![\"datetime\"](\"img/파이썬과학기술라이브러리.jpg\")
\n",
+ "# 1. Pandas 소개\n",
+ "![\"datetime\"](\"img/6pandas소개.jpg\")
\n",
+ "# [ Pandas API 공식 문서 ]\n",
+ "## http://pandas.pydata.org/pandas-docs/stable/api.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Pandas 주요 데이터 타입"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.1. Series (시리즈)\n",
+ "![\"datetime\"](\"img/Series소개.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 인덱스 라벨로 접근 가능한 점은 사전 타입 특징과 비슷하다"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Pandas 라이브러리 사용 선언, 별칭 부여\n",
+ "import pandas as pd\n",
+ "# 자주 사용하는 개체 import, 쓰기 귀찮으므로 처음에 임포트\n",
+ "from pandas import Series"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.1.1 Series 생성"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 3\n",
+ "1 5\n",
+ "2 1\n",
+ "3 2\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#기본 Series 생성\n",
+ "data1 = Series([3,5,1,2])\n",
+ "data1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1[2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 big data\n",
+ "1 30\n",
+ "2 [1, 2, 3]\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#값은 모든 데이터 타입 가능\n",
+ "data2 = Series(['big data',30,[1,2,3]])\n",
+ "data2\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "강의명 big data\n",
+ "수강생 수 30\n",
+ "비고 [1, 2, 3]\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#인덱스 라벨 추가\n",
+ "data2.index = ['강의명','수강생 수','비고']\n",
+ "data2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#숫자 인덱스로 값 접근\n",
+ "data2[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 인덱스 라벨로 값 접근\n",
+ "data2['수강생 수']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 1\n",
+ "b 3\n",
+ "c 10\n",
+ "d 5\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#사전 타입처럼 (인덱스:값)의 형태로도 생성 가능\n",
+ "data3 = Series({'a':1, 'b':3, 'c':10, 'd':5})\n",
+ "data3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 10\n",
+ "b 20\n",
+ "c 30\n",
+ "d 40\n",
+ "e 50\n",
+ "f 60\n",
+ "g 70\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 값과 Index 인자를 사용하여, Series 생성\n",
+ "data4 = Series([10,20,30,40,50,60,70,80,90,100],\n",
+ " index=['a','b','c','d','e','f','g','h','i','j'])\n",
+ "data4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.2.2. Series 색인"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "60"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 특정 인덱스 접근 (숫자 인덱스)\n",
+ "data4[5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 특정 인덱스 접근 (인덱스 라벨)\n",
+ "data4['c']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 10\n",
+ "c 30\n",
+ "f 60\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 여러 인덱스 접근\n",
+ "#'a','c','f' 라벨만 보고 싶을 때, [[]]\n",
+ "data4[['a','c','f']]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "c 30\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 숫자 인덱스가 2,7,8,9번 항목만 출력\n",
+ "# 출력은 인덱스 라벨로 출력\n",
+ "data4[[2,7,8,9]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b 20\n",
+ "c 30\n",
+ "d 40\n",
+ "e 50\n",
+ "f 60\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 숫자 인덱스가 1,2,3,4,5번 항목 출력\n",
+ "data4[[1,2,3,4,5]]\n",
+ "data4[1:6]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 인덱스 슬라이싱 (숫자 인덱스)\n",
+ "![\"datetime\"](\"img/3문자열슬라이싱.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1) :을 기점으로 왼쪽에 start index, 오른쪽에 end index 지정\n",
+ "### 2) start index 보다 크거나 같고, end index보다 작은 항목을 선택\n",
+ "### 3) 예를 들어, a[2:5]는 a[[2,3,4]]와 동일\n",
+ "### 4) start index와 end index는 생략 가능하며, 생략 시 start index는 첫 인덱스, end index는 끝 인덱스로 지정됨.\n",
+ "### 5) 음수 인덱스는 끝에서 시작되는 역인덱스임."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "d 40\n",
+ "e 50\n",
+ "f 60\n",
+ "g 70\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# end index도 default가 있으며 끝 값이 default\n",
+ "# 숫자 인덱스가 3부터 6까지 슬라싱\n",
+ "data4[3:7]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "f 60\n",
+ "g 70\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# data4의 마지막 5개 출력\n",
+ "data4[-5:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 10\n",
+ "b 20\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#인덱스 라벨 슬라이싱\n",
+ "# 기본적으로 숫자 인덱스와 동일하나, end index 포함여부가 다름.\n",
+ "# 숫자 인덱스는 end index가 포함되지 않으나, 인덱스 라벨 슬라이싱은 end index가 포함됨.\n",
+ "data4[0:2]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 조건을 활용한 인덱스 선택 (조건 색인)\n",
+ "# 매우 많이 활용되므로, 꼭 알고 있어야 함!\n",
+ "![\"datetime\"](\"img/조건색인.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "e 50\n",
+ "f 60\n",
+ "g 70\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ " 벡터 : 1차원 배열(data4), 스칼라 : 값 하나(50)\n",
+ "# data4의 값이 50 이상인 경우만 선택\n",
+ "data4[data4>=50]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "f 60\n",
+ "g 70\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# data4의 항목값의 평균보다 큰 항목 선택\n",
+ "data4[data4>data4.mean()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.2.3. Series 속성 조회 및 주요 함수"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 속성 (Attributes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'], dtype='object')"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1) index - 인덱스 출력\n",
+ "data4.index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], dtype=int64)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2) values - 값 출력\n",
+ "data4.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dtype('int64')"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3) dtype - 데이터 타입 출력\n",
+ "data4.dtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 4) size - Series의 크기 (갯수)\n",
+ "data4.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 주요 함수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1) 인덱스 라벨이 있는지 체크\n",
+ "'c' in data4\n",
+ "'bb' in data4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2) 특정 값이 있는지 체크\n",
+ "55 in data4\n",
+ "50 in data4.values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10\n",
+ "20\n",
+ "30\n",
+ "40\n",
+ "50\n",
+ "60\n",
+ "70\n",
+ "80\n",
+ "90\n",
+ "100\n"
+ ]
+ }
+ ],
+ "source": [
+ "# data4의 모든 항목 값을 출력\n",
+ "for i in data4.values:\n",
+ " print(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a\n",
+ "b\n",
+ "c\n",
+ "d\n",
+ "e\n",
+ "f\n",
+ "g\n",
+ "h\n",
+ "i\n",
+ "j\n"
+ ]
+ }
+ ],
+ "source": [
+ "# data4의 모든 인덱스 라벨을 출력\n",
+ "for i in data4.index:\n",
+ " print(i)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a 10\n",
+ "b 20\n",
+ "c 30\n",
+ "d 40\n",
+ "e 50\n",
+ "f 60\n",
+ "g 70\n",
+ "h 80\n",
+ "i 90\n",
+ "j 100\n"
+ ]
+ }
+ ],
+ "source": [
+ "# data4의 모든 인덱스 라벨과 값의 쌍을 출력\n",
+ "for index, value in data4.items():\n",
+ " print(index, value)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "=============================================================\n",
+ "# 실습 문제 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 4\n",
+ "B 20\n",
+ "C 28\n",
+ "D 46\n",
+ "E 99\n",
+ "F 45\n",
+ "G 96\n",
+ "H 94\n",
+ "I 5\n",
+ "J 15\n",
+ "K 33\n",
+ "L 62\n",
+ "M 31\n",
+ "N 50\n",
+ "O 97\n",
+ "P 52\n",
+ "Q 1\n",
+ "R 83\n",
+ "S 39\n",
+ "T 48\n",
+ "U 26\n",
+ "V 16\n",
+ "W 90\n",
+ "X 36\n",
+ "Y 18\n",
+ "Z 61\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습 데이터 생성\n",
+ "sample = random.sample(range(1,100),26) # 1~100사이의 값을 26개 생성하여 list로 반환\n",
+ "data = Series(sample, index = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ'))\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "33"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1) 인덱스 라벨이 'K' 항목의 값 출력\n",
+ "data['K']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 4\n",
+ "F 45\n",
+ "C 28\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2) 인덱스 라벨이 'A','F','C' 항목의 값 출력\n",
+ "data[['A','F','C']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "F 45\n",
+ "G 96\n",
+ "H 94\n",
+ "I 5\n",
+ "J 15\n",
+ "K 33\n",
+ "L 62\n",
+ "M 31\n",
+ "N 50\n",
+ "O 97\n",
+ "P 52\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3) 5번 인덱스부터 15번 인덱스까지의 항목 출력\n",
+ "data[5:16]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "V 16\n",
+ "W 90\n",
+ "X 36\n",
+ "Y 18\n",
+ "Z 61\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 4) 뒤에서 5개 항목 출력\n",
+ "data[-5:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "26"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 5) data의 항목의 갯수를 출력\n",
+ "data.size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "D 46\n",
+ "E 99\n",
+ "G 96\n",
+ "H 94\n",
+ "L 62\n",
+ "N 50\n",
+ "O 97\n",
+ "P 52\n",
+ "R 83\n",
+ "T 48\n",
+ "W 90\n",
+ "Z 61\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 6) data 항목 값들의 평균보다 큰 항목만 출력 \n",
+ "## Hint. data 항목 값들의 평균은 data.mean()으로 구할 수 있다.\n",
+ "data[data>data.mean()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 7) data의 항목 값 중에 50이 있는지 확인하여, 있으면 True, 없으면 False를 출력\n",
+ "50 in data.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "index: A , value: 4\n",
+ "index: B , value: 20\n",
+ "index: C , value: 28\n",
+ "index: D , value: 46\n",
+ "index: E , value: 99\n",
+ "index: F , value: 45\n",
+ "index: G , value: 96\n",
+ "index: H , value: 94\n",
+ "index: I , value: 5\n",
+ "index: J , value: 15\n",
+ "index: K , value: 33\n",
+ "index: L , value: 62\n",
+ "index: M , value: 31\n",
+ "index: N , value: 50\n",
+ "index: O , value: 97\n",
+ "index: P , value: 52\n",
+ "index: Q , value: 1\n",
+ "index: R , value: 83\n",
+ "index: S , value: 39\n",
+ "index: T , value: 48\n",
+ "index: U , value: 26\n",
+ "index: V , value: 16\n",
+ "index: W , value: 90\n",
+ "index: X , value: 36\n",
+ "index: Y , value: 18\n",
+ "index: Z , value: 61\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 8) data의 인덱스 라벨과 각 항목 값을 아래와 같이 출력\n",
+ "# index: A, value: XX\n",
+ "# index: B, value: XX\n",
+ "# index: C, value: XX\n",
+ "for index, value in data.items():\n",
+ " print(\"index: \",index, \", value: \",value)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "index :A, value: 4\n",
+ "index :B, value: 20\n",
+ "index :C, value: 28\n",
+ "index :D, value: 46\n",
+ "index :E, value: 99\n",
+ "index :F, value: 45\n",
+ "index :G, value: 96\n",
+ "index :H, value: 94\n",
+ "index :I, value: 5\n",
+ "index :J, value: 15\n",
+ "index :K, value: 33\n",
+ "index :L, value: 62\n",
+ "index :M, value: 31\n",
+ "index :N, value: 50\n",
+ "index :O, value: 97\n",
+ "index :P, value: 52\n",
+ "index :Q, value: 1\n",
+ "index :R, value: 83\n",
+ "index :S, value: 39\n",
+ "index :T, value: 48\n",
+ "index :U, value: 26\n",
+ "index :V, value: 16\n",
+ "index :W, value: 90\n",
+ "index :X, value: 36\n",
+ "index :Y, value: 18\n",
+ "index :Z, value: 61\n"
+ ]
+ }
+ ],
+ "source": [
+ "for index, value in data.items():\n",
+ " str = 'index :{0}, value: {1}'.format(index, value)\n",
+ " print(str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 2. Pandas \352\260\234\354\232\224 \353\260\217 \353\215\260\354\235\264\355\204\260 \355\203\200\354\236\205 \354\206\214\352\260\234 2(DataFrame).ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 2. Pandas \352\260\234\354\232\224 \353\260\217 \353\215\260\354\235\264\355\204\260 \355\203\200\354\236\205 \354\206\214\352\260\234 2(DataFrame).ipynb"
new file mode 100644
index 0000000..379f1e8
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 2. Pandas \352\260\234\354\232\224 \353\260\217 \353\215\260\354\235\264\355\204\260 \355\203\200\354\236\205 \354\206\214\352\260\234 2(DataFrame).ipynb"
@@ -0,0 +1,2563 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Pandas 주요 데이터 타입\n",
+ "## 2.2 DataFrame (데이터프레임)\n",
+ "![\"datetime\"](\"img/dataframe구조.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Pandas 라이브러리 임포트\n",
+ "import pandas as pd \n",
+ "from pandas import Series, DataFrame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2.1 데이터프레임 생성\n",
+ "### 생성자 인자\n",
+ "#### 1) data : dataFrame에 저장할 데이터 (numpy ndarray, dict, DataFrame 등 여러 형태 가능)\n",
+ "#### 2) index : 행(row) 이름, 기본값 = 0부터 1씩 증가하는 정수\n",
+ "#### 3) columns : 열(column) 이름, 기본값 = 0부터 1씩 증가하는 정수\n",
+ "#### 4) dtype : 데이터 형태(type), 만약 지정하지 않으면 파이썬이 자동으로 값의 형태를 보고 결정\n",
+ "#### 5) copy : 입력 데이터를 복사할지 지정. 디폴트는 False 임. (복사할 거 아니면 메모리 관리 차원에서 디폴트인 False 설정 사용하면 됨)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"./img/데이터프레임구조1.jpg\")
\n",
+ "![](\"./img/데이터프레임구조2.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2.2. DataFrame 속성 조회하기 (속성을 조회할 때에는 ()를 사용하지 않으니 유의하기 바람.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"./img/데이터프레임조회.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A True\n",
+ "B True\n",
+ "C False\n",
+ "D False\n",
+ "E True\n",
+ "Name: Col4, dtype: bool"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1) col4만 조회하기, 한 컬럼 조회는 Series로 반환\n",
+ "df4['Col4']\n",
+ "df4.Col4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A big\n",
+ "B data\n",
+ "C is\n",
+ "D very\n",
+ "E good\n",
+ "Name: Col2, dtype: object"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df4['Col2']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![\"csv파일예제\"](\"img/csv파일예제.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
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+ "973 FNBB CAPITAL MARKETS \n",
+ "974 FOSHEE MANAGEMENT \n",
+ "975 THE COOPER-LYON GROUP LLC \n",
+ "976 NATIONAL BANK OF COMMERCE \n",
+ "977 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "978 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "979 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "980 STAR TRUCK PARTS \n",
+ "981 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "982 ALMON AND ASSOCIATES \n",
+ "983 SELF-EMPLOYED \n",
+ "984 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "985 EYE CENTER SOUTH \n",
+ "986 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "987 RETIRED \n",
+ "988 MERILL LYNCH \n",
+ "989 RETIRED \n",
+ "990 MAYNARD COOPER & GALE P.C. \n",
+ "991 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "992 RETIRED \n",
+ "993 REMCO INDUSTRIAL PIPING MATERIALS INC \n",
+ "994 WADE SAND & GRAVEL INC. \n",
+ "995 BROWN & ASSOCIATES PLLC \n",
+ "996 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "997 RETIRED \n",
+ "998 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "999 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "\n",
+ " contbr_occupation contb_receipt_amt \\\n",
+ "0 RETIRED 250.0 \n",
+ "1 RETIRED 50.0 \n",
+ "2 INFORMATION REQUESTED 250.0 \n",
+ "3 RETIRED 250.0 \n",
+ "4 RETIRED 300.0 \n",
+ "5 RETIRED 500.0 \n",
+ "6 INFORMATION REQUESTED 250.0 \n",
+ "7 RETIRED 250.0 \n",
+ "8 RN 250.0 \n",
+ "9 ELECTRICAL ENGINEER 250.0 \n",
+ "10 RETIRED 250.0 \n",
+ "11 LAND DEVELOPER 500.0 \n",
+ "12 INFORMATION REQUESTED 250.0 \n",
+ "13 INFORMATION REQUESTED 250.0 \n",
+ "14 INFORMATION REQUESTED 250.0 \n",
+ "15 PHYSICIAN 300.0 \n",
+ "16 RETIRED 500.0 \n",
+ "17 RETIRED 1000.0 \n",
+ "18 RETIRED 250.0 \n",
+ "19 RETIRED 300.0 \n",
+ "20 OFFICE FURNITURE BROKER 500.0 \n",
+ "21 RETIRED 250.0 \n",
+ "22 MANAGER 2500.0 \n",
+ "23 MANAGER 2500.0 \n",
+ "24 RETIRED 150.0 \n",
+ "25 RETIRED 200.0 \n",
+ "26 RETIRED 100.0 \n",
+ "27 INFORMATION REQUESTED 250.0 \n",
+ "28 INFORMATION REQUESTED 500.0 \n",
+ "29 REALTOR 250.0 \n",
+ ".. ... ... \n",
+ "970 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "971 HOMEMAKER 60.0 \n",
+ "972 HOMEMAKER 60.0 \n",
+ "973 BROKER 500.0 \n",
+ "974 MULTI FAMILY DEVELOPER 500.0 \n",
+ "975 CONSULTANT 2500.0 \n",
+ "976 BANKER 1000.0 \n",
+ "977 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "978 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "979 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "980 OWNER 300.0 \n",
+ "981 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "982 PRESIDENT 250.0 \n",
+ "983 SELF-EMPLOYED 250.0 \n",
+ "984 INFORMATION REQUESTED PER BEST EFFORTS 55.0 \n",
+ "985 PHYSICIAN 300.0 \n",
+ "986 INFORMATION REQUESTED PER BEST EFFORTS 250.0 \n",
+ "987 RETIRED 25.0 \n",
+ "988 STOCKBROKER 1000.0 \n",
+ "989 RETIRED 500.0 \n",
+ "990 ATTORNEY 500.0 \n",
+ "991 INFORMATION REQUESTED PER BEST EFFORTS 200.0 \n",
+ "992 RETIRED 100.0 \n",
+ "993 PRESIDENT/CEO 250.0 \n",
+ "994 SAFETY DIRECTOR 125.0 \n",
+ "995 ATTORNEY 500.0 \n",
+ "996 PHYSICIAN 100.0 \n",
+ "997 RETIRED 250.0 \n",
+ "998 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "999 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "\n",
+ " contb_receipt_dt receipt_desc memo_cd memo_text form_tp file_num \n",
+ "0 20-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "1 23-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "2 05-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ "3 01-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "4 20-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "5 23-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "6 21-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "7 05-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ "8 21-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "9 20-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "10 22-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "11 22-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "12 21-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "13 27-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "14 11-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ "15 08-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ "16 17-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "17 05-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ "18 13-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "19 16-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "20 16-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "21 16-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "22 17-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "23 17-Jun-11 NaN NaN NaN SA17A 736166 \n",
+ "24 05-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "25 08-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "26 15-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "27 16-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "28 18-Aug-11 NaN NaN NaN SA17A 749073 \n",
+ "29 05-Jul-11 NaN NaN NaN SA17A 749073 \n",
+ ".. ... ... ... ... ... ... \n",
+ "970 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "971 17-Jan-12 NaN NaN NaN SA17A 771933 \n",
+ "972 17-Jan-12 NaN NaN NaN SA17A 771933 \n",
+ "973 10-Jan-12 NaN NaN NaN SA17A 771933 \n",
+ "974 19-Jan-12 NaN NaN NaN SA17A 771933 \n",
+ "975 30-Jan-12 NaN NaN NaN SA17A 771933 \n",
+ "976 22-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "977 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "978 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "979 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "980 01-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "981 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "982 20-Mar-12 NaN NaN NaN SA17A 780128 \n",
+ "983 14-Mar-12 NaN NaN NaN SA17A 780128 \n",
+ "984 07-Mar-12 NaN NaN NaN SA17A 780128 \n",
+ "985 28-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "986 16-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "987 21-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "988 22-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "989 13-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "990 22-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "991 28-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "992 06-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "993 01-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "994 06-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "995 20-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "996 20-Mar-12 NaN NaN NaN SA17A 780128 \n",
+ "997 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "998 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "999 29-Feb-12 NaN NaN NaN SA17A 780124 \n",
+ "\n",
+ "[1000 rows x 16 columns]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#chunksize만큼 데이터를 읽어옴.\n",
+ "next(리더)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | cmte_id | \n", + "cand_id | \n", + "cand_nm | \n", + "contbr_nm | \n", + "contbr_city | \n", + "contbr_st | \n", + "contbr_zip | \n", + "contbr_employer | \n", + "contbr_occupation | \n", + "contb_receipt_amt | \n", + "contb_receipt_dt | \n", + "receipt_desc | \n", + "memo_cd | \n", + "memo_text | \n", + "form_tp | \n", + "file_num | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "HARVEY, WILLIAM | \n", + "MOBILE | \n", + "AL | \n", + "366010290.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "250.0 | \n", + "20-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 1 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "HARVEY, WILLIAM | \n", + "MOBILE | \n", + "AL | \n", + "366010290.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "50.0 | \n", + "23-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 2 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "SMITH, LANIER | \n", + "LANETT | \n", + "AL | \n", + "368633403.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "05-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 3 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "BLEVINS, DARONDA | \n", + "PIGGOTT | \n", + "AR | \n", + "724548253.0 | \n", + "NONE | \n", + "RETIRED | \n", + "250.0 | \n", + "01-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 4 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "WARDENBURG, HAROLD | \n", + "HOT SPRINGS NATION | \n", + "AR | \n", + "719016467.0 | \n", + "NONE | \n", + "RETIRED | \n", + "300.0 | \n", + "20-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 5 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "BECKMAN, JAMES | \n", + "SPRINGDALE | \n", + "AR | \n", + "727647190.0 | \n", + "NONE | \n", + "RETIRED | \n", + "500.0 | \n", + "23-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 6 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "BLEVINS, DARONDA | \n", + "PIGGOTT | \n", + "AR | \n", + "724548253.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "21-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 7 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "BLEVINS, DARONDA | \n", + "PIGGOTT | \n", + "AR | \n", + "724548253.0 | \n", + "NONE | \n", + "RETIRED | \n", + "250.0 | \n", + "05-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 8 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "COLLINS, SARAH | \n", + "MESA | \n", + "AZ | \n", + "852106725.0 | \n", + "ST. JOSEPH HOSPITAL | \n", + "RN | \n", + "250.0 | \n", + "21-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 9 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "COLEMAN, RONALD | \n", + "TUCSON | \n", + "AZ | \n", + "857498865.0 | \n", + "RAYTHEON | \n", + "ELECTRICAL ENGINEER | \n", + "250.0 | \n", + "20-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 10 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "ATCHLEY, JR, KEITH | \n", + "MESA | \n", + "AZ | \n", + "85215.0 | \n", + "NONE | \n", + "RETIRED | \n", + "250.0 | \n", + "22-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 11 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "FARNSWORTH, ROSS | \n", + "MESA | \n", + "AZ | \n", + "852062062.0 | \n", + "FARNSWORTH COMPANIES | \n", + "LAND DEVELOPER | \n", + "500.0 | \n", + "22-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 12 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "PRESTON, CLIFFORD | \n", + "ORO VALLEY | \n", + "AZ | \n", + "857379072.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "21-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 13 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "PETERSEN, W | \n", + "GILBERT | \n", + "AZ | \n", + "85295.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "27-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 14 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "MILLER, WILLIAM | \n", + "SCOTTSDALE | \n", + "AZ | \n", + "852513055.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "11-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 15 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "DOLAN, WILLIAM | \n", + "PHOENIX | \n", + "AZ | \n", + "850145248.0 | \n", + "VA MEDICAL CENTER | \n", + "PHYSICIAN | \n", + "300.0 | \n", + "08-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 16 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "WILSON, RICHARD | \n", + "FLAGSTAFF | \n", + "AZ | \n", + "860010717.0 | \n", + "NONE | \n", + "RETIRED | \n", + "500.0 | \n", + "17-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 17 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "REULING, RICHARD | \n", + "GREEN VALLEY | \n", + "AZ | \n", + "856225413.0 | \n", + "NONE | \n", + "RETIRED | \n", + "1000.0 | \n", + "05-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 18 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "ALLUMBAUGH, KATHY | \n", + "SANTA ANA | \n", + "CA | \n", + "927051427.0 | \n", + "NONE | \n", + "RETIRED | \n", + "250.0 | \n", + "13-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 19 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "DEL POZO, JOSE | \n", + "DEL MAR | \n", + "CA | \n", + "920142938.0 | \n", + "RETIRES | \n", + "RETIRED | \n", + "300.0 | \n", + "16-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 20 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "KIEFFER, PIERRE | \n", + "SACRAMENTO | \n", + "CA | \n", + "958256640.0 | \n", + "SELF | \n", + "OFFICE FURNITURE BROKER | \n", + "500.0 | \n", + "16-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 21 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "HANNAH, STEPHEN | \n", + "SHERMAN OAKS | \n", + "CA | \n", + "914131654.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "250.0 | \n", + "16-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 22 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "MINNIS, RITA | \n", + "MILPITAS | \n", + "CA | \n", + "950357610.0 | \n", + "MILPITS MATERIALS | \n", + "MANAGER | \n", + "2500.0 | \n", + "17-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 23 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "MINNIS, RITA | \n", + "MILPITAS | \n", + "CA | \n", + "950357610.0 | \n", + "MILPITS MATERIALS | \n", + "MANAGER | \n", + "2500.0 | \n", + "17-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "736166 | \n", + "
| 24 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "EDWARDS, MARK | \n", + "LA JOLLA | \n", + "CA | \n", + "920377643.0 | \n", + "NONE | \n", + "RETIRED | \n", + "150.0 | \n", + "05-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 25 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "HANNAH, STEPHEN | \n", + "SHERMAN OAKS | \n", + "CA | \n", + "914131654.0 | \n", + "NONE | \n", + "RETIRED | \n", + "200.0 | \n", + "08-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 26 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "HENRY, JOAN | \n", + "YUBA CITY | \n", + "CA | \n", + "959938506.0 | \n", + "NONE | \n", + "RETIRED | \n", + "100.0 | \n", + "15-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 27 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "SKEREN, THOMAS | \n", + "CALABASAS | \n", + "CA | \n", + "913021547.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "250.0 | \n", + "16-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 28 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "EDWARDS, WILLIAM | \n", + "LOS ANGELES | \n", + "CA | \n", + "900243008.0 | \n", + "INFORMATION REQUESTED | \n", + "INFORMATION REQUESTED | \n", + "500.0 | \n", + "18-Aug-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| 29 | \n", + "C00410118 | \n", + "P20002978 | \n", + "Bachmann, Michelle | \n", + "PORTER, NANCY | \n", + "CARMEL VALLEY | \n", + "CA | \n", + "939249482.0 | \n", + "NANCY PORTER | \n", + "REALTOR | \n", + "250.0 | \n", + "05-Jul-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "749073 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 970 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COLEMAN, JEFFREY MR. | \n", + "DOTHAN | \n", + "AL | \n", + "363056899.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 971 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FUTRELL, JOANN M. MRS. | \n", + "DOTHAN | \n", + "AL | \n", + "363056962.0 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "60.0 | \n", + "17-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 972 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FUTRELL, JOANN M. MRS. | \n", + "DOTHAN | \n", + "AL | \n", + "363056962.0 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "60.0 | \n", + "17-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 973 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CRABTREE, REAVES M. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352133130.0 | \n", + "FNBB CAPITAL MARKETS | \n", + "BROKER | \n", + "500.0 | \n", + "10-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 974 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FOSHEEJOHNF, JOHN | \n", + "MONTGOMERY | \n", + "AL | \n", + "361062224.0 | \n", + "FOSHEE MANAGEMENT | \n", + "MULTI FAMILY DEVELOPER | \n", + "500.0 | \n", + "19-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 975 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COOPER, COLBY | \n", + "FAIRHOPE | \n", + "AL | \n", + "36532.0 | \n", + "THE COOPER-LYON GROUP LLC | \n", + "CONSULTANT | \n", + "2500.0 | \n", + "30-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 976 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "MURRAY, RICHARD MR. IV | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352133833.0 | \n", + "NATIONAL BANK OF COMMERCE | \n", + "BANKER | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 977 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "MALONE, W. DAVIS MR. III | \n", + "DOTHAN | \n", + "AL | \n", + "363056319.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 978 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HIGHTOWER, TIM MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352261904.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 979 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COOK, BRENT T. MR. | \n", + "DOTHAN | \n", + "AL | \n", + "363033832.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 980 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BROOKS, JERALD MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352341010.0 | \n", + "STAR TRUCK PARTS | \n", + "OWNER | \n", + "300.0 | \n", + "01-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 981 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CONNORS, CAITLIN A. MS. | \n", + "ALABASTER | \n", + "AL | \n", + "350073108.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 982 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "ALMON, ROBERT N. MR. | \n", + "TUSCALOOSA | \n", + "AL | \n", + "354032687.0 | \n", + "ALMON AND ASSOCIATES | \n", + "PRESIDENT | \n", + "250.0 | \n", + "20-Mar-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780128 | \n", + "
| 983 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "ALTZSMITH, MARY | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352096808.0 | \n", + "SELF-EMPLOYED | \n", + "SELF-EMPLOYED | \n", + "250.0 | \n", + "14-Mar-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780128 | \n", + "
| 984 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "AMARE, BARBARA V. MS. | \n", + "FAIRHOPE | \n", + "AL | \n", + "365324201.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "55.0 | \n", + "07-Mar-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780128 | \n", + "
| 985 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "STURRIDGE, KIRK DR. | \n", + "DOTHAN | \n", + "AL | \n", + "363056953.0 | \n", + "EYE CENTER SOUTH | \n", + "PHYSICIAN | \n", + "300.0 | \n", + "28-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 986 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "SUGGS, WILLARD C. MR. | \n", + "GULF SHORES | \n", + "AL | \n", + "365428232.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "250.0 | \n", + "16-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 987 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "WHITT, MARY A. DR. | \n", + "GADSDEN | \n", + "AL | \n", + "359014001.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "25.0 | \n", + "21-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 988 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "TAYLOR, CRAWFORD L. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352161326.0 | \n", + "MERILL LYNCH | \n", + "STOCKBROKER | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 989 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "TANKERSLEY, WILL HILL MG. | \n", + "MONTGOMERY | \n", + "AL | \n", + "361010470.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "500.0 | \n", + "13-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 990 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "NABERS, DRAYTON MR. JR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352231320.0 | \n", + "MAYNARD COOPER & GALE P.C. | \n", + "ATTORNEY | \n", + "500.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 991 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "KING, WAYNE M. MR. | \n", + "BREWTON | \n", + "AL | \n", + "364264006.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "200.0 | \n", + "28-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 992 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "KIMMERLY, JAMES W. MR. JR. | \n", + "ATHENS | \n", + "AL | \n", + "356112026.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "100.0 | \n", + "06-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 993 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "EMMERTSON, ROBERT | \n", + "SPANISH FORT | \n", + "AL | \n", + "365275128.0 | \n", + "REMCO INDUSTRIAL PIPING MATERIALS INC | \n", + "PRESIDENT/CEO | \n", + "250.0 | \n", + "01-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 994 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BROWN, CHESTER MR. | \n", + "OAKMAN | \n", + "AL | \n", + "355794512.0 | \n", + "WADE SAND & GRAVEL INC. | \n", + "SAFETY DIRECTOR | \n", + "125.0 | \n", + "06-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 995 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BROWN, ERIN | \n", + "MONTGOMERY | \n", + "AL | \n", + "361111428.0 | \n", + "BROWN & ASSOCIATES PLLC | \n", + "ATTORNEY | \n", + "500.0 | \n", + "20-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 996 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BLACKWELL, RICHARD DR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352052931.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "PHYSICIAN | \n", + "100.0 | \n", + "20-Mar-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780128 | \n", + "
| 997 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "SWIFT, JOHN MR. | \n", + "ATMORE | \n", + "AL | \n", + "365021007.0 | \n", + "RETIRED | \n", + "RETIRED | \n", + "250.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 998 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "SWAID, CHRISTY J. MS. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352660827.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 999 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "THEUNE, ROBERT J. MR. | \n", + "DOTHAN | \n", + "AL | \n", + "363055842.0 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
1000 rows × 16 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " cmte_id cand_id cand_nm contbr_nm \\\n",
+ "1000 C00431171 P80003353 Romney, Mitt NEUWIEN, SUSAN W. MS. \n",
+ "1001 C00431171 P80003353 Romney, Mitt NEUWIEN, SUSAN W. MS. \n",
+ "1002 C00431171 P80003353 Romney, Mitt NELSON, ARTHUR R. MR. \n",
+ "1003 C00431171 P80003353 Romney, Mitt HOLLIS, HAYNE MR. \n",
+ "1004 C00431171 P80003353 Romney, Mitt GORRIE, ALISON MRS. \n",
+ "1005 C00431171 P80003353 Romney, Mitt GORRIE, JIM MR. \n",
+ "1006 C00431171 P80003353 Romney, Mitt EVANS, NANCY D. MRS. \n",
+ "1007 C00431171 P80003353 Romney, Mitt COX, ANNETTE MS. \n",
+ "1008 C00431171 P80003353 Romney, Mitt COX, ANNETTE P. MS. \n",
+ "1009 C00431171 P80003353 Romney, Mitt BALLARD, HALYCON \n",
+ "1010 C00431171 P80003353 Romney, Mitt BALLARD, HALYCON \n",
+ "1011 C00431171 P80003353 Romney, Mitt BOHANNON, PATRICIA COPLEY MS. \n",
+ "1012 C00431171 P80003353 Romney, Mitt WHITT, MARY A. DR. \n",
+ "1013 C00431171 P80003353 Romney, Mitt FEARERINGILL, WILLIAM W. MR. \n",
+ "1014 C00431171 P80003353 Romney, Mitt WILSON, JAMES W. MR. III \n",
+ "1015 C00431171 P80003353 Romney, Mitt SHERLING, GEORGE L. MR. \n",
+ "1016 C00431171 P80003353 Romney, Mitt SHERLING, GEORGE L. MR. \n",
+ "1017 C00431171 P80003353 Romney, Mitt THOMAS, SUSAN E. MS. \n",
+ "1018 C00431171 P80003353 Romney, Mitt THOMAS, SUSAN E. MS. \n",
+ "1019 C00431171 P80003353 Romney, Mitt RAMSAY-NAILE, ASHLEY \n",
+ "1020 C00431171 P80003353 Romney, Mitt NIELSEN, CLAUDE B. MR. \n",
+ "1021 C00431171 P80003353 Romney, Mitt MATHIS, ALAN \n",
+ "1022 C00431171 P80003353 Romney, Mitt MATTHEWS, JOHN E. MR. \n",
+ "1023 C00431171 P80003353 Romney, Mitt MATTHEWS, WILLIAM MR. \n",
+ "1024 C00431171 P80003353 Romney, Mitt KNOWLES, KENNETH J. MR. \n",
+ "1025 C00431171 P80003353 Romney, Mitt FERRELL, PATRICK \n",
+ "1026 C00431171 P80003353 Romney, Mitt DANDURAND, RICHARD L. MR. \n",
+ "1027 C00431171 P80003353 Romney, Mitt CABANISS, CATHERINE C. MS. \n",
+ "1028 C00431171 P80003353 Romney, Mitt OUTLAND, JAMES \n",
+ "1029 C00431171 P80003353 Romney, Mitt THOMPSON, PATRICIA ANN MS. \n",
+ "... ... ... ... ... \n",
+ "1970 C00431171 P80003353 Romney, Mitt CROCKETT, C. WEBB MR. \n",
+ "1971 C00431171 P80003353 Romney, Mitt CROCKETT, JEFF W. MR. \n",
+ "1972 C00431171 P80003353 Romney, Mitt BEUS, ANNETTE MRS. \n",
+ "1973 C00431171 P80003353 Romney, Mitt CASE, ARTHUR D. MR. \n",
+ "1974 C00431171 P80003353 Romney, Mitt CASE, NANCY I. MRS. \n",
+ "1975 C00431171 P80003353 Romney, Mitt COURT, STEPHEN G. MR. \n",
+ "1976 C00431171 P80003353 Romney, Mitt COWLEY, SAM MR. \n",
+ "1977 C00431171 P80003353 Romney, Mitt COWMAN, STEVE MR. \n",
+ "1978 C00431171 P80003353 Romney, Mitt CARDON, CRAIG D. MR. \n",
+ "1979 C00431171 P80003353 Romney, Mitt CARDON, KASTLE K. MS. \n",
+ "1980 C00431171 P80003353 Romney, Mitt GARNER, RICHARD MR. \n",
+ "1981 C00431171 P80003353 Romney, Mitt HANSEN, VAN MR. \n",
+ "1982 C00431171 P80003353 Romney, Mitt GARDNER, ELBERT \n",
+ "1983 C00431171 P80003353 Romney, Mitt GARDNER, KATHLEEN \n",
+ "1984 C00431171 P80003353 Romney, Mitt JOHNSON, BECKY H. MS. \n",
+ "1985 C00431171 P80003353 Romney, Mitt HANLEY, LEE T. MR. \n",
+ "1986 C00431171 P80003353 Romney, Mitt HANLEY, LEE T. MR. \n",
+ "1987 C00431171 P80003353 Romney, Mitt HANLEY, LEE T. MR. \n",
+ "1988 C00431171 P80003353 Romney, Mitt HANLEY, NANCY R. MRS. \n",
+ "1989 C00431171 P80003353 Romney, Mitt HANLEY, NANCY R. MRS. \n",
+ "1990 C00431171 P80003353 Romney, Mitt HANLEY, NANCY R. MRS. \n",
+ "1991 C00431171 P80003353 Romney, Mitt JENSON, JEFF \n",
+ "1992 C00431171 P80003353 Romney, Mitt HALES, DONALD D. DR. \n",
+ "1993 C00431171 P80003353 Romney, Mitt HALES, DONALD D. DR. \n",
+ "1994 C00431171 P80003353 Romney, Mitt HALL, DIANNA M. MS. \n",
+ "1995 C00431171 P80003353 Romney, Mitt HALL, KENNETH T. MR. \n",
+ "1996 C00431171 P80003353 Romney, Mitt DEMENNA, KEVIN B. MR. \n",
+ "1997 C00431171 P80003353 Romney, Mitt DEMENNA, MELISSA MRS. \n",
+ "1998 C00431171 P80003353 Romney, Mitt JACKSON, WILLIAM G. MR. \n",
+ "1999 C00431171 P80003353 Romney, Mitt FOYLE, DOLORES D. MRS. \n",
+ "\n",
+ " contbr_city contbr_st contbr_zip \\\n",
+ "1000 ENTERPRISE AL 363302385 \n",
+ "1001 ENTERPRISE AL 363302385 \n",
+ "1002 DECATUR AL 356034622 \n",
+ "1003 DOTHAN AL 363033008 \n",
+ "1004 BIRMINGHAM AL 352231228 \n",
+ "1005 BIRMINGHAM AL 352231228 \n",
+ "1006 DOTHAN AL 363059355 \n",
+ "1007 DELL CITY AL 351286719 \n",
+ "1008 SPRINGVILLE AL 35146 \n",
+ "1009 BIRMINGHAM AL 352103049 \n",
+ "1010 BIRMINGHAM AL 352103049 \n",
+ "1011 ATTALLA AL 359547827 \n",
+ "1012 GADSDEN AL 359014001 \n",
+ "1013 BIRMINGHAM AL 352097801 \n",
+ "1014 MONTGOMERY AL 361177024 \n",
+ "1015 BIRMINGHAM AL 352435239 \n",
+ "1016 BIRMINGHAM AL 352435239 \n",
+ "1017 BIRMINGHAM AL 352131813 \n",
+ "1018 BIRMINGHAM AL 352131813 \n",
+ "1019 MOBILE AL 366194419 \n",
+ "1020 BIRMINGHAM AL 352133956 \n",
+ "1021 BIRMINGHAM AL 352262418 \n",
+ "1022 MONTGOMERY AL 361112013 \n",
+ "1023 BIRMINGHAM AL 352133024 \n",
+ "1024 HUNTSVILLE AL 358033361 \n",
+ "1025 MONTGOMERY AL 361240185 \n",
+ "1026 VESTAVIA AL 352432822 \n",
+ "1027 BIRMINGHAM AL 352133916 \n",
+ "1028 BIRMINGHAM AL 352231232 \n",
+ "1029 OWENS CROSS ROADS AL 357638677 \n",
+ "... ... ... ... \n",
+ "1970 MESA AZ 852134001 \n",
+ "1971 MESA AZ 852133242 \n",
+ "1972 PHOENIX AZ 850484526 \n",
+ "1973 PHOENIX AZ 850166040 \n",
+ "1974 PHOENIX AZ 850166027 \n",
+ "1975 MESA AZ 852033603 \n",
+ "1976 PHOENIX AZ 850481828 \n",
+ "1977 SCOTTSDALE AZ 852504615 \n",
+ "1978 QUEEN CREEK AZ 851423667 \n",
+ "1979 GILBERT AZ 852969451 \n",
+ "1980 MESA AZ 852133248 \n",
+ "1981 MESA AZ 852135521 \n",
+ "1982 MESA AZ 852053802 \n",
+ "1983 MESA AZ 852053802 \n",
+ "1984 MESA AZ 852132931 \n",
+ "1985 PHOENIX AZ 850164261 \n",
+ "1986 PHOENIX AZ 850164261 \n",
+ "1987 PHOENIX AZ 850164261 \n",
+ "1988 PHOENIX AZ 85253 \n",
+ "1989 PHOENIX AZ 85253 \n",
+ "1990 PHOENIX AZ 85253 \n",
+ "1991 PEORIA AZ 853835107 \n",
+ "1992 FLAGSTAFF AZ 860012000 \n",
+ "1993 FLAGSTAFF AZ 860012000 \n",
+ "1994 GLENDALE AZ 853086132 \n",
+ "1995 GLENDALE AZ 853086132 \n",
+ "1996 PHOENIX AZ 850072603 \n",
+ "1997 PHOENIX AZ 850072603 \n",
+ "1998 PARADISE VALLEY AZ 852531824 \n",
+ "1999 PHOENIX AZ 850329213 \n",
+ "\n",
+ " contbr_employer \\\n",
+ "1000 RETIRED \n",
+ "1001 RETIRED \n",
+ "1002 TFA \n",
+ "1003 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1004 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1005 B. & G. \n",
+ "1006 WALLACE COLLEGE \n",
+ "1007 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1008 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1009 SELF-EMPLOYED \n",
+ "1010 SELF-EMPLOYED \n",
+ "1011 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1012 RETIRED \n",
+ "1013 PRIVATE CAPITAL \n",
+ "1014 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1015 RETIRED \n",
+ "1016 RETIRED \n",
+ "1017 RETIRED \n",
+ "1018 RETIRED \n",
+ "1019 CROWDERGULF \n",
+ "1020 COCA-COLA BOTTLING CO. UNITED INC. \n",
+ "1021 JOHNSTON BARTON PROCTOR & ROSE LLP \n",
+ "1022 RETIRED \n",
+ "1023 NATIONAL BANK OF COMMERCE \n",
+ "1024 RETIRED \n",
+ "1025 THE PATRICK COMPANY \n",
+ "1026 RETIRED \n",
+ "1027 SELF-EMPLOYED \n",
+ "1028 NEW CAPITAL PARTNERS \n",
+ "1029 RETIRED \n",
+ "... ... \n",
+ "1970 FENNEMORE \n",
+ "1971 BROWNSTEIN HYATT FARBER SCHRECK \n",
+ "1972 HOMEMAKER \n",
+ "1973 LOW MOUNTAIN CONSTRUCTION INC. \n",
+ "1974 HOMEMAKER \n",
+ "1975 STATE OF ARIZONA \n",
+ "1976 SELF-EMPLOYED \n",
+ "1977 ENERGY DEVELOPMENTS INC. \n",
+ "1978 THE VINEYARD GROUP \n",
+ "1979 HOMEMAKER \n",
+ "1980 PIVOTAL GROUP INC \n",
+ "1981 ODYSSEY CAPITAL GROUP \n",
+ "1982 RETIRED \n",
+ "1983 INFORMATION REQUESTED PER BEST EFFORTS \n",
+ "1984 HOMEMAKER \n",
+ "1985 VESTAR \n",
+ "1986 VESTAR \n",
+ "1987 VESTAR \n",
+ "1988 HOMEMAKER \n",
+ "1989 HOMEMAKER \n",
+ "1990 HOMEMAKER \n",
+ "1991 VESCOR HOLDINGS \n",
+ "1992 NORTHERN ARIZONA ORTHOPAEDICS \n",
+ "1993 NORTHERN ARIZONA ORTHOPAEDICS \n",
+ "1994 SELF-EMPLOYED \n",
+ "1995 SELF-EMPLOYED \n",
+ "1996 DEMENNA & ASSOCIATES \n",
+ "1997 HOMEMAKER \n",
+ "1998 CABLE AMERICA COORPORATION \n",
+ "1999 RETIRED \n",
+ "\n",
+ " contbr_occupation contb_receipt_amt \\\n",
+ "1000 RETIRED 1000.0 \n",
+ "1001 RETIRED 100.0 \n",
+ "1002 PRESIDENT 200.0 \n",
+ "1003 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "1004 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "1005 CONTRACTOR 1000.0 \n",
+ "1006 CLINICAL NURSING INSTRUCTOR 100.0 \n",
+ "1007 INFORMATION REQUESTED PER BEST EFFORTS 500.0 \n",
+ "1008 INFORMATION REQUESTED PER BEST EFFORTS 500.0 \n",
+ "1009 H R EXECUTIVE 50.0 \n",
+ "1010 H R EXECUTIVE 50.0 \n",
+ "1011 INFORMATION REQUESTED PER BEST EFFORTS 50.0 \n",
+ "1012 RETIRED 25.0 \n",
+ "1013 PRESIDENT 2000.0 \n",
+ "1014 INFORMATION REQUESTED PER BEST EFFORTS 1000.0 \n",
+ "1015 RETIRED 18.0 \n",
+ "1016 RETIRED 1000.0 \n",
+ "1017 RETIRED 50.0 \n",
+ "1018 RETIRED 25.0 \n",
+ "1019 GENERAL MANAGER / VICE PRESIDENT 500.0 \n",
+ "1020 CHAIRMAN/C.E.O. 2500.0 \n",
+ "1021 ATTORNEY 250.0 \n",
+ "1022 RETIRED 100.0 \n",
+ "1023 BANKING/C.F.O. 1000.0 \n",
+ "1024 RETIRED 50.0 \n",
+ "1025 OWNER - CONSULTANT 1000.0 \n",
+ "1026 RETIRED 250.0 \n",
+ "1027 ARTIST 500.0 \n",
+ "1028 INVESTMENTS 250.0 \n",
+ "1029 RETIRED 25.0 \n",
+ "... ... ... \n",
+ "1970 ATTORNEY 1000.0 \n",
+ "1971 ATTORNEY 1000.0 \n",
+ "1972 HOMEMAKER 2500.0 \n",
+ "1973 CONSTRUCTION MANAGER 2500.0 \n",
+ "1974 HOMEMAKER 2500.0 \n",
+ "1975 LEGISLATOR 250.0 \n",
+ "1976 ATTORNEY 2500.0 \n",
+ "1977 C.E.O. & PRESIDENT 2500.0 \n",
+ "1978 EXECUTIVE 2500.0 \n",
+ "1979 HOMEMAKER 2500.0 \n",
+ "1980 C.P.A. 2500.0 \n",
+ "1981 CONSULTANT 500.0 \n",
+ "1982 RETIRED 2500.0 \n",
+ "1983 INFORMATION REQUESTED PER BEST EFFORTS 2500.0 \n",
+ "1984 HOMEMAKER 2500.0 \n",
+ "1985 REAL ESTATE MANAGER 2500.0 \n",
+ "1986 REAL ESTATE MANAGER 2500.0 \n",
+ "1987 REAL ESTATE MANAGER -2500.0 \n",
+ "1988 HOMEMAKER 2500.0 \n",
+ "1989 HOMEMAKER 2500.0 \n",
+ "1990 HOMEMAKER -2500.0 \n",
+ "1991 FINANCE 500.0 \n",
+ "1992 ORTHOPEDIC SURGEON 1000.0 \n",
+ "1993 ORTHOPEDIC SURGEON 1500.0 \n",
+ "1994 SELF-EMPLOYED 2500.0 \n",
+ "1995 SELF-EMPLOYED 2500.0 \n",
+ "1996 LOBBYIST 2500.0 \n",
+ "1997 HOMEMAKER 2500.0 \n",
+ "1998 PRESIDENT 250.0 \n",
+ "1999 RETIRED 250.0 \n",
+ "\n",
+ " contb_receipt_dt receipt_desc memo_cd \\\n",
+ "1000 13-Feb-12 NaN NaN \n",
+ "1001 18-Feb-12 NaN NaN \n",
+ "1002 29-Feb-12 NaN NaN \n",
+ "1003 29-Feb-12 NaN NaN \n",
+ "1004 22-Feb-12 NaN NaN \n",
+ "1005 22-Feb-12 NaN NaN \n",
+ "1006 08-Feb-12 NaN NaN \n",
+ "1007 28-Feb-12 NaN NaN \n",
+ "1008 28-Feb-12 NaN NaN \n",
+ "1009 11-Feb-12 NaN NaN \n",
+ "1010 22-Feb-12 NaN NaN \n",
+ "1011 13-Mar-12 NaN NaN \n",
+ "1012 03-Feb-12 NaN NaN \n",
+ "1013 22-Feb-12 NaN NaN \n",
+ "1014 21-Feb-12 NaN NaN \n",
+ "1015 29-Feb-12 NaN NaN \n",
+ "1016 29-Feb-12 NaN NaN \n",
+ "1017 06-Feb-12 NaN NaN \n",
+ "1018 21-Feb-12 NaN NaN \n",
+ "1019 27-Feb-12 NaN NaN \n",
+ "1020 14-Feb-12 NaN NaN \n",
+ "1021 28-Feb-12 NaN NaN \n",
+ "1022 27-Feb-12 NaN NaN \n",
+ "1023 22-Feb-12 NaN NaN \n",
+ "1024 13-Feb-12 NaN NaN \n",
+ "1025 22-Feb-12 NaN NaN \n",
+ "1026 10-Feb-12 NaN NaN \n",
+ "1027 29-Feb-12 NaN NaN \n",
+ "1028 09-Jan-12 NaN NaN \n",
+ "1029 31-Jan-12 NaN NaN \n",
+ "... ... ... ... \n",
+ "1970 24-May-11 NaN NaN \n",
+ "1971 30-Jun-11 NaN NaN \n",
+ "1972 24-May-11 NaN NaN \n",
+ "1973 26-Apr-11 NaN NaN \n",
+ "1974 26-Apr-11 NaN NaN \n",
+ "1975 16-May-11 NaN NaN \n",
+ "1976 30-Jun-11 NaN NaN \n",
+ "1977 29-Jun-11 NaN NaN \n",
+ "1978 20-Jun-11 NaN NaN \n",
+ "1979 24-May-11 NaN NaN \n",
+ "1980 04-May-11 NaN NaN \n",
+ "1981 25-Apr-11 NaN NaN \n",
+ "1982 28-Jun-11 NaN NaN \n",
+ "1983 30-Jun-11 NaN NaN \n",
+ "1984 30-Jun-11 NaN NaN \n",
+ "1985 13-Apr-11 NaN NaN \n",
+ "1986 16-May-11 SEE REATTRIBUTION NaN \n",
+ "1987 25-May-11 REATTRIBUTION TO SPOUSE X \n",
+ "1988 25-May-11 REATTRIBUTION FROM SPOUSE X \n",
+ "1989 15-Jun-11 NaN NaN \n",
+ "1990 22-Jun-11 NaN NaN \n",
+ "1991 16-May-11 NaN NaN \n",
+ "1992 12-Apr-11 NaN NaN \n",
+ "1993 30-Jun-11 NaN NaN \n",
+ "1994 16-May-11 NaN NaN \n",
+ "1995 16-May-11 NaN NaN \n",
+ "1996 16-May-11 NaN NaN \n",
+ "1997 16-May-11 NaN NaN \n",
+ "1998 10-Jun-11 NaN NaN \n",
+ "1999 06-Jun-11 NaN NaN \n",
+ "\n",
+ " memo_text form_tp file_num \n",
+ "1000 NaN SA17A 780124 \n",
+ "1001 NaN SA17A 780124 \n",
+ "1002 NaN SA17A 780124 \n",
+ "1003 NaN SA17A 780124 \n",
+ "1004 NaN SA17A 780124 \n",
+ "1005 NaN SA17A 780124 \n",
+ "1006 NaN SA17A 780124 \n",
+ "1007 NaN SA17A 780124 \n",
+ "1008 NaN SA17A 780124 \n",
+ "1009 NaN SA17A 780124 \n",
+ "1010 NaN SA17A 780124 \n",
+ "1011 NaN SA17A 780128 \n",
+ "1012 NaN SA17A 780124 \n",
+ "1013 NaN SA17A 780124 \n",
+ "1014 NaN SA17A 780124 \n",
+ "1015 NaN SA17A 780124 \n",
+ "1016 NaN SA17A 780124 \n",
+ "1017 NaN SA17A 780124 \n",
+ "1018 NaN SA17A 780124 \n",
+ "1019 NaN SA17A 780124 \n",
+ "1020 NaN SA17A 780124 \n",
+ "1021 NaN SA17A 780124 \n",
+ "1022 NaN SA17A 780124 \n",
+ "1023 NaN SA17A 780124 \n",
+ "1024 NaN SA17A 780124 \n",
+ "1025 NaN SA17A 780124 \n",
+ "1026 NaN SA17A 780124 \n",
+ "1027 NaN SA17A 780124 \n",
+ "1028 NaN SA17A 771933 \n",
+ "1029 NaN SA17A 771933 \n",
+ "... ... ... ... \n",
+ "1970 NaN SA17A 760248 \n",
+ "1971 NaN SA17A 760248 \n",
+ "1972 NaN SA17A 760248 \n",
+ "1973 NaN SA17A 760248 \n",
+ "1974 NaN SA17A 760248 \n",
+ "1975 NaN SA17A 760248 \n",
+ "1976 NaN SA17A 760248 \n",
+ "1977 NaN SA17A 760248 \n",
+ "1978 NaN SA17A 760248 \n",
+ "1979 NaN SA17A 760248 \n",
+ "1980 NaN SA17A 760248 \n",
+ "1981 NaN SA17A 760248 \n",
+ "1982 NaN SA17A 760248 \n",
+ "1983 NaN SA17A 760248 \n",
+ "1984 NaN SA17A 760248 \n",
+ "1985 NaN SA17A 760248 \n",
+ "1986 SEE REATTRIBUTION SA17A 760248 \n",
+ "1987 REATTRIBUTION TO SPOUSE SA17A 760248 \n",
+ "1988 REATTRIBUTION FROM SPOUSE SA17A 760248 \n",
+ "1989 CHARGED BACK $2500.00 ON 06/22/2011 SA17A 760248 \n",
+ "1990 CHARGED BACK SA17A 760248 \n",
+ "1991 NaN SA17A 760248 \n",
+ "1992 NaN SA17A 760248 \n",
+ "1993 NaN SA17A 760248 \n",
+ "1994 NaN SA17A 760248 \n",
+ "1995 NaN SA17A 760248 \n",
+ "1996 NaN SA17A 760248 \n",
+ "1997 NaN SA17A 760248 \n",
+ "1998 NaN SA17A 760248 \n",
+ "1999 NaN SA17A 760248 \n",
+ "\n",
+ "[1000 rows x 16 columns]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "next(리더)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. 클립보드를 이용하여 데이터 읽기"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#IE에서는 안되고, 크롬에서 저장하고자 하는 테이블 형태의 데이터를 복사한 후에 아래 명령어 출력하면, 데이터프레임으로 생성됨.\n",
+ "주식정보 = pd.read_clipboard(engine ='python')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | cmte_id | \n", + "cand_id | \n", + "cand_nm | \n", + "contbr_nm | \n", + "contbr_city | \n", + "contbr_st | \n", + "contbr_zip | \n", + "contbr_employer | \n", + "contbr_occupation | \n", + "contb_receipt_amt | \n", + "contb_receipt_dt | \n", + "receipt_desc | \n", + "memo_cd | \n", + "memo_text | \n", + "form_tp | \n", + "file_num | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1000 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "NEUWIEN, SUSAN W. MS. | \n", + "ENTERPRISE | \n", + "AL | \n", + "363302385 | \n", + "RETIRED | \n", + "RETIRED | \n", + "1000.0 | \n", + "13-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1001 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "NEUWIEN, SUSAN W. MS. | \n", + "ENTERPRISE | \n", + "AL | \n", + "363302385 | \n", + "RETIRED | \n", + "RETIRED | \n", + "100.0 | \n", + "18-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1002 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "NELSON, ARTHUR R. MR. | \n", + "DECATUR | \n", + "AL | \n", + "356034622 | \n", + "TFA | \n", + "PRESIDENT | \n", + "200.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1003 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HOLLIS, HAYNE MR. | \n", + "DOTHAN | \n", + "AL | \n", + "363033008 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1004 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "GORRIE, ALISON MRS. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352231228 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1005 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "GORRIE, JIM MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352231228 | \n", + "B. & G. | \n", + "CONTRACTOR | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1006 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "EVANS, NANCY D. MRS. | \n", + "DOTHAN | \n", + "AL | \n", + "363059355 | \n", + "WALLACE COLLEGE | \n", + "CLINICAL NURSING INSTRUCTOR | \n", + "100.0 | \n", + "08-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1007 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COX, ANNETTE MS. | \n", + "DELL CITY | \n", + "AL | \n", + "351286719 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "500.0 | \n", + "28-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1008 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COX, ANNETTE P. MS. | \n", + "SPRINGVILLE | \n", + "AL | \n", + "35146 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "500.0 | \n", + "28-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1009 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BALLARD, HALYCON | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352103049 | \n", + "SELF-EMPLOYED | \n", + "H R EXECUTIVE | \n", + "50.0 | \n", + "11-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1010 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BALLARD, HALYCON | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352103049 | \n", + "SELF-EMPLOYED | \n", + "H R EXECUTIVE | \n", + "50.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1011 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BOHANNON, PATRICIA COPLEY MS. | \n", + "ATTALLA | \n", + "AL | \n", + "359547827 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "50.0 | \n", + "13-Mar-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780128 | \n", + "
| 1012 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "WHITT, MARY A. DR. | \n", + "GADSDEN | \n", + "AL | \n", + "359014001 | \n", + "RETIRED | \n", + "RETIRED | \n", + "25.0 | \n", + "03-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1013 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FEARERINGILL, WILLIAM W. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352097801 | \n", + "PRIVATE CAPITAL | \n", + "PRESIDENT | \n", + "2000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1014 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "WILSON, JAMES W. MR. III | \n", + "MONTGOMERY | \n", + "AL | \n", + "361177024 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "1000.0 | \n", + "21-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1015 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "SHERLING, GEORGE L. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352435239 | \n", + "RETIRED | \n", + "RETIRED | \n", + "18.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1016 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "SHERLING, GEORGE L. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352435239 | \n", + "RETIRED | \n", + "RETIRED | \n", + "1000.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1017 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "THOMAS, SUSAN E. MS. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352131813 | \n", + "RETIRED | \n", + "RETIRED | \n", + "50.0 | \n", + "06-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1018 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "THOMAS, SUSAN E. MS. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352131813 | \n", + "RETIRED | \n", + "RETIRED | \n", + "25.0 | \n", + "21-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1019 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "RAMSAY-NAILE, ASHLEY | \n", + "MOBILE | \n", + "AL | \n", + "366194419 | \n", + "CROWDERGULF | \n", + "GENERAL MANAGER / VICE PRESIDENT | \n", + "500.0 | \n", + "27-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1020 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "NIELSEN, CLAUDE B. MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352133956 | \n", + "COCA-COLA BOTTLING CO. UNITED INC. | \n", + "CHAIRMAN/C.E.O. | \n", + "2500.0 | \n", + "14-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1021 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "MATHIS, ALAN | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352262418 | \n", + "JOHNSTON BARTON PROCTOR & ROSE LLP | \n", + "ATTORNEY | \n", + "250.0 | \n", + "28-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1022 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "MATTHEWS, JOHN E. MR. | \n", + "MONTGOMERY | \n", + "AL | \n", + "361112013 | \n", + "RETIRED | \n", + "RETIRED | \n", + "100.0 | \n", + "27-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1023 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "MATTHEWS, WILLIAM MR. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352133024 | \n", + "NATIONAL BANK OF COMMERCE | \n", + "BANKING/C.F.O. | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1024 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "KNOWLES, KENNETH J. MR. | \n", + "HUNTSVILLE | \n", + "AL | \n", + "358033361 | \n", + "RETIRED | \n", + "RETIRED | \n", + "50.0 | \n", + "13-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1025 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FERRELL, PATRICK | \n", + "MONTGOMERY | \n", + "AL | \n", + "361240185 | \n", + "THE PATRICK COMPANY | \n", + "OWNER - CONSULTANT | \n", + "1000.0 | \n", + "22-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1026 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "DANDURAND, RICHARD L. MR. | \n", + "VESTAVIA | \n", + "AL | \n", + "352432822 | \n", + "RETIRED | \n", + "RETIRED | \n", + "250.0 | \n", + "10-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1027 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CABANISS, CATHERINE C. MS. | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352133916 | \n", + "SELF-EMPLOYED | \n", + "ARTIST | \n", + "500.0 | \n", + "29-Feb-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "780124 | \n", + "
| 1028 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "OUTLAND, JAMES | \n", + "BIRMINGHAM | \n", + "AL | \n", + "352231232 | \n", + "NEW CAPITAL PARTNERS | \n", + "INVESTMENTS | \n", + "250.0 | \n", + "09-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| 1029 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "THOMPSON, PATRICIA ANN MS. | \n", + "OWENS CROSS ROADS | \n", + "AL | \n", + "357638677 | \n", + "RETIRED | \n", + "RETIRED | \n", + "25.0 | \n", + "31-Jan-12 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "771933 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1970 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CROCKETT, C. WEBB MR. | \n", + "MESA | \n", + "AZ | \n", + "852134001 | \n", + "FENNEMORE | \n", + "ATTORNEY | \n", + "1000.0 | \n", + "24-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1971 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CROCKETT, JEFF W. MR. | \n", + "MESA | \n", + "AZ | \n", + "852133242 | \n", + "BROWNSTEIN HYATT FARBER SCHRECK | \n", + "ATTORNEY | \n", + "1000.0 | \n", + "30-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1972 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "BEUS, ANNETTE MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "850484526 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "24-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1973 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CASE, ARTHUR D. MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850166040 | \n", + "LOW MOUNTAIN CONSTRUCTION INC. | \n", + "CONSTRUCTION MANAGER | \n", + "2500.0 | \n", + "26-Apr-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1974 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CASE, NANCY I. MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "850166027 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "26-Apr-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1975 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COURT, STEPHEN G. MR. | \n", + "MESA | \n", + "AZ | \n", + "852033603 | \n", + "STATE OF ARIZONA | \n", + "LEGISLATOR | \n", + "250.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1976 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COWLEY, SAM MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850481828 | \n", + "SELF-EMPLOYED | \n", + "ATTORNEY | \n", + "2500.0 | \n", + "30-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1977 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "COWMAN, STEVE MR. | \n", + "SCOTTSDALE | \n", + "AZ | \n", + "852504615 | \n", + "ENERGY DEVELOPMENTS INC. | \n", + "C.E.O. & PRESIDENT | \n", + "2500.0 | \n", + "29-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1978 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CARDON, CRAIG D. MR. | \n", + "QUEEN CREEK | \n", + "AZ | \n", + "851423667 | \n", + "THE VINEYARD GROUP | \n", + "EXECUTIVE | \n", + "2500.0 | \n", + "20-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1979 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "CARDON, KASTLE K. MS. | \n", + "GILBERT | \n", + "AZ | \n", + "852969451 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "24-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1980 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "GARNER, RICHARD MR. | \n", + "MESA | \n", + "AZ | \n", + "852133248 | \n", + "PIVOTAL GROUP INC | \n", + "C.P.A. | \n", + "2500.0 | \n", + "04-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1981 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANSEN, VAN MR. | \n", + "MESA | \n", + "AZ | \n", + "852135521 | \n", + "ODYSSEY CAPITAL GROUP | \n", + "CONSULTANT | \n", + "500.0 | \n", + "25-Apr-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1982 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "GARDNER, ELBERT | \n", + "MESA | \n", + "AZ | \n", + "852053802 | \n", + "RETIRED | \n", + "RETIRED | \n", + "2500.0 | \n", + "28-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1983 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "GARDNER, KATHLEEN | \n", + "MESA | \n", + "AZ | \n", + "852053802 | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "INFORMATION REQUESTED PER BEST EFFORTS | \n", + "2500.0 | \n", + "30-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1984 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "JOHNSON, BECKY H. MS. | \n", + "MESA | \n", + "AZ | \n", + "852132931 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "30-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1985 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, LEE T. MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850164261 | \n", + "VESTAR | \n", + "REAL ESTATE MANAGER | \n", + "2500.0 | \n", + "13-Apr-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1986 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, LEE T. MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850164261 | \n", + "VESTAR | \n", + "REAL ESTATE MANAGER | \n", + "2500.0 | \n", + "16-May-11 | \n", + "SEE REATTRIBUTION | \n", + "NaN | \n", + "SEE REATTRIBUTION | \n", + "SA17A | \n", + "760248 | \n", + "
| 1987 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, LEE T. MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850164261 | \n", + "VESTAR | \n", + "REAL ESTATE MANAGER | \n", + "-2500.0 | \n", + "25-May-11 | \n", + "REATTRIBUTION TO SPOUSE | \n", + "X | \n", + "REATTRIBUTION TO SPOUSE | \n", + "SA17A | \n", + "760248 | \n", + "
| 1988 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, NANCY R. MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "85253 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "25-May-11 | \n", + "REATTRIBUTION FROM SPOUSE | \n", + "X | \n", + "REATTRIBUTION FROM SPOUSE | \n", + "SA17A | \n", + "760248 | \n", + "
| 1989 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, NANCY R. MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "85253 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "15-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "CHARGED BACK $2500.00 ON 06/22/2011 | \n", + "SA17A | \n", + "760248 | \n", + "
| 1990 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HANLEY, NANCY R. MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "85253 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "-2500.0 | \n", + "22-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "CHARGED BACK | \n", + "SA17A | \n", + "760248 | \n", + "
| 1991 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "JENSON, JEFF | \n", + "PEORIA | \n", + "AZ | \n", + "853835107 | \n", + "VESCOR HOLDINGS | \n", + "FINANCE | \n", + "500.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1992 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HALES, DONALD D. DR. | \n", + "FLAGSTAFF | \n", + "AZ | \n", + "860012000 | \n", + "NORTHERN ARIZONA ORTHOPAEDICS | \n", + "ORTHOPEDIC SURGEON | \n", + "1000.0 | \n", + "12-Apr-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1993 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HALES, DONALD D. DR. | \n", + "FLAGSTAFF | \n", + "AZ | \n", + "860012000 | \n", + "NORTHERN ARIZONA ORTHOPAEDICS | \n", + "ORTHOPEDIC SURGEON | \n", + "1500.0 | \n", + "30-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1994 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HALL, DIANNA M. MS. | \n", + "GLENDALE | \n", + "AZ | \n", + "853086132 | \n", + "SELF-EMPLOYED | \n", + "SELF-EMPLOYED | \n", + "2500.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1995 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "HALL, KENNETH T. MR. | \n", + "GLENDALE | \n", + "AZ | \n", + "853086132 | \n", + "SELF-EMPLOYED | \n", + "SELF-EMPLOYED | \n", + "2500.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1996 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "DEMENNA, KEVIN B. MR. | \n", + "PHOENIX | \n", + "AZ | \n", + "850072603 | \n", + "DEMENNA & ASSOCIATES | \n", + "LOBBYIST | \n", + "2500.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1997 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "DEMENNA, MELISSA MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "850072603 | \n", + "HOMEMAKER | \n", + "HOMEMAKER | \n", + "2500.0 | \n", + "16-May-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1998 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "JACKSON, WILLIAM G. MR. | \n", + "PARADISE VALLEY | \n", + "AZ | \n", + "852531824 | \n", + "CABLE AMERICA COORPORATION | \n", + "PRESIDENT | \n", + "250.0 | \n", + "10-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
| 1999 | \n", + "C00431171 | \n", + "P80003353 | \n", + "Romney, Mitt | \n", + "FOYLE, DOLORES D. MRS. | \n", + "PHOENIX | \n", + "AZ | \n", + "850329213 | \n", + "RETIRED | \n", + "RETIRED | \n", + "250.0 | \n", + "06-Jun-11 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "SA17A | \n", + "760248 | \n", + "
1000 rows × 16 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " N 종목명 현재가 전일비 등락률 액면가 시가총액 상장주식수 \\\n",
+ "0 1 삼성전자 45,500 상승 600 +1.34% 100 2,920,793 6,419,325 \n",
+ "1 2 SK하이닉스 86,100 상승 800 +0.94% 5,000 626,810 728,002 \n",
+ "2 3 셀트리온 290,000 상승 500 +0.17% 1,000 363,648 125,396 \n",
+ "3 4 삼성전자우 36,550 상승 200 +0.55% 100 330,276 903,629 \n",
+ "4 5 삼성바이오로직스 428,500 상승 18,500 +4.51% 2,500 283,517 66,165 \n",
+ "\n",
+ " 외국인비율 거래량 PER ROE 토론실 \n",
+ "0 52.50 4,484,323 8.39 21.01 토론실 \n",
+ "1 50.65 657,996 5.89 36.80 토론실 \n",
+ "2 22.07 160,216 90.77 17.84 토론실 \n",
+ "3 84.00 200,064 6.74 NaN 토론실 \n",
+ "4 9.62 133,487 -292.29 -2.41 토론실 "
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "주식정보"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. 데이터베이스에서 읽기\n",
+ "### 파이썬에서 sqlite를 내장하고 있음"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sqlite3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#테이블 생성 SQL 정의\n",
+ "테이블생성 = \"\"\"CREATE TABLE locations\n",
+ " (시 varchar[20], 구 varchar[20], 선호도 integer)\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sqlite 접속\n",
+ "conn = sqlite3.connect('test.db')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | N | \n", + "종목명 | \n", + "현재가 | \n", + "전일비 | \n", + "등락률 | \n", + "액면가 | \n", + "시가총액 | \n", + "상장주식수 | \n", + "외국인비율 | \n", + "거래량 | \n", + "PER | \n", + "ROE | \n", + "토론실 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "삼성전자 | \n", + "45,500 | \n", + "상승 600 | \n", + "+1.34% | \n", + "100 | \n", + "2,920,793 | \n", + "6,419,325 | \n", + "52.50 | \n", + "4,484,323 | \n", + "8.39 | \n", + "21.01 | \n", + "토론실 | \n", + "
| 1 | \n", + "2 | \n", + "SK하이닉스 | \n", + "86,100 | \n", + "상승 800 | \n", + "+0.94% | \n", + "5,000 | \n", + "626,810 | \n", + "728,002 | \n", + "50.65 | \n", + "657,996 | \n", + "5.89 | \n", + "36.80 | \n", + "토론실 | \n", + "
| 2 | \n", + "3 | \n", + "셀트리온 | \n", + "290,000 | \n", + "상승 500 | \n", + "+0.17% | \n", + "1,000 | \n", + "363,648 | \n", + "125,396 | \n", + "22.07 | \n", + "160,216 | \n", + "90.77 | \n", + "17.84 | \n", + "토론실 | \n", + "
| 3 | \n", + "4 | \n", + "삼성전자우 | \n", + "36,550 | \n", + "상승 200 | \n", + "+0.55% | \n", + "100 | \n", + "330,276 | \n", + "903,629 | \n", + "84.00 | \n", + "200,064 | \n", + "6.74 | \n", + "NaN | \n", + "토론실 | \n", + "
| 4 | \n", + "5 | \n", + "삼성바이오로직스 | \n", + "428,500 | \n", + "상승 18,500 | \n", + "+4.51% | \n", + "2,500 | \n", + "283,517 | \n", + "66,165 | \n", + "9.62 | \n", + "133,487 | \n", + "-292.29 | \n", + "-2.41 | \n", + "토론실 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 시 구 선호도\n",
+ "0 서울 강남 15\n",
+ "1 서울 강동 10\n",
+ "2 서울 동대문 7\n",
+ "3 서울 양천 12\n",
+ "4 성남 분당 12\n",
+ "5 수원 영통 15\n",
+ "6 성남 중원 11\n",
+ "7 수원 팔달 14"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Select SQL 실행1\n",
+ "pd.read_sql('SELECT *FROM locations', conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 시 | \n", + "구 | \n", + "선호도 | \n", + "
|---|---|---|---|
| 0 | \n", + "서울 | \n", + "강남 | \n", + "15 | \n", + "
| 1 | \n", + "서울 | \n", + "강동 | \n", + "10 | \n", + "
| 2 | \n", + "서울 | \n", + "동대문 | \n", + "7 | \n", + "
| 3 | \n", + "서울 | \n", + "양천 | \n", + "12 | \n", + "
| 4 | \n", + "성남 | \n", + "분당 | \n", + "12 | \n", + "
| 5 | \n", + "수원 | \n", + "영통 | \n", + "15 | \n", + "
| 6 | \n", + "성남 | \n", + "중원 | \n", + "11 | \n", + "
| 7 | \n", + "수원 | \n", + "팔달 | \n", + "14 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 시 sum(선호도)\n",
+ "0 서울 44\n",
+ "1 성남 23\n",
+ "2 수원 29"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Select SQL 실행2 # 줄바꿈 시 \\ 사용\n",
+ "pd.read_sql('SELECT 시, sum(선호도)\\\n",
+ " FROM locations GROUP BY 시', conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 데이터베이스의 테이블 크기가 큰 경우에는 cursor(커서) 사용하기\n",
+ "# 커서까지 읽어오는 것\n",
+ "cursor = conn.execute('SELECT * FROM locations')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | 시 | \n", + "sum(선호도) | \n", + "
|---|---|---|
| 0 | \n", + "서울 | \n", + "44 | \n", + "
| 1 | \n", + "성남 | \n", + "23 | \n", + "
| 2 | \n", + "수원 | \n", + "29 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 0 1 2\n",
+ "0 성남 분당 12\n",
+ "1 수원 영통 15"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 데이터프레임에 넣어 읽어오기\n",
+ "DataFrame(rows)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. 엑셀 파일 읽기\n",
+ "### 기본적으로 첫번째 시트에 있는 데이터를 읽어와서 데이터프레임으로 저장함. \n",
+ "### 모든 시트를 읽기 위해서는 sheetname인자를 None으로 설정\n",
+ "### sheetname = None. 모든 시트의 데이터를 읽어서 사전 형태로 저장됨. \n",
+ "### key = 시트의 이름, value = 각 시트에 있는 데이터들을 저장한 데이터프레임 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ex6.xlsx 파일 \n",
+ "| \n", + " | 0 | \n", + "1 | \n", + "2 | \n", + "
|---|---|---|---|
| 0 | \n", + "성남 | \n", + "분당 | \n", + "12 | \n", + "
| 1 | \n", + "수원 | \n", + "영통 | \n", + "15 | \n", + "
![\"csv파일예제\"](\"img/엑셀파일예제.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " a b\n",
+ "0 1 2\n",
+ "1 5 6\n",
+ "2 9 10"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 첫번째 시트 읽기\n",
+ "pd.read_excel('data/ex6.xlsx')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | a | \n", + "b | \n", + "
|---|---|---|
| 0 | \n", + "1 | \n", + "2 | \n", + "
| 1 | \n", + "5 | \n", + "6 | \n", + "
| 2 | \n", + "9 | \n", + "10 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " c d message\n",
+ "0 3 4 hello\n",
+ "1 7 8 world\n",
+ "2 11 12 foo"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 두번째 시트 읽기\n",
+ "pd.read_excel('data/ex6.xlsx',sheet_name = '시트2')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "OrderedDict([('시트1', a b\n",
+ " 0 1 2\n",
+ " 1 5 6\n",
+ " 2 9 10), ('시트2', c d message\n",
+ " 0 3 4 hello\n",
+ " 1 7 8 world\n",
+ " 2 11 12 foo)])"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 모든 시트 다 읽어오기\n",
+ "data = pd.read_excel('data/ex6.xlsx', sheet_name = None)\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "odict_keys(['시트1', '시트2'])"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "odict_values([ a b\n",
+ "0 1 2\n",
+ "1 5 6\n",
+ "2 9 10, c d message\n",
+ "0 3 4 hello\n",
+ "1 7 8 world\n",
+ "2 11 12 foo])"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.values()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 시트1, 시트2 데이터를 각각 data1, datda2에 저장\n",
+ "data1,data2 = data.values()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | c | \n", + "d | \n", + "message | \n", + "
|---|---|---|---|
| 0 | \n", + "3 | \n", + "4 | \n", + "hello | \n", + "
| 1 | \n", + "7 | \n", + "8 | \n", + "world | \n", + "
| 2 | \n", + "11 | \n", + "12 | \n", + "foo | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " a b\n",
+ "0 1 2\n",
+ "1 5 6\n",
+ "2 9 10"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | a | \n", + "b | \n", + "
|---|---|---|
| 0 | \n", + "1 | \n", + "2 | \n", + "
| 1 | \n", + "5 | \n", + "6 | \n", + "
| 2 | \n", + "9 | \n", + "10 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " c d message\n",
+ "0 3 4 hello\n",
+ "1 7 8 world\n",
+ "2 11 12 foo"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 4. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 1 - \353\215\260\354\235\264\355\204\260 \355\225\251\354\271\230\352\270\260 (merge, concat).ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 4. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 1 - \353\215\260\354\235\264\355\204\260 \355\225\251\354\271\230\352\270\260 (merge, concat).ipynb"
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@@ -0,0 +1,7059 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. 데이터 가공 1 - 데이터 합치기\n",
+ "## 4-1. pandas.merge()\n",
+ "| \n", + " | c | \n", + "d | \n", + "message | \n", + "
|---|---|---|---|
| 0 | \n", + "3 | \n", + "4 | \n", + "hello | \n", + "
| 1 | \n", + "7 | \n", + "8 | \n", + "world | \n", + "
| 2 | \n", + "11 | \n", + "12 | \n", + "foo | \n", + "
![\"merge예제\"](\"img/merge예제.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#샘플 데이터 생성\n",
+ "df1 = DataFrame([[1,3],[2,4]], \n",
+ " columns = list('AB'))\n",
+ "df2 = DataFrame([[1,'aa'],[2,'bb']], \n",
+ " columns = list('AC'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " A B C\n",
+ "0 1 3 aa\n",
+ "1 2 4 bb"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.merge(df1,df2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " ### 주요 파라미터\n",
+ " - 키를 기준으로 DataFrame의 로우를 합친다. SQL이나 다른 관계형 데이터베이스의 join 연산과 동일함.\n",
+ " - 주요 파라미터 \n",
+ " . left, right : merge할 DataFrame 객체이름\n",
+ " \n",
+ " . how = 'inner', #left, right, outer\n",
+ " \n",
+ " . on = None, #merge의 기준이 되는 컬럼\n",
+ " \n",
+ " . left_on = None, #left DataFrame의 기준 컬럼\n",
+ " \n",
+ " . right_on = None, #right DataFrame의 기준 컬럼"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_left = DataFrame({'KEY': ['k0', 'k1', 'k2', 'k3'],\n",
+ " 'A': ['a0', 'a1', 'a2', 'a3'],\n",
+ " 'B': ['b0', 'b1', 'b2', 'b3']})\n",
+ "df_right = DataFrame({'KEY': ['k2', 'k3', 'k4', 'k5'],\n",
+ " 'C': ['c2', 'c3', 'c4', 'c5'],\n",
+ " 'D': ['d2', 'd3', 'd4', 'd5']})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "C | \n", + "
|---|---|---|---|
| 0 | \n", + "1 | \n", + "3 | \n", + "aa | \n", + "
| 1 | \n", + "2 | \n", + "4 | \n", + "bb | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY A B\n",
+ "0 k0 a0 b0\n",
+ "1 k1 a1 b1\n",
+ "2 k2 a2 b2\n",
+ "3 k3 a3 b3"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_left"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "A | \n", + "B | \n", + "
|---|---|---|---|
| 0 | \n", + "k0 | \n", + "a0 | \n", + "b0 | \n", + "
| 1 | \n", + "k1 | \n", + "a1 | \n", + "b1 | \n", + "
| 2 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "
| 3 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY C D\n",
+ "0 k2 c2 d2\n",
+ "1 k3 c3 d3\n",
+ "2 k4 c4 d4\n",
+ "3 k5 c5 d5"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_right"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|
| 0 | \n", + "k2 | \n", + "c2 | \n", + "d2 | \n", + "
| 1 | \n", + "k3 | \n", + "c3 | \n", + "d3 | \n", + "
| 2 | \n", + "k4 | \n", + "c4 | \n", + "d4 | \n", + "
| 3 | \n", + "k5 | \n", + "c5 | \n", + "d5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY A B C D\n",
+ "0 k2 a2 b2 c2 d2\n",
+ "1 k3 a3 b3 c3 d3"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# inner join(default)\n",
+ "pd.merge(df_left, df_right)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "c2 | \n", + "d2 | \n", + "
| 1 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "c3 | \n", + "d3 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY A B C D\n",
+ "0 k0 a0 b0 NaN NaN\n",
+ "1 k1 a1 b1 NaN NaN\n",
+ "2 k2 a2 b2 c2 d2\n",
+ "3 k3 a3 b3 c3 d3"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# left outer join\n",
+ "pd.merge(df_left,df_right, how='left')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "k0 | \n", + "a0 | \n", + "b0 | \n", + "NaN | \n", + "NaN | \n", + "
| 1 | \n", + "k1 | \n", + "a1 | \n", + "b1 | \n", + "NaN | \n", + "NaN | \n", + "
| 2 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "c2 | \n", + "d2 | \n", + "
| 3 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "c3 | \n", + "d3 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY A B C D\n",
+ "0 k2 a2 b2 c2 d2\n",
+ "1 k3 a3 b3 c3 d3\n",
+ "2 k4 NaN NaN c4 d4\n",
+ "3 k5 NaN NaN c5 d5"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#right outer join\n",
+ "pd.merge(df_left, df_right, how='right')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "c2 | \n", + "d2 | \n", + "
| 1 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "c3 | \n", + "d3 | \n", + "
| 2 | \n", + "k4 | \n", + "NaN | \n", + "NaN | \n", + "c4 | \n", + "d4 | \n", + "
| 3 | \n", + "k5 | \n", + "NaN | \n", + "NaN | \n", + "c5 | \n", + "d5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " KEY A B C D\n",
+ "0 k0 a0 b0 NaN NaN\n",
+ "1 k1 a1 b1 NaN NaN\n",
+ "2 k2 a2 b2 c2 d2\n",
+ "3 k3 a3 b3 c3 d3\n",
+ "4 k4 NaN NaN c4 d4\n",
+ "5 k5 NaN NaN c5 d5"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# fully outer join\n",
+ "pd.merge(df_left, df_right, how='outer')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 실습, 아래 2개의 DataFrame을 K1과 K2 컬럼으로 outer merge\n",
+ "df_left2 = DataFrame({'K1': ['k0', 'k1', 'k2', 'k3'],\n",
+ " 'A': ['a0', 'a1', 'a2', 'a3'],\n",
+ " 'B': ['b0', 'b1', 'b2', 'b3']})\n",
+ "df_right2 = DataFrame({'K2': ['k2', 'k3', 'k4', 'k5'],\n",
+ " 'C': ['c2', 'c3', 'c4', 'c5'],\n",
+ " 'D': ['d2', 'd3', 'd4', 'd5']}) \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | KEY | \n", + "A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "k0 | \n", + "a0 | \n", + "b0 | \n", + "NaN | \n", + "NaN | \n", + "
| 1 | \n", + "k1 | \n", + "a1 | \n", + "b1 | \n", + "NaN | \n", + "NaN | \n", + "
| 2 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "c2 | \n", + "d2 | \n", + "
| 3 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "c3 | \n", + "d3 | \n", + "
| 4 | \n", + "k4 | \n", + "NaN | \n", + "NaN | \n", + "c4 | \n", + "d4 | \n", + "
| 5 | \n", + "k5 | \n", + "NaN | \n", + "NaN | \n", + "c5 | \n", + "d5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " K1 A B K2 C D\n",
+ "0 k0 a0 b0 NaN NaN NaN\n",
+ "1 k1 a1 b1 NaN NaN NaN\n",
+ "2 k2 a2 b2 k2 c2 d2\n",
+ "3 k3 a3 b3 k3 c3 d3\n",
+ "4 NaN NaN NaN k4 c4 d4\n",
+ "5 NaN NaN NaN k5 c5 d5"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습\n",
+ "pd.merge(df_left2, df_right2, how = 'outer', left_on = 'K1', right_on = 'K2')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | K1 | \n", + "A | \n", + "B | \n", + "K2 | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "k0 | \n", + "a0 | \n", + "b0 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 1 | \n", + "k1 | \n", + "a1 | \n", + "b1 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 2 | \n", + "k2 | \n", + "a2 | \n", + "b2 | \n", + "k2 | \n", + "c2 | \n", + "d2 | \n", + "
| 3 | \n", + "k3 | \n", + "a3 | \n", + "b3 | \n", + "k3 | \n", + "c3 | \n", + "d3 | \n", + "
| 4 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "k4 | \n", + "c4 | \n", + "d4 | \n", + "
| 5 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "k5 | \n", + "c5 | \n", + "d5 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역\n",
+ "0 1 F 1 10 48067\n",
+ "1 2 M 56 16 70072\n",
+ "2 3 M 25 15 55117\n",
+ "3 4 M 45 7 02460\n",
+ "4 5 M 25 20 55455"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실제 데이터 실습\n",
+ "#1. 사용자 데이터 읽어오기\n",
+ "user = pd.read_csv('data/movielens/users.dat', sep='::', \n",
+ " names=['사용자아이디', '성별', '연령','직업','지역'], engine='python') # sep이 콤마 아니여도 csv라고 통칭 함 \n",
+ "user.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "
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| 2 | \n", + "3 | \n", + "M | \n", + "25 | \n", + "15 | \n", + "55117 | \n", + "
| 3 | \n", + "4 | \n", + "M | \n", + "45 | \n", + "7 | \n", + "02460 | \n", + "
| 4 | \n", + "5 | \n", + "M | \n", + "25 | \n", + "20 | \n", + "55455 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역\n",
+ "6035 6036 F 25 15 32603\n",
+ "6036 6037 F 45 1 76006\n",
+ "6037 6038 F 56 1 14706\n",
+ "6038 6039 F 45 0 01060\n",
+ "6039 6040 M 25 6 11106"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "user.tail()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "
|---|---|---|---|---|---|
| 6035 | \n", + "6036 | \n", + "F | \n", + "25 | \n", + "15 | \n", + "32603 | \n", + "
| 6036 | \n", + "6037 | \n", + "F | \n", + "45 | \n", + "1 | \n", + "76006 | \n", + "
| 6037 | \n", + "6038 | \n", + "F | \n", + "56 | \n", + "1 | \n", + "14706 | \n", + "
| 6038 | \n", + "6039 | \n", + "F | \n", + "45 | \n", + "0 | \n", + "01060 | \n", + "
| 6039 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 영화아이디 평점 타임스탬프\n",
+ "0 1 1193 5 978300760\n",
+ "1 1 661 3 978302109\n",
+ "2 1 914 3 978301968\n",
+ "3 1 3408 4 978300275\n",
+ "4 1 2355 5 978824291"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2. 평점 데이터 읽어오기\n",
+ "# 데이터 파일 : data/movielens/ratings.dat\n",
+ "# 컬럼명들은 ['사용자아이디', '영화아이디', '평점', '타임스탬프']\n",
+ "ratings = pd.read_csv('data/movielens/ratings.dat', sep='::', engine = 'python', \n",
+ " names = ['사용자아이디','영화아이디', '평점','타임스탬프'])\n",
+ "ratings.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "
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| 2 | \n", + "1 | \n", + "914 | \n", + "3 | \n", + "978301968 | \n", + "
| 3 | \n", + "1 | \n", + "3408 | \n", + "4 | \n", + "978300275 | \n", + "
| 4 | \n", + "1 | \n", + "2355 | \n", + "5 | \n", + "978824291 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프\n",
+ "0 1 F 1 10 48067 1193 5 978300760\n",
+ "1 1 F 1 10 48067 661 3 978302109\n",
+ "2 1 F 1 10 48067 914 3 978301968\n",
+ "3 1 F 1 10 48067 3408 4 978300275\n",
+ "4 1 F 1 10 48067 2355 5 978824291\n",
+ "5 1 F 1 10 48067 1197 3 978302268\n",
+ "6 1 F 1 10 48067 1287 5 978302039\n",
+ "7 1 F 1 10 48067 2804 5 978300719\n",
+ "8 1 F 1 10 48067 594 4 978302268\n",
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+ "10 1 F 1 10 48067 595 5 978824268\n",
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+ "12 1 F 1 10 48067 2398 4 978302281\n",
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+ "14 1 F 1 10 48067 1035 5 978301753\n",
+ "15 1 F 1 10 48067 2791 4 978302188\n",
+ "16 1 F 1 10 48067 2687 3 978824268\n",
+ "17 1 F 1 10 48067 2018 4 978301777\n",
+ "18 1 F 1 10 48067 3105 5 978301713\n",
+ "19 1 F 1 10 48067 2797 4 978302039\n",
+ "20 1 F 1 10 48067 2321 3 978302205\n",
+ "21 1 F 1 10 48067 720 3 978300760\n",
+ "22 1 F 1 10 48067 1270 5 978300055\n",
+ "23 1 F 1 10 48067 527 5 978824195\n",
+ "24 1 F 1 10 48067 2340 3 978300103\n",
+ "25 1 F 1 10 48067 48 5 978824351\n",
+ "26 1 F 1 10 48067 1097 4 978301953\n",
+ "27 1 F 1 10 48067 1721 4 978300055\n",
+ "28 1 F 1 10 48067 1545 4 978824139\n",
+ "29 1 F 1 10 48067 745 3 978824268\n",
+ "... ... .. .. .. ... ... .. ...\n",
+ "1000179 6040 M 25 6 11106 2762 4 956704584\n",
+ "1000180 6040 M 25 6 11106 1036 3 956715455\n",
+ "1000181 6040 M 25 6 11106 508 4 956704972\n",
+ "1000182 6040 M 25 6 11106 1041 4 957717678\n",
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+ "1000184 6040 M 25 6 11106 2791 4 956715569\n",
+ "1000185 6040 M 25 6 11106 2794 1 956716438\n",
+ "1000186 6040 M 25 6 11106 527 5 956704219\n",
+ "1000187 6040 M 25 6 11106 2003 1 956716294\n",
+ "1000188 6040 M 25 6 11106 535 4 964828734\n",
+ "1000189 6040 M 25 6 11106 2010 5 957716795\n",
+ "1000190 6040 M 25 6 11106 2011 4 956716113\n",
+ "1000191 6040 M 25 6 11106 3751 4 964828782\n",
+ "1000192 6040 M 25 6 11106 2019 5 956703977\n",
+ "1000193 6040 M 25 6 11106 541 4 956715288\n",
+ "1000194 6040 M 25 6 11106 1077 5 964828799\n",
+ "1000195 6040 M 25 6 11106 1079 2 956715648\n",
+ "1000196 6040 M 25 6 11106 549 4 956704746\n",
+ "1000197 6040 M 25 6 11106 2020 3 956715288\n",
+ "1000198 6040 M 25 6 11106 2021 3 956716374\n",
+ "1000199 6040 M 25 6 11106 2022 5 956716207\n",
+ "1000200 6040 M 25 6 11106 2028 5 956704519\n",
+ "1000201 6040 M 25 6 11106 1080 4 957717322\n",
+ "1000202 6040 M 25 6 11106 1089 4 956704996\n",
+ "1000203 6040 M 25 6 11106 1090 3 956715518\n",
+ "1000204 6040 M 25 6 11106 1091 1 956716541\n",
+ "1000205 6040 M 25 6 11106 1094 5 956704887\n",
+ "1000206 6040 M 25 6 11106 562 5 956704746\n",
+ "1000207 6040 M 25 6 11106 1096 4 956715648\n",
+ "1000208 6040 M 25 6 11106 1097 4 956715569\n",
+ "\n",
+ "[1000209 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3. 사용자와 평점 데이터 합치기\n",
+ "pd.merge(user, ratings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 4\n",
+ "movies = pd.read_csv('data/movielens/movies.dat', sep ='::', engine= 'python', names=[])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "
|---|---|---|---|---|---|---|---|---|
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| 2 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "914 | \n", + "3 | \n", + "978301968 | \n", + "
| 3 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "3408 | \n", + "4 | \n", + "978300275 | \n", + "
| 4 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2355 | \n", + "5 | \n", + "978824291 | \n", + "
| 5 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1197 | \n", + "3 | \n", + "978302268 | \n", + "
| 6 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1287 | \n", + "5 | \n", + "978302039 | \n", + "
| 7 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2804 | \n", + "5 | \n", + "978300719 | \n", + "
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| 9 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "919 | \n", + "4 | \n", + "978301368 | \n", + "
| 10 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "595 | \n", + "5 | \n", + "978824268 | \n", + "
| 11 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "938 | \n", + "4 | \n", + "978301752 | \n", + "
| 12 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2398 | \n", + "4 | \n", + "978302281 | \n", + "
| 13 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2918 | \n", + "4 | \n", + "978302124 | \n", + "
| 14 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1035 | \n", + "5 | \n", + "978301753 | \n", + "
| 15 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2791 | \n", + "4 | \n", + "978302188 | \n", + "
| 16 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2687 | \n", + "3 | \n", + "978824268 | \n", + "
| 17 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2018 | \n", + "4 | \n", + "978301777 | \n", + "
| 18 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "3105 | \n", + "5 | \n", + "978301713 | \n", + "
| 19 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2797 | \n", + "4 | \n", + "978302039 | \n", + "
| 20 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2321 | \n", + "3 | \n", + "978302205 | \n", + "
| 21 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "720 | \n", + "3 | \n", + "978300760 | \n", + "
| 22 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1270 | \n", + "5 | \n", + "978300055 | \n", + "
| 23 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "527 | \n", + "5 | \n", + "978824195 | \n", + "
| 24 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2340 | \n", + "3 | \n", + "978300103 | \n", + "
| 25 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "48 | \n", + "5 | \n", + "978824351 | \n", + "
| 26 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1097 | \n", + "4 | \n", + "978301953 | \n", + "
| 27 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1721 | \n", + "4 | \n", + "978300055 | \n", + "
| 28 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1545 | \n", + "4 | \n", + "978824139 | \n", + "
| 29 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "745 | \n", + "3 | \n", + "978824268 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1000179 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2762 | \n", + "4 | \n", + "956704584 | \n", + "
| 1000180 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1036 | \n", + "3 | \n", + "956715455 | \n", + "
| 1000181 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "508 | \n", + "4 | \n", + "956704972 | \n", + "
| 1000182 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1041 | \n", + "4 | \n", + "957717678 | \n", + "
| 1000183 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "3735 | \n", + "4 | \n", + "960971654 | \n", + "
| 1000184 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2791 | \n", + "4 | \n", + "956715569 | \n", + "
| 1000185 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2794 | \n", + "1 | \n", + "956716438 | \n", + "
| 1000186 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "527 | \n", + "5 | \n", + "956704219 | \n", + "
| 1000187 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2003 | \n", + "1 | \n", + "956716294 | \n", + "
| 1000188 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "535 | \n", + "4 | \n", + "964828734 | \n", + "
| 1000189 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2010 | \n", + "5 | \n", + "957716795 | \n", + "
| 1000190 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2011 | \n", + "4 | \n", + "956716113 | \n", + "
| 1000191 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "3751 | \n", + "4 | \n", + "964828782 | \n", + "
| 1000192 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2019 | \n", + "5 | \n", + "956703977 | \n", + "
| 1000193 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "541 | \n", + "4 | \n", + "956715288 | \n", + "
| 1000194 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1077 | \n", + "5 | \n", + "964828799 | \n", + "
| 1000195 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1079 | \n", + "2 | \n", + "956715648 | \n", + "
| 1000196 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "549 | \n", + "4 | \n", + "956704746 | \n", + "
| 1000197 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2020 | \n", + "3 | \n", + "956715288 | \n", + "
| 1000198 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2021 | \n", + "3 | \n", + "956716374 | \n", + "
| 1000199 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2022 | \n", + "5 | \n", + "956716207 | \n", + "
| 1000200 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2028 | \n", + "5 | \n", + "956704519 | \n", + "
| 1000201 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1080 | \n", + "4 | \n", + "957717322 | \n", + "
| 1000202 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1089 | \n", + "4 | \n", + "956704996 | \n", + "
| 1000203 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1090 | \n", + "3 | \n", + "956715518 | \n", + "
| 1000204 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1091 | \n", + "1 | \n", + "956716541 | \n", + "
| 1000205 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1094 | \n", + "5 | \n", + "956704887 | \n", + "
| 1000206 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "562 | \n", + "5 | \n", + "956704746 | \n", + "
| 1000207 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1096 | \n", + "4 | \n", + "956715648 | \n", + "
| 1000208 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1097 | \n", + "4 | \n", + "956715569 | \n", + "
1000209 rows × 8 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프\n",
+ "0 1 F 1 10 48067 1193 5 978300760\n",
+ "1 1 F 1 10 48067 661 3 978302109\n",
+ "2 1 F 1 10 48067 914 3 978301968\n",
+ "3 1 F 1 10 48067 3408 4 978300275\n",
+ "4 1 F 1 10 48067 2355 5 978824291\n",
+ "5 1 F 1 10 48067 1197 3 978302268\n",
+ "6 1 F 1 10 48067 1287 5 978302039\n",
+ "7 1 F 1 10 48067 2804 5 978300719\n",
+ "8 1 F 1 10 48067 594 4 978302268\n",
+ "9 1 F 1 10 48067 919 4 978301368\n",
+ "10 1 F 1 10 48067 595 5 978824268\n",
+ "11 1 F 1 10 48067 938 4 978301752\n",
+ "12 1 F 1 10 48067 2398 4 978302281\n",
+ "13 1 F 1 10 48067 2918 4 978302124\n",
+ "14 1 F 1 10 48067 1035 5 978301753\n",
+ "15 1 F 1 10 48067 2791 4 978302188\n",
+ "16 1 F 1 10 48067 2687 3 978824268\n",
+ "17 1 F 1 10 48067 2018 4 978301777\n",
+ "18 1 F 1 10 48067 3105 5 978301713\n",
+ "19 1 F 1 10 48067 2797 4 978302039\n",
+ "20 1 F 1 10 48067 2321 3 978302205\n",
+ "21 1 F 1 10 48067 720 3 978300760\n",
+ "22 1 F 1 10 48067 1270 5 978300055\n",
+ "23 1 F 1 10 48067 527 5 978824195\n",
+ "24 1 F 1 10 48067 2340 3 978300103\n",
+ "25 1 F 1 10 48067 48 5 978824351\n",
+ "26 1 F 1 10 48067 1097 4 978301953\n",
+ "27 1 F 1 10 48067 1721 4 978300055\n",
+ "28 1 F 1 10 48067 1545 4 978824139\n",
+ "29 1 F 1 10 48067 745 3 978824268\n",
+ "... ... .. .. .. ... ... .. ...\n",
+ "1000179 6040 M 25 6 11106 2762 4 956704584\n",
+ "1000180 6040 M 25 6 11106 1036 3 956715455\n",
+ "1000181 6040 M 25 6 11106 508 4 956704972\n",
+ "1000182 6040 M 25 6 11106 1041 4 957717678\n",
+ "1000183 6040 M 25 6 11106 3735 4 960971654\n",
+ "1000184 6040 M 25 6 11106 2791 4 956715569\n",
+ "1000185 6040 M 25 6 11106 2794 1 956716438\n",
+ "1000186 6040 M 25 6 11106 527 5 956704219\n",
+ "1000187 6040 M 25 6 11106 2003 1 956716294\n",
+ "1000188 6040 M 25 6 11106 535 4 964828734\n",
+ "1000189 6040 M 25 6 11106 2010 5 957716795\n",
+ "1000190 6040 M 25 6 11106 2011 4 956716113\n",
+ "1000191 6040 M 25 6 11106 3751 4 964828782\n",
+ "1000192 6040 M 25 6 11106 2019 5 956703977\n",
+ "1000193 6040 M 25 6 11106 541 4 956715288\n",
+ "1000194 6040 M 25 6 11106 1077 5 964828799\n",
+ "1000195 6040 M 25 6 11106 1079 2 956715648\n",
+ "1000196 6040 M 25 6 11106 549 4 956704746\n",
+ "1000197 6040 M 25 6 11106 2020 3 956715288\n",
+ "1000198 6040 M 25 6 11106 2021 3 956716374\n",
+ "1000199 6040 M 25 6 11106 2022 5 956716207\n",
+ "1000200 6040 M 25 6 11106 2028 5 956704519\n",
+ "1000201 6040 M 25 6 11106 1080 4 957717322\n",
+ "1000202 6040 M 25 6 11106 1089 4 956704996\n",
+ "1000203 6040 M 25 6 11106 1090 3 956715518\n",
+ "1000204 6040 M 25 6 11106 1091 1 956716541\n",
+ "1000205 6040 M 25 6 11106 1094 5 956704887\n",
+ "1000206 6040 M 25 6 11106 562 5 956704746\n",
+ "1000207 6040 M 25 6 11106 1096 4 956715648\n",
+ "1000208 6040 M 25 6 11106 1097 4 956715569\n",
+ "\n",
+ "[1000209 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 사용자아이디 없거나 평점 안 매긴 사람도 포함하고 싶을 때 \n",
+ "pd.merge(user, ratings, how='outer')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4-2. pandas.concat(axis = 0)\n",
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "
| 1 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "661 | \n", + "3 | \n", + "978302109 | \n", + "
| 2 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "914 | \n", + "3 | \n", + "978301968 | \n", + "
| 3 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "3408 | \n", + "4 | \n", + "978300275 | \n", + "
| 4 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2355 | \n", + "5 | \n", + "978824291 | \n", + "
| 5 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1197 | \n", + "3 | \n", + "978302268 | \n", + "
| 6 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1287 | \n", + "5 | \n", + "978302039 | \n", + "
| 7 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2804 | \n", + "5 | \n", + "978300719 | \n", + "
| 8 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "594 | \n", + "4 | \n", + "978302268 | \n", + "
| 9 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "919 | \n", + "4 | \n", + "978301368 | \n", + "
| 10 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "595 | \n", + "5 | \n", + "978824268 | \n", + "
| 11 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "938 | \n", + "4 | \n", + "978301752 | \n", + "
| 12 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2398 | \n", + "4 | \n", + "978302281 | \n", + "
| 13 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2918 | \n", + "4 | \n", + "978302124 | \n", + "
| 14 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1035 | \n", + "5 | \n", + "978301753 | \n", + "
| 15 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2791 | \n", + "4 | \n", + "978302188 | \n", + "
| 16 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2687 | \n", + "3 | \n", + "978824268 | \n", + "
| 17 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2018 | \n", + "4 | \n", + "978301777 | \n", + "
| 18 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "3105 | \n", + "5 | \n", + "978301713 | \n", + "
| 19 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2797 | \n", + "4 | \n", + "978302039 | \n", + "
| 20 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2321 | \n", + "3 | \n", + "978302205 | \n", + "
| 21 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "720 | \n", + "3 | \n", + "978300760 | \n", + "
| 22 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1270 | \n", + "5 | \n", + "978300055 | \n", + "
| 23 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "527 | \n", + "5 | \n", + "978824195 | \n", + "
| 24 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2340 | \n", + "3 | \n", + "978300103 | \n", + "
| 25 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "48 | \n", + "5 | \n", + "978824351 | \n", + "
| 26 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1097 | \n", + "4 | \n", + "978301953 | \n", + "
| 27 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1721 | \n", + "4 | \n", + "978300055 | \n", + "
| 28 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1545 | \n", + "4 | \n", + "978824139 | \n", + "
| 29 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "745 | \n", + "3 | \n", + "978824268 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1000179 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2762 | \n", + "4 | \n", + "956704584 | \n", + "
| 1000180 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1036 | \n", + "3 | \n", + "956715455 | \n", + "
| 1000181 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "508 | \n", + "4 | \n", + "956704972 | \n", + "
| 1000182 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1041 | \n", + "4 | \n", + "957717678 | \n", + "
| 1000183 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "3735 | \n", + "4 | \n", + "960971654 | \n", + "
| 1000184 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2791 | \n", + "4 | \n", + "956715569 | \n", + "
| 1000185 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2794 | \n", + "1 | \n", + "956716438 | \n", + "
| 1000186 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "527 | \n", + "5 | \n", + "956704219 | \n", + "
| 1000187 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2003 | \n", + "1 | \n", + "956716294 | \n", + "
| 1000188 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "535 | \n", + "4 | \n", + "964828734 | \n", + "
| 1000189 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2010 | \n", + "5 | \n", + "957716795 | \n", + "
| 1000190 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2011 | \n", + "4 | \n", + "956716113 | \n", + "
| 1000191 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "3751 | \n", + "4 | \n", + "964828782 | \n", + "
| 1000192 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2019 | \n", + "5 | \n", + "956703977 | \n", + "
| 1000193 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "541 | \n", + "4 | \n", + "956715288 | \n", + "
| 1000194 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1077 | \n", + "5 | \n", + "964828799 | \n", + "
| 1000195 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1079 | \n", + "2 | \n", + "956715648 | \n", + "
| 1000196 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "549 | \n", + "4 | \n", + "956704746 | \n", + "
| 1000197 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2020 | \n", + "3 | \n", + "956715288 | \n", + "
| 1000198 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2021 | \n", + "3 | \n", + "956716374 | \n", + "
| 1000199 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2022 | \n", + "5 | \n", + "956716207 | \n", + "
| 1000200 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "2028 | \n", + "5 | \n", + "956704519 | \n", + "
| 1000201 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1080 | \n", + "4 | \n", + "957717322 | \n", + "
| 1000202 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1089 | \n", + "4 | \n", + "956704996 | \n", + "
| 1000203 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1090 | \n", + "3 | \n", + "956715518 | \n", + "
| 1000204 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1091 | \n", + "1 | \n", + "956716541 | \n", + "
| 1000205 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1094 | \n", + "5 | \n", + "956704887 | \n", + "
| 1000206 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "562 | \n", + "5 | \n", + "956704746 | \n", + "
| 1000207 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1096 | \n", + "4 | \n", + "956715648 | \n", + "
| 1000208 | \n", + "6040 | \n", + "M | \n", + "25 | \n", + "6 | \n", + "11106 | \n", + "1097 | \n", + "4 | \n", + "956715569 | \n", + "
1000209 rows × 8 columns
\n", + "![\"csv파일예제\"](\"img/concat0예제.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df1 = DataFrame([[1,'aa'],[2,'bb']], \n",
+ " columns = list('AB'))\n",
+ "df2 = DataFrame([[3,'cc'],[4,'dd']], \n",
+ " columns = list('AB'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " A B\n",
+ "0 1 aa\n",
+ "1 2 bb\n",
+ "2 3 cc\n",
+ "3 4 dd"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# df1과 df2 concat 수행하기\n",
+ "pd.concat([df1, df2],ignore_index = 'True') # axis = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 실제 데이터 실습\n",
+ "# data/NC Dinos.xlsx 파일에서 2013, 2014, 2015시즌 정보를 읽어서, 각각 NC13, NC14, NC15에 저장하시오. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC = pd.read_excel('data/NC Dinos.xlsx', sheet_name=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC13,NC14,NC15 = NC.values()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC13['년도']=2013\n",
+ "NC14['년도']=2014\n",
+ "NC15['년도']=2015"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "
|---|---|---|
| 0 | \n", + "1 | \n", + "aa | \n", + "
| 1 | \n", + "2 | \n", + "bb | \n", + "
| 2 | \n", + "3 | \n", + "cc | \n", + "
| 3 | \n", + "4 | \n", + "dd | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 \n",
+ "0 테임즈 NC 125 514 443 152 37 95 121 58 99 11 0.367 0.343 \n",
+ "1 나성범 NC 123 536 477 157 30 88 101 43 128 14 0.397 0.329 \n",
+ "2 박민우 NC 118 491 416 124 1 87 40 56 89 50 0.373 0.298 \n",
+ "3 손시헌 NC 97 361 307 90 5 39 39 34 53 2 0.331 0.293 \n",
+ "4 지석훈 NC 114 238 212 58 6 26 34 16 46 1 0.323 0.274 \n",
+ "5 이호준 NC 122 500 424 115 23 59 78 67 104 2 0.305 0.271 \n",
+ "6 권희동 NC 101 252 221 63 7 39 36 25 43 6 0.324 0.285 \n",
+ "7 모창민 NC 122 468 419 110 16 62 72 37 82 14 0.289 0.263 \n",
+ "8 이종욱 NC 124 495 438 126 6 73 79 40 60 15 0.313 0.288 \n",
+ "9 김준완 NC 6 5 4 2 0 1 0 1 1 1 0.667 0.5 \n",
+ "10 최재원 NC 2 2 1 0 0 1 0 1 1 0 - 0 \n",
+ "11 마낙길 NC 2 2 1 0 0 1 0 1 0 0 0.000 0 \n",
+ "12 조영훈 NC 92 124 111 29 6 15 22 10 26 1 0.288 0.261 \n",
+ "13 김태우 NC 4 5 5 1 0 0 0 0 3 0 0.500 0.2 \n",
+ "14 박명환* NC 2 1 1 0 0 0 0 0 1 0 - 0 \n",
+ "15 조평호 NC 2 2 2 0 0 0 0 0 0 0 0.000 0 \n",
+ "16 강민국 NC 6 3 3 0 0 0 0 0 0 0 0.000 0 \n",
+ "17 허준 NC 28 36 29 5 0 2 1 5 10 0 0.263 0.172 \n",
+ "18 김성욱 NC 26 26 23 4 1 6 1 3 9 1 0.231 0.174 \n",
+ "19 노진혁 NC 25 16 16 3 1 3 2 0 7 0 0.250 0.188 \n",
+ "0 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.39 0.381 \n",
+ "1 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "2 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "3 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "4 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "5 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "6 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.26 0.245 \n",
+ "7 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "8 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.29 \n",
+ "9 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "10 용덕한 NC 50 49 42 12 0 4 5 5 8 2 0.353 0.286 \n",
+ "11 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "12 박정준 NC 19 24 21 8 0 2 2 0 5 0 0.5 0.381 \n",
+ "13 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "14 조평호 NC 22 22 22 8 1 4 3 0 6 0 0.467 0.364 \n",
+ "15 이창섭 NC 15 8 5 0 0 0 0 3 3 0 0 0 \n",
+ "16 김준완 NC 30 54 44 10 0 12 2 10 10 2 0.294 0.227 \n",
+ "17 강구성 NC 4 5 5 1 0 0 0 0 0 0 0.2 0.2 \n",
+ "18 강민국 NC 1 2 2 0 0 0 0 0 1 0 0 0 \n",
+ "19 김태진 NC 1 3 3 0 0 0 0 0 0 0 0 0 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.339 0.443 0.782 0.353 2.31 2013 \n",
+ "1 0.362 0.475 0.837 0.373 1.85 2013 \n",
+ "2 0.376 0.333 0.709 0.339 1.55 2013 \n",
+ "3 0.319 0.416 0.735 0.329 1.50 2013 \n",
+ "4 0.350 0.413 0.763 0.348 0.83 2013 \n",
+ "5 0.361 0.324 0.685 0.327 0.52 2013 \n",
+ "6 0.299 0.320 0.619 0.289 0.16 2013 \n",
+ "7 0.667 1.000 1.667 0.671 0.10 2013 \n",
+ "8 0.318 0.418 0.736 0.329 0.09 2013 \n",
+ "9 0.333 0.286 0.619 0.296 0.07 2013 \n",
+ "10 0.335 0.353 0.688 0.320 0.06 2013 \n",
+ "11 1.000 - - 0.712 0.06 2013 \n",
+ "12 0.333 0.345 0.678 0.317 -0.03 2013 \n",
+ "13 0.250 0.250 0.500 0.234 -0.03 2013 \n",
+ "14 0.000 0.000 0.000 0.000 -0.06 2013 \n",
+ "15 0.300 0.330 0.630 0.293 -0.09 2013 \n",
+ "16 0.143 0.143 0.286 0.134 -0.11 2013 \n",
+ "17 0.306 0.311 0.617 0.289 -0.12 2013 \n",
+ "18 0.176 0.313 0.489 0.216 -0.14 2013 \n",
+ "19 0.000 0.000 0.000 0.000 -0.14 2013 \n",
+ "0 0.422 0.688 1.11 0.456 6.36 2014 \n",
+ "1 0.400 0.597 0.997 0.424 5.94 2014 \n",
+ "2 0.392 0.399 0.791 0.365 2.73 2014 \n",
+ "3 0.368 0.414 0.782 0.349 1.52 2014 \n",
+ "4 0.340 0.462 0.802 0.352 0.97 2014 \n",
+ "5 0.371 0.481 0.852 0.369 0.88 2014 \n",
+ "6 0.363 0.443 0.806 0.353 0.62 2014 \n",
+ "7 0.320 0.413 0.733 0.319 0.17 2014 \n",
+ "8 0.343 0.411 0.754 0.332 0.13 2014 \n",
+ "9 0.600 0.5 1.1 0.509 0.11 2014 \n",
+ "10 0.500 0 0.5 0.363 0.01 2014 \n",
+ "11 0.500 0 0.5 0.363 0.01 2014 \n",
+ "12 0.325 0.459 0.784 0.337 -0.02 2014 \n",
+ "13 0.200 0.4 0.6 0.244 -0.03 2014 \n",
+ "14 0.000 0 0 0.000 -0.03 2014 \n",
+ "15 0.000 0 0 0.000 -0.07 2014 \n",
+ "16 0.000 0 0 0.000 -0.10 2014 \n",
+ "17 0.294 0.276 0.57 0.268 -0.11 2014 \n",
+ "18 0.269 0.348 0.617 0.272 -0.15 2014 \n",
+ "19 0.188 0.375 0.563 0.231 -0.18 2014 \n",
+ "0 0.497 0.79 1.287 0.520 11.73 2015 \n",
+ "1 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "2 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "3 0.381 0.51 0.891 0.381 2.19 2015 \n",
+ "4 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "5 0.372 0.54 0.912 0.389 1.00 2015 \n",
+ "6 0.319 0.398 0.717 0.316 0.85 2015 \n",
+ "7 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "8 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "9 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "10 0.375 0.31 0.685 0.326 0.44 2015 \n",
+ "11 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "12 0.458 0.476 0.934 0.417 0.25 2015 \n",
+ "13 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "14 0.364 0.591 0.955 0.401 0.18 2015 \n",
+ "15 0.375 0 0.375 0.286 -0.01 2015 \n",
+ "16 0.370 0.227 0.597 0.308 -0.04 2015 \n",
+ "17 0.200 0.4 0.6 0.242 -0.04 2015 \n",
+ "18 0.000 0 0 0.000 -0.06 2015 \n",
+ "19 0.000 0 0 0.000 -0.09 2015 "
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.concat([NC13,NC14,NC15])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4-3. concat(axis = 1)\n",
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "2013 | \n", + "
| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "2013 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "2013 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "2013 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "2013 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "2013 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "2013 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "2013 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "2013 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "2013 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "2013 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2013 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "2013 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "2013 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "2013 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "125 | \n", + "514 | \n", + "443 | \n", + "152 | \n", + "37 | \n", + "95 | \n", + "121 | \n", + "58 | \n", + "99 | \n", + "11 | \n", + "0.367 | \n", + "0.343 | \n", + "0.422 | \n", + "0.688 | \n", + "1.11 | \n", + "0.456 | \n", + "6.36 | \n", + "2014 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "123 | \n", + "536 | \n", + "477 | \n", + "157 | \n", + "30 | \n", + "88 | \n", + "101 | \n", + "43 | \n", + "128 | \n", + "14 | \n", + "0.397 | \n", + "0.329 | \n", + "0.400 | \n", + "0.597 | \n", + "0.997 | \n", + "0.424 | \n", + "5.94 | \n", + "2014 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "118 | \n", + "491 | \n", + "416 | \n", + "124 | \n", + "1 | \n", + "87 | \n", + "40 | \n", + "56 | \n", + "89 | \n", + "50 | \n", + "0.373 | \n", + "0.298 | \n", + "0.392 | \n", + "0.399 | \n", + "0.791 | \n", + "0.365 | \n", + "2.73 | \n", + "2014 | \n", + "
| 3 | \n", + "손시헌 | \n", + "NC | \n", + "97 | \n", + "361 | \n", + "307 | \n", + "90 | \n", + "5 | \n", + "39 | \n", + "39 | \n", + "34 | \n", + "53 | \n", + "2 | \n", + "0.331 | \n", + "0.293 | \n", + "0.368 | \n", + "0.414 | \n", + "0.782 | \n", + "0.349 | \n", + "1.52 | \n", + "2014 | \n", + "
| 4 | \n", + "지석훈 | \n", + "NC | \n", + "114 | \n", + "238 | \n", + "212 | \n", + "58 | \n", + "6 | \n", + "26 | \n", + "34 | \n", + "16 | \n", + "46 | \n", + "1 | \n", + "0.323 | \n", + "0.274 | \n", + "0.340 | \n", + "0.462 | \n", + "0.802 | \n", + "0.352 | \n", + "0.97 | \n", + "2014 | \n", + "
| 5 | \n", + "이호준 | \n", + "NC | \n", + "122 | \n", + "500 | \n", + "424 | \n", + "115 | \n", + "23 | \n", + "59 | \n", + "78 | \n", + "67 | \n", + "104 | \n", + "2 | \n", + "0.305 | \n", + "0.271 | \n", + "0.371 | \n", + "0.481 | \n", + "0.852 | \n", + "0.369 | \n", + "0.88 | \n", + "2014 | \n", + "
| 6 | \n", + "권희동 | \n", + "NC | \n", + "101 | \n", + "252 | \n", + "221 | \n", + "63 | \n", + "7 | \n", + "39 | \n", + "36 | \n", + "25 | \n", + "43 | \n", + "6 | \n", + "0.324 | \n", + "0.285 | \n", + "0.363 | \n", + "0.443 | \n", + "0.806 | \n", + "0.353 | \n", + "0.62 | \n", + "2014 | \n", + "
| 7 | \n", + "모창민 | \n", + "NC | \n", + "122 | \n", + "468 | \n", + "419 | \n", + "110 | \n", + "16 | \n", + "62 | \n", + "72 | \n", + "37 | \n", + "82 | \n", + "14 | \n", + "0.289 | \n", + "0.263 | \n", + "0.320 | \n", + "0.413 | \n", + "0.733 | \n", + "0.319 | \n", + "0.17 | \n", + "2014 | \n", + "
| 8 | \n", + "이종욱 | \n", + "NC | \n", + "124 | \n", + "495 | \n", + "438 | \n", + "126 | \n", + "6 | \n", + "73 | \n", + "79 | \n", + "40 | \n", + "60 | \n", + "15 | \n", + "0.313 | \n", + "0.288 | \n", + "0.343 | \n", + "0.411 | \n", + "0.754 | \n", + "0.332 | \n", + "0.13 | \n", + "2014 | \n", + "
| 9 | \n", + "김준완 | \n", + "NC | \n", + "6 | \n", + "5 | \n", + "4 | \n", + "2 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "0.667 | \n", + "0.5 | \n", + "0.600 | \n", + "0.5 | \n", + "1.1 | \n", + "0.509 | \n", + "0.11 | \n", + "2014 | \n", + "
| 10 | \n", + "최재원 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 11 | \n", + "마낙길 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 12 | \n", + "조영훈 | \n", + "NC | \n", + "92 | \n", + "124 | \n", + "111 | \n", + "29 | \n", + "6 | \n", + "15 | \n", + "22 | \n", + "10 | \n", + "26 | \n", + "1 | \n", + "0.288 | \n", + "0.261 | \n", + "0.325 | \n", + "0.459 | \n", + "0.784 | \n", + "0.337 | \n", + "-0.02 | \n", + "2014 | \n", + "
| 13 | \n", + "김태우 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.500 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.244 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 14 | \n", + "박명환* | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 15 | \n", + "조평호 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.07 | \n", + "2014 | \n", + "
| 16 | \n", + "강민국 | \n", + "NC | \n", + "6 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.10 | \n", + "2014 | \n", + "
| 17 | \n", + "허준 | \n", + "NC | \n", + "28 | \n", + "36 | \n", + "29 | \n", + "5 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "5 | \n", + "10 | \n", + "0 | \n", + "0.263 | \n", + "0.172 | \n", + "0.294 | \n", + "0.276 | \n", + "0.57 | \n", + "0.268 | \n", + "-0.11 | \n", + "2014 | \n", + "
| 18 | \n", + "김성욱 | \n", + "NC | \n", + "26 | \n", + "26 | \n", + "23 | \n", + "4 | \n", + "1 | \n", + "6 | \n", + "1 | \n", + "3 | \n", + "9 | \n", + "1 | \n", + "0.231 | \n", + "0.174 | \n", + "0.269 | \n", + "0.348 | \n", + "0.617 | \n", + "0.272 | \n", + "-0.15 | \n", + "2014 | \n", + "
| 19 | \n", + "노진혁 | \n", + "NC | \n", + "25 | \n", + "16 | \n", + "16 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "2 | \n", + "0 | \n", + "7 | \n", + "0 | \n", + "0.250 | \n", + "0.188 | \n", + "0.188 | \n", + "0.375 | \n", + "0.563 | \n", + "0.231 | \n", + "-0.18 | \n", + "2014 | \n", + "
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.39 | \n", + "0.381 | \n", + "0.497 | \n", + "0.79 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 3 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.51 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 4 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 5 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.54 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 6 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.26 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
| 7 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 8 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.29 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 9 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 10 | \n", + "용덕한 | \n", + "NC | \n", + "50 | \n", + "49 | \n", + "42 | \n", + "12 | \n", + "0 | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "8 | \n", + "2 | \n", + "0.353 | \n", + "0.286 | \n", + "0.375 | \n", + "0.31 | \n", + "0.685 | \n", + "0.326 | \n", + "0.44 | \n", + "2015 | \n", + "
| 11 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 12 | \n", + "박정준 | \n", + "NC | \n", + "19 | \n", + "24 | \n", + "21 | \n", + "8 | \n", + "0 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "0.5 | \n", + "0.381 | \n", + "0.458 | \n", + "0.476 | \n", + "0.934 | \n", + "0.417 | \n", + "0.25 | \n", + "2015 | \n", + "
| 13 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 14 | \n", + "조평호 | \n", + "NC | \n", + "22 | \n", + "22 | \n", + "22 | \n", + "8 | \n", + "1 | \n", + "4 | \n", + "3 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "0.467 | \n", + "0.364 | \n", + "0.364 | \n", + "0.591 | \n", + "0.955 | \n", + "0.401 | \n", + "0.18 | \n", + "2015 | \n", + "
| 15 | \n", + "이창섭 | \n", + "NC | \n", + "15 | \n", + "8 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.375 | \n", + "0 | \n", + "0.375 | \n", + "0.286 | \n", + "-0.01 | \n", + "2015 | \n", + "
| 16 | \n", + "김준완 | \n", + "NC | \n", + "30 | \n", + "54 | \n", + "44 | \n", + "10 | \n", + "0 | \n", + "12 | \n", + "2 | \n", + "10 | \n", + "10 | \n", + "2 | \n", + "0.294 | \n", + "0.227 | \n", + "0.370 | \n", + "0.227 | \n", + "0.597 | \n", + "0.308 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 17 | \n", + "강구성 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.2 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.242 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 18 | \n", + "강민국 | \n", + "NC | \n", + "1 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.06 | \n", + "2015 | \n", + "
| 19 | \n", + "김태진 | \n", + "NC | \n", + "1 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.09 | \n", + "2015 | \n", + "
![\"csv파일예제\"](\"img/concat1예제.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 샘플 데이터 생성\n",
+ "df1 = DataFrame([[1,10],[2,20]], \n",
+ " columns = list('AB'), index = ['aa','bb'])\n",
+ "df2 = DataFrame([[3,'xx'],[4,'yy']], \n",
+ " columns = list('CD'), index = ['aa','bb'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " A B\n",
+ "aa 1 10\n",
+ "bb 2 20"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# concat with axis = 1 수행\n",
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "
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| aa | \n", + "1 | \n", + "10 | \n", + "
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+ "\n",
+ " \n",
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+ "
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+ ],
+ "text/plain": [
+ " C D\n",
+ "aa 3 xx\n",
+ "bb 4 yy"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | C | \n", + "D | \n", + "
|---|---|---|
| aa | \n", + "3 | \n", + "xx | \n", + "
| bb | \n", + "4 | \n", + "yy | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " A B C D\n",
+ "aa 1 10 3 xx\n",
+ "bb 2 20 4 yy"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.concat([df1,df2], axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 12\n",
+ "1 20\n",
+ "2 0\n",
+ "3 14\n",
+ "4 6\n",
+ "5 0\n",
+ "6 0\n",
+ "7 0\n",
+ "8 2\n",
+ "9 0\n",
+ "10 4\n",
+ "11 0\n",
+ "12 0\n",
+ "13 0\n",
+ "14 0\n",
+ "15 3\n",
+ "16 0\n",
+ "17 1\n",
+ "18 1\n",
+ "19 0\n",
+ "Name: 홈런, dtype: int64"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC13.홈런"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 37\n",
+ "1 30\n",
+ "2 1\n",
+ "3 5\n",
+ "4 6\n",
+ "5 23\n",
+ "6 7\n",
+ "7 16\n",
+ "8 6\n",
+ "9 0\n",
+ "10 0\n",
+ "11 0\n",
+ "12 6\n",
+ "13 0\n",
+ "14 0\n",
+ "15 0\n",
+ "16 0\n",
+ "17 0\n",
+ "18 1\n",
+ "19 1\n",
+ "Name: 홈런, dtype: int64"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC14.홈런"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|
| aa | \n", + "1 | \n", + "10 | \n", + "3 | \n", + "xx | \n", + "
| bb | \n", + "2 | \n", + "20 | \n", + "4 | \n", + "yy | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 출루율 \\\n",
+ "선수명 \n",
+ "모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 0.339 \n",
+ "이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 0.362 \n",
+ "김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 0.376 \n",
+ "나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 0.319 \n",
+ "조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 0.350 \n",
+ "이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 0.361 \n",
+ "이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 0.299 \n",
+ "강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 0.667 \n",
+ "조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 0.318 \n",
+ "박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 0.333 \n",
+ "박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 0.335 \n",
+ "이승호 NC 2 1 0 0 0 0 0 1 0 1 - - 1.000 \n",
+ "마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 0.333 \n",
+ "김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 0.250 \n",
+ "강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 0.000 \n",
+ "지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 0.300 \n",
+ "김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 0.143 \n",
+ "차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 0.306 \n",
+ "김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 0.176 \n",
+ "이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 0.000 \n",
+ "테임즈 NC 125 514 443 152 37 95 121 58 99 11 0.367 0.343 0.422 \n",
+ "나성범 NC 123 536 477 157 30 88 101 43 128 14 0.397 0.329 0.400 \n",
+ "박민우 NC 118 491 416 124 1 87 40 56 89 50 0.373 0.298 0.392 \n",
+ "손시헌 NC 97 361 307 90 5 39 39 34 53 2 0.331 0.293 0.368 \n",
+ "지석훈 NC 114 238 212 58 6 26 34 16 46 1 0.323 0.274 0.340 \n",
+ "이호준 NC 122 500 424 115 23 59 78 67 104 2 0.305 0.271 0.371 \n",
+ "권희동 NC 101 252 221 63 7 39 36 25 43 6 0.324 0.285 0.363 \n",
+ "모창민 NC 122 468 419 110 16 62 72 37 82 14 0.289 0.263 0.320 \n",
+ "이종욱 NC 124 495 438 126 6 73 79 40 60 15 0.313 0.288 0.343 \n",
+ "김준완 NC 6 5 4 2 0 1 0 1 1 1 0.667 0.5 0.600 \n",
+ "최재원 NC 2 2 1 0 0 1 0 1 1 0 - 0 0.500 \n",
+ "마낙길 NC 2 2 1 0 0 1 0 1 0 0 0.000 0 0.500 \n",
+ "조영훈 NC 92 124 111 29 6 15 22 10 26 1 0.288 0.261 0.325 \n",
+ "김태우 NC 4 5 5 1 0 0 0 0 3 0 0.500 0.2 0.200 \n",
+ "박명환* NC 2 1 1 0 0 0 0 0 1 0 - 0 0.000 \n",
+ "조평호 NC 2 2 2 0 0 0 0 0 0 0 0.000 0 0.000 \n",
+ "강민국 NC 6 3 3 0 0 0 0 0 0 0 0.000 0 0.000 \n",
+ "허준 NC 28 36 29 5 0 2 1 5 10 0 0.263 0.172 0.294 \n",
+ "김성욱 NC 26 26 23 4 1 6 1 3 9 1 0.231 0.174 0.269 \n",
+ "노진혁 NC 25 16 16 3 1 3 2 0 7 0 0.250 0.188 0.188 \n",
+ "테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.39 0.381 0.497 \n",
+ "나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 0.373 \n",
+ "박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 0.399 \n",
+ "이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 0.381 \n",
+ "김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 0.364 \n",
+ "조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 0.372 \n",
+ "손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.26 0.245 0.319 \n",
+ "이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 0.351 \n",
+ "모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.29 0.340 \n",
+ "지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 0.328 \n",
+ "용덕한 NC 50 49 42 12 0 4 5 5 8 2 0.353 0.286 0.375 \n",
+ "최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 0.366 \n",
+ "박정준 NC 19 24 21 8 0 2 2 0 5 0 0.5 0.381 0.458 \n",
+ "김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 0.307 \n",
+ "조평호 NC 22 22 22 8 1 4 3 0 6 0 0.467 0.364 0.364 \n",
+ "이창섭 NC 15 8 5 0 0 0 0 3 3 0 0 0 0.375 \n",
+ "김준완 NC 30 54 44 10 0 12 2 10 10 2 0.294 0.227 0.370 \n",
+ "강구성 NC 4 5 5 1 0 0 0 0 0 0 0.2 0.2 0.200 \n",
+ "강민국 NC 1 2 2 0 0 0 0 0 1 0 0 0 0.000 \n",
+ "김태진 NC 1 3 3 0 0 0 0 0 0 0 0 0 0.000 \n",
+ "\n",
+ " 장타율 OPS wOBA WAR 년도 \n",
+ "선수명 \n",
+ "모창민 0.443 0.782 0.353 2.31 2013 \n",
+ "이호준 0.475 0.837 0.373 1.85 2013 \n",
+ "김종호 0.333 0.709 0.339 1.55 2013 \n",
+ "나성범 0.416 0.735 0.329 1.50 2013 \n",
+ "조영훈 0.413 0.763 0.348 0.83 2013 \n",
+ "이현곤 0.324 0.685 0.327 0.52 2013 \n",
+ "이상호 0.320 0.619 0.289 0.16 2013 \n",
+ "강진성 1.000 1.667 0.671 0.10 2013 \n",
+ "조평호 0.418 0.736 0.329 0.09 2013 \n",
+ "박민우 0.286 0.619 0.296 0.07 2013 \n",
+ "박정준 0.353 0.688 0.320 0.06 2013 \n",
+ "이승호 - - 0.712 0.06 2013 \n",
+ "마낙길 0.345 0.678 0.317 -0.03 2013 \n",
+ "김성욱 0.250 0.500 0.234 -0.03 2013 \n",
+ "강구성 0.000 0.000 0.000 -0.06 2013 \n",
+ "지석훈 0.330 0.630 0.293 -0.09 2013 \n",
+ "김종찬 0.143 0.286 0.134 -0.11 2013 \n",
+ "차화준 0.311 0.617 0.289 -0.12 2013 \n",
+ "김동건 0.313 0.489 0.216 -0.14 2013 \n",
+ "이창섭 0.000 0.000 0.000 -0.14 2013 \n",
+ "테임즈 0.688 1.11 0.456 6.36 2014 \n",
+ "나성범 0.597 0.997 0.424 5.94 2014 \n",
+ "박민우 0.399 0.791 0.365 2.73 2014 \n",
+ "손시헌 0.414 0.782 0.349 1.52 2014 \n",
+ "지석훈 0.462 0.802 0.352 0.97 2014 \n",
+ "이호준 0.481 0.852 0.369 0.88 2014 \n",
+ "권희동 0.443 0.806 0.353 0.62 2014 \n",
+ "모창민 0.413 0.733 0.319 0.17 2014 \n",
+ "이종욱 0.411 0.754 0.332 0.13 2014 \n",
+ "김준완 0.5 1.1 0.509 0.11 2014 \n",
+ "최재원 0 0.5 0.363 0.01 2014 \n",
+ "마낙길 0 0.5 0.363 0.01 2014 \n",
+ "조영훈 0.459 0.784 0.337 -0.02 2014 \n",
+ "김태우 0.4 0.6 0.244 -0.03 2014 \n",
+ "박명환* 0 0 0.000 -0.03 2014 \n",
+ "조평호 0 0 0.000 -0.07 2014 \n",
+ "강민국 0 0 0.000 -0.10 2014 \n",
+ "허준 0.276 0.57 0.268 -0.11 2014 \n",
+ "김성욱 0.348 0.617 0.272 -0.15 2014 \n",
+ "노진혁 0.375 0.563 0.231 -0.18 2014 \n",
+ "테임즈 0.79 1.287 0.520 11.73 2015 \n",
+ "나성범 0.553 0.926 0.390 4.46 2015 \n",
+ "박민우 0.404 0.803 0.364 3.52 2015 \n",
+ "이호준 0.51 0.891 0.381 2.19 2015 \n",
+ "김종호 0.394 0.758 0.340 1.03 2015 \n",
+ "조영훈 0.54 0.912 0.389 1.00 2015 \n",
+ "손시헌 0.398 0.717 0.316 0.85 2015 \n",
+ "이종욱 0.368 0.719 0.322 0.84 2015 \n",
+ "모창민 0.439 0.779 0.340 0.76 2015 \n",
+ "지석훈 0.383 0.711 0.317 0.51 2015 \n",
+ "용덕한 0.31 0.685 0.326 0.44 2015 \n",
+ "최재원 0.412 0.778 0.350 0.30 2015 \n",
+ "박정준 0.476 0.934 0.417 0.25 2015 \n",
+ "김태군 0.354 0.661 0.294 0.25 2015 \n",
+ "조평호 0.591 0.955 0.401 0.18 2015 \n",
+ "이창섭 0 0.375 0.286 -0.01 2015 \n",
+ "김준완 0.227 0.597 0.308 -0.04 2015 \n",
+ "강구성 0.4 0.6 0.242 -0.04 2015 \n",
+ "강민국 0 0 0.000 -0.06 2015 \n",
+ "김태진 0 0 0.000 -0.09 2015 "
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 선수 이름 순서가 년도별로 다르므로 올바른 데이터 합치기가 아니다\n",
+ "pd.concat([NC13, NC14, NC15])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC13=NC13.set_index('선수명')\n",
+ "NC14=NC14.set_index('선수명')\n",
+ "NC15=NC15.set_index('선수명')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "homerun = pd.concat([NC13.홈런, NC14.홈런, NC15.홈런], axis = 1, sort = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "2013 | \n", + "
| 이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "2013 | \n", + "
| 김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "2013 | \n", + "
| 나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "2013 | \n", + "
| 조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "2013 | \n", + "
| 이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "2013 | \n", + "
| 이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "2013 | \n", + "
| 강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "2013 | \n", + "
| 조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "2013 | \n", + "
| 박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "2013 | \n", + "
| 박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "2013 | \n", + "
| 이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "
| 마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2013 | \n", + "
| 지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "2013 | \n", + "
| 김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "2013 | \n", + "
| 차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "2013 | \n", + "
| 김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 테임즈 | \n", + "NC | \n", + "125 | \n", + "514 | \n", + "443 | \n", + "152 | \n", + "37 | \n", + "95 | \n", + "121 | \n", + "58 | \n", + "99 | \n", + "11 | \n", + "0.367 | \n", + "0.343 | \n", + "0.422 | \n", + "0.688 | \n", + "1.11 | \n", + "0.456 | \n", + "6.36 | \n", + "2014 | \n", + "
| 나성범 | \n", + "NC | \n", + "123 | \n", + "536 | \n", + "477 | \n", + "157 | \n", + "30 | \n", + "88 | \n", + "101 | \n", + "43 | \n", + "128 | \n", + "14 | \n", + "0.397 | \n", + "0.329 | \n", + "0.400 | \n", + "0.597 | \n", + "0.997 | \n", + "0.424 | \n", + "5.94 | \n", + "2014 | \n", + "
| 박민우 | \n", + "NC | \n", + "118 | \n", + "491 | \n", + "416 | \n", + "124 | \n", + "1 | \n", + "87 | \n", + "40 | \n", + "56 | \n", + "89 | \n", + "50 | \n", + "0.373 | \n", + "0.298 | \n", + "0.392 | \n", + "0.399 | \n", + "0.791 | \n", + "0.365 | \n", + "2.73 | \n", + "2014 | \n", + "
| 손시헌 | \n", + "NC | \n", + "97 | \n", + "361 | \n", + "307 | \n", + "90 | \n", + "5 | \n", + "39 | \n", + "39 | \n", + "34 | \n", + "53 | \n", + "2 | \n", + "0.331 | \n", + "0.293 | \n", + "0.368 | \n", + "0.414 | \n", + "0.782 | \n", + "0.349 | \n", + "1.52 | \n", + "2014 | \n", + "
| 지석훈 | \n", + "NC | \n", + "114 | \n", + "238 | \n", + "212 | \n", + "58 | \n", + "6 | \n", + "26 | \n", + "34 | \n", + "16 | \n", + "46 | \n", + "1 | \n", + "0.323 | \n", + "0.274 | \n", + "0.340 | \n", + "0.462 | \n", + "0.802 | \n", + "0.352 | \n", + "0.97 | \n", + "2014 | \n", + "
| 이호준 | \n", + "NC | \n", + "122 | \n", + "500 | \n", + "424 | \n", + "115 | \n", + "23 | \n", + "59 | \n", + "78 | \n", + "67 | \n", + "104 | \n", + "2 | \n", + "0.305 | \n", + "0.271 | \n", + "0.371 | \n", + "0.481 | \n", + "0.852 | \n", + "0.369 | \n", + "0.88 | \n", + "2014 | \n", + "
| 권희동 | \n", + "NC | \n", + "101 | \n", + "252 | \n", + "221 | \n", + "63 | \n", + "7 | \n", + "39 | \n", + "36 | \n", + "25 | \n", + "43 | \n", + "6 | \n", + "0.324 | \n", + "0.285 | \n", + "0.363 | \n", + "0.443 | \n", + "0.806 | \n", + "0.353 | \n", + "0.62 | \n", + "2014 | \n", + "
| 모창민 | \n", + "NC | \n", + "122 | \n", + "468 | \n", + "419 | \n", + "110 | \n", + "16 | \n", + "62 | \n", + "72 | \n", + "37 | \n", + "82 | \n", + "14 | \n", + "0.289 | \n", + "0.263 | \n", + "0.320 | \n", + "0.413 | \n", + "0.733 | \n", + "0.319 | \n", + "0.17 | \n", + "2014 | \n", + "
| 이종욱 | \n", + "NC | \n", + "124 | \n", + "495 | \n", + "438 | \n", + "126 | \n", + "6 | \n", + "73 | \n", + "79 | \n", + "40 | \n", + "60 | \n", + "15 | \n", + "0.313 | \n", + "0.288 | \n", + "0.343 | \n", + "0.411 | \n", + "0.754 | \n", + "0.332 | \n", + "0.13 | \n", + "2014 | \n", + "
| 김준완 | \n", + "NC | \n", + "6 | \n", + "5 | \n", + "4 | \n", + "2 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "0.667 | \n", + "0.5 | \n", + "0.600 | \n", + "0.5 | \n", + "1.1 | \n", + "0.509 | \n", + "0.11 | \n", + "2014 | \n", + "
| 최재원 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 마낙길 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 조영훈 | \n", + "NC | \n", + "92 | \n", + "124 | \n", + "111 | \n", + "29 | \n", + "6 | \n", + "15 | \n", + "22 | \n", + "10 | \n", + "26 | \n", + "1 | \n", + "0.288 | \n", + "0.261 | \n", + "0.325 | \n", + "0.459 | \n", + "0.784 | \n", + "0.337 | \n", + "-0.02 | \n", + "2014 | \n", + "
| 김태우 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.500 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.244 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 박명환* | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 조평호 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.07 | \n", + "2014 | \n", + "
| 강민국 | \n", + "NC | \n", + "6 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.10 | \n", + "2014 | \n", + "
| 허준 | \n", + "NC | \n", + "28 | \n", + "36 | \n", + "29 | \n", + "5 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "5 | \n", + "10 | \n", + "0 | \n", + "0.263 | \n", + "0.172 | \n", + "0.294 | \n", + "0.276 | \n", + "0.57 | \n", + "0.268 | \n", + "-0.11 | \n", + "2014 | \n", + "
| 김성욱 | \n", + "NC | \n", + "26 | \n", + "26 | \n", + "23 | \n", + "4 | \n", + "1 | \n", + "6 | \n", + "1 | \n", + "3 | \n", + "9 | \n", + "1 | \n", + "0.231 | \n", + "0.174 | \n", + "0.269 | \n", + "0.348 | \n", + "0.617 | \n", + "0.272 | \n", + "-0.15 | \n", + "2014 | \n", + "
| 노진혁 | \n", + "NC | \n", + "25 | \n", + "16 | \n", + "16 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "2 | \n", + "0 | \n", + "7 | \n", + "0 | \n", + "0.250 | \n", + "0.188 | \n", + "0.188 | \n", + "0.375 | \n", + "0.563 | \n", + "0.231 | \n", + "-0.18 | \n", + "2014 | \n", + "
| 테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.39 | \n", + "0.381 | \n", + "0.497 | \n", + "0.79 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.51 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.54 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.26 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
| 이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.29 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 용덕한 | \n", + "NC | \n", + "50 | \n", + "49 | \n", + "42 | \n", + "12 | \n", + "0 | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "8 | \n", + "2 | \n", + "0.353 | \n", + "0.286 | \n", + "0.375 | \n", + "0.31 | \n", + "0.685 | \n", + "0.326 | \n", + "0.44 | \n", + "2015 | \n", + "
| 최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 박정준 | \n", + "NC | \n", + "19 | \n", + "24 | \n", + "21 | \n", + "8 | \n", + "0 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "0.5 | \n", + "0.381 | \n", + "0.458 | \n", + "0.476 | \n", + "0.934 | \n", + "0.417 | \n", + "0.25 | \n", + "2015 | \n", + "
| 김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 조평호 | \n", + "NC | \n", + "22 | \n", + "22 | \n", + "22 | \n", + "8 | \n", + "1 | \n", + "4 | \n", + "3 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "0.467 | \n", + "0.364 | \n", + "0.364 | \n", + "0.591 | \n", + "0.955 | \n", + "0.401 | \n", + "0.18 | \n", + "2015 | \n", + "
| 이창섭 | \n", + "NC | \n", + "15 | \n", + "8 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.375 | \n", + "0 | \n", + "0.375 | \n", + "0.286 | \n", + "-0.01 | \n", + "2015 | \n", + "
| 김준완 | \n", + "NC | \n", + "30 | \n", + "54 | \n", + "44 | \n", + "10 | \n", + "0 | \n", + "12 | \n", + "2 | \n", + "10 | \n", + "10 | \n", + "2 | \n", + "0.294 | \n", + "0.227 | \n", + "0.370 | \n", + "0.227 | \n", + "0.597 | \n", + "0.308 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 강구성 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.2 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.242 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 강민국 | \n", + "NC | \n", + "1 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.06 | \n", + "2015 | \n", + "
| 김태진 | \n", + "NC | \n", + "1 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.09 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2013 2014 2015\n",
+ "강구성 0.0 NaN 0.0\n",
+ "강민국 NaN 0.0 0.0\n",
+ "강진성 0.0 NaN NaN\n",
+ "권희동 NaN 7.0 NaN\n",
+ "김동건 1.0 NaN NaN\n",
+ "김성욱 0.0 1.0 NaN\n",
+ "김종찬 0.0 NaN NaN\n",
+ "김종호 0.0 NaN 4.0\n",
+ "김준완 NaN 0.0 0.0\n",
+ "김태군 NaN NaN 6.0\n",
+ "김태우 NaN 0.0 NaN\n",
+ "김태진 NaN NaN 0.0\n",
+ "나성범 14.0 30.0 28.0\n",
+ "노진혁 NaN 1.0 NaN\n",
+ "마낙길 0.0 0.0 NaN\n",
+ "모창민 12.0 16.0 6.0\n",
+ "박명환* NaN 0.0 NaN\n",
+ "박민우 0.0 1.0 3.0\n",
+ "박정준 4.0 NaN 0.0\n",
+ "손시헌 NaN 5.0 13.0\n",
+ "용덕한 NaN NaN 0.0\n",
+ "이상호 0.0 NaN NaN\n",
+ "이승호 0.0 NaN NaN\n",
+ "이종욱 NaN 6.0 5.0\n",
+ "이창섭 0.0 NaN 0.0\n",
+ "이현곤 0.0 NaN NaN\n",
+ "이호준 20.0 23.0 24.0\n",
+ "조영훈 6.0 6.0 8.0\n",
+ "조평호 2.0 0.0 1.0\n",
+ "지석훈 3.0 6.0 11.0\n",
+ "차화준 1.0 NaN NaN\n",
+ "최재원 NaN 0.0 2.0\n",
+ "테임즈 NaN 37.0 47.0\n",
+ "허준 NaN 0.0 NaN"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "homerun.columns = [2013,2014,2015]\n",
+ "homerun"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2013 | \n", + "2014 | \n", + "2015 | \n", + "
|---|---|---|---|
| 강구성 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 강민국 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "
| 강진성 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 권희동 | \n", + "NaN | \n", + "7.0 | \n", + "NaN | \n", + "
| 김동건 | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 김성욱 | \n", + "0.0 | \n", + "1.0 | \n", + "NaN | \n", + "
| 김종찬 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 김종호 | \n", + "0.0 | \n", + "NaN | \n", + "4.0 | \n", + "
| 김준완 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "
| 김태군 | \n", + "NaN | \n", + "NaN | \n", + "6.0 | \n", + "
| 김태우 | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
| 김태진 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "
| 나성범 | \n", + "14.0 | \n", + "30.0 | \n", + "28.0 | \n", + "
| 노진혁 | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "
| 마낙길 | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
| 모창민 | \n", + "12.0 | \n", + "16.0 | \n", + "6.0 | \n", + "
| 박명환* | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
| 박민우 | \n", + "0.0 | \n", + "1.0 | \n", + "3.0 | \n", + "
| 박정준 | \n", + "4.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 손시헌 | \n", + "NaN | \n", + "5.0 | \n", + "13.0 | \n", + "
| 용덕한 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "
| 이상호 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이승호 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이종욱 | \n", + "NaN | \n", + "6.0 | \n", + "5.0 | \n", + "
| 이창섭 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 이현곤 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "20.0 | \n", + "23.0 | \n", + "24.0 | \n", + "
| 조영훈 | \n", + "6.0 | \n", + "6.0 | \n", + "8.0 | \n", + "
| 조평호 | \n", + "2.0 | \n", + "0.0 | \n", + "1.0 | \n", + "
| 지석훈 | \n", + "3.0 | \n", + "6.0 | \n", + "11.0 | \n", + "
| 차화준 | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 최재원 | \n", + "NaN | \n", + "0.0 | \n", + "2.0 | \n", + "
| 테임즈 | \n", + "NaN | \n", + "37.0 | \n", + "47.0 | \n", + "
| 허준 | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2013 2014 2015\n",
+ "나성범 14.0 30.0 28.0\n",
+ "노진혁 NaN 1.0 NaN\n",
+ "마낙길 0.0 0.0 NaN\n",
+ "모창민 12.0 16.0 6.0\n",
+ "박명환* NaN 0.0 NaN\n",
+ "박민우 0.0 1.0 3.0\n",
+ "박정준 4.0 NaN 0.0"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "homerun.loc['나성범':'박정준']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2013 | \n", + "2014 | \n", + "2015 | \n", + "
|---|---|---|---|
| 나성범 | \n", + "14.0 | \n", + "30.0 | \n", + "28.0 | \n", + "
| 노진혁 | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "
| 마낙길 | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
| 모창민 | \n", + "12.0 | \n", + "16.0 | \n", + "6.0 | \n", + "
| 박명환* | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
| 박민우 | \n", + "0.0 | \n", + "1.0 | \n", + "3.0 | \n", + "
| 박정준 | \n", + "4.0 | \n", + "NaN | \n", + "0.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2013 2014 2015\n",
+ "나성범 14.0 30.0 28.0\n",
+ "모창민 12.0 16.0 6.0\n",
+ "이호준 20.0 23.0 24.0"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2013년도 홈런 10개 이상\n",
+ "homerun[homerun[2013]>=10]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2013 | \n", + "2014 | \n", + "2015 | \n", + "
|---|---|---|---|
| 나성범 | \n", + "14.0 | \n", + "30.0 | \n", + "28.0 | \n", + "
| 모창민 | \n", + "12.0 | \n", + "16.0 | \n", + "6.0 | \n", + "
| 이호준 | \n", + "20.0 | \n", + "23.0 | \n", + "24.0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2013 2014 2015\n",
+ "강구성 0 - 0\n",
+ "강민국 - 0 0\n",
+ "강진성 0 - -\n",
+ "권희동 - 7 -\n",
+ "김동건 1 - -\n",
+ "김성욱 0 1 -\n",
+ "김종찬 0 - -\n",
+ "김종호 0 - 4\n",
+ "김준완 - 0 0\n",
+ "김태군 - - 6\n",
+ "김태우 - 0 -\n",
+ "김태진 - - 0\n",
+ "나성범 14 30 28\n",
+ "노진혁 - 1 -\n",
+ "마낙길 0 0 -\n",
+ "모창민 12 16 6\n",
+ "박명환* - 0 -\n",
+ "박민우 0 1 3\n",
+ "박정준 4 - 0\n",
+ "손시헌 - 5 13\n",
+ "용덕한 - - 0\n",
+ "이상호 0 - -\n",
+ "이승호 0 - -\n",
+ "이종욱 - 6 5\n",
+ "이창섭 0 - 0\n",
+ "이현곤 0 - -\n",
+ "이호준 20 23 24\n",
+ "조영훈 6 6 8\n",
+ "조평호 2 0 1\n",
+ "지석훈 3 6 11\n",
+ "차화준 1 - -\n",
+ "최재원 - 0 2\n",
+ "테임즈 - 37 47\n",
+ "허준 - 0 -"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "homerun.fillna('-')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "강구성 0.0\n",
+ "강민국 0.0\n",
+ "강진성 NaN\n",
+ "권희동 NaN\n",
+ "김동건 NaN\n",
+ "김성욱 NaN\n",
+ "김종찬 NaN\n",
+ "김종호 4.0\n",
+ "김준완 0.0\n",
+ "김태군 6.0\n",
+ "김태우 NaN\n",
+ "김태진 0.0\n",
+ "나성범 28.0\n",
+ "노진혁 NaN\n",
+ "마낙길 NaN\n",
+ "모창민 6.0\n",
+ "박명환* NaN\n",
+ "박민우 3.0\n",
+ "박정준 0.0\n",
+ "손시헌 13.0\n",
+ "용덕한 0.0\n",
+ "이상호 NaN\n",
+ "이승호 NaN\n",
+ "이종욱 5.0\n",
+ "이창섭 0.0\n",
+ "이현곤 NaN\n",
+ "이호준 24.0\n",
+ "조영훈 8.0\n",
+ "조평호 1.0\n",
+ "지석훈 11.0\n",
+ "차화준 NaN\n",
+ "최재원 2.0\n",
+ "테임즈 47.0\n",
+ "허준 NaN\n",
+ "Name: 2015, dtype: float64"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "homerun[2015]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2013 14.0\n",
+ "2014 30.0\n",
+ "2015 28.0\n",
+ "Name: 나성범, dtype: float64"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 나성범 선수 데이터\n",
+ "homerun.loc['나성범']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 5. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 2 - \354\202\260\354\210\240 \354\227\260\354\202\260, \354\273\254\353\237\274 \354\266\224\352\260\200, \355\225\250\354\210\230 \354\240\201\354\232\251.ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 5. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 2 - \354\202\260\354\210\240 \354\227\260\354\202\260, \354\273\254\353\237\274 \354\266\224\352\260\200, \355\225\250\354\210\230 \354\240\201\354\232\251.ipynb"
new file mode 100644
index 0000000..cebb14c
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 5. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 2 - \354\202\260\354\210\240 \354\227\260\354\202\260, \354\273\254\353\237\274 \354\266\224\352\260\200, \355\225\250\354\210\230 \354\240\201\354\232\251.ipynb"
@@ -0,0 +1,3285 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5-1. 산술연산\n",
+ "## 1) Series 산술연산\n",
+ "### - 같은 인덱스 라벨을 가진 항목끼리 산술 연산을 수행함.\n",
+ "### - 겹치는 인덱스가 없다면, 데이터는 NA가 된다. \n",
+ "| \n", + " | 2013 | \n", + "2014 | \n", + "2015 | \n", + "
|---|---|---|---|
| 강구성 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "
| 강민국 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "
| 강진성 | \n", + "0 | \n", + "- | \n", + "- | \n", + "
| 권희동 | \n", + "- | \n", + "7 | \n", + "- | \n", + "
| 김동건 | \n", + "1 | \n", + "- | \n", + "- | \n", + "
| 김성욱 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "
| 김종찬 | \n", + "0 | \n", + "- | \n", + "- | \n", + "
| 김종호 | \n", + "0 | \n", + "- | \n", + "4 | \n", + "
| 김준완 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "
| 김태군 | \n", + "- | \n", + "- | \n", + "6 | \n", + "
| 김태우 | \n", + "- | \n", + "0 | \n", + "- | \n", + "
| 김태진 | \n", + "- | \n", + "- | \n", + "0 | \n", + "
| 나성범 | \n", + "14 | \n", + "30 | \n", + "28 | \n", + "
| 노진혁 | \n", + "- | \n", + "1 | \n", + "- | \n", + "
| 마낙길 | \n", + "0 | \n", + "0 | \n", + "- | \n", + "
| 모창민 | \n", + "12 | \n", + "16 | \n", + "6 | \n", + "
| 박명환* | \n", + "- | \n", + "0 | \n", + "- | \n", + "
| 박민우 | \n", + "0 | \n", + "1 | \n", + "3 | \n", + "
| 박정준 | \n", + "4 | \n", + "- | \n", + "0 | \n", + "
| 손시헌 | \n", + "- | \n", + "5 | \n", + "13 | \n", + "
| 용덕한 | \n", + "- | \n", + "- | \n", + "0 | \n", + "
| 이상호 | \n", + "0 | \n", + "- | \n", + "- | \n", + "
| 이승호 | \n", + "0 | \n", + "- | \n", + "- | \n", + "
| 이종욱 | \n", + "- | \n", + "6 | \n", + "5 | \n", + "
| 이창섭 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "
| 이현곤 | \n", + "0 | \n", + "- | \n", + "- | \n", + "
| 이호준 | \n", + "20 | \n", + "23 | \n", + "24 | \n", + "
| 조영훈 | \n", + "6 | \n", + "6 | \n", + "8 | \n", + "
| 조평호 | \n", + "2 | \n", + "0 | \n", + "1 | \n", + "
| 지석훈 | \n", + "3 | \n", + "6 | \n", + "11 | \n", + "
| 차화준 | \n", + "1 | \n", + "- | \n", + "- | \n", + "
| 최재원 | \n", + "- | \n", + "0 | \n", + "2 | \n", + "
| 테임즈 | \n", + "- | \n", + "37 | \n", + "47 | \n", + "
| 허준 | \n", + "- | \n", + "0 | \n", + "- | \n", + "
![\"산술연산표\"](\"img/산술연산.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sr1 = Series([1,2,3], index = list('abc'))\n",
+ "sr2 = Series([4,5,6], index = list('abd'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 4.0\n",
+ "b 10.0\n",
+ "c NaN\n",
+ "d NaN\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sr1*sr2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 5.0\n",
+ "b 7.0\n",
+ "c NaN\n",
+ "d NaN\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sr1.add(sr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#같은 인덱스 라벨을 가진 항목끼리 산술 연산을 수행함.\n",
+ "#겹치는 인덱스가 없다면, 데이터는 NA가 된다. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#sr1 + sr2와 동일하지만, 함수로 사용하는 경우에는 다양한 파라미터 설정이 가능해짐.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#산술 연산 시, 데이터가 없는 경우 fill_value인자의 값으로 간주함.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2) DataFrame 산술연산"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d\n",
+ "Ohio 0 1 2\n",
+ "Texas 3 4 5\n",
+ "Colorado 6 7 8"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr1 = [[0, 1, 2],\n",
+ " [3, 4, 5],\n",
+ " [6, 7, 8]]\n",
+ "df1 = DataFrame(arr1, columns = list('bcd'), index = ['Ohio', 'Texas', 'Colorado'])\n",
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b d e\n",
+ "Utah 0 1 2\n",
+ "Ohio 3 4 5\n",
+ "Texas 6 7 8\n",
+ "Oregon 9 10 11"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr2 = [[ 0, 1, 2],\n",
+ " [ 3, 4, 5],\n",
+ " [ 6, 7, 8],\n",
+ " [ 9, 10, 11]]\n",
+ "df2 = DataFrame(arr2, columns = list('bde'), index = ['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "d | \n", + "e | \n", + "
|---|---|---|---|
| Utah | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "
| Ohio | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "
| Texas | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "
| Oregon | \n", + "9 | \n", + "10 | \n", + "11 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d e\n",
+ "Colorado NaN NaN NaN NaN\n",
+ "Ohio 3.0 NaN 6.0 NaN\n",
+ "Oregon NaN NaN NaN NaN\n",
+ "Texas 9.0 NaN 12.0 NaN\n",
+ "Utah NaN NaN NaN NaN"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 동일 값인 부분만 연산\n",
+ "df1+df2\n",
+ "df1.add(df2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 컬럼과 로우 인덱스 모두 동일한 항목끼리 연산이 수행됨.\n",
+ "# 둘 중 하나라도 없으면 NaN \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "e | \n", + "
|---|---|---|---|---|
| Colorado | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| Ohio | \n", + "3.0 | \n", + "NaN | \n", + "6.0 | \n", + "NaN | \n", + "
| Oregon | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| Texas | \n", + "9.0 | \n", + "NaN | \n", + "12.0 | \n", + "NaN | \n", + "
| Utah | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d e\n",
+ "Colorado 6.0 7.0 8.0 NaN\n",
+ "Ohio 3.0 1.0 6.0 5.0\n",
+ "Oregon 9.0 NaN 10.0 11.0\n",
+ "Texas 9.0 4.0 12.0 8.0\n",
+ "Utah 0.0 NaN 1.0 2.0"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.add(df2,fill_value = 0)\n",
+ "#fill_value = <값> : Missing Value를 <값>으로 간주함. 두 데이터프레임에 모두 없는 경우에는 결과도 Missing\n",
+ "# http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.add.html#pandas.DataFrame.add"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## DataFrame 산술 연산\n",
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "e | \n", + "
|---|---|---|---|---|
| Colorado | \n", + "6.0 | \n", + "7.0 | \n", + "8.0 | \n", + "NaN | \n", + "
| Ohio | \n", + "3.0 | \n", + "1.0 | \n", + "6.0 | \n", + "5.0 | \n", + "
| Oregon | \n", + "9.0 | \n", + "NaN | \n", + "10.0 | \n", + "11.0 | \n", + "
| Texas | \n", + "9.0 | \n", + "4.0 | \n", + "12.0 | \n", + "8.0 | \n", + "
| Utah | \n", + "0.0 | \n", + "NaN | \n", + "1.0 | \n", + "2.0 | \n", + "
![\"Add\"](\"img/데이터프레임add.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3) DataFrame과 Series 간의 연산\n",
+ "\n",
+ "### 기본적으로 DataFrame과 Series 간의 산술연산은 Series의 색인을 DataFrame의 칼럼에 맞추고 아래로 전파한다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " A B C D\n",
+ "X 1 3 5 7\n",
+ "Y 2 4 6 8\n",
+ "Z 10 20 30 40"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = DataFrame([[1,3, 5, 7],[2,4, 6, 8], [10, 20, 30, 40]], \n",
+ " columns = list('ABCD'), index = ['X','Y','Z'])\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "A 10\n",
+ "B 20\n",
+ "C 30\n",
+ "D 40\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#A, B, C, D 컬럼 각각에 10, 20, 30, 40을 더하기\n",
+ "sr = Series([10,20,30,40], index= ['A','B','C','D'])\n",
+ "sr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|
| X | \n", + "1 | \n", + "3 | \n", + "5 | \n", + "7 | \n", + "
| Y | \n", + "2 | \n", + "4 | \n", + "6 | \n", + "8 | \n", + "
| Z | \n", + "10 | \n", + "20 | \n", + "30 | \n", + "40 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " A B C D\n",
+ "X 11 23 35 47\n",
+ "Y 12 24 36 48\n",
+ "Z 20 40 60 80"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "df+sr\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.add(sr)\n",
+ "df.add(sr, axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#실습. X,Y,Z 로우에 각각에 10, 100, 1000을 더하시오."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "X 10\n",
+ "Y 100\n",
+ "Z 1000\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sr2= Series([10,100,1000], index = ['X','Y','Z'])\n",
+ "sr2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|
| X | \n", + "11 | \n", + "23 | \n", + "35 | \n", + "47 | \n", + "
| Y | \n", + "12 | \n", + "24 | \n", + "36 | \n", + "48 | \n", + "
| Z | \n", + "20 | \n", + "40 | \n", + "60 | \n", + "80 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " A B C D\n",
+ "X 11 13 15 17\n",
+ "Y 102 104 106 108\n",
+ "Z 1010 1020 1030 1040"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.add(sr2, axis = 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4) 함수 적용과 매핑\n",
+ "\n",
+ "### apply() :각 로우나 컬럼의 1차원 배열에 함수를 적용\n",
+ "### applymap() : 각 항목에 함수를 적용"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4-1) apply() 함수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def diff(x):\n",
+ " return x.max()-x.min()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3-1\n",
+ "diff(sr1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 1\n",
+ "b 2\n",
+ "c 3\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sr1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b 6\n",
+ "c 6\n",
+ "d 6\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.apply(diff)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | A | \n", + "B | \n", + "C | \n", + "D | \n", + "
|---|---|---|---|---|
| X | \n", + "11 | \n", + "13 | \n", + "15 | \n", + "17 | \n", + "
| Y | \n", + "102 | \n", + "104 | \n", + "106 | \n", + "108 | \n", + "
| Z | \n", + "1010 | \n", + "1020 | \n", + "1030 | \n", + "1040 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d\n",
+ "Ohio 0 1 2\n",
+ "Texas 3 4 5\n",
+ "Colorado 6 7 8"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ohio 2\n",
+ "Texas 2\n",
+ "Colorado 2\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.apply(diff, axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 4-2) applymap() 함수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mul2(x):\n",
+ " return x*2\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "
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+ ],
+ "text/plain": [
+ " b c d\n",
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+ "Texas 3 4 5\n",
+ "Colorado 6 7 8"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " b c d\n",
+ "Ohio 0 2 4\n",
+ "Texas 6 8 10\n",
+ "Colorado 12 14 16"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1.applymap(mul2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b 6\n",
+ "c 6\n",
+ "d 6\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "diff(df1)\n",
+ "# df1.apply(diff)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "2 | \n", + "4 | \n", + "
| Texas | \n", + "6 | \n", + "8 | \n", + "10 | \n", + "
| Colorado | \n", + "12 | \n", + "14 | \n", + "16 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d\n",
+ "Ohio 0 2 4\n",
+ "Texas 6 8 10\n",
+ "Colorado 12 14 16"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mul2(df1)\n",
+ "# df1.applymap(mul2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. 컬럼 추가하기"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "2 | \n", + "4 | \n", + "
| Texas | \n", + "6 | \n", + "8 | \n", + "10 | \n", + "
| Colorado | \n", + "12 | \n", + "14 | \n", + "16 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d\n",
+ "Ohio 0 1 2\n",
+ "Texas 3 4 5\n",
+ "Colorado 6 7 8"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 동일 값 할당 시에는 특정 스칼라 값을 입력하면 broadcasting \n",
+ "df1['e']=10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "
|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d e f\n",
+ "Ohio 0 1 2 10 12\n",
+ "Texas 3 4 5 10 15\n",
+ "Colorado 6 7 8 10 18"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df1['f']=df1['d']+df1['e']\n",
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "e | \n", + "f | \n", + "
|---|---|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "10 | \n", + "12 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "10 | \n", + "15 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "10 | \n", + "18 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " b c d e f g\n",
+ "Ohio 0 1 2 10 12 0\n",
+ "Texas 3 4 5 10 15 3\n",
+ "Colorado 6 7 8 10 18 6"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#실습. df1에 'g'라는 새로운 컬럼을 추가하고, 값은 각 로우(row)에 있는 값들 중 가장 작은 값으로 할당\n",
+ "\n",
+ " \n",
+ "df1['g']=df1.apply(min, axis=1)\n",
+ "df1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | b | \n", + "c | \n", + "d | \n", + "e | \n", + "f | \n", + "g | \n", + "
|---|---|---|---|---|---|---|
| Ohio | \n", + "0 | \n", + "1 | \n", + "2 | \n", + "10 | \n", + "12 | \n", + "0 | \n", + "
| Texas | \n", + "3 | \n", + "4 | \n", + "5 | \n", + "10 | \n", + "15 | \n", + "3 | \n", + "
| Colorado | \n", + "6 | \n", + "7 | \n", + "8 | \n", + "10 | \n", + "18 | \n", + "6 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 출루율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 0.339 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 0.362 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 0.376 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 0.319 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 0.350 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 0.361 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 0.299 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 0.667 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 0.318 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 0.333 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 0.335 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - 1.000 \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 0.333 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 0.300 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 0.306 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 0.176 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 0.000 \n",
+ "\n",
+ " 장타율 OPS wOBA WAR \n",
+ "0 0.443 0.782 0.353 2.31 \n",
+ "1 0.475 0.837 0.373 1.85 \n",
+ "2 0.333 0.709 0.339 1.55 \n",
+ "3 0.416 0.735 0.329 1.50 \n",
+ "4 0.413 0.763 0.348 0.83 \n",
+ "5 0.324 0.685 0.327 0.52 \n",
+ "6 0.320 0.619 0.289 0.16 \n",
+ "7 1.000 1.667 0.671 0.10 \n",
+ "8 0.418 0.736 0.329 0.09 \n",
+ "9 0.286 0.619 0.296 0.07 \n",
+ "10 0.353 0.688 0.320 0.06 \n",
+ "11 - - 0.712 0.06 \n",
+ "12 0.345 0.678 0.317 -0.03 \n",
+ "13 0.250 0.500 0.234 -0.03 \n",
+ "14 0.000 0.000 0.000 -0.06 \n",
+ "15 0.330 0.630 0.293 -0.09 \n",
+ "16 0.143 0.286 0.134 -0.11 \n",
+ "17 0.311 0.617 0.289 -0.12 \n",
+ "18 0.313 0.489 0.216 -0.14 \n",
+ "19 0.000 0.000 0.000 -0.14 "
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습을 위한 데이터 읽기 (NC Dinos.xlsx의 첫번째 시트값만 읽어오기)\n",
+ "# 첫번째 시트는 2013년도 선수 기록 정보\n",
+ "NC = pd.read_excel('data/NC Dinos.xlsx')\n",
+ "NC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "
| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 출루율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 0.339 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 0.362 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 0.376 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 0.319 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 0.350 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 0.361 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 0.299 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 0.667 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 0.318 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 0.333 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 0.335 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - 1.000 \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 0.333 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 0.300 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 0.306 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 0.176 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 0.000 \n",
+ "\n",
+ " 장타율 OPS wOBA WAR 연도 \n",
+ "0 0.443 0.782 0.353 2.31 2013 \n",
+ "1 0.475 0.837 0.373 1.85 2013 \n",
+ "2 0.333 0.709 0.339 1.55 2013 \n",
+ "3 0.416 0.735 0.329 1.50 2013 \n",
+ "4 0.413 0.763 0.348 0.83 2013 \n",
+ "5 0.324 0.685 0.327 0.52 2013 \n",
+ "6 0.320 0.619 0.289 0.16 2013 \n",
+ "7 1.000 1.667 0.671 0.10 2013 \n",
+ "8 0.418 0.736 0.329 0.09 2013 \n",
+ "9 0.286 0.619 0.296 0.07 2013 \n",
+ "10 0.353 0.688 0.320 0.06 2013 \n",
+ "11 - - 0.712 0.06 2013 \n",
+ "12 0.345 0.678 0.317 -0.03 2013 \n",
+ "13 0.250 0.500 0.234 -0.03 2013 \n",
+ "14 0.000 0.000 0.000 -0.06 2013 \n",
+ "15 0.330 0.630 0.293 -0.09 2013 \n",
+ "16 0.143 0.286 0.134 -0.11 2013 \n",
+ "17 0.311 0.617 0.289 -0.12 2013 \n",
+ "18 0.313 0.489 0.216 -0.14 2013 \n",
+ "19 0.000 0.000 0.000 -0.14 2013 "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. '연도' 라는 새로운 컬럼을 추가하고, 값을 2013 으로 초기화하기\n",
+ "NC['연도']=2013\n",
+ "NC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "연도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "2013 | \n", + "
| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "2013 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "2013 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "2013 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "2013 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "2013 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "2013 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "2013 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "2013 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "2013 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "2013 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2013 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "2013 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "2013 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "2013 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "2013 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 ... 도루 BABIP 타율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 ... 16 0.307 0.276 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 ... 2 0.324 0.278 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 ... 50 0.352 0.277 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 ... 12 0.279 0.243 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 ... 4 0.316 0.282 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 ... 2 0.304 0.273 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 ... 24 0.298 0.248 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 ... 0 0.500 0.500 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 ... 1 0.358 0.266 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 ... 9 0.306 0.262 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 ... 4 0.303 0.246 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 ... 1 - - \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 ... 2 0.375 0.310 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 ... 0 0.500 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 ... 0 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 ... 2 0.285 0.220 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 ... 0 0.250 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 ... 4 0.295 0.226 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 ... 0 0.125 0.125 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 ... 0 0.000 0.000 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 연도 안타홈런 \n",
+ "0 0.339 0.443 0.782 0.353 2.31 2013 121 \n",
+ "1 0.362 0.475 0.837 0.373 1.85 2013 143 \n",
+ "2 0.376 0.333 0.709 0.339 1.55 2013 129 \n",
+ "3 0.319 0.416 0.735 0.329 1.50 2013 112 \n",
+ "4 0.350 0.413 0.763 0.348 0.83 2013 113 \n",
+ "5 0.361 0.324 0.685 0.327 0.52 2013 38 \n",
+ "6 0.299 0.320 0.619 0.289 0.16 2013 31 \n",
+ "7 0.667 1.000 1.667 0.671 0.10 2013 1 \n",
+ "8 0.318 0.418 0.736 0.329 0.09 2013 23 \n",
+ "9 0.333 0.286 0.619 0.296 0.07 2013 11 \n",
+ "10 0.335 0.353 0.688 0.320 0.06 2013 45 \n",
+ "11 1.000 - - 0.712 0.06 2013 0 \n",
+ "12 0.333 0.345 0.678 0.317 -0.03 2013 9 \n",
+ "13 0.250 0.250 0.500 0.234 -0.03 2013 1 \n",
+ "14 0.000 0.000 0.000 0.000 -0.06 2013 0 \n",
+ "15 0.300 0.330 0.630 0.293 -0.09 2013 71 \n",
+ "16 0.143 0.143 0.286 0.134 -0.11 2013 1 \n",
+ "17 0.306 0.311 0.617 0.289 -0.12 2013 25 \n",
+ "18 0.176 0.313 0.489 0.216 -0.14 2013 3 \n",
+ "19 0.000 0.000 0.000 0.000 -0.14 2013 0 \n",
+ "\n",
+ "[20 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. 안타와 홈런의 수를 합한 값을 '안타홈런'이라는 컬럼으로 추가\n",
+ "NC['안타홈런']=NC['안타']+NC['홈런']\n",
+ "\n",
+ "NC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def 안타홈런더하기 (x):\n",
+ " return x.홈런+x.안타"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC['안타홈런2']=NC.apply(안타홈런더하기, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 6. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 3 - \352\263\204\354\270\265 \354\203\211\354\235\270 (1).ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 6. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 3 - \352\263\204\354\270\265 \354\203\211\354\235\270 (1).ipynb"
new file mode 100644
index 0000000..7c0fd03
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 6. \353\215\260\354\235\264\355\204\260 \352\260\200\352\263\265 3 - \352\263\204\354\270\265 \354\203\211\354\235\270 (1).ipynb"
@@ -0,0 +1,3544 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6. 계층 색인 (다중 색인)\n",
+ "### 2개 이상의 색인(인덱스)를 지정할 수 있다. \n",
+ "### 차원이 높은 (고차원) 데이터를 낮은 차원의 형식으로 다룰 수 있게 해주는 기능"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 샘플 데이터 생성\n",
+ "np.random.seed(0)\n",
+ "df = pd.DataFrame(np.random.randint(50, 100, (5, 4)), \n",
+ " columns=[[2016, 2016, 2017, 2017], ['영어','수학','영어','수학']], index = ['Kim','Park','Lee','Jung','Moon'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "... | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "2013 | \n", + "143 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "... | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "2013 | \n", + "129 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "... | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "2013 | \n", + "112 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "... | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "2013 | \n", + "113 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "... | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "2013 | \n", + "38 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "... | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "2013 | \n", + "31 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "... | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "2013 | \n", + "1 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "... | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "2013 | \n", + "23 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "... | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "2013 | \n", + "11 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "... | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "2013 | \n", + "45 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "... | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "0 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "... | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "2013 | \n", + "9 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "2013 | \n", + "1 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2013 | \n", + "0 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "... | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "2013 | \n", + "71 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "2013 | \n", + "1 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "... | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "2013 | \n", + "25 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "... | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "2013 | \n", + "3 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "... | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "2013 | \n", + "0 | \n", + "
20 rows × 21 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2016 2017 \n",
+ " 영어 수학 영어 수학\n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6-1. 인덱싱"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| \n", + " | 영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 영어 수학\n",
+ "Kim 94 97\n",
+ "Park 53 89\n",
+ "Lee 71 86\n",
+ "Jung 74 74\n",
+ "Moon 88 89"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#2016년 영어, 수학 성적 조회\n",
+ "df[2016]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Kim 94\n",
+ "Park 53\n",
+ "Lee 71\n",
+ "Jung 74\n",
+ "Moon 88\n",
+ "Name: (2016, 영어), dtype: int32"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#2016년 영어 성적만 조회\n",
+ "df[(2016,'영어')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2016 영어 94\n",
+ " 수학 97\n",
+ "2017 영어 50\n",
+ " 수학 53\n",
+ "Name: Kim, dtype: int32"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Kim의 성적만 선택\n",
+ "df.loc['Kim']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 영어 | \n", + "수학 | \n", + "
|---|---|---|
| Kim | \n", + "94 | \n", + "97 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2016 2017 \n",
+ " 영어 수학 영어 수학\n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. Kim, Park, Lee의 성적만 선택\n",
+ "df.loc[['Kim','Park','Lee']]\n",
+ "df.loc['Kim':'Lee']\n",
+ "df['Kim':'Lee']\n",
+ "df.iloc[0:3]\n",
+ "df[0:3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| \n", + " | 영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2016 2017\n",
+ "Kim 94 50\n",
+ "Park 53 59\n",
+ "Lee 71 73\n",
+ "Jung 74 62\n",
+ "Moon 88 73"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 최상위 색인이 아닌 색인으로만 인덱싱하고 싶은 경우에는 xs() 함수를 사용.\n",
+ "# 2016, 2017년도 영어 성적만 선택\n",
+ "# level :계층 (최상위:0, 1, 2,...)\n",
+ "# axis (row: 0, col: 1)\n",
+ "df.xs('영어',axis=1, level = 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "## 6-2. 메타데이터 설정 (set_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2016 | \n", + "2017 | \n", + "
|---|---|---|
| Kim | \n", + "94 | \n", + "50 | \n", + "
| Park | \n", + "53 | \n", + "59 | \n", + "
| Lee | \n", + "71 | \n", + "73 | \n", + "
| Jung | \n", + "74 | \n", + "62 | \n", + "
| Moon | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 2016 2017 \n",
+ " 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 인덱스에 이름 부여하기 (set_names)\n",
+ "# 로우 인덱스의 이름을 '학생명'이라고 정의하기\n",
+ "# inplace : True 시 로우 인덱스로 올라감 \n",
+ "df.index.set_names('학생명', inplace = True)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| \n", + " | 영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목명 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. 컬럼들의 이름을 각각 year와 subject로 정의하기\n",
+ "df.columns.set_names(['년도','과목명'], inplace= True)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 6-3. 몇 가지 주요 함수들\n",
+ "### 1) swaplevel(index1, index2, axis)\n",
+ "##### index1과 index2의 위치를 변경함. \n",
+ "##### index1과 index2가 로우 인덱스인 경우, axis = 0, 컬럼인덱스면 1 (기본값은 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목명 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ "과목명 영어 수학 영어 수학\n",
+ "년도 2016 2016 2017 2017\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# year과 subject의 위치를 변경\n",
+ "df.swaplevel('년도','과목명', axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목명 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|---|---|
| 년도 | \n", + "2016 | \n", + "2016 | \n", + "2017 | \n", + "2017 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목명 영어 수학 영어 수학\n",
+ "년도 2016 2016 2017 2017\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.swaplevel(0,1, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2) stack(), unstack() 함수\n",
+ "### stack(level) : 컬럼 인덱스를 로우 인덱스로 옮길 때 사용.\n",
+ "### unstack(level): 로우 인덱스를 컬럼 인덱스로 옮길 때 사용.\n",
+ "### level 인자는 옮기고자 하는 인덱스의 위치를 표기함. 명시하지 않은 경우, 최하단의 인덱스를 이동시킴.\n",
+ "### level은 최상위가 0이고, 1씩 증가함"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목명 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|---|---|
| 년도 | \n", + "2016 | \n", + "2016 | \n", + "2017 | \n", + "2017 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목명 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목명 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "학생명 과목명 \n",
+ "Kim 수학 97 53\n",
+ " 영어 94 50\n",
+ "Park 수학 89 69\n",
+ " 영어 53 59\n",
+ "Lee 수학 86 56\n",
+ " 영어 71 73\n",
+ "Jung 수학 74 51\n",
+ " 영어 74 62\n",
+ "Moon 수학 89 96\n",
+ " 영어 88 73"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.stack() # level 인자 : 기본값 = 최하위"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|---|
| 학생명 | \n", + "과목명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "수학 | \n", + "97 | \n", + "53 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "|
| Park | \n", + "수학 | \n", + "89 | \n", + "69 | \n", + "
| 영어 | \n", + "53 | \n", + "59 | \n", + "|
| Lee | \n", + "수학 | \n", + "86 | \n", + "56 | \n", + "
| 영어 | \n", + "71 | \n", + "73 | \n", + "|
| Jung | \n", + "수학 | \n", + "74 | \n", + "51 | \n", + "
| 영어 | \n", + "74 | \n", + "62 | \n", + "|
| Moon | \n", + "수학 | \n", + "89 | \n", + "96 | \n", + "
| 영어 | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "학생명 과목명 \n",
+ "Kim 수학 97 53\n",
+ " 영어 94 50\n",
+ "Park 수학 89 69\n",
+ " 영어 53 59\n",
+ "Lee 수학 86 56\n",
+ " 영어 71 73\n",
+ "Jung 수학 74 51\n",
+ " 영어 74 62\n",
+ "Moon 수학 89 96\n",
+ " 영어 88 73"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.stack(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|---|
| 학생명 | \n", + "과목명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "수학 | \n", + "97 | \n", + "53 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "|
| Park | \n", + "수학 | \n", + "89 | \n", + "69 | \n", + "
| 영어 | \n", + "53 | \n", + "59 | \n", + "|
| Lee | \n", + "수학 | \n", + "86 | \n", + "56 | \n", + "
| 영어 | \n", + "71 | \n", + "73 | \n", + "|
| Jung | \n", + "수학 | \n", + "74 | \n", + "51 | \n", + "
| 영어 | \n", + "74 | \n", + "62 | \n", + "|
| Moon | \n", + "수학 | \n", + "89 | \n", + "96 | \n", + "
| 영어 | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "학생명 과목명 \n",
+ "Kim 수학 97 53\n",
+ " 영어 94 50\n",
+ "Park 수학 89 69\n",
+ " 영어 53 59\n",
+ "Lee 수학 86 56\n",
+ " 영어 71 73\n",
+ "Jung 수학 74 51\n",
+ " 영어 74 62\n",
+ "Moon 수학 89 96\n",
+ " 영어 88 73"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 컬럼 인덱스 과목을 로우 인덱스로 변경하고 df2에 저장\n",
+ "df.stack()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|---|
| 학생명 | \n", + "과목명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "수학 | \n", + "97 | \n", + "53 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "|
| Park | \n", + "수학 | \n", + "89 | \n", + "69 | \n", + "
| 영어 | \n", + "53 | \n", + "59 | \n", + "|
| Lee | \n", + "수학 | \n", + "86 | \n", + "56 | \n", + "
| 영어 | \n", + "71 | \n", + "73 | \n", + "|
| Jung | \n", + "수학 | \n", + "74 | \n", + "51 | \n", + "
| 영어 | \n", + "74 | \n", + "62 | \n", + "|
| Moon | \n", + "수학 | \n", + "89 | \n", + "96 | \n", + "
| 영어 | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목명 수학 영어\n",
+ "학생명 년도 \n",
+ "Kim 2016 97 94\n",
+ " 2017 53 50\n",
+ "Park 2016 89 53\n",
+ " 2017 69 59\n",
+ "Lee 2016 86 71\n",
+ " 2017 56 73\n",
+ "Jung 2016 74 74\n",
+ " 2017 51 62\n",
+ "Moon 2016 89 88\n",
+ " 2017 96 73"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 컬럼 인덱스 '년도'를 로우 인덱스로 변경\n",
+ "df.stack(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### df2를 대상으로 아래 실습 문제 수행"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 과목명 | \n", + "수학 | \n", + "영어 | \n", + "
|---|---|---|---|
| 학생명 | \n", + "년도 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "2016 | \n", + "97 | \n", + "94 | \n", + "
| 2017 | \n", + "53 | \n", + "50 | \n", + "|
| Park | \n", + "2016 | \n", + "89 | \n", + "53 | \n", + "
| 2017 | \n", + "69 | \n", + "59 | \n", + "|
| Lee | \n", + "2016 | \n", + "86 | \n", + "71 | \n", + "
| 2017 | \n", + "56 | \n", + "73 | \n", + "|
| Jung | \n", + "2016 | \n", + "74 | \n", + "74 | \n", + "
| 2017 | \n", + "51 | \n", + "62 | \n", + "|
| Moon | \n", + "2016 | \n", + "89 | \n", + "88 | \n", + "
| 2017 | \n", + "96 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "학생명 과목명 \n",
+ "Kim 수학 97 53\n",
+ " 영어 94 50\n",
+ "Park 수학 89 69\n",
+ " 영어 53 59\n",
+ "Lee 수학 86 56\n",
+ " 영어 71 73\n",
+ "Jung 수학 74 51\n",
+ " 영어 74 62\n",
+ "Moon 수학 89 96\n",
+ " 영어 88 73"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2= df.stack(1)\n",
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|---|
| 학생명 | \n", + "과목명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "수학 | \n", + "97 | \n", + "53 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "|
| Park | \n", + "수학 | \n", + "89 | \n", + "69 | \n", + "
| 영어 | \n", + "53 | \n", + "59 | \n", + "|
| Lee | \n", + "수학 | \n", + "86 | \n", + "56 | \n", + "
| 영어 | \n", + "71 | \n", + "73 | \n", + "|
| Jung | \n", + "수학 | \n", + "74 | \n", + "51 | \n", + "
| 영어 | \n", + "74 | \n", + "62 | \n", + "|
| Moon | \n", + "수학 | \n", + "89 | \n", + "96 | \n", + "
| 영어 | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "과목명 \n",
+ "수학 97 53\n",
+ "영어 94 50"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. Kim의 성적만 선택\n",
+ "df2.loc['Kim']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|
| 과목명 | \n", + "\n", + " | \n", + " |
| 수학 | \n", + "97 | \n", + "53 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Park\n",
+ " 수학\n",
+ "년도 \n",
+ "2016 89\n",
+ "2017 69"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. Park의 수학 성적만 선택\n",
+ "# 튜플 형태로\n",
+ "df2.loc[('Park','수학')]\n",
+ "DataFrame(df2.loc[('Park','수학')])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Park | \n", + "
|---|---|
| \n", + " | 수학 | \n", + "
| 년도 | \n", + "\n", + " |
| 2016 | \n", + "89 | \n", + "
| 2017 | \n", + "69 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017\n",
+ "학생명 \n",
+ "Kim 94 50\n",
+ "Park 53 59\n",
+ "Lee 71 73\n",
+ "Jung 74 62\n",
+ "Moon 88 73"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. 모든 학생들의 영어 성적만 선택 \n",
+ "df2.xs('영어',axis =0, level=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "학생명 과목명\n",
+ "Park 수학 89\n",
+ " 영어 53\n",
+ "Name: 2016, dtype: int32"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. Park 학생의 2016년 영어 성적만 출력\n",
+ "df2[2016][('Park','영어')]\n",
+ "df2.loc[['Park','영어'],2016]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|
| 학생명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "50 | \n", + "
| Park | \n", + "53 | \n", + "59 | \n", + "
| Lee | \n", + "71 | \n", + "73 | \n", + "
| Jung | \n", + "74 | \n", + "62 | \n", + "
| Moon | \n", + "88 | \n", + "73 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 평균\n",
+ "학생명 과목명 \n",
+ "Kim 수학 97 53 75.0\n",
+ " 영어 94 50 72.0\n",
+ "Park 수학 89 69 79.0\n",
+ " 영어 53 59 56.0\n",
+ "Lee 수학 86 56 71.0\n",
+ " 영어 71 73 72.0\n",
+ "Jung 수학 74 51 62.5\n",
+ " 영어 74 62 68.0\n",
+ "Moon 수학 89 96 92.5\n",
+ " 영어 88 73 80.5"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. 학생들의 과목별 평균 \n",
+ "\n",
+ "df2['평균']=df2.apply(np.mean, axis = 1)\n",
+ "df2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'mean' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | 년도 | \n", + "2016 | \n", + "2017 | \n", + "평균 | \n", + "
|---|---|---|---|---|
| 학생명 | \n", + "과목명 | \n", + "\n", + " | \n", + " | \n", + " |
| Kim | \n", + "수학 | \n", + "97 | \n", + "53 | \n", + "75.0 | \n", + "
| 영어 | \n", + "94 | \n", + "50 | \n", + "72.0 | \n", + "|
| Park | \n", + "수학 | \n", + "89 | \n", + "69 | \n", + "79.0 | \n", + "
| 영어 | \n", + "53 | \n", + "59 | \n", + "56.0 | \n", + "|
| Lee | \n", + "수학 | \n", + "86 | \n", + "56 | \n", + "71.0 | \n", + "
| 영어 | \n", + "71 | \n", + "73 | \n", + "72.0 | \n", + "|
| Jung | \n", + "수학 | \n", + "74 | \n", + "51 | \n", + "62.5 | \n", + "
| 영어 | \n", + "74 | \n", + "62 | \n", + "68.0 | \n", + "|
| Moon | \n", + "수학 | \n", + "89 | \n", + "96 | \n", + "92.5 | \n", + "
| 영어 | \n", + "88 | \n", + "73 | \n", + "80.5 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " 선수명 년도 안타 홈런\n",
+ "0 모창민 2013 109 12\n",
+ "1 이호준 2013 123 20\n",
+ "2 김종호 2013 129 0\n",
+ "3 나성범 2013 98 14\n",
+ "4 조영훈 2013 107 6"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NC Dinos 선수 기록 적재\n",
+ "NC = pd.read_excel('data/NC Dinos.xlsx', sheet_name = None)\n",
+ "NC13, NC14, NC15 = NC.values()\n",
+ "NC13['년도'] = 2013\n",
+ "NC14['년도'] = 2014\n",
+ "NC15['년도'] = 2015\n",
+ "NC13 = NC13[['선수명', '년도', '안타','홈런']]\n",
+ "NC14 = NC14[['선수명', '년도', '안타','홈런']]\n",
+ "NC15 = NC15[['선수명', '년도', '안타','홈런']]\n",
+ "NCAll = pd.concat([NC13, NC14, NC15])\n",
+ "NCAll.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "15 조평호 2014 0 0\n",
+ "16 강민국 2014 0 0\n",
+ "17 허준 2014 5 0\n",
+ "18 김성욱 2014 4 1\n",
+ "19 노진혁 2014 3 1\n",
+ "0 테임즈 2015 180 47\n",
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+ "10 용덕한 2015 12 0\n",
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+ "14 조평호 2015 8 1\n",
+ "15 이창섭 2015 0 0\n",
+ "16 김준완 2015 10 0\n",
+ "17 강구성 2015 1 0\n",
+ "18 강민국 2015 0 0\n",
+ "19 김태진 2015 0 0"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NCAll"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
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| 19 | \n", + "노진혁 | \n", + "2014 | \n", + "3 | \n", + "1 | \n", + "
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| 5 | \n", + "조영훈 | \n", + "2015 | \n", + "35 | \n", + "8 | \n", + "
| 6 | \n", + "손시헌 | \n", + "2015 | \n", + "108 | \n", + "13 | \n", + "
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| 8 | \n", + "모창민 | \n", + "2015 | \n", + "62 | \n", + "6 | \n", + "
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| 10 | \n", + "용덕한 | \n", + "2015 | \n", + "12 | \n", + "0 | \n", + "
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+ "metadata": {},
+ "output_type": "execute_result"
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+ ]
+ },
+ {
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| 박민우 | \n", + "11.0 | \n", + "124.0 | \n", + "158.0 | \n", + "0.0 | \n", + "1.0 | \n", + "3.0 | \n", + "
| 박정준 | \n", + "41.0 | \n", + "NaN | \n", + "8.0 | \n", + "4.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 손시헌 | \n", + "NaN | \n", + "90.0 | \n", + "108.0 | \n", + "NaN | \n", + "5.0 | \n", + "13.0 | \n", + "
| 용덕한 | \n", + "NaN | \n", + "NaN | \n", + "12.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "
| 이상호 | \n", + "31.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이승호 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이종욱 | \n", + "NaN | \n", + "126.0 | \n", + "118.0 | \n", + "NaN | \n", + "6.0 | \n", + "5.0 | \n", + "
| 이창섭 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 이현곤 | \n", + "38.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "123.0 | \n", + "115.0 | \n", + "132.0 | \n", + "20.0 | \n", + "23.0 | \n", + "24.0 | \n", + "
| 조영훈 | \n", + "107.0 | \n", + "29.0 | \n", + "35.0 | \n", + "6.0 | \n", + "6.0 | \n", + "8.0 | \n", + "
| 조평호 | \n", + "21.0 | \n", + "0.0 | \n", + "8.0 | \n", + "2.0 | \n", + "0.0 | \n", + "1.0 | \n", + "
| 지석훈 | \n", + "68.0 | \n", + "58.0 | \n", + "111.0 | \n", + "3.0 | \n", + "6.0 | \n", + "11.0 | \n", + "
| 차화준 | \n", + "24.0 | \n", + "NaN | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 최재원 | \n", + "NaN | \n", + "0.0 | \n", + "21.0 | \n", + "NaN | \n", + "0.0 | \n", + "2.0 | \n", + "
| 테임즈 | \n", + "NaN | \n", + "152.0 | \n", + "180.0 | \n", + "NaN | \n", + "37.0 | \n", + "47.0 | \n", + "
| 허준 | \n", + "NaN | \n", + "5.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 안타 홈런 \n",
+ "년도 2013 2014 2015 2013 2014 2015\n",
+ "선수명 \n",
+ "강구성 0.0 NaN 1.0 0.0 NaN 0.0\n",
+ "강민국 NaN 0.0 0.0 NaN 0.0 0.0\n",
+ "강진성 1.0 NaN NaN 0.0 NaN NaN\n",
+ "권희동 NaN 63.0 NaN NaN 7.0 NaN\n",
+ "김동건 2.0 NaN NaN 1.0 NaN NaN\n",
+ "김성욱 1.0 4.0 NaN 0.0 1.0 NaN\n",
+ "김종찬 1.0 NaN NaN 0.0 NaN NaN\n",
+ "김종호 129.0 NaN 125.0 0.0 NaN 4.0\n",
+ "김준완 NaN 2.0 10.0 NaN 0.0 0.0\n",
+ "김태군 NaN NaN 107.0 NaN NaN 6.0\n",
+ "김태우 NaN 1.0 NaN NaN 0.0 NaN\n",
+ "김태진 NaN NaN 0.0 NaN NaN 0.0\n",
+ "나성범 98.0 157.0 184.0 14.0 30.0 28.0\n",
+ "노진혁 NaN 3.0 NaN NaN 1.0 NaN\n",
+ "마낙길 9.0 0.0 NaN 0.0 0.0 NaN\n",
+ "모창민 109.0 110.0 62.0 12.0 16.0 6.0\n",
+ "박명환* NaN 0.0 NaN NaN 0.0 NaN\n",
+ "박민우 11.0 124.0 158.0 0.0 1.0 3.0\n",
+ "박정준 41.0 NaN 8.0 4.0 NaN 0.0\n",
+ "손시헌 NaN 90.0 108.0 NaN 5.0 13.0\n",
+ "용덕한 NaN NaN 12.0 NaN NaN 0.0\n",
+ "이상호 31.0 NaN NaN 0.0 NaN NaN\n",
+ "이승호 0.0 NaN NaN 0.0 NaN NaN\n",
+ "이종욱 NaN 126.0 118.0 NaN 6.0 5.0\n",
+ "이창섭 0.0 NaN 0.0 0.0 NaN 0.0\n",
+ "이현곤 38.0 NaN NaN 0.0 NaN NaN\n",
+ "이호준 123.0 115.0 132.0 20.0 23.0 24.0\n",
+ "조영훈 107.0 29.0 35.0 6.0 6.0 8.0\n",
+ "조평호 21.0 0.0 8.0 2.0 0.0 1.0\n",
+ "지석훈 68.0 58.0 111.0 3.0 6.0 11.0\n",
+ "차화준 24.0 NaN NaN 1.0 NaN NaN\n",
+ "최재원 NaN 0.0 21.0 NaN 0.0 2.0\n",
+ "테임즈 NaN 152.0 180.0 NaN 37.0 47.0\n",
+ "허준 NaN 5.0 NaN NaN 0.0 NaN"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NCAll을 아래 결과처럼 나오도록 변경\n",
+ "NCAll.set_index(['선수명','년도']).unstack()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 7. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 1 - \355\206\265\352\263\204, \354\240\225\353\240\254.ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 7. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 1 - \355\206\265\352\263\204, \354\240\225\353\240\254.ipynb"
new file mode 100644
index 0000000..5ba3023
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 7. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 1 - \355\206\265\352\263\204, \354\240\225\353\240\254.ipynb"
@@ -0,0 +1,1572 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 7-1. 통계 함수\n",
+ "## 주요 통계 함수 목록\n",
+ "| \n", + " | 안타 | \n", + "홈런 | \n", + "||||
|---|---|---|---|---|---|---|
| 년도 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 강구성 | \n", + "0.0 | \n", + "NaN | \n", + "1.0 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 강민국 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "
| 강진성 | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 권희동 | \n", + "NaN | \n", + "63.0 | \n", + "NaN | \n", + "NaN | \n", + "7.0 | \n", + "NaN | \n", + "
| 김동건 | \n", + "2.0 | \n", + "NaN | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 김성욱 | \n", + "1.0 | \n", + "4.0 | \n", + "NaN | \n", + "0.0 | \n", + "1.0 | \n", + "NaN | \n", + "
| 김종찬 | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 김종호 | \n", + "129.0 | \n", + "NaN | \n", + "125.0 | \n", + "0.0 | \n", + "NaN | \n", + "4.0 | \n", + "
| 김준완 | \n", + "NaN | \n", + "2.0 | \n", + "10.0 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "
| 김태군 | \n", + "NaN | \n", + "NaN | \n", + "107.0 | \n", + "NaN | \n", + "NaN | \n", + "6.0 | \n", + "
| 김태우 | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
| 김태진 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "
| 나성범 | \n", + "98.0 | \n", + "157.0 | \n", + "184.0 | \n", + "14.0 | \n", + "30.0 | \n", + "28.0 | \n", + "
| 노진혁 | \n", + "NaN | \n", + "3.0 | \n", + "NaN | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "
| 마낙길 | \n", + "9.0 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "
| 모창민 | \n", + "109.0 | \n", + "110.0 | \n", + "62.0 | \n", + "12.0 | \n", + "16.0 | \n", + "6.0 | \n", + "
| 박명환* | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
| 박민우 | \n", + "11.0 | \n", + "124.0 | \n", + "158.0 | \n", + "0.0 | \n", + "1.0 | \n", + "3.0 | \n", + "
| 박정준 | \n", + "41.0 | \n", + "NaN | \n", + "8.0 | \n", + "4.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 손시헌 | \n", + "NaN | \n", + "90.0 | \n", + "108.0 | \n", + "NaN | \n", + "5.0 | \n", + "13.0 | \n", + "
| 용덕한 | \n", + "NaN | \n", + "NaN | \n", + "12.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "
| 이상호 | \n", + "31.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이승호 | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이종욱 | \n", + "NaN | \n", + "126.0 | \n", + "118.0 | \n", + "NaN | \n", + "6.0 | \n", + "5.0 | \n", + "
| 이창섭 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "0.0 | \n", + "NaN | \n", + "0.0 | \n", + "
| 이현곤 | \n", + "38.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "123.0 | \n", + "115.0 | \n", + "132.0 | \n", + "20.0 | \n", + "23.0 | \n", + "24.0 | \n", + "
| 조영훈 | \n", + "107.0 | \n", + "29.0 | \n", + "35.0 | \n", + "6.0 | \n", + "6.0 | \n", + "8.0 | \n", + "
| 조평호 | \n", + "21.0 | \n", + "0.0 | \n", + "8.0 | \n", + "2.0 | \n", + "0.0 | \n", + "1.0 | \n", + "
| 지석훈 | \n", + "68.0 | \n", + "58.0 | \n", + "111.0 | \n", + "3.0 | \n", + "6.0 | \n", + "11.0 | \n", + "
| 차화준 | \n", + "24.0 | \n", + "NaN | \n", + "NaN | \n", + "1.0 | \n", + "NaN | \n", + "NaN | \n", + "
| 최재원 | \n", + "NaN | \n", + "0.0 | \n", + "21.0 | \n", + "NaN | \n", + "0.0 | \n", + "2.0 | \n", + "
| 테임즈 | \n", + "NaN | \n", + "152.0 | \n", + "180.0 | \n", + "NaN | \n", + "37.0 | \n", + "47.0 | \n", + "
| 허준 | \n", + "NaN | \n", + "5.0 | \n", + "NaN | \n", + "NaN | \n", + "0.0 | \n", + "NaN | \n", + "
![\"통계함수\"](\"img/통계함수.jpg\")
\n",
+ "\n",
+ "## 주요 함수 옵션\n",
+ "![\"주요옵션\"](\"img/주요함수옵션.jpg\")
\n",
+ "\n",
+ "### ** axis는 통계함수 뿐만 아니라, 많은 함수에서 동일하게 적용됨. **"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 샘플 데이터 생성\n",
+ "np.random.seed(0)\n",
+ "df = pd.DataFrame(np.random.randint(50, 100, (5, 4)), \n",
+ " columns=[[2016, 2016, 2017, 2017], ['영어','수학','영어','수학']], index = ['Kim','Park','Lee','Jung','Moon'])\n",
+ "df\n",
+ "df.index.set_names('학생명', inplace = True)\n",
+ "df.columns.set_names(['년도','과목'], inplace = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97\n",
+ "Park 53 89\n",
+ "Lee 71 86\n",
+ "Jung 74 74\n",
+ "Moon 88 89"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2016년 성적만 선택해서 df2016에 저장\n",
+ "df2016 = df[2016]\n",
+ "df2016"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "과목\n",
+ "영어 5\n",
+ "수학 5\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1) count() - 컬럼 단위로 NaN 값을 제외한 데이터의 갯수 \n",
+ "df2016.count()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "과목\n",
+ "영어 380\n",
+ "수학 435\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2) sum() - 값의 합을 계산\n",
+ "## 기본적으로 함수는 각 컬럼에 있는 로우(row)들의 값들로 수행됨. (기본값 axis =0) \n",
+ "## 모든 통계 함수는 동일함.\n",
+ "df2016.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "과목\n",
+ "영어 76.0\n",
+ "수학 87.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 전체 학생들의 과목 평균 구하기\n",
+ "df2016.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "학생명\n",
+ "Kim 95.5\n",
+ "Park 71.0\n",
+ "Lee 78.5\n",
+ "Jung 74.0\n",
+ "Moon 88.5\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2016.mean(axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|
| 학생명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목 영어 수학\n",
+ "count 5.000000 5.000000\n",
+ "mean 76.000000 87.000000\n",
+ "std 16.015617 8.336666\n",
+ "min 53.000000 74.000000\n",
+ "25% 71.000000 86.000000\n",
+ "50% 74.000000 89.000000\n",
+ "75% 88.000000 89.000000\n",
+ "max 94.000000 97.000000"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 3) describe() - 각 컬럼에 대한 요약 통계\n",
+ "df2016.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|
| count | \n", + "5.000000 | \n", + "5.000000 | \n", + "
| mean | \n", + "76.000000 | \n", + "87.000000 | \n", + "
| std | \n", + "16.015617 | \n", + "8.336666 | \n", + "
| min | \n", + "53.000000 | \n", + "74.000000 | \n", + "
| 25% | \n", + "71.000000 | \n", + "86.000000 | \n", + "
| 50% | \n", + "74.000000 | \n", + "89.000000 | \n", + "
| 75% | \n", + "88.000000 | \n", + "89.000000 | \n", + "
| max | \n", + "94.000000 | \n", + "97.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "년도 과목\n",
+ "2016 영어 76.0\n",
+ " 수학 87.0\n",
+ "2017 영어 63.4\n",
+ " 수학 65.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습.df 대상으로 년도별 과목별 평균 성적을 출력\n",
+ "df.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "학생명\n",
+ "Kim 73.50\n",
+ "Park 67.50\n",
+ "Lee 71.50\n",
+ "Jung 65.25\n",
+ "Moon 86.50\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. df 대상으로 학생별로 모든 년도/과목에 대한 전체 평균을 출력\n",
+ "df.mean(axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 7-2. 정렬\n",
+ "## * 데이터 정렬 : sort_values()\n",
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
![\"DataFrame.sort_values\"](\"img/데이터프레임정렬.jpg\")
\n",
+ "## * 인덱스 정렬 : sort_index() - 인자는 sort_values()와 동일\n",
+ "### http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1) Series 정렬"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b 3\n",
+ "c 5\n",
+ "a 2\n",
+ "f 1\n",
+ "e 7\n",
+ "d 10\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Series 정렬을 위한 샘플 데이터\n",
+ "sr = Series([3,5,2,1,7,10], index = list('bcafed'))\n",
+ "sr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 2\n",
+ "b 3\n",
+ "c 5\n",
+ "d 10\n",
+ "e 7\n",
+ "f 1\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 로우 인덱스 라벨의 값으로 정렬\n",
+ "sr.sort_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "f 1\n",
+ "a 2\n",
+ "b 3\n",
+ "c 5\n",
+ "e 7\n",
+ "d 10\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Series의 값으로 정렬 (기본값 = 오름차순)\n",
+ "sr.sort_values()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "d 10\n",
+ "e 7\n",
+ "c 5\n",
+ "b 3\n",
+ "a 2\n",
+ "f 1\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Series의 값으로 내림차순 정렬\n",
+ "sr.sort_values(ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2) DataFrame 정렬"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97\n",
+ "Park 53 89\n",
+ "Lee 71 86\n",
+ "Jung 74 74\n",
+ "Moon 88 89"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df2016"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|
| 학생명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Jung 74 74 62 51\n",
+ "Kim 94 97 50 53\n",
+ "Lee 71 86 73 56\n",
+ "Moon 88 89 73 96\n",
+ "Park 53 89 59 69"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 학생명으로 알파벳 정렬하여 보고 싶을 때 \n",
+ "df.sort_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목 영어 수학\n",
+ "학생명 \n",
+ "Jung 74 74\n",
+ "Lee 71 86\n",
+ "Park 53 89\n",
+ "Moon 88 89\n",
+ "Kim 94 97"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 수학 성적을 오름 차순 정렬\n",
+ "df2016.sort_values(by = '수학')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|
| 학생명 | \n", + "\n", + " | \n", + " |
| Jung | \n", + "74 | \n", + "74 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "
| Kim | \n", + "94 | \n", + "97 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "과목 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97\n",
+ "Park 53 89\n",
+ "Moon 88 89\n",
+ "Lee 71 86\n",
+ "Jung 74 74"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 수학성적으로 내림차순 정렬\n",
+ "df2016.sort_values(by = '수학', ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 과목 | \n", + "영어 | \n", + "수학 | \n", + "
|---|---|---|
| 학생명 | \n", + "\n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51\n",
+ "Moon 88 89 73 96"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Moon 88 89 73 96\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2016년 수학 성적을 기준으로 내림차순 정렬\n",
+ "# by 인자는 튜플로\n",
+ "df.sort_values(by=(2016,'수학'), ascending= False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Moon 88 89 73 96\n",
+ "Park 53 89 59 69\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2016, 2017 수학성적으로 내림차순 정렬\n",
+ "df.sort_values(by=[(2016,'수학'),(2017,'수학')], ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "년도 2016 2017 \n",
+ "과목 영어 수학 영어 수학\n",
+ "학생명 \n",
+ "Kim 94 97 50 53\n",
+ "Park 53 89 59 69\n",
+ "Moon 88 89 73 96\n",
+ "Lee 71 86 73 56\n",
+ "Jung 74 74 62 51"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2016 수학 내림차순, 2017 수학 오름차순\n",
+ "df.sort_values(by=[(2016,'수학'),(2017,'수학')], ascending = [False, True])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 8. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 2 - \354\247\221\352\263\204 (Groupby, pivot_table) (1).ipynb" "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 8. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 2 - \354\247\221\352\263\204 (Groupby, pivot_table) (1).ipynb"
new file mode 100644
index 0000000..a4eff0f
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\352\270\260\354\264\210 8. \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 2 - \354\247\221\352\263\204 (Groupby, pivot_table) (1).ipynb"
@@ -0,0 +1,1345 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "| 년도 | \n", + "2016 | \n", + "2017 | \n", + "||
|---|---|---|---|---|
| 과목 | \n", + "영어 | \n", + "수학 | \n", + "영어 | \n", + "수학 | \n", + "
| 학생명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| Kim | \n", + "94 | \n", + "97 | \n", + "50 | \n", + "53 | \n", + "
| Park | \n", + "53 | \n", + "89 | \n", + "59 | \n", + "69 | \n", + "
| Moon | \n", + "88 | \n", + "89 | \n", + "73 | \n", + "96 | \n", + "
| Lee | \n", + "71 | \n", + "86 | \n", + "73 | \n", + "56 | \n", + "
| Jung | \n", + "74 | \n", + "74 | \n", + "62 | \n", + "51 | \n", + "
![\"그룹분석_groupby\"](\"img/그룹분석_groupby.jpg\")
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#groupby() 실습\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 실습 데이터 적재\n",
+ "data = pd.read_excel('data/인구수예제.xlsx')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 도시 자치구 연도 남자인구 여자인구 총인구\n",
+ "0 서울 강남구 2013 73 92 165\n",
+ "1 서울 강남구 2014 139 55 194\n",
+ "2 서울 강남구 2015 123 83 206\n",
+ "3 서울 강남구 2016 147 150 297\n",
+ "4 서울 강남구 2017 57 133 190\n",
+ "5 서울 서대문구 2013 95 111 206\n",
+ "6 서울 서대문구 2014 149 150 299\n",
+ "7 서울 서대문구 2015 106 77 183\n",
+ "8 서울 서대문구 2016 56 109 165\n",
+ "9 서울 서대문구 2017 82 96 178\n",
+ "10 서울 종로구 2013 121 68 189\n",
+ "11 서울 종로구 2014 107 55 162\n",
+ "12 서울 종로구 2015 50 79 129\n",
+ "13 서울 종로구 2016 100 80 180\n",
+ "14 서울 종로구 2017 105 91 196\n",
+ "15 서울 영등포구 2013 146 113 259\n",
+ "16 서울 영등포구 2014 127 117 244\n",
+ "17 서울 영등포구 2015 70 72 142\n",
+ "18 서울 영등포구 2016 141 136 277\n",
+ "19 서울 영등포구 2017 145 124 269\n",
+ "20 서울 송파구 2013 90 130 220\n",
+ "21 서울 송파구 2014 121 66 187\n",
+ "22 서울 송파구 2015 62 121 183\n",
+ "23 서울 송파구 2016 80 92 172\n",
+ "24 서울 송파구 2017 62 150 212\n",
+ "25 서울 도봉구 2013 113 138 251\n",
+ "26 서울 도봉구 2014 145 140 285\n",
+ "27 서울 도봉구 2015 56 139 195\n",
+ "28 서울 도봉구 2016 60 71 131\n",
+ "29 서울 도봉구 2017 111 62 173\n",
+ "30 서울 동작구 2013 120 117 237\n",
+ "31 서울 동작구 2014 94 108 202\n",
+ "32 서울 동작구 2015 74 139 213\n",
+ "33 서울 동작구 2016 87 84 171\n",
+ "34 서울 동작구 2017 79 134 213\n",
+ "35 부산 해운대구 2013 124 103 227\n",
+ "36 부산 해운대구 2014 101 144 245\n",
+ "37 부산 해운대구 2015 115 70 185\n",
+ "38 부산 해운대구 2016 134 126 260\n",
+ "39 부산 해운대구 2017 146 72 218\n",
+ "40 부산 수영구 2013 134 94 228\n",
+ "41 부산 수영구 2014 74 138 212\n",
+ "42 부산 수영구 2015 69 81 150\n",
+ "43 부산 수영구 2016 81 148 229\n",
+ "44 부산 수영구 2017 144 98 242\n",
+ "45 부산 동래구 2013 83 65 148\n",
+ "46 부산 동래구 2014 139 87 226\n",
+ "47 부산 동래구 2015 147 115 262\n",
+ "48 부산 동래구 2016 61 102 163\n",
+ "49 부산 동래구 2017 132 105 237"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "연도\n",
+ "2013 2130\n",
+ "2014 2256\n",
+ "2015 1848\n",
+ "2016 2045\n",
+ "2017 2128\n",
+ "Name: 총인구, dtype: int64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 연도별 총인구수\n",
+ "data.groupby('연도')['총인구'].sum()\n",
+ "\n",
+ "#SELECT COL1, SUM(총인구)\n",
+ "#FROM DATA\n",
+ "#GROUP BY 연도"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 도시 | \n", + "자치구 | \n", + "연도 | \n", + "남자인구 | \n", + "여자인구 | \n", + "총인구 | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "서울 | \n", + "강남구 | \n", + "2013 | \n", + "73 | \n", + "92 | \n", + "165 | \n", + "
| 1 | \n", + "서울 | \n", + "강남구 | \n", + "2014 | \n", + "139 | \n", + "55 | \n", + "194 | \n", + "
| 2 | \n", + "서울 | \n", + "강남구 | \n", + "2015 | \n", + "123 | \n", + "83 | \n", + "206 | \n", + "
| 3 | \n", + "서울 | \n", + "강남구 | \n", + "2016 | \n", + "147 | \n", + "150 | \n", + "297 | \n", + "
| 4 | \n", + "서울 | \n", + "강남구 | \n", + "2017 | \n", + "57 | \n", + "133 | \n", + "190 | \n", + "
| 5 | \n", + "서울 | \n", + "서대문구 | \n", + "2013 | \n", + "95 | \n", + "111 | \n", + "206 | \n", + "
| 6 | \n", + "서울 | \n", + "서대문구 | \n", + "2014 | \n", + "149 | \n", + "150 | \n", + "299 | \n", + "
| 7 | \n", + "서울 | \n", + "서대문구 | \n", + "2015 | \n", + "106 | \n", + "77 | \n", + "183 | \n", + "
| 8 | \n", + "서울 | \n", + "서대문구 | \n", + "2016 | \n", + "56 | \n", + "109 | \n", + "165 | \n", + "
| 9 | \n", + "서울 | \n", + "서대문구 | \n", + "2017 | \n", + "82 | \n", + "96 | \n", + "178 | \n", + "
| 10 | \n", + "서울 | \n", + "종로구 | \n", + "2013 | \n", + "121 | \n", + "68 | \n", + "189 | \n", + "
| 11 | \n", + "서울 | \n", + "종로구 | \n", + "2014 | \n", + "107 | \n", + "55 | \n", + "162 | \n", + "
| 12 | \n", + "서울 | \n", + "종로구 | \n", + "2015 | \n", + "50 | \n", + "79 | \n", + "129 | \n", + "
| 13 | \n", + "서울 | \n", + "종로구 | \n", + "2016 | \n", + "100 | \n", + "80 | \n", + "180 | \n", + "
| 14 | \n", + "서울 | \n", + "종로구 | \n", + "2017 | \n", + "105 | \n", + "91 | \n", + "196 | \n", + "
| 15 | \n", + "서울 | \n", + "영등포구 | \n", + "2013 | \n", + "146 | \n", + "113 | \n", + "259 | \n", + "
| 16 | \n", + "서울 | \n", + "영등포구 | \n", + "2014 | \n", + "127 | \n", + "117 | \n", + "244 | \n", + "
| 17 | \n", + "서울 | \n", + "영등포구 | \n", + "2015 | \n", + "70 | \n", + "72 | \n", + "142 | \n", + "
| 18 | \n", + "서울 | \n", + "영등포구 | \n", + "2016 | \n", + "141 | \n", + "136 | \n", + "277 | \n", + "
| 19 | \n", + "서울 | \n", + "영등포구 | \n", + "2017 | \n", + "145 | \n", + "124 | \n", + "269 | \n", + "
| 20 | \n", + "서울 | \n", + "송파구 | \n", + "2013 | \n", + "90 | \n", + "130 | \n", + "220 | \n", + "
| 21 | \n", + "서울 | \n", + "송파구 | \n", + "2014 | \n", + "121 | \n", + "66 | \n", + "187 | \n", + "
| 22 | \n", + "서울 | \n", + "송파구 | \n", + "2015 | \n", + "62 | \n", + "121 | \n", + "183 | \n", + "
| 23 | \n", + "서울 | \n", + "송파구 | \n", + "2016 | \n", + "80 | \n", + "92 | \n", + "172 | \n", + "
| 24 | \n", + "서울 | \n", + "송파구 | \n", + "2017 | \n", + "62 | \n", + "150 | \n", + "212 | \n", + "
| 25 | \n", + "서울 | \n", + "도봉구 | \n", + "2013 | \n", + "113 | \n", + "138 | \n", + "251 | \n", + "
| 26 | \n", + "서울 | \n", + "도봉구 | \n", + "2014 | \n", + "145 | \n", + "140 | \n", + "285 | \n", + "
| 27 | \n", + "서울 | \n", + "도봉구 | \n", + "2015 | \n", + "56 | \n", + "139 | \n", + "195 | \n", + "
| 28 | \n", + "서울 | \n", + "도봉구 | \n", + "2016 | \n", + "60 | \n", + "71 | \n", + "131 | \n", + "
| 29 | \n", + "서울 | \n", + "도봉구 | \n", + "2017 | \n", + "111 | \n", + "62 | \n", + "173 | \n", + "
| 30 | \n", + "서울 | \n", + "동작구 | \n", + "2013 | \n", + "120 | \n", + "117 | \n", + "237 | \n", + "
| 31 | \n", + "서울 | \n", + "동작구 | \n", + "2014 | \n", + "94 | \n", + "108 | \n", + "202 | \n", + "
| 32 | \n", + "서울 | \n", + "동작구 | \n", + "2015 | \n", + "74 | \n", + "139 | \n", + "213 | \n", + "
| 33 | \n", + "서울 | \n", + "동작구 | \n", + "2016 | \n", + "87 | \n", + "84 | \n", + "171 | \n", + "
| 34 | \n", + "서울 | \n", + "동작구 | \n", + "2017 | \n", + "79 | \n", + "134 | \n", + "213 | \n", + "
| 35 | \n", + "부산 | \n", + "해운대구 | \n", + "2013 | \n", + "124 | \n", + "103 | \n", + "227 | \n", + "
| 36 | \n", + "부산 | \n", + "해운대구 | \n", + "2014 | \n", + "101 | \n", + "144 | \n", + "245 | \n", + "
| 37 | \n", + "부산 | \n", + "해운대구 | \n", + "2015 | \n", + "115 | \n", + "70 | \n", + "185 | \n", + "
| 38 | \n", + "부산 | \n", + "해운대구 | \n", + "2016 | \n", + "134 | \n", + "126 | \n", + "260 | \n", + "
| 39 | \n", + "부산 | \n", + "해운대구 | \n", + "2017 | \n", + "146 | \n", + "72 | \n", + "218 | \n", + "
| 40 | \n", + "부산 | \n", + "수영구 | \n", + "2013 | \n", + "134 | \n", + "94 | \n", + "228 | \n", + "
| 41 | \n", + "부산 | \n", + "수영구 | \n", + "2014 | \n", + "74 | \n", + "138 | \n", + "212 | \n", + "
| 42 | \n", + "부산 | \n", + "수영구 | \n", + "2015 | \n", + "69 | \n", + "81 | \n", + "150 | \n", + "
| 43 | \n", + "부산 | \n", + "수영구 | \n", + "2016 | \n", + "81 | \n", + "148 | \n", + "229 | \n", + "
| 44 | \n", + "부산 | \n", + "수영구 | \n", + "2017 | \n", + "144 | \n", + "98 | \n", + "242 | \n", + "
| 45 | \n", + "부산 | \n", + "동래구 | \n", + "2013 | \n", + "83 | \n", + "65 | \n", + "148 | \n", + "
| 46 | \n", + "부산 | \n", + "동래구 | \n", + "2014 | \n", + "139 | \n", + "87 | \n", + "226 | \n", + "
| 47 | \n", + "부산 | \n", + "동래구 | \n", + "2015 | \n", + "147 | \n", + "115 | \n", + "262 | \n", + "
| 48 | \n", + "부산 | \n", + "동래구 | \n", + "2016 | \n", + "61 | \n", + "102 | \n", + "163 | \n", + "
| 49 | \n", + "부산 | \n", + "동래구 | \n", + "2017 | \n", + "132 | \n", + "105 | \n", + "237 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "연도 2013 2014 2015 2016 2017\n",
+ "도시 \n",
+ "부산 603 683 597 652 697\n",
+ "서울 1527 1573 1251 1393 1431"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 도시별, 연도별 총인구수\n",
+ "data.groupby(['도시','연도'])['총인구'].sum()\n",
+ "# 도시를 컬럼으로 삼아 데이터프레임 만듦\n",
+ "data.groupby(['도시','연도'])['총인구'].sum().unstack()\n",
+ "# 도시-서울, 부산\n",
+ "# 연도-2013,2014,2015,2016,2017\n",
+ "# 도시, 연도 컬럼값의 조합으로 구릅핑\n",
+ "# 그럼 총 10가지 (서울, 2013),(서울, 2014)~~~(부산, 2017)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "| 연도 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2016 | \n", + "2017 | \n", + "
|---|---|---|---|---|---|
| 도시 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 부산 | \n", + "603 | \n", + "683 | \n", + "597 | \n", + "652 | \n", + "697 | \n", + "
| 서울 | \n", + "1527 | \n", + "1573 | \n", + "1251 | \n", + "1393 | \n", + "1431 | \n", + "
![\"그룹분석_groupby\"](\"img/그룹분석_pivottable.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "도시 부산 서울\n",
+ "연도 \n",
+ "2013 603 1527\n",
+ "2014 683 1573\n",
+ "2015 597 1251\n",
+ "2016 652 1393\n",
+ "2017 697 1431"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# pivot_table() 실습\n",
+ "data.pivot_table(index= '연도', columns='도시', aggfunc='sum', values ='총인구')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 도시 | \n", + "부산 | \n", + "서울 | \n", + "
|---|---|---|
| 연도 | \n", + "\n", + " | \n", + " |
| 2013 | \n", + "603 | \n", + "1527 | \n", + "
| 2014 | \n", + "683 | \n", + "1573 | \n", + "
| 2015 | \n", + "597 | \n", + "1251 | \n", + "
| 2016 | \n", + "652 | \n", + "1393 | \n", + "
| 2017 | \n", + "697 | \n", + "1431 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 남자인구\n",
+ "도시 자치구 \n",
+ "서울 영등포구 125.8\n",
+ "부산 해운대구 124.0\n",
+ " 동래구 112.4\n",
+ "서울 강남구 107.8\n",
+ "부산 수영구 100.4"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 실습. 자치구 별로 남자인구의 5년치 평균을 구한 후, 평균 남자인구수가 가장 많은 상위 5개 구를 찾아보기\n",
+ "data.pivot_table(index=['도시','자치구'], aggfunc='mean', values='남자인구' ).sort_values(by='남자인구', ascending = False).head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | 남자인구 | \n", + "
|---|---|---|
| 도시 | \n", + "자치구 | \n", + "\n", + " |
| 서울 | \n", + "영등포구 | \n", + "125.8 | \n", + "
| 부산 | \n", + "해운대구 | \n", + "124.0 | \n", + "
| 동래구 | \n", + "112.4 | \n", + "|
| 서울 | \n", + "강남구 | \n", + "107.8 | \n", + "
| 부산 | \n", + "수영구 | \n", + "100.4 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 인구차\n",
+ "도시 자치구 \n",
+ "서울 송파구 50.8\n",
+ "부산 수영구 45.8\n",
+ "서울 강남구 44.4\n",
+ "부산 해운대구 38.2\n",
+ "서울 도봉구 34.6"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 남자와 여자의 인구수가 가장 많이 차이나는 도시 및 자치구 찾기\n",
+ "data['인구차']=abs(data.남자인구-data.여자인구)\n",
+ "data\n",
+ "data.pivot_table(index=['도시','자치구'], aggfunc = 'mean', values = '인구차').sort_values(by = '인구차', ascending = False).head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "남녀차이 = data.pivot_table(index=['도시','자치구'], aggfunc = 'mean', values= ['남자인구','여자인구'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | 인구차 | \n", + "
|---|---|---|
| 도시 | \n", + "자치구 | \n", + "\n", + " |
| 서울 | \n", + "송파구 | \n", + "50.8 | \n", + "
| 부산 | \n", + "수영구 | \n", + "45.8 | \n", + "
| 서울 | \n", + "강남구 | \n", + "44.4 | \n", + "
| 부산 | \n", + "해운대구 | \n", + "38.2 | \n", + "
| 서울 | \n", + "도봉구 | \n", + "34.6 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 남자인구 여자인구 차이\n",
+ "도시 자치구 \n",
+ "서울 송파구 83.0 111.8 28.8\n",
+ " 동작구 90.8 116.4 25.6\n",
+ " 종로구 96.6 74.6 22.0\n",
+ "부산 해운대구 124.0 103.0 21.0\n",
+ " 동래구 112.4 94.8 17.6"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "남녀차이['차이'] = abs(남녀차이.남자인구 - 남녀차이.여자인구)\n",
+ "남녀차이.sort_values(by = '차이', ascending = False).head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | \n", + " | 남자인구 | \n", + "여자인구 | \n", + "차이 | \n", + "
|---|---|---|---|---|
| 도시 | \n", + "자치구 | \n", + "\n", + " | \n", + " | \n", + " |
| 서울 | \n", + "송파구 | \n", + "83.0 | \n", + "111.8 | \n", + "28.8 | \n", + "
| 동작구 | \n", + "90.8 | \n", + "116.4 | \n", + "25.6 | \n", + "|
| 종로구 | \n", + "96.6 | \n", + "74.6 | \n", + "22.0 | \n", + "|
| 부산 | \n", + "해운대구 | \n", + "124.0 | \n", + "103.0 | \n", + "21.0 | \n", + "
| 동래구 | \n", + "112.4 | \n", + "94.8 | \n", + "17.6 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 남자인구 여자인구 총인구\n",
+ "연도 \n",
+ "2013 109.9 103.1 213.0\n",
+ "2014 119.6 106.0 225.6\n",
+ "2015 87.2 97.6 184.8\n",
+ "2016 94.7 109.8 204.5\n",
+ "2017 106.3 106.5 212.8"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.pivot_table(index='연도', aggfunc= 'mean', values= ['남자인구','여자인구','총인구'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 남자인구 | \n", + "여자인구 | \n", + "총인구 | \n", + "
|---|---|---|---|
| 연도 | \n", + "\n", + " | \n", + " | \n", + " |
| 2013 | \n", + "109.9 | \n", + "103.1 | \n", + "213.0 | \n", + "
| 2014 | \n", + "119.6 | \n", + "106.0 | \n", + "225.6 | \n", + "
| 2015 | \n", + "87.2 | \n", + "97.6 | \n", + "184.8 | \n", + "
| 2016 | \n", + "94.7 | \n", + "109.8 | \n", + "204.5 | \n", + "
| 2017 | \n", + "106.3 | \n", + "106.5 | \n", + "212.8 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 남자인구 여자인구 총인구\n",
+ "연도 \n",
+ "2013 109.9 103.1 2130\n",
+ "2014 119.6 106.0 2256\n",
+ "2015 87.2 97.6 1848\n",
+ "2016 94.7 109.8 2045\n",
+ "2017 106.3 106.5 2128"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.pivot_table(index = '연도',aggfunc={'남자인구':'mean','여자인구':'mean','총인구':'sum'}, values = ['남자인구','여자인구','총인구'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 1. \354\230\201\355\231\224 \355\217\211\354\240\220 \353\266\204\354\204\235.ipynb" "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 1. \354\230\201\355\231\224 \355\217\211\354\240\220 \353\266\204\354\204\235.ipynb"
new file mode 100644
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+++ "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 1. \354\230\201\355\231\224 \355\217\211\354\240\220 \353\266\204\354\204\235.ipynb"
@@ -0,0 +1,4265 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 영화 평점 분석 실습"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. 영화 평점 데이터 적재 및 전처리"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 남자인구 | \n", + "여자인구 | \n", + "총인구 | \n", + "
|---|---|---|---|
| 연도 | \n", + "\n", + " | \n", + " | \n", + " |
| 2013 | \n", + "109.9 | \n", + "103.1 | \n", + "2130 | \n", + "
| 2014 | \n", + "119.6 | \n", + "106.0 | \n", + "2256 | \n", + "
| 2015 | \n", + "87.2 | \n", + "97.6 | \n", + "1848 | \n", + "
| 2016 | \n", + "94.7 | \n", + "109.8 | \n", + "2045 | \n", + "
| 2017 | \n", + "106.3 | \n", + "106.5 | \n", + "2128 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역\n",
+ "0 1 F 1 10 48067\n",
+ "1 2 M 56 16 70072\n",
+ "2 3 M 25 15 55117\n",
+ "3 4 M 45 7 02460\n",
+ "4 5 M 25 20 55455\n",
+ "5 6 F 50 9 55117\n",
+ "6 7 M 35 1 06810\n",
+ "7 8 M 25 12 11413\n",
+ "8 9 M 25 17 61614\n",
+ "9 10 F 35 1 95370\n",
+ "10 11 F 25 1 04093\n",
+ "11 12 M 25 12 32793\n",
+ "12 13 M 45 1 93304\n",
+ "13 14 M 35 0 60126\n",
+ "14 15 M 25 7 22903\n",
+ "15 16 F 35 0 20670\n",
+ "16 17 M 50 1 95350\n",
+ "17 18 F 18 3 95825\n",
+ "18 19 M 1 10 48073\n",
+ "19 20 M 25 14 55113\n",
+ "20 21 M 18 16 99353\n",
+ "21 22 M 18 15 53706\n",
+ "22 23 M 35 0 90049\n",
+ "23 24 F 25 7 10023\n",
+ "24 25 M 18 4 01609\n",
+ "25 26 M 25 7 23112\n",
+ "26 27 M 25 11 19130\n",
+ "27 28 F 25 1 14607\n",
+ "28 29 M 35 7 33407\n",
+ "29 30 F 35 7 19143\n",
+ "... ... .. .. .. ...\n",
+ "6010 6011 M 35 15 80538\n",
+ "6011 6012 M 35 15 02871\n",
+ "6012 6013 F 25 20 32301\n",
+ "6013 6014 M 45 1 80634\n",
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+ "6019 6020 M 50 16 10023\n",
+ "6020 6021 M 25 12 08876\n",
+ "6021 6022 M 25 17 57006\n",
+ "6022 6023 M 25 0 43213\n",
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+ "6024 6025 F 25 1 32607\n",
+ "6025 6026 M 35 6 11210\n",
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+ "6030 6031 F 18 0 45123\n",
+ "6031 6032 M 45 7 55108\n",
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+ "6037 6038 F 56 1 14706\n",
+ "6038 6039 F 45 0 01060\n",
+ "6039 6040 M 25 6 11106\n",
+ "\n",
+ "[6040 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "users = pd.read_csv('data/movielens/users.dat', sep=\"::\", engine = 'python', names = ['사용자아이디','성별','연령', '직업','지역'])\n",
+ "users"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "
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\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 영화아이디 평점 타임스탬프\n",
+ "0 1 1193 5 978300760\n",
+ "1 1 661 3 978302109\n",
+ "2 1 914 3 978301968\n",
+ "3 1 3408 4 978300275\n",
+ "4 1 2355 5 978824291"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 2.평점 데이터 불러오기\n",
+ "ratings= pd.read_csv('data/movielens/ratings.dat', sep=\"::\", engine = 'python', names = ['사용자아이디','영화아이디','평점', '타임스탬프'])\n",
+ "ratings.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1000209"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(ratings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 영화데이터 불러오기 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "
| 1 | \n", + "1 | \n", + "661 | \n", + "3 | \n", + "978302109 | \n", + "
| 2 | \n", + "1 | \n", + "914 | \n", + "3 | \n", + "978301968 | \n", + "
| 3 | \n", + "1 | \n", + "3408 | \n", + "4 | \n", + "978300275 | \n", + "
| 4 | \n", + "1 | \n", + "2355 | \n", + "5 | \n", + "978824291 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 영화아이디 영화제목 장르\n",
+ "0 1 Toy Story (1995) Animation|Children's|Comedy\n",
+ "1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
+ "2 3 Grumpier Old Men (1995) Comedy|Romance\n",
+ "3 4 Waiting to Exhale (1995) Comedy|Drama\n",
+ "4 5 Father of the Bride Part II (1995) Comedy"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movies= pd.read_csv('data/movielens/movies.dat', sep=\"::\", engine = 'python', names = ['영화아이디','영화제목', '장르'], encoding= 'latin-1')\n",
+ "movies.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 영화아이디 | \n", + "영화제목 | \n", + "장르 | \n", + "
|---|---|---|---|
| 0 | \n", + "1 | \n", + "Toy Story (1995) | \n", + "Animation|Children's|Comedy | \n", + "
| 1 | \n", + "2 | \n", + "Jumanji (1995) | \n", + "Adventure|Children's|Fantasy | \n", + "
| 2 | \n", + "3 | \n", + "Grumpier Old Men (1995) | \n", + "Comedy|Romance | \n", + "
| 3 | \n", + "4 | \n", + "Waiting to Exhale (1995) | \n", + "Comedy|Drama | \n", + "
| 4 | \n", + "5 | \n", + "Father of the Bride Part II (1995) | \n", + "Comedy | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프\n",
+ "0 1 F 1 10 48067 1193 5 978300760\n",
+ "1 1 F 1 10 48067 661 3 978302109\n",
+ "2 1 F 1 10 48067 914 3 978301968\n",
+ "3 1 F 1 10 48067 3408 4 978300275\n",
+ "4 1 F 1 10 48067 2355 5 978824291"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data=pd.merge(users, ratings)\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "
| 1 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "661 | \n", + "3 | \n", + "978302109 | \n", + "
| 2 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "914 | \n", + "3 | \n", + "978301968 | \n", + "
| 3 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "3408 | \n", + "4 | \n", + "978300275 | \n", + "
| 4 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "2355 | \n", + "5 | \n", + "978824291 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프 \\\n",
+ "0 1 F 1 10 48067 1193 5 978300760 \n",
+ "1 2 M 56 16 70072 1193 5 978298413 \n",
+ "2 12 M 25 12 32793 1193 4 978220179 \n",
+ "3 15 M 25 7 22903 1193 4 978199279 \n",
+ "4 17 M 50 1 95350 1193 5 978158471 \n",
+ "\n",
+ " 영화제목 장르 \n",
+ "0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "1 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "2 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "3 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "4 One Flew Over the Cuckoo's Nest (1975) Drama "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.merge(data, movies)\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. 보고 싶은 영화 찾기 (평점 높은 영화 찾기)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "영화제목 | \n", + "장르 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| 1 | \n", + "2 | \n", + "M | \n", + "56 | \n", + "16 | \n", + "70072 | \n", + "1193 | \n", + "5 | \n", + "978298413 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| 2 | \n", + "12 | \n", + "M | \n", + "25 | \n", + "12 | \n", + "32793 | \n", + "1193 | \n", + "4 | \n", + "978220179 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| 3 | \n", + "15 | \n", + "M | \n", + "25 | \n", + "7 | \n", + "22903 | \n", + "1193 | \n", + "4 | \n", + "978199279 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| 4 | \n", + "17 | \n", + "M | \n", + "50 | \n", + "1 | \n", + "95350 | \n", + "1193 | \n", + "5 | \n", + "978158471 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 평점\n",
+ "영화제목 \n",
+ "Ulysses (Ulisse) (1954) 5.000000\n",
+ "Lured (1947) 5.000000\n",
+ "Follow the Bitch (1998) 5.000000\n",
+ "Bittersweet Motel (2000) 5.000000\n",
+ "Song of Freedom (1936) 5.000000\n",
+ "One Little Indian (1973) 5.000000\n",
+ "Smashing Time (1967) 5.000000\n",
+ "Schlafes Bruder (Brother of Sleep) (1995) 5.000000\n",
+ "Gate of Heavenly Peace, The (1995) 5.000000\n",
+ "Baby, The (1973) 5.000000\n",
+ "I Am Cuba (Soy Cuba/Ya Kuba) (1964) 4.800000\n",
+ "Lamerica (1994) 4.750000\n",
+ "Apple, The (Sib) (1998) 4.666667\n",
+ "Sanjuro (1962) 4.608696\n",
+ "Seven Samurai (The Magnificent Seven) (Shichini... 4.560510"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.pivot_table(index= '영화제목', values = '평점', aggfunc = 'mean').sort_values(by='평점', ascending = False).head(15)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "영화평점 = data.pivot_table(index= '영화제목', values = '평점', aggfunc = ('mean','count'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 평점 | \n", + "
|---|---|
| 영화제목 | \n", + "\n", + " |
| Ulysses (Ulisse) (1954) | \n", + "5.000000 | \n", + "
| Lured (1947) | \n", + "5.000000 | \n", + "
| Follow the Bitch (1998) | \n", + "5.000000 | \n", + "
| Bittersweet Motel (2000) | \n", + "5.000000 | \n", + "
| Song of Freedom (1936) | \n", + "5.000000 | \n", + "
| One Little Indian (1973) | \n", + "5.000000 | \n", + "
| Smashing Time (1967) | \n", + "5.000000 | \n", + "
| Schlafes Bruder (Brother of Sleep) (1995) | \n", + "5.000000 | \n", + "
| Gate of Heavenly Peace, The (1995) | \n", + "5.000000 | \n", + "
| Baby, The (1973) | \n", + "5.000000 | \n", + "
| I Am Cuba (Soy Cuba/Ya Kuba) (1964) | \n", + "4.800000 | \n", + "
| Lamerica (1994) | \n", + "4.750000 | \n", + "
| Apple, The (Sib) (1998) | \n", + "4.666667 | \n", + "
| Sanjuro (1962) | \n", + "4.608696 | \n", + "
| Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954) | \n", + "4.560510 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean\n",
+ "영화제목 \n",
+ "Ulysses (Ulisse) (1954) 1 5.000000\n",
+ "Lured (1947) 1 5.000000\n",
+ "Follow the Bitch (1998) 1 5.000000\n",
+ "Bittersweet Motel (2000) 1 5.000000\n",
+ "Song of Freedom (1936) 1 5.000000\n",
+ "One Little Indian (1973) 1 5.000000\n",
+ "Smashing Time (1967) 2 5.000000\n",
+ "Schlafes Bruder (Brother of Sleep) (1995) 1 5.000000\n",
+ "Gate of Heavenly Peace, The (1995) 3 5.000000\n",
+ "Baby, The (1973) 1 5.000000\n",
+ "I Am Cuba (Soy Cuba/Ya Kuba) (1964) 5 4.800000\n",
+ "Lamerica (1994) 8 4.750000\n",
+ "Apple, The (Sib) (1998) 9 4.666667\n",
+ "Sanjuro (1962) 69 4.608696\n",
+ "Seven Samurai (The Magnificent Seven) (Shichini... 628 4.560510\n",
+ "Shawshank Redemption, The (1994) 2227 4.554558\n",
+ "Godfather, The (1972) 2223 4.524966\n",
+ "Close Shave, A (1995) 657 4.520548\n",
+ "Usual Suspects, The (1995) 1783 4.517106\n",
+ "Schindler's List (1993) 2304 4.510417\n",
+ "Wrong Trousers, The (1993) 882 4.507937\n",
+ "Dry Cleaning (Nettoyage à sec) (1997) 2 4.500000\n",
+ "Inheritors, The (Die Siebtelbauern) (1998) 2 4.500000\n",
+ "Mamma Roma (1962) 2 4.500000\n",
+ "Bells, The (1926) 2 4.500000\n",
+ "Dangerous Game (1993) 2 4.500000\n",
+ "Hour of the Pig, The (1993) 2 4.500000\n",
+ "Callejón de los milagros, El (1995) 2 4.500000\n",
+ "Skipped Parts (2000) 2 4.500000\n",
+ "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) 470 4.491489\n",
+ "... ... ...\n",
+ "Diebinnen (1995) 1 1.000000\n",
+ "Blood Spattered Bride, The (La Novia Ensangrent... 2 1.000000\n",
+ "Bloody Child, The (1996) 1 1.000000\n",
+ "Uninvited Guest, An (2000) 1 1.000000\n",
+ "Little Indian, Big City (Un indien dans la vill... 1 1.000000\n",
+ "Shadows (Cienie) (1988) 1 1.000000\n",
+ "McCullochs, The (1975) 1 1.000000\n",
+ "Hillbillys in a Haunted House (1967) 1 1.000000\n",
+ "Terror in a Texas Town (1958) 1 1.000000\n",
+ "White Boys (1999) 1 1.000000\n",
+ "Lotto Land (1995) 1 1.000000\n",
+ "Nueba Yol (1995) 1 1.000000\n",
+ "Even Dwarfs Started Small (Auch Zwerge haben kl... 1 1.000000\n",
+ "Get Over It (1996) 2 1.000000\n",
+ "Venice/Venice (1992) 2 1.000000\n",
+ "Sleepover (1995) 1 1.000000\n",
+ "Silence of the Palace, The (Saimt el Qusur) (1994) 1 1.000000\n",
+ "Waltzes from Vienna (1933) 1 1.000000\n",
+ "Wirey Spindell (1999) 2 1.000000\n",
+ "Kestrel's Eye (Falkens öga) (1998) 1 1.000000\n",
+ "Spring Fever USA (a.k.a. Lauderdale) (1989) 3 1.000000\n",
+ "Loves of Carmen, The (1948) 1 1.000000\n",
+ "Underworld (1997) 1 1.000000\n",
+ "Low Life, The (1994) 1 1.000000\n",
+ "Santa with Muscles (1996) 7 1.000000\n",
+ "Fantastic Night, The (La Nuit Fantastique) (1949) 1 1.000000\n",
+ "Cheetah (1989) 1 1.000000\n",
+ "Torso (Corpi Presentano Tracce di Violenza Carn... 2 1.000000\n",
+ "Mutters Courage (1995) 1 1.000000\n",
+ "Windows (1980) 1 1.000000\n",
+ "\n",
+ "[3706 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "영화평점.sort_values(by='mean', ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | count | \n", + "mean | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| Ulysses (Ulisse) (1954) | \n", + "1 | \n", + "5.000000 | \n", + "
| Lured (1947) | \n", + "1 | \n", + "5.000000 | \n", + "
| Follow the Bitch (1998) | \n", + "1 | \n", + "5.000000 | \n", + "
| Bittersweet Motel (2000) | \n", + "1 | \n", + "5.000000 | \n", + "
| Song of Freedom (1936) | \n", + "1 | \n", + "5.000000 | \n", + "
| One Little Indian (1973) | \n", + "1 | \n", + "5.000000 | \n", + "
| Smashing Time (1967) | \n", + "2 | \n", + "5.000000 | \n", + "
| Schlafes Bruder (Brother of Sleep) (1995) | \n", + "1 | \n", + "5.000000 | \n", + "
| Gate of Heavenly Peace, The (1995) | \n", + "3 | \n", + "5.000000 | \n", + "
| Baby, The (1973) | \n", + "1 | \n", + "5.000000 | \n", + "
| I Am Cuba (Soy Cuba/Ya Kuba) (1964) | \n", + "5 | \n", + "4.800000 | \n", + "
| Lamerica (1994) | \n", + "8 | \n", + "4.750000 | \n", + "
| Apple, The (Sib) (1998) | \n", + "9 | \n", + "4.666667 | \n", + "
| Sanjuro (1962) | \n", + "69 | \n", + "4.608696 | \n", + "
| Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954) | \n", + "628 | \n", + "4.560510 | \n", + "
| Shawshank Redemption, The (1994) | \n", + "2227 | \n", + "4.554558 | \n", + "
| Godfather, The (1972) | \n", + "2223 | \n", + "4.524966 | \n", + "
| Close Shave, A (1995) | \n", + "657 | \n", + "4.520548 | \n", + "
| Usual Suspects, The (1995) | \n", + "1783 | \n", + "4.517106 | \n", + "
| Schindler's List (1993) | \n", + "2304 | \n", + "4.510417 | \n", + "
| Wrong Trousers, The (1993) | \n", + "882 | \n", + "4.507937 | \n", + "
| Dry Cleaning (Nettoyage à sec) (1997) | \n", + "2 | \n", + "4.500000 | \n", + "
| Inheritors, The (Die Siebtelbauern) (1998) | \n", + "2 | \n", + "4.500000 | \n", + "
| Mamma Roma (1962) | \n", + "2 | \n", + "4.500000 | \n", + "
| Bells, The (1926) | \n", + "2 | \n", + "4.500000 | \n", + "
| Dangerous Game (1993) | \n", + "2 | \n", + "4.500000 | \n", + "
| Hour of the Pig, The (1993) | \n", + "2 | \n", + "4.500000 | \n", + "
| Callejón de los milagros, El (1995) | \n", + "2 | \n", + "4.500000 | \n", + "
| Skipped Parts (2000) | \n", + "2 | \n", + "4.500000 | \n", + "
| Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) | \n", + "470 | \n", + "4.491489 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| Diebinnen (1995) | \n", + "1 | \n", + "1.000000 | \n", + "
| Blood Spattered Bride, The (La Novia Ensangrentada) (1972) | \n", + "2 | \n", + "1.000000 | \n", + "
| Bloody Child, The (1996) | \n", + "1 | \n", + "1.000000 | \n", + "
| Uninvited Guest, An (2000) | \n", + "1 | \n", + "1.000000 | \n", + "
| Little Indian, Big City (Un indien dans la ville) (1994) | \n", + "1 | \n", + "1.000000 | \n", + "
| Shadows (Cienie) (1988) | \n", + "1 | \n", + "1.000000 | \n", + "
| McCullochs, The (1975) | \n", + "1 | \n", + "1.000000 | \n", + "
| Hillbillys in a Haunted House (1967) | \n", + "1 | \n", + "1.000000 | \n", + "
| Terror in a Texas Town (1958) | \n", + "1 | \n", + "1.000000 | \n", + "
| White Boys (1999) | \n", + "1 | \n", + "1.000000 | \n", + "
| Lotto Land (1995) | \n", + "1 | \n", + "1.000000 | \n", + "
| Nueba Yol (1995) | \n", + "1 | \n", + "1.000000 | \n", + "
| Even Dwarfs Started Small (Auch Zwerge haben klein angefangen) (1971) | \n", + "1 | \n", + "1.000000 | \n", + "
| Get Over It (1996) | \n", + "2 | \n", + "1.000000 | \n", + "
| Venice/Venice (1992) | \n", + "2 | \n", + "1.000000 | \n", + "
| Sleepover (1995) | \n", + "1 | \n", + "1.000000 | \n", + "
| Silence of the Palace, The (Saimt el Qusur) (1994) | \n", + "1 | \n", + "1.000000 | \n", + "
| Waltzes from Vienna (1933) | \n", + "1 | \n", + "1.000000 | \n", + "
| Wirey Spindell (1999) | \n", + "2 | \n", + "1.000000 | \n", + "
| Kestrel's Eye (Falkens öga) (1998) | \n", + "1 | \n", + "1.000000 | \n", + "
| Spring Fever USA (a.k.a. Lauderdale) (1989) | \n", + "3 | \n", + "1.000000 | \n", + "
| Loves of Carmen, The (1948) | \n", + "1 | \n", + "1.000000 | \n", + "
| Underworld (1997) | \n", + "1 | \n", + "1.000000 | \n", + "
| Low Life, The (1994) | \n", + "1 | \n", + "1.000000 | \n", + "
| Santa with Muscles (1996) | \n", + "7 | \n", + "1.000000 | \n", + "
| Fantastic Night, The (La Nuit Fantastique) (1949) | \n", + "1 | \n", + "1.000000 | \n", + "
| Cheetah (1989) | \n", + "1 | \n", + "1.000000 | \n", + "
| Torso (Corpi Presentano Tracce di Violenza Carnale) (1973) | \n", + "2 | \n", + "1.000000 | \n", + "
| Mutters Courage (1995) | \n", + "1 | \n", + "1.000000 | \n", + "
| Windows (1980) | \n", + "1 | \n", + "1.000000 | \n", + "
3706 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean\n",
+ "영화제목 \n",
+ "American Beauty (1999) 3428 4.317386\n",
+ "Star Wars: Episode IV - A New Hope (1977) 2991 4.453694\n",
+ "Star Wars: Episode V - The Empire Strikes Back ... 2990 4.292977\n",
+ "Star Wars: Episode VI - Return of the Jedi (1983) 2883 4.022893\n",
+ "Jurassic Park (1993) 2672 3.763847"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#1. 평점 개수가 가장 많은 영화 상위 10개 찾기\n",
+ "영화평점.sort_values(by = 'count', ascending = False).head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | count | \n", + "mean | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| American Beauty (1999) | \n", + "3428 | \n", + "4.317386 | \n", + "
| Star Wars: Episode IV - A New Hope (1977) | \n", + "2991 | \n", + "4.453694 | \n", + "
| Star Wars: Episode V - The Empire Strikes Back (1980) | \n", + "2990 | \n", + "4.292977 | \n", + "
| Star Wars: Episode VI - Return of the Jedi (1983) | \n", + "2883 | \n", + "4.022893 | \n", + "
| Jurassic Park (1993) | \n", + "2672 | \n", + "3.763847 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean\n",
+ "영화제목 \n",
+ "Schindler's List (1993) 2304 4.510417\n",
+ "Shawshank Redemption, The (1994) 2227 4.554558\n",
+ "Godfather, The (1972) 2223 4.524966\n",
+ "Usual Suspects, The (1995) 1783 4.517106\n",
+ "Wrong Trousers, The (1993) 882 4.507937\n",
+ "Close Shave, A (1995) 657 4.520548\n",
+ "Seven Samurai (The Magnificent Seven) (Shichini... 628 4.560510\n",
+ "Sanjuro (1962) 69 4.608696\n",
+ "Apple, The (Sib) (1998) 9 4.666667\n",
+ "Lamerica (1994) 8 4.750000\n",
+ "I Am Cuba (Soy Cuba/Ya Kuba) (1964) 5 4.800000\n",
+ "Gate of Heavenly Peace, The (1995) 3 5.000000\n",
+ "Hour of the Pig, The (1993) 2 4.500000\n",
+ "Inheritors, The (Die Siebtelbauern) (1998) 2 4.500000\n",
+ "Bells, The (1926) 2 4.500000\n",
+ "Smashing Time (1967) 2 5.000000\n",
+ "Skipped Parts (2000) 2 4.500000\n",
+ "Callejón de los milagros, El (1995) 2 4.500000\n",
+ "Dangerous Game (1993) 2 4.500000\n",
+ "Dry Cleaning (Nettoyage à sec) (1997) 2 4.500000\n",
+ "Mamma Roma (1962) 2 4.500000\n",
+ "Follow the Bitch (1998) 1 5.000000\n",
+ "One Little Indian (1973) 1 5.000000\n",
+ "Bittersweet Motel (2000) 1 5.000000\n",
+ "Lured (1947) 1 5.000000\n",
+ "Song of Freedom (1936) 1 5.000000\n",
+ "Ulysses (Ulisse) (1954) 1 5.000000\n",
+ "Baby, The (1973) 1 5.000000\n",
+ "Schlafes Bruder (Brother of Sleep) (1995) 1 5.000000"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#2. 평점이 4.5 이상인 영화들 중에 평점의 개수가 많은 순으로 정렬해 보고 싶다\n",
+ "영화평점높 = 영화평점[영화평점['mean']>=4.5]\n",
+ "영화평점높.head()\n",
+ "영화평점높.sort_values(by='count', ascending =False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | count | \n", + "mean | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| Schindler's List (1993) | \n", + "2304 | \n", + "4.510417 | \n", + "
| Shawshank Redemption, The (1994) | \n", + "2227 | \n", + "4.554558 | \n", + "
| Godfather, The (1972) | \n", + "2223 | \n", + "4.524966 | \n", + "
| Usual Suspects, The (1995) | \n", + "1783 | \n", + "4.517106 | \n", + "
| Wrong Trousers, The (1993) | \n", + "882 | \n", + "4.507937 | \n", + "
| Close Shave, A (1995) | \n", + "657 | \n", + "4.520548 | \n", + "
| Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954) | \n", + "628 | \n", + "4.560510 | \n", + "
| Sanjuro (1962) | \n", + "69 | \n", + "4.608696 | \n", + "
| Apple, The (Sib) (1998) | \n", + "9 | \n", + "4.666667 | \n", + "
| Lamerica (1994) | \n", + "8 | \n", + "4.750000 | \n", + "
| I Am Cuba (Soy Cuba/Ya Kuba) (1964) | \n", + "5 | \n", + "4.800000 | \n", + "
| Gate of Heavenly Peace, The (1995) | \n", + "3 | \n", + "5.000000 | \n", + "
| Hour of the Pig, The (1993) | \n", + "2 | \n", + "4.500000 | \n", + "
| Inheritors, The (Die Siebtelbauern) (1998) | \n", + "2 | \n", + "4.500000 | \n", + "
| Bells, The (1926) | \n", + "2 | \n", + "4.500000 | \n", + "
| Smashing Time (1967) | \n", + "2 | \n", + "5.000000 | \n", + "
| Skipped Parts (2000) | \n", + "2 | \n", + "4.500000 | \n", + "
| Callejón de los milagros, El (1995) | \n", + "2 | \n", + "4.500000 | \n", + "
| Dangerous Game (1993) | \n", + "2 | \n", + "4.500000 | \n", + "
| Dry Cleaning (Nettoyage à sec) (1997) | \n", + "2 | \n", + "4.500000 | \n", + "
| Mamma Roma (1962) | \n", + "2 | \n", + "4.500000 | \n", + "
| Follow the Bitch (1998) | \n", + "1 | \n", + "5.000000 | \n", + "
| One Little Indian (1973) | \n", + "1 | \n", + "5.000000 | \n", + "
| Bittersweet Motel (2000) | \n", + "1 | \n", + "5.000000 | \n", + "
| Lured (1947) | \n", + "1 | \n", + "5.000000 | \n", + "
| Song of Freedom (1936) | \n", + "1 | \n", + "5.000000 | \n", + "
| Ulysses (Ulisse) (1954) | \n", + "1 | \n", + "5.000000 | \n", + "
| Baby, The (1973) | \n", + "1 | \n", + "5.000000 | \n", + "
| Schlafes Bruder (Brother of Sleep) (1995) | \n", + "1 | \n", + "5.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean\n",
+ "영화제목 \n",
+ "American Beauty (1999) 3428 4.317386\n",
+ "Godfather, The (1972) 2223 4.524966\n",
+ "Matrix, The (1999) 2590 4.315830\n",
+ "Princess Bride, The (1987) 2318 4.303710\n",
+ "Raiders of the Lost Ark (1981) 2514 4.477725\n",
+ "Saving Private Ryan (1998) 2653 4.337354\n",
+ "Schindler's List (1993) 2304 4.510417\n",
+ "Shawshank Redemption, The (1994) 2227 4.554558\n",
+ "Silence of the Lambs, The (1991) 2578 4.351823\n",
+ "Sixth Sense, The (1999) 2459 4.406263\n",
+ "Star Wars: Episode IV - A New Hope (1977) 2991 4.453694"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 평점이 4.3이상이고 평점의 수가 2000개 이상\n",
+ "평점_평점수 = 영화평점[(영화평점['mean']>=4.3)&( 영화평점['count']>=2000)]\n",
+ "평점_평점수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "'>=' not supported between instances of 'method' and 'float'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | count | \n", + "mean | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| American Beauty (1999) | \n", + "3428 | \n", + "4.317386 | \n", + "
| Godfather, The (1972) | \n", + "2223 | \n", + "4.524966 | \n", + "
| Matrix, The (1999) | \n", + "2590 | \n", + "4.315830 | \n", + "
| Princess Bride, The (1987) | \n", + "2318 | \n", + "4.303710 | \n", + "
| Raiders of the Lost Ark (1981) | \n", + "2514 | \n", + "4.477725 | \n", + "
| Saving Private Ryan (1998) | \n", + "2653 | \n", + "4.337354 | \n", + "
| Schindler's List (1993) | \n", + "2304 | \n", + "4.510417 | \n", + "
| Shawshank Redemption, The (1994) | \n", + "2227 | \n", + "4.554558 | \n", + "
| Silence of the Lambs, The (1991) | \n", + "2578 | \n", + "4.351823 | \n", + "
| Sixth Sense, The (1999) | \n", + "2459 | \n", + "4.406263 | \n", + "
| Star Wars: Episode IV - A New Hope (1977) | \n", + "2991 | \n", + "4.453694 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프 \\\n",
+ "0 1 F 1 10 48067 1193 5 978300760 \n",
+ "1 2 M 56 16 70072 1193 5 978298413 \n",
+ "2 12 M 25 12 32793 1193 4 978220179 \n",
+ "3 15 M 25 7 22903 1193 4 978199279 \n",
+ "4 17 M 50 1 95350 1193 5 978158471 \n",
+ "\n",
+ " 영화제목 장르 \n",
+ "0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "1 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "2 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "3 One Flew Over the Cuckoo's Nest (1975) Drama \n",
+ "4 One Flew Over the Cuckoo's Nest (1975) Drama "
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data3 = data[data.성별 == 'F']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 사용자아이디 | \n", + "성별 | \n", + "연령 | \n", + "직업 | \n", + "지역 | \n", + "영화아이디 | \n", + "평점 | \n", + "타임스탬프 | \n", + "영화제목 | \n", + "장르 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "48067 | \n", + "1193 | \n", + "5 | \n", + "978300760 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
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\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 사용자아이디 성별 연령 직업 지역 영화아이디 평점 타임스탬프 \\\n",
+ "0 1 F 1 10 48067 1193 5 978300760 \n",
+ "5 18 F 18 3 95825 1193 4 978156168 \n",
+ "7 24 F 25 7 10023 1193 5 978136709 \n",
+ "8 28 F 25 1 14607 1193 3 978125194 \n",
+ "19 59 F 50 1 55413 1193 4 977934292 \n",
+ "20 62 F 35 3 98105 1193 4 977968584 \n",
+ "22 81 F 25 0 60640 1193 5 977785864 \n",
+ "23 88 F 45 1 02476 1193 5 977694161 \n",
+ "24 89 F 56 9 85749 1193 5 977683596 \n",
+ "26 96 F 25 16 78028 1193 3 977621789 \n",
+ "27 99 F 1 10 19390 1193 2 982791053 \n",
+ "36 139 F 25 20 45409 1193 4 977359402 \n",
+ "37 146 F 35 20 10954 1193 4 979940868 \n",
+ "39 151 F 25 20 85013 1193 4 993121122 \n",
+ "43 175 F 25 2 95123 1193 5 978051929 \n",
+ "50 196 F 35 9 94587 1193 4 977004875 \n",
+ "55 224 F 18 4 14850 1193 4 976833559 \n",
+ "62 242 F 18 4 53706 1193 4 977267440 \n",
+ "67 265 F 35 7 55116 1193 5 976649008 \n",
+ "71 281 F 35 0 94117 1193 5 976571257 \n",
+ "72 285 F 35 0 94109 1193 4 976579214 \n",
+ "77 294 F 45 9 43147 1193 4 976541860 \n",
+ "87 312 F 50 2 22207 1193 4 976475038 \n",
+ "88 314 F 56 9 46911 1193 3 976751458 \n",
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+ "102 340 F 25 3 28001 1193 3 976341657 \n",
+ "105 353 F 35 7 92625 1193 4 976323254 \n",
+ "... ... .. .. .. ... ... .. ... \n",
+ "1000118 3790 F 25 17 94618 2623 4 966019247 \n",
+ "1000124 3842 F 35 16 92054 396 4 979988590 \n",
+ "1000126 3945 F 45 1 96778 3413 4 965690592 \n",
+ "1000128 3952 F 45 1 12449 1316 4 974595248 \n",
+ "1000145 4888 F 56 0 08055 820 4 962739636 \n",
+ "1000150 6001 F 25 7 94117 138 1 956807238 \n",
+ "1000160 5334 F 56 13 46140 3123 1 960795763 \n",
+ "1000163 4553 F 35 16 48237 3290 3 964551353 \n",
+ "1000164 4553 F 35 16 48237 1709 2 964551353 \n",
+ "1000165 4572 F 1 10 17036 3164 4 964460301 \n",
+ "1000166 4579 F 18 4 63332 3315 2 1033002064 \n",
+ "1000167 4628 F 56 6 23225 1832 4 964050337 \n",
+ "1000169 4888 F 56 0 08055 439 4 962737163 \n",
+ "1000170 4651 F 45 0 10036 134 4 963953346 \n",
+ "1000175 5878 F 25 0 60640 1842 4 1043783975 \n",
+ "1000176 4790 F 25 2 94133 2309 5 963105678 \n",
+ "1000177 4816 F 45 6 04240 2309 4 965869069 \n",
+ "1000182 4874 F 25 4 70808 624 4 962781918 \n",
+ "1000184 5947 F 45 16 97215 1434 4 957190428 \n",
+ "1000186 5944 F 18 10 27606 1868 1 957197520 \n",
+ "1000188 5185 F 35 4 44485 404 4 963402617 \n",
+ "1000192 5754 F 18 1 60640 2543 4 958272316 \n",
+ "1000196 5328 F 25 4 91740 2438 4 960838075 \n",
+ "1000197 5334 F 56 13 46140 3323 3 960796159 \n",
+ "1000198 5334 F 56 13 46140 127 1 960795494 \n",
+ "1000199 5334 F 56 13 46140 3382 5 960796159 \n",
+ "1000200 5420 F 1 19 14850 1843 3 960156505 \n",
+ "1000201 5433 F 35 17 45014 286 3 960240881 \n",
+ "1000202 5494 F 35 17 94306 3530 4 959816296 \n",
+ "1000207 5851 F 18 20 55410 3607 5 957756608 \n",
+ "\n",
+ " 영화제목 \\\n",
+ "0 One Flew Over the Cuckoo's Nest (1975) \n",
+ "5 One Flew Over the Cuckoo's Nest (1975) \n",
+ "7 One Flew Over the Cuckoo's Nest (1975) \n",
+ "8 One Flew Over the Cuckoo's Nest (1975) \n",
+ "19 One Flew Over the Cuckoo's Nest (1975) \n",
+ "20 One Flew Over the Cuckoo's Nest (1975) \n",
+ "22 One Flew Over the Cuckoo's Nest (1975) \n",
+ "23 One Flew Over the Cuckoo's Nest (1975) \n",
+ "24 One Flew Over the Cuckoo's Nest (1975) \n",
+ "26 One Flew Over the Cuckoo's Nest (1975) \n",
+ "27 One Flew Over the Cuckoo's Nest (1975) \n",
+ "36 One Flew Over the Cuckoo's Nest (1975) \n",
+ "37 One Flew Over the Cuckoo's Nest (1975) \n",
+ "39 One Flew Over the Cuckoo's Nest (1975) \n",
+ "43 One Flew Over the Cuckoo's Nest (1975) \n",
+ "50 One Flew Over the Cuckoo's Nest (1975) \n",
+ "55 One Flew Over the Cuckoo's Nest (1975) \n",
+ "62 One Flew Over the Cuckoo's Nest (1975) \n",
+ "67 One Flew Over the Cuckoo's Nest (1975) \n",
+ "71 One Flew Over the Cuckoo's Nest (1975) \n",
+ "72 One Flew Over the Cuckoo's Nest (1975) \n",
+ "77 One Flew Over the Cuckoo's Nest (1975) \n",
+ "87 One Flew Over the Cuckoo's Nest (1975) \n",
+ "88 One Flew Over the Cuckoo's Nest (1975) \n",
+ "89 One Flew Over the Cuckoo's Nest (1975) \n",
+ "91 One Flew Over the Cuckoo's Nest (1975) \n",
+ "92 One Flew Over the Cuckoo's Nest (1975) \n",
+ "99 One Flew Over the Cuckoo's Nest (1975) \n",
+ "102 One Flew Over the Cuckoo's Nest (1975) \n",
+ "105 One Flew Over the Cuckoo's Nest (1975) \n",
+ "... ... \n",
+ "1000118 Trippin' (1999) \n",
+ "1000124 Fall Time (1995) \n",
+ "1000126 Impact (1949) \n",
+ "1000128 Anna (1996) \n",
+ "1000145 Death in the Garden (Mort en ce jardin, La) (1... \n",
+ "1000150 Neon Bible, The (1995) \n",
+ "1000160 Spring Fever USA (a.k.a. Lauderdale) (1989) \n",
+ "1000163 Soft Toilet Seats (1999) \n",
+ "1000164 Legal Deceit (1997) \n",
+ "1000165 Alley Cats, The (1968) \n",
+ "1000166 Happy Go Lovely (1951) \n",
+ "1000167 Heaven's Burning (1997) \n",
+ "1000169 Dangerous Game (1993) \n",
+ "1000170 Sonic Outlaws (1995) \n",
+ "1000175 Illtown (1996) \n",
+ "1000176 Inheritors, The (Die Siebtelbauern) (1998) \n",
+ "1000177 Inheritors, The (Die Siebtelbauern) (1998) \n",
+ "1000182 Condition Red (1995) \n",
+ "1000184 Stranger, The (1994) \n",
+ "1000186 Truce, The (1996) \n",
+ "1000188 Brother Minister: The Assassination of Malcolm... \n",
+ "1000192 Six Ways to Sunday (1997) \n",
+ "1000196 Outside Ozona (1998) \n",
+ "1000197 Chain of Fools (2000) \n",
+ "1000198 Silence of the Palace, The (Saimt el Qusur) (1... \n",
+ "1000199 Song of Freedom (1936) \n",
+ "1000200 Slappy and the Stinkers (1998) \n",
+ "1000201 Nemesis 2: Nebula (1995) \n",
+ "1000202 Smoking/No Smoking (1993) \n",
+ "1000207 One Little Indian (1973) \n",
+ "\n",
+ " 장르 \n",
+ "0 Drama \n",
+ "5 Drama \n",
+ "7 Drama \n",
+ "8 Drama \n",
+ "19 Drama \n",
+ "20 Drama \n",
+ "22 Drama \n",
+ "23 Drama \n",
+ "24 Drama \n",
+ "26 Drama \n",
+ "27 Drama \n",
+ "36 Drama \n",
+ "37 Drama \n",
+ "39 Drama \n",
+ "43 Drama \n",
+ "50 Drama \n",
+ "55 Drama \n",
+ "62 Drama \n",
+ "67 Drama \n",
+ "71 Drama \n",
+ "72 Drama \n",
+ "77 Drama \n",
+ "87 Drama \n",
+ "88 Drama \n",
+ "89 Drama \n",
+ "91 Drama \n",
+ "92 Drama \n",
+ "99 Drama \n",
+ "102 Drama \n",
+ "105 Drama \n",
+ "... ... \n",
+ "1000118 Comedy \n",
+ "1000124 Drama \n",
+ "1000126 Crime|Drama \n",
+ "1000128 Drama \n",
+ "1000145 Drama \n",
+ "1000150 Drama \n",
+ "1000160 Comedy \n",
+ "1000163 Comedy \n",
+ "1000164 Thriller \n",
+ "1000165 Drama \n",
+ "1000166 Musical \n",
+ "1000167 Action|Drama \n",
+ "1000169 Drama \n",
+ "1000170 Documentary \n",
+ "1000175 Crime|Drama \n",
+ "1000176 Drama \n",
+ "1000177 Drama \n",
+ "1000182 Action|Drama|Thriller \n",
+ "1000184 Action \n",
+ "1000186 Drama|War \n",
+ "1000188 Documentary \n",
+ "1000192 Comedy \n",
+ "1000196 Drama|Thriller \n",
+ "1000197 Comedy|Crime \n",
+ "1000198 Drama \n",
+ "1000199 Drama \n",
+ "1000200 Children's|Comedy \n",
+ "1000201 Action|Sci-Fi|Thriller \n",
+ "1000202 Comedy \n",
+ "1000207 Comedy|Drama|Western \n",
+ "\n",
+ "[246440 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 62 | \n", + "242 | \n", + "F | \n", + "18 | \n", + "4 | \n", + "53706 | \n", + "1193 | \n", + "4 | \n", + "977267440 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
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| 102 | \n", + "340 | \n", + "F | \n", + "25 | \n", + "3 | \n", + "28001 | \n", + "1193 | \n", + "3 | \n", + "976341657 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| 105 | \n", + "353 | \n", + "F | \n", + "35 | \n", + "7 | \n", + "92625 | \n", + "1193 | \n", + "4 | \n", + "976323254 | \n", + "One Flew Over the Cuckoo's Nest (1975) | \n", + "Drama | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1000118 | \n", + "3790 | \n", + "F | \n", + "25 | \n", + "17 | \n", + "94618 | \n", + "2623 | \n", + "4 | \n", + "966019247 | \n", + "Trippin' (1999) | \n", + "Comedy | \n", + "
| 1000124 | \n", + "3842 | \n", + "F | \n", + "35 | \n", + "16 | \n", + "92054 | \n", + "396 | \n", + "4 | \n", + "979988590 | \n", + "Fall Time (1995) | \n", + "Drama | \n", + "
| 1000126 | \n", + "3945 | \n", + "F | \n", + "45 | \n", + "1 | \n", + "96778 | \n", + "3413 | \n", + "4 | \n", + "965690592 | \n", + "Impact (1949) | \n", + "Crime|Drama | \n", + "
| 1000128 | \n", + "3952 | \n", + "F | \n", + "45 | \n", + "1 | \n", + "12449 | \n", + "1316 | \n", + "4 | \n", + "974595248 | \n", + "Anna (1996) | \n", + "Drama | \n", + "
| 1000145 | \n", + "4888 | \n", + "F | \n", + "56 | \n", + "0 | \n", + "08055 | \n", + "820 | \n", + "4 | \n", + "962739636 | \n", + "Death in the Garden (Mort en ce jardin, La) (1... | \n", + "Drama | \n", + "
| 1000150 | \n", + "6001 | \n", + "F | \n", + "25 | \n", + "7 | \n", + "94117 | \n", + "138 | \n", + "1 | \n", + "956807238 | \n", + "Neon Bible, The (1995) | \n", + "Drama | \n", + "
| 1000160 | \n", + "5334 | \n", + "F | \n", + "56 | \n", + "13 | \n", + "46140 | \n", + "3123 | \n", + "1 | \n", + "960795763 | \n", + "Spring Fever USA (a.k.a. Lauderdale) (1989) | \n", + "Comedy | \n", + "
| 1000163 | \n", + "4553 | \n", + "F | \n", + "35 | \n", + "16 | \n", + "48237 | \n", + "3290 | \n", + "3 | \n", + "964551353 | \n", + "Soft Toilet Seats (1999) | \n", + "Comedy | \n", + "
| 1000164 | \n", + "4553 | \n", + "F | \n", + "35 | \n", + "16 | \n", + "48237 | \n", + "1709 | \n", + "2 | \n", + "964551353 | \n", + "Legal Deceit (1997) | \n", + "Thriller | \n", + "
| 1000165 | \n", + "4572 | \n", + "F | \n", + "1 | \n", + "10 | \n", + "17036 | \n", + "3164 | \n", + "4 | \n", + "964460301 | \n", + "Alley Cats, The (1968) | \n", + "Drama | \n", + "
| 1000166 | \n", + "4579 | \n", + "F | \n", + "18 | \n", + "4 | \n", + "63332 | \n", + "3315 | \n", + "2 | \n", + "1033002064 | \n", + "Happy Go Lovely (1951) | \n", + "Musical | \n", + "
| 1000167 | \n", + "4628 | \n", + "F | \n", + "56 | \n", + "6 | \n", + "23225 | \n", + "1832 | \n", + "4 | \n", + "964050337 | \n", + "Heaven's Burning (1997) | \n", + "Action|Drama | \n", + "
| 1000169 | \n", + "4888 | \n", + "F | \n", + "56 | \n", + "0 | \n", + "08055 | \n", + "439 | \n", + "4 | \n", + "962737163 | \n", + "Dangerous Game (1993) | \n", + "Drama | \n", + "
| 1000170 | \n", + "4651 | \n", + "F | \n", + "45 | \n", + "0 | \n", + "10036 | \n", + "134 | \n", + "4 | \n", + "963953346 | \n", + "Sonic Outlaws (1995) | \n", + "Documentary | \n", + "
| 1000175 | \n", + "5878 | \n", + "F | \n", + "25 | \n", + "0 | \n", + "60640 | \n", + "1842 | \n", + "4 | \n", + "1043783975 | \n", + "Illtown (1996) | \n", + "Crime|Drama | \n", + "
| 1000176 | \n", + "4790 | \n", + "F | \n", + "25 | \n", + "2 | \n", + "94133 | \n", + "2309 | \n", + "5 | \n", + "963105678 | \n", + "Inheritors, The (Die Siebtelbauern) (1998) | \n", + "Drama | \n", + "
| 1000177 | \n", + "4816 | \n", + "F | \n", + "45 | \n", + "6 | \n", + "04240 | \n", + "2309 | \n", + "4 | \n", + "965869069 | \n", + "Inheritors, The (Die Siebtelbauern) (1998) | \n", + "Drama | \n", + "
| 1000182 | \n", + "4874 | \n", + "F | \n", + "25 | \n", + "4 | \n", + "70808 | \n", + "624 | \n", + "4 | \n", + "962781918 | \n", + "Condition Red (1995) | \n", + "Action|Drama|Thriller | \n", + "
| 1000184 | \n", + "5947 | \n", + "F | \n", + "45 | \n", + "16 | \n", + "97215 | \n", + "1434 | \n", + "4 | \n", + "957190428 | \n", + "Stranger, The (1994) | \n", + "Action | \n", + "
| 1000186 | \n", + "5944 | \n", + "F | \n", + "18 | \n", + "10 | \n", + "27606 | \n", + "1868 | \n", + "1 | \n", + "957197520 | \n", + "Truce, The (1996) | \n", + "Drama|War | \n", + "
| 1000188 | \n", + "5185 | \n", + "F | \n", + "35 | \n", + "4 | \n", + "44485 | \n", + "404 | \n", + "4 | \n", + "963402617 | \n", + "Brother Minister: The Assassination of Malcolm... | \n", + "Documentary | \n", + "
| 1000192 | \n", + "5754 | \n", + "F | \n", + "18 | \n", + "1 | \n", + "60640 | \n", + "2543 | \n", + "4 | \n", + "958272316 | \n", + "Six Ways to Sunday (1997) | \n", + "Comedy | \n", + "
| 1000196 | \n", + "5328 | \n", + "F | \n", + "25 | \n", + "4 | \n", + "91740 | \n", + "2438 | \n", + "4 | \n", + "960838075 | \n", + "Outside Ozona (1998) | \n", + "Drama|Thriller | \n", + "
| 1000197 | \n", + "5334 | \n", + "F | \n", + "56 | \n", + "13 | \n", + "46140 | \n", + "3323 | \n", + "3 | \n", + "960796159 | \n", + "Chain of Fools (2000) | \n", + "Comedy|Crime | \n", + "
| 1000198 | \n", + "5334 | \n", + "F | \n", + "56 | \n", + "13 | \n", + "46140 | \n", + "127 | \n", + "1 | \n", + "960795494 | \n", + "Silence of the Palace, The (Saimt el Qusur) (1... | \n", + "Drama | \n", + "
| 1000199 | \n", + "5334 | \n", + "F | \n", + "56 | \n", + "13 | \n", + "46140 | \n", + "3382 | \n", + "5 | \n", + "960796159 | \n", + "Song of Freedom (1936) | \n", + "Drama | \n", + "
| 1000200 | \n", + "5420 | \n", + "F | \n", + "1 | \n", + "19 | \n", + "14850 | \n", + "1843 | \n", + "3 | \n", + "960156505 | \n", + "Slappy and the Stinkers (1998) | \n", + "Children's|Comedy | \n", + "
| 1000201 | \n", + "5433 | \n", + "F | \n", + "35 | \n", + "17 | \n", + "45014 | \n", + "286 | \n", + "3 | \n", + "960240881 | \n", + "Nemesis 2: Nebula (1995) | \n", + "Action|Sci-Fi|Thriller | \n", + "
| 1000202 | \n", + "5494 | \n", + "F | \n", + "35 | \n", + "17 | \n", + "94306 | \n", + "3530 | \n", + "4 | \n", + "959816296 | \n", + "Smoking/No Smoking (1993) | \n", + "Comedy | \n", + "
| 1000207 | \n", + "5851 | \n", + "F | \n", + "18 | \n", + "20 | \n", + "55410 | \n", + "3607 | \n", + "5 | \n", + "957756608 | \n", + "One Little Indian (1973) | \n", + "Comedy|Drama|Western | \n", + "
246440 rows × 10 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count mean\n",
+ "영화제목 \n",
+ "$1,000,000 Duck (1971) 16 3.375000\n",
+ "'Night Mother (1986) 36 3.388889\n",
+ "'Til There Was You (1997) 37 2.675676\n",
+ "'burbs, The (1989) 92 2.793478\n",
+ "...And Justice for All (1979) 35 3.828571\n",
+ "1-900 (1994) 1 2.000000\n",
+ "10 Things I Hate About You (1999) 232 3.646552\n",
+ "101 Dalmatians (1961) 187 3.791444\n",
+ "101 Dalmatians (1996) 150 3.240000\n",
+ "12 Angry Men (1957) 141 4.184397\n",
+ "13th Warrior, The (1999) 125 3.112000\n",
+ "187 (1997) 7 2.428571\n",
+ "2 Days in the Valley (1996) 45 3.488889\n",
+ "20 Dates (1998) 29 2.620690\n",
+ "20,000 Leagues Under the Sea (1954) 97 3.670103\n",
+ "200 Cigarettes (1999) 71 3.169014\n",
+ "2001: A Space Odyssey (1968) 344 3.825581\n",
+ "2010 (1984) 47 3.446809\n",
+ "24 7: Twenty Four Seven (1997) 1 5.000000\n",
+ "24-hour Woman (1998) 4 2.000000\n",
+ "28 Days (2000) 191 3.209424\n",
+ "3 Ninjas: High Noon On Mega Mountain (1998) 10 1.400000\n",
+ "301, 302 (1995) 2 3.000000\n",
+ "39 Steps, The (1935) 58 3.965517\n",
+ "400 Blows, The (Les Quatre cents coups) (1959) 47 4.489362\n",
+ "42 Up (1998) 30 3.966667\n",
+ "52 Pick-Up (1986) 23 3.304348\n",
+ "54 (1998) 57 2.701754\n",
+ "7th Voyage of Sinbad, The (1958) 44 3.409091\n",
+ "8 1/2 (1963) 52 4.096154\n",
+ "... ... ...\n",
+ "World of Apu, The (Apur Sansar) (1959) 19 4.842105\n",
+ "Wrong Man, The (1956) 27 3.703704\n",
+ "Wrong Trousers, The (1993) 238 4.588235\n",
+ "Wrongfully Accused (1998) 12 2.666667\n",
+ "Wyatt Earp (1994) 34 3.147059\n",
+ "X-Files: Fight the Future, The (1998) 190 3.489474\n",
+ "X-Men (2000) 277 3.682310\n",
+ "X: The Unknown (1956) 4 2.250000\n",
+ "Xiu Xiu: The Sent-Down Girl (Tian yu) (1998) 28 3.821429\n",
+ "Yankee Zulu (1994) 1 3.000000\n",
+ "Yards, The (1999) 16 2.687500\n",
+ "Year My Voice Broke, The (1987) 10 4.000000\n",
+ "Year of Living Dangerously (1982) 123 3.951220\n",
+ "Yellow Submarine (1968) 119 3.714286\n",
+ "Yojimbo (1961) 26 4.423077\n",
+ "You Can't Take It With You (1938) 26 4.192308\n",
+ "You So Crazy (1994) 3 3.666667\n",
+ "You've Got Mail (1998) 330 3.542424\n",
+ "Young Doctors in Love (1982) 13 1.923077\n",
+ "Young Frankenstein (1974) 269 4.289963\n",
+ "Young Guns (1988) 78 3.371795\n",
+ "Young Guns II (1990) 46 2.934783\n",
+ "Young Poisoner's Handbook, The (1995) 17 4.000000\n",
+ "Young Sherlock Holmes (1985) 68 3.514706\n",
+ "Young and Innocent (1937) 2 2.500000\n",
+ "Your Friends and Neighbors (1998) 27 2.888889\n",
+ "Zed & Two Noughts, A (1985) 8 3.500000\n",
+ "Zero Effect (1998) 59 3.864407\n",
+ "Zeus and Roxanne (1997) 9 2.777778\n",
+ "eXistenZ (1999) 71 3.098592\n",
+ "\n",
+ "[3481 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data3.pivot_table(index = '영화제목', values = '평점', aggfunc = ('mean', 'count'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "여성평점 = data3.pivot_table(index = '영화제목', values = '평점', aggfunc = ('mean', 'count'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "여성인기영화 = 여성평점[(여성평점['count']>= 500) & (여성평점['mean']>= 4.0)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | count | \n", + "mean | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| $1,000,000 Duck (1971) | \n", + "16 | \n", + "3.375000 | \n", + "
| 'Night Mother (1986) | \n", + "36 | \n", + "3.388889 | \n", + "
| 'Til There Was You (1997) | \n", + "37 | \n", + "2.675676 | \n", + "
| 'burbs, The (1989) | \n", + "92 | \n", + "2.793478 | \n", + "
| ...And Justice for All (1979) | \n", + "35 | \n", + "3.828571 | \n", + "
| 1-900 (1994) | \n", + "1 | \n", + "2.000000 | \n", + "
| 10 Things I Hate About You (1999) | \n", + "232 | \n", + "3.646552 | \n", + "
| 101 Dalmatians (1961) | \n", + "187 | \n", + "3.791444 | \n", + "
| 101 Dalmatians (1996) | \n", + "150 | \n", + "3.240000 | \n", + "
| 12 Angry Men (1957) | \n", + "141 | \n", + "4.184397 | \n", + "
| 13th Warrior, The (1999) | \n", + "125 | \n", + "3.112000 | \n", + "
| 187 (1997) | \n", + "7 | \n", + "2.428571 | \n", + "
| 2 Days in the Valley (1996) | \n", + "45 | \n", + "3.488889 | \n", + "
| 20 Dates (1998) | \n", + "29 | \n", + "2.620690 | \n", + "
| 20,000 Leagues Under the Sea (1954) | \n", + "97 | \n", + "3.670103 | \n", + "
| 200 Cigarettes (1999) | \n", + "71 | \n", + "3.169014 | \n", + "
| 2001: A Space Odyssey (1968) | \n", + "344 | \n", + "3.825581 | \n", + "
| 2010 (1984) | \n", + "47 | \n", + "3.446809 | \n", + "
| 24 7: Twenty Four Seven (1997) | \n", + "1 | \n", + "5.000000 | \n", + "
| 24-hour Woman (1998) | \n", + "4 | \n", + "2.000000 | \n", + "
| 28 Days (2000) | \n", + "191 | \n", + "3.209424 | \n", + "
| 3 Ninjas: High Noon On Mega Mountain (1998) | \n", + "10 | \n", + "1.400000 | \n", + "
| 301, 302 (1995) | \n", + "2 | \n", + "3.000000 | \n", + "
| 39 Steps, The (1935) | \n", + "58 | \n", + "3.965517 | \n", + "
| 400 Blows, The (Les Quatre cents coups) (1959) | \n", + "47 | \n", + "4.489362 | \n", + "
| 42 Up (1998) | \n", + "30 | \n", + "3.966667 | \n", + "
| 52 Pick-Up (1986) | \n", + "23 | \n", + "3.304348 | \n", + "
| 54 (1998) | \n", + "57 | \n", + "2.701754 | \n", + "
| 7th Voyage of Sinbad, The (1958) | \n", + "44 | \n", + "3.409091 | \n", + "
| 8 1/2 (1963) | \n", + "52 | \n", + "4.096154 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| World of Apu, The (Apur Sansar) (1959) | \n", + "19 | \n", + "4.842105 | \n", + "
| Wrong Man, The (1956) | \n", + "27 | \n", + "3.703704 | \n", + "
| Wrong Trousers, The (1993) | \n", + "238 | \n", + "4.588235 | \n", + "
| Wrongfully Accused (1998) | \n", + "12 | \n", + "2.666667 | \n", + "
| Wyatt Earp (1994) | \n", + "34 | \n", + "3.147059 | \n", + "
| X-Files: Fight the Future, The (1998) | \n", + "190 | \n", + "3.489474 | \n", + "
| X-Men (2000) | \n", + "277 | \n", + "3.682310 | \n", + "
| X: The Unknown (1956) | \n", + "4 | \n", + "2.250000 | \n", + "
| Xiu Xiu: The Sent-Down Girl (Tian yu) (1998) | \n", + "28 | \n", + "3.821429 | \n", + "
| Yankee Zulu (1994) | \n", + "1 | \n", + "3.000000 | \n", + "
| Yards, The (1999) | \n", + "16 | \n", + "2.687500 | \n", + "
| Year My Voice Broke, The (1987) | \n", + "10 | \n", + "4.000000 | \n", + "
| Year of Living Dangerously (1982) | \n", + "123 | \n", + "3.951220 | \n", + "
| Yellow Submarine (1968) | \n", + "119 | \n", + "3.714286 | \n", + "
| Yojimbo (1961) | \n", + "26 | \n", + "4.423077 | \n", + "
| You Can't Take It With You (1938) | \n", + "26 | \n", + "4.192308 | \n", + "
| You So Crazy (1994) | \n", + "3 | \n", + "3.666667 | \n", + "
| You've Got Mail (1998) | \n", + "330 | \n", + "3.542424 | \n", + "
| Young Doctors in Love (1982) | \n", + "13 | \n", + "1.923077 | \n", + "
| Young Frankenstein (1974) | \n", + "269 | \n", + "4.289963 | \n", + "
| Young Guns (1988) | \n", + "78 | \n", + "3.371795 | \n", + "
| Young Guns II (1990) | \n", + "46 | \n", + "2.934783 | \n", + "
| Young Poisoner's Handbook, The (1995) | \n", + "17 | \n", + "4.000000 | \n", + "
| Young Sherlock Holmes (1985) | \n", + "68 | \n", + "3.514706 | \n", + "
| Young and Innocent (1937) | \n", + "2 | \n", + "2.500000 | \n", + "
| Your Friends and Neighbors (1998) | \n", + "27 | \n", + "2.888889 | \n", + "
| Zed & Two Noughts, A (1985) | \n", + "8 | \n", + "3.500000 | \n", + "
| Zero Effect (1998) | \n", + "59 | \n", + "3.864407 | \n", + "
| Zeus and Roxanne (1997) | \n", + "9 | \n", + "2.777778 | \n", + "
| eXistenZ (1999) | \n", + "71 | \n", + "3.098592 | \n", + "
3481 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 영화아이디 장르\n",
+ "영화제목 \n",
+ "Toy Story (1995) 1 Animation|Children's|Comedy\n",
+ "Jumanji (1995) 2 Adventure|Children's|Fantasy\n",
+ "Grumpier Old Men (1995) 3 Comedy|Romance\n",
+ "Waiting to Exhale (1995) 4 Comedy|Drama\n",
+ "Father of the Bride Part II (1995) 5 Comedy\n",
+ "Heat (1995) 6 Action|Crime|Thriller\n",
+ "Sabrina (1995) 7 Comedy|Romance\n",
+ "Tom and Huck (1995) 8 Adventure|Children's\n",
+ "Sudden Death (1995) 9 Action\n",
+ "GoldenEye (1995) 10 Action|Adventure|Thriller"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movies2= movies.set_index('영화제목')\n",
+ "movies2.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'장르'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 3108\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3109\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3110\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_box\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | 영화아이디 | \n", + "장르 | \n", + "
|---|---|---|
| 영화제목 | \n", + "\n", + " | \n", + " |
| Toy Story (1995) | \n", + "1 | \n", + "Animation|Children's|Comedy | \n", + "
| Jumanji (1995) | \n", + "2 | \n", + "Adventure|Children's|Fantasy | \n", + "
| Grumpier Old Men (1995) | \n", + "3 | \n", + "Comedy|Romance | \n", + "
| Waiting to Exhale (1995) | \n", + "4 | \n", + "Comedy|Drama | \n", + "
| Father of the Bride Part II (1995) | \n", + "5 | \n", + "Comedy | \n", + "
| Heat (1995) | \n", + "6 | \n", + "Action|Crime|Thriller | \n", + "
| Sabrina (1995) | \n", + "7 | \n", + "Comedy|Romance | \n", + "
| Tom and Huck (1995) | \n", + "8 | \n", + "Adventure|Children's | \n", + "
| Sudden Death (1995) | \n", + "9 | \n", + "Action | \n", + "
| GoldenEye (1995) | \n", + "10 | \n", + "Action|Adventure|Thriller | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 출루율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 0.339 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 0.362 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 0.376 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 0.319 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 0.350 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 0.361 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 0.299 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 0.667 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 0.318 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 0.333 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 0.335 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - 1.000 \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 0.333 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 0.300 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 0.306 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 0.176 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 0.000 \n",
+ "\n",
+ " 장타율 OPS wOBA WAR \n",
+ "0 0.443 0.782 0.353 2.31 \n",
+ "1 0.475 0.837 0.373 1.85 \n",
+ "2 0.333 0.709 0.339 1.55 \n",
+ "3 0.416 0.735 0.329 1.50 \n",
+ "4 0.413 0.763 0.348 0.83 \n",
+ "5 0.324 0.685 0.327 0.52 \n",
+ "6 0.320 0.619 0.289 0.16 \n",
+ "7 1.000 1.667 0.671 0.10 \n",
+ "8 0.418 0.736 0.329 0.09 \n",
+ "9 0.286 0.619 0.296 0.07 \n",
+ "10 0.353 0.688 0.320 0.06 \n",
+ "11 - - 0.712 0.06 \n",
+ "12 0.345 0.678 0.317 -0.03 \n",
+ "13 0.250 0.500 0.234 -0.03 \n",
+ "14 0.000 0.000 0.000 -0.06 \n",
+ "15 0.330 0.630 0.293 -0.09 \n",
+ "16 0.143 0.286 0.134 -0.11 \n",
+ "17 0.311 0.617 0.289 -0.12 \n",
+ "18 0.313 0.489 0.216 -0.14 \n",
+ "19 0.000 0.000 0.000 -0.14 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC13"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC13['년도'] = 2013\n",
+ "NC14['년도'] = 2014\n",
+ "NC15['년도'] = 2015"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC = pd.concat([NC13,NC14,NC15],axis= 0 , ignore_index = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. 2015년도 선수 기록을 '타율'로 내림차순 정렬"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "
| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.390 0.381 \n",
+ "12 박정준 NC 19 24 21 8 0 2 2 0 5 0 0.500 0.381 \n",
+ "14 조평호 NC 22 22 22 8 1 4 3 0 6 0 0.467 0.364 \n",
+ "1 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "2 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "4 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "3 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "8 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.290 \n",
+ "10 용덕한 NC 50 49 42 12 0 4 5 5 8 2 0.353 0.286 \n",
+ "5 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "7 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "9 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "13 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "11 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "6 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.260 0.245 \n",
+ "16 김준완 NC 30 54 44 10 0 12 2 10 10 2 0.294 0.227 \n",
+ "17 강구성 NC 4 5 5 1 0 0 0 0 0 0 0.200 0.200 \n",
+ "15 이창섭 NC 15 8 5 0 0 0 0 3 3 0 0.000 0.000 \n",
+ "18 강민국 NC 1 2 2 0 0 0 0 0 1 0 0.000 0.000 \n",
+ "19 김태진 NC 1 3 3 0 0 0 0 0 0 0 0.000 0.000 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.497 0.790 1.287 0.520 11.73 2015 \n",
+ "12 0.458 0.476 0.934 0.417 0.25 2015 \n",
+ "14 0.364 0.591 0.955 0.401 0.18 2015 \n",
+ "1 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "2 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "4 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "3 0.381 0.510 0.891 0.381 2.19 2015 \n",
+ "8 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "10 0.375 0.310 0.685 0.326 0.44 2015 \n",
+ "5 0.372 0.540 0.912 0.389 1.00 2015 \n",
+ "7 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "9 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "13 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "11 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "6 0.319 0.398 0.717 0.316 0.85 2015 \n",
+ "16 0.370 0.227 0.597 0.308 -0.04 2015 \n",
+ "17 0.200 0.400 0.600 0.242 -0.04 2015 \n",
+ "15 0.375 0.000 0.375 0.286 -0.01 2015 \n",
+ "18 0.000 0.000 0.000 0.000 -0.06 2015 \n",
+ "19 0.000 0.000 0.000 0.000 -0.09 2015 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC15.sort_values(by = '타율', ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.390 | \n", + "0.381 | \n", + "0.497 | \n", + "0.790 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 12 | \n", + "박정준 | \n", + "NC | \n", + "19 | \n", + "24 | \n", + "21 | \n", + "8 | \n", + "0 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "0.500 | \n", + "0.381 | \n", + "0.458 | \n", + "0.476 | \n", + "0.934 | \n", + "0.417 | \n", + "0.25 | \n", + "2015 | \n", + "
| 14 | \n", + "조평호 | \n", + "NC | \n", + "22 | \n", + "22 | \n", + "22 | \n", + "8 | \n", + "1 | \n", + "4 | \n", + "3 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "0.467 | \n", + "0.364 | \n", + "0.364 | \n", + "0.591 | \n", + "0.955 | \n", + "0.401 | \n", + "0.18 | \n", + "2015 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 4 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 3 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.510 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 8 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.290 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 10 | \n", + "용덕한 | \n", + "NC | \n", + "50 | \n", + "49 | \n", + "42 | \n", + "12 | \n", + "0 | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "8 | \n", + "2 | \n", + "0.353 | \n", + "0.286 | \n", + "0.375 | \n", + "0.310 | \n", + "0.685 | \n", + "0.326 | \n", + "0.44 | \n", + "2015 | \n", + "
| 5 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.540 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 7 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 9 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 13 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 11 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 6 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.260 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
| 16 | \n", + "김준완 | \n", + "NC | \n", + "30 | \n", + "54 | \n", + "44 | \n", + "10 | \n", + "0 | \n", + "12 | \n", + "2 | \n", + "10 | \n", + "10 | \n", + "2 | \n", + "0.294 | \n", + "0.227 | \n", + "0.370 | \n", + "0.227 | \n", + "0.597 | \n", + "0.308 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 17 | \n", + "강구성 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.200 | \n", + "0.200 | \n", + "0.200 | \n", + "0.400 | \n", + "0.600 | \n", + "0.242 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 15 | \n", + "이창섭 | \n", + "NC | \n", + "15 | \n", + "8 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.375 | \n", + "0.000 | \n", + "0.375 | \n", + "0.286 | \n", + "-0.01 | \n", + "2015 | \n", + "
| 18 | \n", + "강민국 | \n", + "NC | \n", + "1 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2015 | \n", + "
| 19 | \n", + "김태진 | \n", + "NC | \n", + "1 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.09 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.390 0.381 \n",
+ "1 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "2 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "4 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "3 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "8 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.290 \n",
+ "5 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "7 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "9 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "13 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "11 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "6 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.260 0.245 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.497 0.790 1.287 0.520 11.73 2015 \n",
+ "1 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "2 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "4 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "3 0.381 0.510 0.891 0.381 2.19 2015 \n",
+ "8 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "5 0.372 0.540 0.912 0.389 1.00 2015 \n",
+ "7 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "9 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "13 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "11 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "6 0.319 0.398 0.717 0.316 0.85 2015 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#조건 추가, 타율이 100이상\n",
+ "#조건색인\n",
+ "NC15[NC15['타석']>=100].sort_values(by = '타율', ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.390 | \n", + "0.381 | \n", + "0.497 | \n", + "0.790 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 4 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 3 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.510 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 8 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.290 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 5 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.540 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 7 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 9 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 13 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 11 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 6 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.260 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.390 0.381 \n",
+ "1 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "2 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "4 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "3 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "8 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.290 \n",
+ "5 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "7 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "9 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "13 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "11 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "6 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.260 0.245 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.497 0.790 1.287 0.520 11.73 2015 \n",
+ "1 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "2 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "4 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "3 0.381 0.510 0.891 0.381 2.19 2015 \n",
+ "8 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "5 0.372 0.540 0.912 0.389 1.00 2015 \n",
+ "7 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "9 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "13 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "11 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "6 0.319 0.398 0.717 0.316 0.85 2015 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 조건 추가2, 타율이 같은 경우, 안타가 많은 순\n",
+ "NC15[NC15['타석']>=100].sort_values(by = ['타율','안타'], ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.390 | \n", + "0.381 | \n", + "0.497 | \n", + "0.790 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 4 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 3 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.510 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 8 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.290 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 5 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.540 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 7 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 9 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 13 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 11 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 6 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.260 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.390 0.381 \n",
+ "1 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "2 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "4 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "3 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "8 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.290 \n",
+ "5 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "7 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "9 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "13 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "11 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "6 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.260 0.245 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.497 0.790 1.287 0.520 11.73 2015 \n",
+ "1 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "2 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "4 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "3 0.381 0.510 0.891 0.381 2.19 2015 \n",
+ "8 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "5 0.372 0.540 0.912 0.389 1.00 2015 \n",
+ "7 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "9 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "13 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "11 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "6 0.319 0.398 0.717 0.316 0.85 2015 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 타율이 내림 차순, 안타 오름차순\n",
+ "NC15[NC15['타석']>=100].sort_values(by = ['타율','안타'], ascending = [False,True])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. (실습) 2013~2015년 선수별로 3년 간 평균 안타수를 구한 후, 평균 안타수가 가장 많은 상위 10명의 선수를 출력"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.390 | \n", + "0.381 | \n", + "0.497 | \n", + "0.790 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 1 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 2 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 4 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 3 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.510 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 8 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.290 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 5 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.540 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 7 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 9 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 13 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 11 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 6 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.260 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "0 모창민 NC 108 436 395 109 12 57 51 37 68 16 0.307 0.276 \n",
+ "1 이호준 NC 126 508 442 123 20 46 87 60 109 2 0.324 0.278 \n",
+ "2 김종호 NC 128 546 465 129 0 72 22 57 100 50 0.352 0.277 \n",
+ "3 나성범 NC 104 458 404 98 14 55 64 33 95 12 0.279 0.243 \n",
+ "4 조영훈 NC 120 426 380 107 6 38 39 39 56 4 0.316 0.282 \n",
+ "5 이현곤 NC 91 161 139 38 0 10 9 16 14 2 0.304 0.273 \n",
+ "6 이상호 NC 102 138 125 31 0 26 13 9 21 24 0.298 0.248 \n",
+ "7 강진성 NC 3 3 2 1 0 1 0 1 0 0 0.500 0.500 \n",
+ "8 조평호 NC 26 86 79 21 2 12 7 6 24 1 0.358 0.266 \n",
+ "9 박민우 NC 32 48 42 11 0 10 6 5 7 9 0.306 0.262 \n",
+ "10 박정준 NC 77 195 167 41 4 24 21 23 44 4 0.303 0.246 \n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - \n",
+ "12 마낙길 NC 24 31 29 9 0 5 0 1 5 2 0.375 0.310 \n",
+ "13 김성욱 NC 4 4 4 1 0 0 1 0 2 0 0.500 0.250 \n",
+ "14 강구성 NC 2 2 2 0 0 0 0 0 0 0 0.000 0.000 \n",
+ "15 지석훈 NC 104 356 309 68 3 25 35 30 82 2 0.285 0.220 \n",
+ "16 김종찬 NC 5 7 7 1 0 0 0 0 3 0 0.250 0.143 \n",
+ "17 차화준 NC 58 121 106 24 1 18 7 12 29 4 0.295 0.226 \n",
+ "18 김동건 NC 7 17 16 2 1 1 1 1 7 0 0.125 0.125 \n",
+ "19 이창섭 NC 5 5 5 0 0 0 0 0 2 0 0.000 0.000 \n",
+ "20 테임즈 NC 125 514 443 152 37 95 121 58 99 11 0.367 0.343 \n",
+ "21 나성범 NC 123 536 477 157 30 88 101 43 128 14 0.397 0.329 \n",
+ "22 박민우 NC 118 491 416 124 1 87 40 56 89 50 0.373 0.298 \n",
+ "23 손시헌 NC 97 361 307 90 5 39 39 34 53 2 0.331 0.293 \n",
+ "24 지석훈 NC 114 238 212 58 6 26 34 16 46 1 0.323 0.274 \n",
+ "25 이호준 NC 122 500 424 115 23 59 78 67 104 2 0.305 0.271 \n",
+ "26 권희동 NC 101 252 221 63 7 39 36 25 43 6 0.324 0.285 \n",
+ "27 모창민 NC 122 468 419 110 16 62 72 37 82 14 0.289 0.263 \n",
+ "28 이종욱 NC 124 495 438 126 6 73 79 40 60 15 0.313 0.288 \n",
+ "29 김준완 NC 6 5 4 2 0 1 0 1 1 1 0.667 0.5 \n",
+ "30 최재원 NC 2 2 1 0 0 1 0 1 1 0 - 0 \n",
+ "31 마낙길 NC 2 2 1 0 0 1 0 1 0 0 0.000 0 \n",
+ "32 조영훈 NC 92 124 111 29 6 15 22 10 26 1 0.288 0.261 \n",
+ "33 김태우 NC 4 5 5 1 0 0 0 0 3 0 0.500 0.2 \n",
+ "34 박명환* NC 2 1 1 0 0 0 0 0 1 0 - 0 \n",
+ "35 조평호 NC 2 2 2 0 0 0 0 0 0 0 0.000 0 \n",
+ "36 강민국 NC 6 3 3 0 0 0 0 0 0 0 0.000 0 \n",
+ "37 허준 NC 28 36 29 5 0 2 1 5 10 0 0.263 0.172 \n",
+ "38 김성욱 NC 26 26 23 4 1 6 1 3 9 1 0.231 0.174 \n",
+ "39 노진혁 NC 25 16 16 3 1 3 2 0 7 0 0.250 0.188 \n",
+ "40 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.39 0.381 \n",
+ "41 나성범 NC 144 622 564 184 28 112 135 33 127 23 0.374 0.326 \n",
+ "42 박민우 NC 141 617 520 158 3 111 47 73 108 46 0.373 0.304 \n",
+ "43 이호준 NC 131 518 449 132 24 48 110 61 102 0 0.332 0.294 \n",
+ "44 김종호 NC 133 486 424 125 4 90 36 44 65 41 0.338 0.295 \n",
+ "45 조영훈 NC 103 146 124 35 8 20 35 19 36 3 0.329 0.282 \n",
+ "46 손시헌 NC 140 508 440 108 13 56 58 36 66 3 0.26 0.245 \n",
+ "47 이종욱 NC 125 504 440 118 5 63 52 54 78 17 0.315 0.268 \n",
+ "48 모창민 NC 103 239 214 62 6 23 35 18 52 5 0.352 0.29 \n",
+ "49 지석훈 NC 137 466 415 111 11 53 46 23 85 4 0.311 0.267 \n",
+ "50 용덕한 NC 50 49 42 12 0 4 5 5 8 2 0.353 0.286 \n",
+ "51 최재원 NC 114 102 85 21 2 31 13 10 29 14 0.352 0.247 \n",
+ "52 박정준 NC 19 24 21 8 0 2 2 0 5 0 0.5 0.381 \n",
+ "53 김태군 NC 144 474 421 107 6 45 52 25 77 1 0.293 0.254 \n",
+ "54 조평호 NC 22 22 22 8 1 4 3 0 6 0 0.467 0.364 \n",
+ "55 이창섭 NC 15 8 5 0 0 0 0 3 3 0 0 0 \n",
+ "56 김준완 NC 30 54 44 10 0 12 2 10 10 2 0.294 0.227 \n",
+ "57 강구성 NC 4 5 5 1 0 0 0 0 0 0 0.2 0.2 \n",
+ "58 강민국 NC 1 2 2 0 0 0 0 0 1 0 0 0 \n",
+ "59 김태진 NC 1 3 3 0 0 0 0 0 0 0 0 0 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "0 0.339 0.443 0.782 0.353 2.31 2013 \n",
+ "1 0.362 0.475 0.837 0.373 1.85 2013 \n",
+ "2 0.376 0.333 0.709 0.339 1.55 2013 \n",
+ "3 0.319 0.416 0.735 0.329 1.50 2013 \n",
+ "4 0.350 0.413 0.763 0.348 0.83 2013 \n",
+ "5 0.361 0.324 0.685 0.327 0.52 2013 \n",
+ "6 0.299 0.320 0.619 0.289 0.16 2013 \n",
+ "7 0.667 1.000 1.667 0.671 0.10 2013 \n",
+ "8 0.318 0.418 0.736 0.329 0.09 2013 \n",
+ "9 0.333 0.286 0.619 0.296 0.07 2013 \n",
+ "10 0.335 0.353 0.688 0.320 0.06 2013 \n",
+ "11 1.000 - - 0.712 0.06 2013 \n",
+ "12 0.333 0.345 0.678 0.317 -0.03 2013 \n",
+ "13 0.250 0.250 0.500 0.234 -0.03 2013 \n",
+ "14 0.000 0.000 0.000 0.000 -0.06 2013 \n",
+ "15 0.300 0.330 0.630 0.293 -0.09 2013 \n",
+ "16 0.143 0.143 0.286 0.134 -0.11 2013 \n",
+ "17 0.306 0.311 0.617 0.289 -0.12 2013 \n",
+ "18 0.176 0.313 0.489 0.216 -0.14 2013 \n",
+ "19 0.000 0.000 0.000 0.000 -0.14 2013 \n",
+ "20 0.422 0.688 1.11 0.456 6.36 2014 \n",
+ "21 0.400 0.597 0.997 0.424 5.94 2014 \n",
+ "22 0.392 0.399 0.791 0.365 2.73 2014 \n",
+ "23 0.368 0.414 0.782 0.349 1.52 2014 \n",
+ "24 0.340 0.462 0.802 0.352 0.97 2014 \n",
+ "25 0.371 0.481 0.852 0.369 0.88 2014 \n",
+ "26 0.363 0.443 0.806 0.353 0.62 2014 \n",
+ "27 0.320 0.413 0.733 0.319 0.17 2014 \n",
+ "28 0.343 0.411 0.754 0.332 0.13 2014 \n",
+ "29 0.600 0.5 1.1 0.509 0.11 2014 \n",
+ "30 0.500 0 0.5 0.363 0.01 2014 \n",
+ "31 0.500 0 0.5 0.363 0.01 2014 \n",
+ "32 0.325 0.459 0.784 0.337 -0.02 2014 \n",
+ "33 0.200 0.4 0.6 0.244 -0.03 2014 \n",
+ "34 0.000 0 0 0.000 -0.03 2014 \n",
+ "35 0.000 0 0 0.000 -0.07 2014 \n",
+ "36 0.000 0 0 0.000 -0.10 2014 \n",
+ "37 0.294 0.276 0.57 0.268 -0.11 2014 \n",
+ "38 0.269 0.348 0.617 0.272 -0.15 2014 \n",
+ "39 0.188 0.375 0.563 0.231 -0.18 2014 \n",
+ "40 0.497 0.79 1.287 0.520 11.73 2015 \n",
+ "41 0.373 0.553 0.926 0.390 4.46 2015 \n",
+ "42 0.399 0.404 0.803 0.364 3.52 2015 \n",
+ "43 0.381 0.51 0.891 0.381 2.19 2015 \n",
+ "44 0.364 0.394 0.758 0.340 1.03 2015 \n",
+ "45 0.372 0.54 0.912 0.389 1.00 2015 \n",
+ "46 0.319 0.398 0.717 0.316 0.85 2015 \n",
+ "47 0.351 0.368 0.719 0.322 0.84 2015 \n",
+ "48 0.340 0.439 0.779 0.340 0.76 2015 \n",
+ "49 0.328 0.383 0.711 0.317 0.51 2015 \n",
+ "50 0.375 0.31 0.685 0.326 0.44 2015 \n",
+ "51 0.366 0.412 0.778 0.350 0.30 2015 \n",
+ "52 0.458 0.476 0.934 0.417 0.25 2015 \n",
+ "53 0.307 0.354 0.661 0.294 0.25 2015 \n",
+ "54 0.364 0.591 0.955 0.401 0.18 2015 \n",
+ "55 0.375 0 0.375 0.286 -0.01 2015 \n",
+ "56 0.370 0.227 0.597 0.308 -0.04 2015 \n",
+ "57 0.200 0.4 0.6 0.242 -0.04 2015 \n",
+ "58 0.000 0 0 0.000 -0.06 2015 \n",
+ "59 0.000 0 0 0.000 -0.09 2015 "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "모창민 | \n", + "NC | \n", + "108 | \n", + "436 | \n", + "395 | \n", + "109 | \n", + "12 | \n", + "57 | \n", + "51 | \n", + "37 | \n", + "68 | \n", + "16 | \n", + "0.307 | \n", + "0.276 | \n", + "0.339 | \n", + "0.443 | \n", + "0.782 | \n", + "0.353 | \n", + "2.31 | \n", + "2013 | \n", + "
| 1 | \n", + "이호준 | \n", + "NC | \n", + "126 | \n", + "508 | \n", + "442 | \n", + "123 | \n", + "20 | \n", + "46 | \n", + "87 | \n", + "60 | \n", + "109 | \n", + "2 | \n", + "0.324 | \n", + "0.278 | \n", + "0.362 | \n", + "0.475 | \n", + "0.837 | \n", + "0.373 | \n", + "1.85 | \n", + "2013 | \n", + "
| 2 | \n", + "김종호 | \n", + "NC | \n", + "128 | \n", + "546 | \n", + "465 | \n", + "129 | \n", + "0 | \n", + "72 | \n", + "22 | \n", + "57 | \n", + "100 | \n", + "50 | \n", + "0.352 | \n", + "0.277 | \n", + "0.376 | \n", + "0.333 | \n", + "0.709 | \n", + "0.339 | \n", + "1.55 | \n", + "2013 | \n", + "
| 3 | \n", + "나성범 | \n", + "NC | \n", + "104 | \n", + "458 | \n", + "404 | \n", + "98 | \n", + "14 | \n", + "55 | \n", + "64 | \n", + "33 | \n", + "95 | \n", + "12 | \n", + "0.279 | \n", + "0.243 | \n", + "0.319 | \n", + "0.416 | \n", + "0.735 | \n", + "0.329 | \n", + "1.50 | \n", + "2013 | \n", + "
| 4 | \n", + "조영훈 | \n", + "NC | \n", + "120 | \n", + "426 | \n", + "380 | \n", + "107 | \n", + "6 | \n", + "38 | \n", + "39 | \n", + "39 | \n", + "56 | \n", + "4 | \n", + "0.316 | \n", + "0.282 | \n", + "0.350 | \n", + "0.413 | \n", + "0.763 | \n", + "0.348 | \n", + "0.83 | \n", + "2013 | \n", + "
| 5 | \n", + "이현곤 | \n", + "NC | \n", + "91 | \n", + "161 | \n", + "139 | \n", + "38 | \n", + "0 | \n", + "10 | \n", + "9 | \n", + "16 | \n", + "14 | \n", + "2 | \n", + "0.304 | \n", + "0.273 | \n", + "0.361 | \n", + "0.324 | \n", + "0.685 | \n", + "0.327 | \n", + "0.52 | \n", + "2013 | \n", + "
| 6 | \n", + "이상호 | \n", + "NC | \n", + "102 | \n", + "138 | \n", + "125 | \n", + "31 | \n", + "0 | \n", + "26 | \n", + "13 | \n", + "9 | \n", + "21 | \n", + "24 | \n", + "0.298 | \n", + "0.248 | \n", + "0.299 | \n", + "0.320 | \n", + "0.619 | \n", + "0.289 | \n", + "0.16 | \n", + "2013 | \n", + "
| 7 | \n", + "강진성 | \n", + "NC | \n", + "3 | \n", + "3 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.500 | \n", + "0.500 | \n", + "0.667 | \n", + "1.000 | \n", + "1.667 | \n", + "0.671 | \n", + "0.10 | \n", + "2013 | \n", + "
| 8 | \n", + "조평호 | \n", + "NC | \n", + "26 | \n", + "86 | \n", + "79 | \n", + "21 | \n", + "2 | \n", + "12 | \n", + "7 | \n", + "6 | \n", + "24 | \n", + "1 | \n", + "0.358 | \n", + "0.266 | \n", + "0.318 | \n", + "0.418 | \n", + "0.736 | \n", + "0.329 | \n", + "0.09 | \n", + "2013 | \n", + "
| 9 | \n", + "박민우 | \n", + "NC | \n", + "32 | \n", + "48 | \n", + "42 | \n", + "11 | \n", + "0 | \n", + "10 | \n", + "6 | \n", + "5 | \n", + "7 | \n", + "9 | \n", + "0.306 | \n", + "0.262 | \n", + "0.333 | \n", + "0.286 | \n", + "0.619 | \n", + "0.296 | \n", + "0.07 | \n", + "2013 | \n", + "
| 10 | \n", + "박정준 | \n", + "NC | \n", + "77 | \n", + "195 | \n", + "167 | \n", + "41 | \n", + "4 | \n", + "24 | \n", + "21 | \n", + "23 | \n", + "44 | \n", + "4 | \n", + "0.303 | \n", + "0.246 | \n", + "0.335 | \n", + "0.353 | \n", + "0.688 | \n", + "0.320 | \n", + "0.06 | \n", + "2013 | \n", + "
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.000 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "
| 12 | \n", + "마낙길 | \n", + "NC | \n", + "24 | \n", + "31 | \n", + "29 | \n", + "9 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "1 | \n", + "5 | \n", + "2 | \n", + "0.375 | \n", + "0.310 | \n", + "0.333 | \n", + "0.345 | \n", + "0.678 | \n", + "0.317 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 13 | \n", + "김성욱 | \n", + "NC | \n", + "4 | \n", + "4 | \n", + "4 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.500 | \n", + "0.250 | \n", + "0.250 | \n", + "0.250 | \n", + "0.500 | \n", + "0.234 | \n", + "-0.03 | \n", + "2013 | \n", + "
| 14 | \n", + "강구성 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.06 | \n", + "2013 | \n", + "
| 15 | \n", + "지석훈 | \n", + "NC | \n", + "104 | \n", + "356 | \n", + "309 | \n", + "68 | \n", + "3 | \n", + "25 | \n", + "35 | \n", + "30 | \n", + "82 | \n", + "2 | \n", + "0.285 | \n", + "0.220 | \n", + "0.300 | \n", + "0.330 | \n", + "0.630 | \n", + "0.293 | \n", + "-0.09 | \n", + "2013 | \n", + "
| 16 | \n", + "김종찬 | \n", + "NC | \n", + "5 | \n", + "7 | \n", + "7 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.250 | \n", + "0.143 | \n", + "0.143 | \n", + "0.143 | \n", + "0.286 | \n", + "0.134 | \n", + "-0.11 | \n", + "2013 | \n", + "
| 17 | \n", + "차화준 | \n", + "NC | \n", + "58 | \n", + "121 | \n", + "106 | \n", + "24 | \n", + "1 | \n", + "18 | \n", + "7 | \n", + "12 | \n", + "29 | \n", + "4 | \n", + "0.295 | \n", + "0.226 | \n", + "0.306 | \n", + "0.311 | \n", + "0.617 | \n", + "0.289 | \n", + "-0.12 | \n", + "2013 | \n", + "
| 18 | \n", + "김동건 | \n", + "NC | \n", + "7 | \n", + "17 | \n", + "16 | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "7 | \n", + "0 | \n", + "0.125 | \n", + "0.125 | \n", + "0.176 | \n", + "0.313 | \n", + "0.489 | \n", + "0.216 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 19 | \n", + "이창섭 | \n", + "NC | \n", + "5 | \n", + "5 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "2 | \n", + "0 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "0.000 | \n", + "-0.14 | \n", + "2013 | \n", + "
| 20 | \n", + "테임즈 | \n", + "NC | \n", + "125 | \n", + "514 | \n", + "443 | \n", + "152 | \n", + "37 | \n", + "95 | \n", + "121 | \n", + "58 | \n", + "99 | \n", + "11 | \n", + "0.367 | \n", + "0.343 | \n", + "0.422 | \n", + "0.688 | \n", + "1.11 | \n", + "0.456 | \n", + "6.36 | \n", + "2014 | \n", + "
| 21 | \n", + "나성범 | \n", + "NC | \n", + "123 | \n", + "536 | \n", + "477 | \n", + "157 | \n", + "30 | \n", + "88 | \n", + "101 | \n", + "43 | \n", + "128 | \n", + "14 | \n", + "0.397 | \n", + "0.329 | \n", + "0.400 | \n", + "0.597 | \n", + "0.997 | \n", + "0.424 | \n", + "5.94 | \n", + "2014 | \n", + "
| 22 | \n", + "박민우 | \n", + "NC | \n", + "118 | \n", + "491 | \n", + "416 | \n", + "124 | \n", + "1 | \n", + "87 | \n", + "40 | \n", + "56 | \n", + "89 | \n", + "50 | \n", + "0.373 | \n", + "0.298 | \n", + "0.392 | \n", + "0.399 | \n", + "0.791 | \n", + "0.365 | \n", + "2.73 | \n", + "2014 | \n", + "
| 23 | \n", + "손시헌 | \n", + "NC | \n", + "97 | \n", + "361 | \n", + "307 | \n", + "90 | \n", + "5 | \n", + "39 | \n", + "39 | \n", + "34 | \n", + "53 | \n", + "2 | \n", + "0.331 | \n", + "0.293 | \n", + "0.368 | \n", + "0.414 | \n", + "0.782 | \n", + "0.349 | \n", + "1.52 | \n", + "2014 | \n", + "
| 24 | \n", + "지석훈 | \n", + "NC | \n", + "114 | \n", + "238 | \n", + "212 | \n", + "58 | \n", + "6 | \n", + "26 | \n", + "34 | \n", + "16 | \n", + "46 | \n", + "1 | \n", + "0.323 | \n", + "0.274 | \n", + "0.340 | \n", + "0.462 | \n", + "0.802 | \n", + "0.352 | \n", + "0.97 | \n", + "2014 | \n", + "
| 25 | \n", + "이호준 | \n", + "NC | \n", + "122 | \n", + "500 | \n", + "424 | \n", + "115 | \n", + "23 | \n", + "59 | \n", + "78 | \n", + "67 | \n", + "104 | \n", + "2 | \n", + "0.305 | \n", + "0.271 | \n", + "0.371 | \n", + "0.481 | \n", + "0.852 | \n", + "0.369 | \n", + "0.88 | \n", + "2014 | \n", + "
| 26 | \n", + "권희동 | \n", + "NC | \n", + "101 | \n", + "252 | \n", + "221 | \n", + "63 | \n", + "7 | \n", + "39 | \n", + "36 | \n", + "25 | \n", + "43 | \n", + "6 | \n", + "0.324 | \n", + "0.285 | \n", + "0.363 | \n", + "0.443 | \n", + "0.806 | \n", + "0.353 | \n", + "0.62 | \n", + "2014 | \n", + "
| 27 | \n", + "모창민 | \n", + "NC | \n", + "122 | \n", + "468 | \n", + "419 | \n", + "110 | \n", + "16 | \n", + "62 | \n", + "72 | \n", + "37 | \n", + "82 | \n", + "14 | \n", + "0.289 | \n", + "0.263 | \n", + "0.320 | \n", + "0.413 | \n", + "0.733 | \n", + "0.319 | \n", + "0.17 | \n", + "2014 | \n", + "
| 28 | \n", + "이종욱 | \n", + "NC | \n", + "124 | \n", + "495 | \n", + "438 | \n", + "126 | \n", + "6 | \n", + "73 | \n", + "79 | \n", + "40 | \n", + "60 | \n", + "15 | \n", + "0.313 | \n", + "0.288 | \n", + "0.343 | \n", + "0.411 | \n", + "0.754 | \n", + "0.332 | \n", + "0.13 | \n", + "2014 | \n", + "
| 29 | \n", + "김준완 | \n", + "NC | \n", + "6 | \n", + "5 | \n", + "4 | \n", + "2 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "1 | \n", + "0.667 | \n", + "0.5 | \n", + "0.600 | \n", + "0.5 | \n", + "1.1 | \n", + "0.509 | \n", + "0.11 | \n", + "2014 | \n", + "
| 30 | \n", + "최재원 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 31 | \n", + "마낙길 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.500 | \n", + "0 | \n", + "0.5 | \n", + "0.363 | \n", + "0.01 | \n", + "2014 | \n", + "
| 32 | \n", + "조영훈 | \n", + "NC | \n", + "92 | \n", + "124 | \n", + "111 | \n", + "29 | \n", + "6 | \n", + "15 | \n", + "22 | \n", + "10 | \n", + "26 | \n", + "1 | \n", + "0.288 | \n", + "0.261 | \n", + "0.325 | \n", + "0.459 | \n", + "0.784 | \n", + "0.337 | \n", + "-0.02 | \n", + "2014 | \n", + "
| 33 | \n", + "김태우 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "0 | \n", + "0.500 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.244 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 34 | \n", + "박명환* | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.03 | \n", + "2014 | \n", + "
| 35 | \n", + "조평호 | \n", + "NC | \n", + "2 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.07 | \n", + "2014 | \n", + "
| 36 | \n", + "강민국 | \n", + "NC | \n", + "6 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.10 | \n", + "2014 | \n", + "
| 37 | \n", + "허준 | \n", + "NC | \n", + "28 | \n", + "36 | \n", + "29 | \n", + "5 | \n", + "0 | \n", + "2 | \n", + "1 | \n", + "5 | \n", + "10 | \n", + "0 | \n", + "0.263 | \n", + "0.172 | \n", + "0.294 | \n", + "0.276 | \n", + "0.57 | \n", + "0.268 | \n", + "-0.11 | \n", + "2014 | \n", + "
| 38 | \n", + "김성욱 | \n", + "NC | \n", + "26 | \n", + "26 | \n", + "23 | \n", + "4 | \n", + "1 | \n", + "6 | \n", + "1 | \n", + "3 | \n", + "9 | \n", + "1 | \n", + "0.231 | \n", + "0.174 | \n", + "0.269 | \n", + "0.348 | \n", + "0.617 | \n", + "0.272 | \n", + "-0.15 | \n", + "2014 | \n", + "
| 39 | \n", + "노진혁 | \n", + "NC | \n", + "25 | \n", + "16 | \n", + "16 | \n", + "3 | \n", + "1 | \n", + "3 | \n", + "2 | \n", + "0 | \n", + "7 | \n", + "0 | \n", + "0.250 | \n", + "0.188 | \n", + "0.188 | \n", + "0.375 | \n", + "0.563 | \n", + "0.231 | \n", + "-0.18 | \n", + "2014 | \n", + "
| 40 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.39 | \n", + "0.381 | \n", + "0.497 | \n", + "0.79 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
| 41 | \n", + "나성범 | \n", + "NC | \n", + "144 | \n", + "622 | \n", + "564 | \n", + "184 | \n", + "28 | \n", + "112 | \n", + "135 | \n", + "33 | \n", + "127 | \n", + "23 | \n", + "0.374 | \n", + "0.326 | \n", + "0.373 | \n", + "0.553 | \n", + "0.926 | \n", + "0.390 | \n", + "4.46 | \n", + "2015 | \n", + "
| 42 | \n", + "박민우 | \n", + "NC | \n", + "141 | \n", + "617 | \n", + "520 | \n", + "158 | \n", + "3 | \n", + "111 | \n", + "47 | \n", + "73 | \n", + "108 | \n", + "46 | \n", + "0.373 | \n", + "0.304 | \n", + "0.399 | \n", + "0.404 | \n", + "0.803 | \n", + "0.364 | \n", + "3.52 | \n", + "2015 | \n", + "
| 43 | \n", + "이호준 | \n", + "NC | \n", + "131 | \n", + "518 | \n", + "449 | \n", + "132 | \n", + "24 | \n", + "48 | \n", + "110 | \n", + "61 | \n", + "102 | \n", + "0 | \n", + "0.332 | \n", + "0.294 | \n", + "0.381 | \n", + "0.51 | \n", + "0.891 | \n", + "0.381 | \n", + "2.19 | \n", + "2015 | \n", + "
| 44 | \n", + "김종호 | \n", + "NC | \n", + "133 | \n", + "486 | \n", + "424 | \n", + "125 | \n", + "4 | \n", + "90 | \n", + "36 | \n", + "44 | \n", + "65 | \n", + "41 | \n", + "0.338 | \n", + "0.295 | \n", + "0.364 | \n", + "0.394 | \n", + "0.758 | \n", + "0.340 | \n", + "1.03 | \n", + "2015 | \n", + "
| 45 | \n", + "조영훈 | \n", + "NC | \n", + "103 | \n", + "146 | \n", + "124 | \n", + "35 | \n", + "8 | \n", + "20 | \n", + "35 | \n", + "19 | \n", + "36 | \n", + "3 | \n", + "0.329 | \n", + "0.282 | \n", + "0.372 | \n", + "0.54 | \n", + "0.912 | \n", + "0.389 | \n", + "1.00 | \n", + "2015 | \n", + "
| 46 | \n", + "손시헌 | \n", + "NC | \n", + "140 | \n", + "508 | \n", + "440 | \n", + "108 | \n", + "13 | \n", + "56 | \n", + "58 | \n", + "36 | \n", + "66 | \n", + "3 | \n", + "0.26 | \n", + "0.245 | \n", + "0.319 | \n", + "0.398 | \n", + "0.717 | \n", + "0.316 | \n", + "0.85 | \n", + "2015 | \n", + "
| 47 | \n", + "이종욱 | \n", + "NC | \n", + "125 | \n", + "504 | \n", + "440 | \n", + "118 | \n", + "5 | \n", + "63 | \n", + "52 | \n", + "54 | \n", + "78 | \n", + "17 | \n", + "0.315 | \n", + "0.268 | \n", + "0.351 | \n", + "0.368 | \n", + "0.719 | \n", + "0.322 | \n", + "0.84 | \n", + "2015 | \n", + "
| 48 | \n", + "모창민 | \n", + "NC | \n", + "103 | \n", + "239 | \n", + "214 | \n", + "62 | \n", + "6 | \n", + "23 | \n", + "35 | \n", + "18 | \n", + "52 | \n", + "5 | \n", + "0.352 | \n", + "0.29 | \n", + "0.340 | \n", + "0.439 | \n", + "0.779 | \n", + "0.340 | \n", + "0.76 | \n", + "2015 | \n", + "
| 49 | \n", + "지석훈 | \n", + "NC | \n", + "137 | \n", + "466 | \n", + "415 | \n", + "111 | \n", + "11 | \n", + "53 | \n", + "46 | \n", + "23 | \n", + "85 | \n", + "4 | \n", + "0.311 | \n", + "0.267 | \n", + "0.328 | \n", + "0.383 | \n", + "0.711 | \n", + "0.317 | \n", + "0.51 | \n", + "2015 | \n", + "
| 50 | \n", + "용덕한 | \n", + "NC | \n", + "50 | \n", + "49 | \n", + "42 | \n", + "12 | \n", + "0 | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "8 | \n", + "2 | \n", + "0.353 | \n", + "0.286 | \n", + "0.375 | \n", + "0.31 | \n", + "0.685 | \n", + "0.326 | \n", + "0.44 | \n", + "2015 | \n", + "
| 51 | \n", + "최재원 | \n", + "NC | \n", + "114 | \n", + "102 | \n", + "85 | \n", + "21 | \n", + "2 | \n", + "31 | \n", + "13 | \n", + "10 | \n", + "29 | \n", + "14 | \n", + "0.352 | \n", + "0.247 | \n", + "0.366 | \n", + "0.412 | \n", + "0.778 | \n", + "0.350 | \n", + "0.30 | \n", + "2015 | \n", + "
| 52 | \n", + "박정준 | \n", + "NC | \n", + "19 | \n", + "24 | \n", + "21 | \n", + "8 | \n", + "0 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "5 | \n", + "0 | \n", + "0.5 | \n", + "0.381 | \n", + "0.458 | \n", + "0.476 | \n", + "0.934 | \n", + "0.417 | \n", + "0.25 | \n", + "2015 | \n", + "
| 53 | \n", + "김태군 | \n", + "NC | \n", + "144 | \n", + "474 | \n", + "421 | \n", + "107 | \n", + "6 | \n", + "45 | \n", + "52 | \n", + "25 | \n", + "77 | \n", + "1 | \n", + "0.293 | \n", + "0.254 | \n", + "0.307 | \n", + "0.354 | \n", + "0.661 | \n", + "0.294 | \n", + "0.25 | \n", + "2015 | \n", + "
| 54 | \n", + "조평호 | \n", + "NC | \n", + "22 | \n", + "22 | \n", + "22 | \n", + "8 | \n", + "1 | \n", + "4 | \n", + "3 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "0.467 | \n", + "0.364 | \n", + "0.364 | \n", + "0.591 | \n", + "0.955 | \n", + "0.401 | \n", + "0.18 | \n", + "2015 | \n", + "
| 55 | \n", + "이창섭 | \n", + "NC | \n", + "15 | \n", + "8 | \n", + "5 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.375 | \n", + "0 | \n", + "0.375 | \n", + "0.286 | \n", + "-0.01 | \n", + "2015 | \n", + "
| 56 | \n", + "김준완 | \n", + "NC | \n", + "30 | \n", + "54 | \n", + "44 | \n", + "10 | \n", + "0 | \n", + "12 | \n", + "2 | \n", + "10 | \n", + "10 | \n", + "2 | \n", + "0.294 | \n", + "0.227 | \n", + "0.370 | \n", + "0.227 | \n", + "0.597 | \n", + "0.308 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 57 | \n", + "강구성 | \n", + "NC | \n", + "4 | \n", + "5 | \n", + "5 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.2 | \n", + "0.2 | \n", + "0.200 | \n", + "0.4 | \n", + "0.6 | \n", + "0.242 | \n", + "-0.04 | \n", + "2015 | \n", + "
| 58 | \n", + "강민국 | \n", + "NC | \n", + "1 | \n", + "2 | \n", + "2 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.06 | \n", + "2015 | \n", + "
| 59 | \n", + "김태진 | \n", + "NC | \n", + "1 | \n", + "3 | \n", + "3 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "0 | \n", + "0 | \n", + "0.000 | \n", + "-0.09 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 안타\n",
+ "선수명 \n",
+ "테임즈 166.000000\n",
+ "나성범 146.333333\n",
+ "김종호 127.000000\n",
+ "이호준 123.333333\n",
+ "이종욱 122.000000\n",
+ "김태군 107.000000\n",
+ "손시헌 99.000000\n",
+ "박민우 97.666667\n",
+ "모창민 93.666667\n",
+ "지석훈 79.000000"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 연도별 평균 안타수 상위 10명\n",
+ "상위10명 = NC.pivot_table(index='선수명', values= '안타', aggfunc = 'mean')\\\n",
+ ".sort_values(by='안타', ascending = False).head(10)\n",
+ "상위10명"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 안타 | \n", + "
|---|---|
| 선수명 | \n", + "\n", + " |
| 테임즈 | \n", + "166.000000 | \n", + "
| 나성범 | \n", + "146.333333 | \n", + "
| 김종호 | \n", + "127.000000 | \n", + "
| 이호준 | \n", + "123.333333 | \n", + "
| 이종욱 | \n", + "122.000000 | \n", + "
| 김태군 | \n", + "107.000000 | \n", + "
| 손시헌 | \n", + "99.000000 | \n", + "
| 박민우 | \n", + "97.666667 | \n", + "
| 모창민 | \n", + "93.666667 | \n", + "
| 지석훈 | \n", + "79.000000 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 \\\n",
+ "20 테임즈 NC 125 514 443 152 37 95 121 58 99 11 0.367 0.343 \n",
+ "40 테임즈 NC 142 595 472 180 47 130 140 103 91 40 0.39 0.381 \n",
+ "\n",
+ " 출루율 장타율 OPS wOBA WAR 년도 \n",
+ "20 0.422 0.688 1.11 0.456 6.36 2014 \n",
+ "40 0.497 0.79 1.287 0.520 11.73 2015 "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC[NC.선수명 =='테임즈']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. (실습) 3에서 구한 상위 10명의 타자들의 3년간 안타, 홈런, 타율을 출력하기"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#1. 출력하고자 하는 모양대로 데이터프레임 변경\n",
+ "#1-2) 상위 10명 조건 추가"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | \n", + "테임즈 | \n", + "NC | \n", + "125 | \n", + "514 | \n", + "443 | \n", + "152 | \n", + "37 | \n", + "95 | \n", + "121 | \n", + "58 | \n", + "99 | \n", + "11 | \n", + "0.367 | \n", + "0.343 | \n", + "0.422 | \n", + "0.688 | \n", + "1.11 | \n", + "0.456 | \n", + "6.36 | \n", + "2014 | \n", + "
| 40 | \n", + "테임즈 | \n", + "NC | \n", + "142 | \n", + "595 | \n", + "472 | \n", + "180 | \n", + "47 | \n", + "130 | \n", + "140 | \n", + "103 | \n", + "91 | \n", + "40 | \n", + "0.39 | \n", + "0.381 | \n", + "0.497 | \n", + "0.79 | \n", + "1.287 | \n", + "0.520 | \n", + "11.73 | \n", + "2015 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 홈런 안타 타율 \n",
+ "년도 2013 2014 2015 2013 2014 2015 2013 2014 2015\n",
+ "선수명 \n",
+ "강구성 0 - 0 0 - 1 0.000 - 0.2\n",
+ "강민국 - 0 0 - 0 0 - 0 0\n",
+ "강진성 0 - - 1 - - 0.500 - -\n",
+ "권희동 - 7 - - 63 - - 0.285 -\n",
+ "김동건 1 - - 2 - - 0.125 - -\n",
+ "김성욱 0 1 - 1 4 - 0.250 0.174 -\n",
+ "김종찬 0 - - 1 - - 0.143 - -\n",
+ "김종호 0 - 4 129 - 125 0.277 - 0.295\n",
+ "김준완 - 0 0 - 2 10 - 0.5 0.227\n",
+ "김태군 - - 6 - - 107 - - 0.254\n",
+ "김태우 - 0 - - 1 - - 0.2 -\n",
+ "김태진 - - 0 - - 0 - - 0\n",
+ "나성범 14 30 28 98 157 184 0.243 0.329 0.326\n",
+ "노진혁 - 1 - - 3 - - 0.188 -\n",
+ "마낙길 0 0 - 9 0 - 0.310 0 -\n",
+ "모창민 12 16 6 109 110 62 0.276 0.263 0.29\n",
+ "박명환* - 0 - - 0 - - 0 -\n",
+ "박민우 0 1 3 11 124 158 0.262 0.298 0.304\n",
+ "박정준 4 - 0 41 - 8 0.246 - 0.381\n",
+ "손시헌 - 5 13 - 90 108 - 0.293 0.245\n",
+ "용덕한 - - 0 - - 12 - - 0.286\n",
+ "이상호 0 - - 31 - - 0.248 - -\n",
+ "이승호 0 - - 0 - - - - -\n",
+ "이종욱 - 6 5 - 126 118 - 0.288 0.268\n",
+ "이창섭 0 - 0 0 - 0 0.000 - 0\n",
+ "이현곤 0 - - 38 - - 0.273 - -\n",
+ "이호준 20 23 24 123 115 132 0.278 0.271 0.294\n",
+ "조영훈 6 6 8 107 29 35 0.282 0.261 0.282\n",
+ "조평호 2 0 1 21 0 8 0.266 0 0.364\n",
+ "지석훈 3 6 11 68 58 111 0.220 0.274 0.267\n",
+ "차화준 1 - - 24 - - 0.226 - -\n",
+ "최재원 - 0 2 - 0 21 - 0 0.247\n",
+ "테임즈 - 37 47 - 152 180 - 0.343 0.381\n",
+ "허준 - 0 - - 5 - - 0.172 -"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1-1.\n",
+ "data4 = NC[['선수명','년도','홈런','안타','타율']]\n",
+ "data4 = data4.set_index(['선수명','년도'])\n",
+ "data4 = data4.unstack(1)\n",
+ "data4 = data4.fillna('-')\n",
+ "data4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 홈런 | \n", + "안타 | \n", + "타율 | \n", + "||||||
|---|---|---|---|---|---|---|---|---|---|
| 년도 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 강구성 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "- | \n", + "1 | \n", + "0.000 | \n", + "- | \n", + "0.2 | \n", + "
| 강민국 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "
| 강진성 | \n", + "0 | \n", + "- | \n", + "- | \n", + "1 | \n", + "- | \n", + "- | \n", + "0.500 | \n", + "- | \n", + "- | \n", + "
| 권희동 | \n", + "- | \n", + "7 | \n", + "- | \n", + "- | \n", + "63 | \n", + "- | \n", + "- | \n", + "0.285 | \n", + "- | \n", + "
| 김동건 | \n", + "1 | \n", + "- | \n", + "- | \n", + "2 | \n", + "- | \n", + "- | \n", + "0.125 | \n", + "- | \n", + "- | \n", + "
| 김성욱 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "1 | \n", + "4 | \n", + "- | \n", + "0.250 | \n", + "0.174 | \n", + "- | \n", + "
| 김종찬 | \n", + "0 | \n", + "- | \n", + "- | \n", + "1 | \n", + "- | \n", + "- | \n", + "0.143 | \n", + "- | \n", + "- | \n", + "
| 김종호 | \n", + "0 | \n", + "- | \n", + "4 | \n", + "129 | \n", + "- | \n", + "125 | \n", + "0.277 | \n", + "- | \n", + "0.295 | \n", + "
| 김준완 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "- | \n", + "2 | \n", + "10 | \n", + "- | \n", + "0.5 | \n", + "0.227 | \n", + "
| 김태군 | \n", + "- | \n", + "- | \n", + "6 | \n", + "- | \n", + "- | \n", + "107 | \n", + "- | \n", + "- | \n", + "0.254 | \n", + "
| 김태우 | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "1 | \n", + "- | \n", + "- | \n", + "0.2 | \n", + "- | \n", + "
| 김태진 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "0 | \n", + "
| 나성범 | \n", + "14 | \n", + "30 | \n", + "28 | \n", + "98 | \n", + "157 | \n", + "184 | \n", + "0.243 | \n", + "0.329 | \n", + "0.326 | \n", + "
| 노진혁 | \n", + "- | \n", + "1 | \n", + "- | \n", + "- | \n", + "3 | \n", + "- | \n", + "- | \n", + "0.188 | \n", + "- | \n", + "
| 마낙길 | \n", + "0 | \n", + "0 | \n", + "- | \n", + "9 | \n", + "0 | \n", + "- | \n", + "0.310 | \n", + "0 | \n", + "- | \n", + "
| 모창민 | \n", + "12 | \n", + "16 | \n", + "6 | \n", + "109 | \n", + "110 | \n", + "62 | \n", + "0.276 | \n", + "0.263 | \n", + "0.29 | \n", + "
| 박명환* | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "
| 박민우 | \n", + "0 | \n", + "1 | \n", + "3 | \n", + "11 | \n", + "124 | \n", + "158 | \n", + "0.262 | \n", + "0.298 | \n", + "0.304 | \n", + "
| 박정준 | \n", + "4 | \n", + "- | \n", + "0 | \n", + "41 | \n", + "- | \n", + "8 | \n", + "0.246 | \n", + "- | \n", + "0.381 | \n", + "
| 손시헌 | \n", + "- | \n", + "5 | \n", + "13 | \n", + "- | \n", + "90 | \n", + "108 | \n", + "- | \n", + "0.293 | \n", + "0.245 | \n", + "
| 용덕한 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "12 | \n", + "- | \n", + "- | \n", + "0.286 | \n", + "
| 이상호 | \n", + "0 | \n", + "- | \n", + "- | \n", + "31 | \n", + "- | \n", + "- | \n", + "0.248 | \n", + "- | \n", + "- | \n", + "
| 이승호 | \n", + "0 | \n", + "- | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "- | \n", + "- | \n", + "- | \n", + "
| 이종욱 | \n", + "- | \n", + "6 | \n", + "5 | \n", + "- | \n", + "126 | \n", + "118 | \n", + "- | \n", + "0.288 | \n", + "0.268 | \n", + "
| 이창섭 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0 | \n", + "- | \n", + "0 | \n", + "0.000 | \n", + "- | \n", + "0 | \n", + "
| 이현곤 | \n", + "0 | \n", + "- | \n", + "- | \n", + "38 | \n", + "- | \n", + "- | \n", + "0.273 | \n", + "- | \n", + "- | \n", + "
| 이호준 | \n", + "20 | \n", + "23 | \n", + "24 | \n", + "123 | \n", + "115 | \n", + "132 | \n", + "0.278 | \n", + "0.271 | \n", + "0.294 | \n", + "
| 조영훈 | \n", + "6 | \n", + "6 | \n", + "8 | \n", + "107 | \n", + "29 | \n", + "35 | \n", + "0.282 | \n", + "0.261 | \n", + "0.282 | \n", + "
| 조평호 | \n", + "2 | \n", + "0 | \n", + "1 | \n", + "21 | \n", + "0 | \n", + "8 | \n", + "0.266 | \n", + "0 | \n", + "0.364 | \n", + "
| 지석훈 | \n", + "3 | \n", + "6 | \n", + "11 | \n", + "68 | \n", + "58 | \n", + "111 | \n", + "0.220 | \n", + "0.274 | \n", + "0.267 | \n", + "
| 차화준 | \n", + "1 | \n", + "- | \n", + "- | \n", + "24 | \n", + "- | \n", + "- | \n", + "0.226 | \n", + "- | \n", + "- | \n", + "
| 최재원 | \n", + "- | \n", + "0 | \n", + "2 | \n", + "- | \n", + "0 | \n", + "21 | \n", + "- | \n", + "0 | \n", + "0.247 | \n", + "
| 테임즈 | \n", + "- | \n", + "37 | \n", + "47 | \n", + "- | \n", + "152 | \n", + "180 | \n", + "- | \n", + "0.343 | \n", + "0.381 | \n", + "
| 허준 | \n", + "- | \n", + "0 | \n", + "- | \n", + "- | \n", + "5 | \n", + "- | \n", + "- | \n", + "0.172 | \n", + "- | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 홈런 안타 타율 \n",
+ "년도 2013 2014 2015 2013 2014 2015 2013 2014 2015\n",
+ "선수명 \n",
+ "테임즈 - 37 47 - 152 180 - 0.343 0.381\n",
+ "나성범 14 30 28 98 157 184 0.243 0.329 0.326\n",
+ "김종호 0 - 4 129 - 125 0.277 - 0.295\n",
+ "이호준 20 23 24 123 115 132 0.278 0.271 0.294\n",
+ "이종욱 - 6 5 - 126 118 - 0.288 0.268\n",
+ "김태군 - - 6 - - 107 - - 0.254\n",
+ "손시헌 - 5 13 - 90 108 - 0.293 0.245\n",
+ "박민우 0 1 3 11 124 158 0.262 0.298 0.304\n",
+ "모창민 12 16 6 109 110 62 0.276 0.263 0.29\n",
+ "지석훈 3 6 11 68 58 111 0.220 0.274 0.267"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1-2\n",
+ "data4.loc[상위10명.index]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2. NC에서 상위10명 정보만 추출한 후에 원하는 형태로 가공"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 홈런 | \n", + "안타 | \n", + "타율 | \n", + "||||||
|---|---|---|---|---|---|---|---|---|---|
| 년도 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 테임즈 | \n", + "- | \n", + "37 | \n", + "47 | \n", + "- | \n", + "152 | \n", + "180 | \n", + "- | \n", + "0.343 | \n", + "0.381 | \n", + "
| 나성범 | \n", + "14 | \n", + "30 | \n", + "28 | \n", + "98 | \n", + "157 | \n", + "184 | \n", + "0.243 | \n", + "0.329 | \n", + "0.326 | \n", + "
| 김종호 | \n", + "0 | \n", + "- | \n", + "4 | \n", + "129 | \n", + "- | \n", + "125 | \n", + "0.277 | \n", + "- | \n", + "0.295 | \n", + "
| 이호준 | \n", + "20 | \n", + "23 | \n", + "24 | \n", + "123 | \n", + "115 | \n", + "132 | \n", + "0.278 | \n", + "0.271 | \n", + "0.294 | \n", + "
| 이종욱 | \n", + "- | \n", + "6 | \n", + "5 | \n", + "- | \n", + "126 | \n", + "118 | \n", + "- | \n", + "0.288 | \n", + "0.268 | \n", + "
| 김태군 | \n", + "- | \n", + "- | \n", + "6 | \n", + "- | \n", + "- | \n", + "107 | \n", + "- | \n", + "- | \n", + "0.254 | \n", + "
| 손시헌 | \n", + "- | \n", + "5 | \n", + "13 | \n", + "- | \n", + "90 | \n", + "108 | \n", + "- | \n", + "0.293 | \n", + "0.245 | \n", + "
| 박민우 | \n", + "0 | \n", + "1 | \n", + "3 | \n", + "11 | \n", + "124 | \n", + "158 | \n", + "0.262 | \n", + "0.298 | \n", + "0.304 | \n", + "
| 모창민 | \n", + "12 | \n", + "16 | \n", + "6 | \n", + "109 | \n", + "110 | \n", + "62 | \n", + "0.276 | \n", + "0.263 | \n", + "0.29 | \n", + "
| 지석훈 | \n", + "3 | \n", + "6 | \n", + "11 | \n", + "68 | \n", + "58 | \n", + "111 | \n", + "0.220 | \n", + "0.274 | \n", + "0.267 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 홈런 안타 타율 \n",
+ "년도 2013 2014 2015 2013 2014 2015 2013 2014 2015\n",
+ "선수명 \n",
+ "김종호 0 - 4.0 129 - 125.0 0.277 - 0.295\n",
+ "김태군 - - 6.0 - - 107.0 - - 0.254\n",
+ "나성범 14 30 28.0 98 157 184.0 0.243 0.329 0.326\n",
+ "모창민 12 16 6.0 109 110 62.0 0.276 0.263 0.290\n",
+ "박민우 0 1 3.0 11 124 158.0 0.262 0.298 0.304\n",
+ "손시헌 - 5 13.0 - 90 108.0 - 0.293 0.245\n",
+ "이종욱 - 6 5.0 - 126 118.0 - 0.288 0.268\n",
+ "이호준 20 23 24.0 123 115 132.0 0.278 0.271 0.294\n",
+ "지석훈 3 6 11.0 68 58 111.0 0.220 0.274 0.267\n",
+ "테임즈 - 37 47.0 - 152 180.0 - 0.343 0.381"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "상위10명기록 = NC[NC.선수명.isin(상위10명.index)]\n",
+ "상위10명기록 = 상위10명기록[['선수명','년도','홈런','안타','타율']]\n",
+ "상위10명기록.set_index(['선수명','년도']).unstack(1).fillna('-')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "상위data = pd.merge(상위10명, NC, left_on = '선수명', right_on = '선수명')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "상위data = 상위data[['선수명','안타_y','홈런','타율']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(상위data, \n",
+ " columns=[['안타','안타','안타','홈런','홈런','홈런','타율','타율','타율'],[2013,2014,2015,2013,2014,2015,2013,2014,2015]], index = 상위data['선수명'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 홈런 | \n", + "안타 | \n", + "타율 | \n", + "||||||
|---|---|---|---|---|---|---|---|---|---|
| 년도 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 김종호 | \n", + "0 | \n", + "- | \n", + "4.0 | \n", + "129 | \n", + "- | \n", + "125.0 | \n", + "0.277 | \n", + "- | \n", + "0.295 | \n", + "
| 김태군 | \n", + "- | \n", + "- | \n", + "6.0 | \n", + "- | \n", + "- | \n", + "107.0 | \n", + "- | \n", + "- | \n", + "0.254 | \n", + "
| 나성범 | \n", + "14 | \n", + "30 | \n", + "28.0 | \n", + "98 | \n", + "157 | \n", + "184.0 | \n", + "0.243 | \n", + "0.329 | \n", + "0.326 | \n", + "
| 모창민 | \n", + "12 | \n", + "16 | \n", + "6.0 | \n", + "109 | \n", + "110 | \n", + "62.0 | \n", + "0.276 | \n", + "0.263 | \n", + "0.290 | \n", + "
| 박민우 | \n", + "0 | \n", + "1 | \n", + "3.0 | \n", + "11 | \n", + "124 | \n", + "158.0 | \n", + "0.262 | \n", + "0.298 | \n", + "0.304 | \n", + "
| 손시헌 | \n", + "- | \n", + "5 | \n", + "13.0 | \n", + "- | \n", + "90 | \n", + "108.0 | \n", + "- | \n", + "0.293 | \n", + "0.245 | \n", + "
| 이종욱 | \n", + "- | \n", + "6 | \n", + "5.0 | \n", + "- | \n", + "126 | \n", + "118.0 | \n", + "- | \n", + "0.288 | \n", + "0.268 | \n", + "
| 이호준 | \n", + "20 | \n", + "23 | \n", + "24.0 | \n", + "123 | \n", + "115 | \n", + "132.0 | \n", + "0.278 | \n", + "0.271 | \n", + "0.294 | \n", + "
| 지석훈 | \n", + "3 | \n", + "6 | \n", + "11.0 | \n", + "68 | \n", + "58 | \n", + "111.0 | \n", + "0.220 | \n", + "0.274 | \n", + "0.267 | \n", + "
| 테임즈 | \n", + "- | \n", + "37 | \n", + "47.0 | \n", + "- | \n", + "152 | \n", + "180.0 | \n", + "- | \n", + "0.343 | \n", + "0.381 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 안타 홈런 타율 \n",
+ " 2013 2014 2015 2013 2014 2015 2013 2014 2015\n",
+ "선수명 \n",
+ "테임즈 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "테임즈 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "나성범 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "나성범 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "나성범 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "김종호 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "김종호 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "이호준 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "이호준 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "이호준 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "이종욱 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "이종욱 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "김태군 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "손시헌 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "손시헌 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "박민우 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "박민우 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "박민우 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "모창민 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "모창민 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "모창민 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "지석훈 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "지석훈 NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
+ "지석훈 NaN NaN NaN NaN NaN NaN NaN NaN NaN"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. (실습) 년도별 팀의 기록 (총 안타수, 총 홈런수, 총 도루수)을 출력"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 안타 | \n", + "홈런 | \n", + "타율 | \n", + "||||||
|---|---|---|---|---|---|---|---|---|---|
| \n", + " | 2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "2013 | \n", + "2014 | \n", + "2015 | \n", + "
| 선수명 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 테임즈 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 테임즈 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 나성범 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 나성범 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 나성범 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 김종호 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 김종호 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 이호준 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 이종욱 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 이종욱 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 김태군 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 손시헌 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 손시헌 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 박민우 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 박민우 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 박민우 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 모창민 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 모창민 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 모창민 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 지석훈 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 지석훈 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
| 지석훈 | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 도루 안타 홈런\n",
+ "년도 \n",
+ "2013 133 814 63\n",
+ "2014 118 1039 139\n",
+ "2015 201 1380 158"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#총 합계\n",
+ "NC.pivot_table(index = '년도', values = ['도루','안타','홈런'], aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## (심화) 조건 추가. 팀 타율도 함께 출력 \n",
+ "### 팀 타율은 선수 타율의 평균으로 가정"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 도루 | \n", + "안타 | \n", + "홈런 | \n", + "
|---|---|---|---|
| 년도 | \n", + "\n", + " | \n", + " | \n", + " |
| 2013 | \n", + "133 | \n", + "814 | \n", + "63 | \n", + "
| 2014 | \n", + "118 | \n", + "1039 | \n", + "139 | \n", + "
| 2015 | \n", + "201 | \n", + "1380 | \n", + "158 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 선수명 팀명 경기 타석 타수 안타 홈런 득점 타점 볼넷 삼진 도루 BABIP 타율 출루율 장타율 OPS \\\n",
+ "11 이승호 NC 2 1 0 0 0 0 0 1 0 1 - - 1.0 - - \n",
+ "\n",
+ " wOBA WAR 년도 \n",
+ "11 0.712 0.06 2013 "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC[NC.타율 =='-']\n",
+ "# 1. 타율이 없는 선수는 기록이 없다고 보고, 통계에서 제외하는 건 다른 기록이 유실되므로 이 방법은 안된다\n",
+ "# 2. '-'를 NaN으로 변경한 후에 '타율'을 실수형[float]로 변환 ->OK"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC.타율 = NC.타율.replace('-',np.nan)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(NC.타율[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "NC.타율 = NC.타율.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 선수명 | \n", + "팀명 | \n", + "경기 | \n", + "타석 | \n", + "타수 | \n", + "안타 | \n", + "홈런 | \n", + "득점 | \n", + "타점 | \n", + "볼넷 | \n", + "삼진 | \n", + "도루 | \n", + "BABIP | \n", + "타율 | \n", + "출루율 | \n", + "장타율 | \n", + "OPS | \n", + "wOBA | \n", + "WAR | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11 | \n", + "이승호 | \n", + "NC | \n", + "2 | \n", + "1 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "0 | \n", + "1 | \n", + "0 | \n", + "1 | \n", + "- | \n", + "- | \n", + "1.0 | \n", + "- | \n", + "- | \n", + "0.712 | \n", + "0.06 | \n", + "2013 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 도루 안타 타율 홈런\n",
+ "년도 \n",
+ "2013 133 814 0.232895 63\n",
+ "2014 118 1039 0.206950 139\n",
+ "2015 201 1380 0.245550 158"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "NC.pivot_table(index='년도', \n",
+ " values = ['안타','홈런','도루', '타율'], \n",
+ " aggfunc={'안타':'sum','홈런':'sum','도루':'sum','타율':'mean'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 3. \354\231\225\354\242\214\354\235\230 \352\262\214\354\236\204 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 (+ \354\213\234\352\260\201\355\231\224).ipynb" "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 3. \354\231\225\354\242\214\354\235\230 \352\262\214\354\236\204 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 (+ \354\213\234\352\260\201\355\231\224).ipynb"
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@@ -0,0 +1,1177 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from pandas import Series, DataFrame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 왕좌의 게임 데이터 분석 (+ 시각화)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Matplotlib 소개\n",
+ "### - 파이썬에서 자료를 차트(chart)나 플롯(plot)으로 시각화(visulaization)하는 패키지\n",
+ "### - Matplotlib는 다음과 같은 정형화된 차트나 플롯 이외에도 저수준 api를 사용한 다양한 시각화 기능 제공\n",
+ "#### * 라인 플롯(line plot)\n",
+ "#### * 스캐터 플롯(scatter plot)\n",
+ "#### * 컨투어 플롯(contour plot)\n",
+ "#### * 서피스 플롯(surface plot)\n",
+ "#### * 바 차트(bar chart)\n",
+ "#### * 히스토그램(histogram)\n",
+ "#### * 박스 플롯(box plot)\n",
+ "\n",
+ "## [Matplotlib 갤러리 웹사이트]\n",
+ "http://matplotlib.org/gallery.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 도루 | \n", + "안타 | \n", + "타율 | \n", + "홈런 | \n", + "
|---|---|---|---|---|
| 년도 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " |
| 2013 | \n", + "133 | \n", + "814 | \n", + "0.232895 | \n", + "63 | \n", + "
| 2014 | \n", + "118 | \n", + "1039 | \n", + "0.206950 | \n", + "139 | \n", + "
| 2015 | \n", + "201 | \n", + "1380 | \n", + "0.245550 | \n", + "158 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 서울 경기 충청 경상 전라\n",
+ "2015 1053.5 1023 512 897 451\n",
+ "2016 1023.0 1067 489 872 421\n",
+ "2017 987.0 1123 487 811 399"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "인구통계 = {'서울': [1053.5, 1023, 987],\n",
+ " '경기':[1023, 1067, 1123],\n",
+ " '충청':[512,489,487],\n",
+ " '경상':[897, 872, 811],\n",
+ " '전라':[451, 421, 399]\n",
+ " }\n",
+ "df = DataFrame(인구통계)\n",
+ "df.index = [2015, 2016, 2017]\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | 서울 | \n", + "경기 | \n", + "충청 | \n", + "경상 | \n", + "전라 | \n", + "
|---|---|---|---|---|---|
| 2015 | \n", + "1053.5 | \n", + "1023 | \n", + "512 | \n", + "897 | \n", + "451 | \n", + "
| 2016 | \n", + "1023.0 | \n", + "1067 | \n", + "489 | \n", + "872 | \n", + "421 | \n", + "
| 2017 | \n", + "987.0 | \n", + "1123 | \n", + "487 | \n", + "811 | \n", + "399 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 기본 그리기\n",
+ "# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n",
+ "df.plot(kind='bar')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'plt' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mlw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m ms = 10)\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'년도'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'인구수'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(\n",
+ " kind = 'line', title = '인구추이',\n",
+ " figsize=(10, 5),\n",
+ " xticks = df.index,\n",
+ " ylim = [200,1300],\n",
+ " style = ['g--o', 'b-d','m-.^','r--<'],\n",
+ " lw = 3,\n",
+ " ms = 10)\n",
+ "plt.xlabel('년도')\n",
+ "plt.ylabel('인구수')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"](\"img/matplotlib인자.jpg\")
\n",
+ "\n",
+ "#### 색상: https://matplotlib.org/examples/color/named_colors.html\n",
+ "#### 기타 스타일: https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 실습. 아래 그림처럼 연도별 지역별 인구수 그래프 그리기\n",
+ "df.T.plot(kind='bar', subplots = True, figsize=(8,10))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"그림의구조\"](\"img/그림의구조.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# matplotlib 기반 최신 시각화 라이브러리 소개\n",
+ "1. seaborn\n",
+ " - matplotlib을 기반으로 만들어진 시각화 라이브러리\n",
+ " - 디자인적으로 훨씬 세련됨. \n",
+ " - matplotlib와 사용방식이 유사하므로 쉽고 빠르게 습득할 수 있음.\n",
+ " - https://seaborn.pydata.org/\n",
+ "\n",
+ "2. bokeh\n",
+ " - 웹브라우저 상에서의 시각화에 효과적인 파이썬 인터랙티브 시각화 라이브러리\n",
+ " - 플롯을 html 파일로 export하여 이를 웹브라우저를 통해 확인할 수 있음.\n",
+ " - matplotlib와 비슷, jupyter와 호환이 잘 됨. \n",
+ " - https://bokeh.pydata.org/en/latest/\n",
+ "\n",
+ "3. Folium \n",
+ " - 지리적 데이터 시각화에 특화된 라이브러리 (leaflet.js 기반)\n",
+ " - 웹브라우저에서 확인 가능\n",
+ " - 지도 데이터 사용을 위해 선행되어야 하는 작업이 원래 매우 많은데, 이러한 선행작업을 간단화함.\n",
+ " - https://github.com/python-visualization/folium\n",
+ " - http://python-visualization.github.io/folium/docs-v0.5.0/\n",
+ " - http://pinkwink.kr/971"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. 왕좌의 게임 데이터 분석 및 시각화 실습\n",
+ "### 1) 데이터 적재"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "battles = pd.read_csv('data/data-society-game-of-thrones/battles.csv')\n",
+ "death = pd.read_csv('data/data-society-game-of-thrones/character-deaths.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2) 책의 권별로 사망자 수 추이 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "death_count_book_chapter = death.pivot_table(index = 'Book of Death', values = 'Name', aggfunc='count')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graph1 = death_count_book_chapter.plot(style = 'k--o')\n",
+ "# ==(color= 'k', linestyle= '--', marker = 'o')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph1.set_title('권별 사망자수')\n",
+ "graph1.set_xticks([1,2,3,4,5])\n",
+ "graph1.set_xlabel('book chapter')\n",
+ "graph1.set_ylabel('# of Death')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graph1.figure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# figure : 도화지\n",
+ "graph1.figure.savefig('book_chapter.jpg')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3) 대규모 전쟁의 공격진영과 수비 진영의 숫자 시각화\n",
+ "#### 대규모 전쟁 = 공격과 수비 모두 합쳐서 10,000명 이상 참가한 전쟁"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = battles[battles['attacker_size']+battles['defender_size']>=10000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = big_battles[['name','attacker_size','defender_size']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles.plot(kind = 'barh', stacked = True, figsize = (8,8))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = big_battles.set_index('name')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles.T.plot(kind= 'pie', subplots = True, layout = (5,2), figsize = (8,15))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4) (실습) 가문 별로 전쟁을 벌인 횟수 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#컬럼별 이름 출력\n",
+ "battles.columns\n",
+ "# house_col = attacker_1~defender4\n",
+ "house_col = battles.columns[5:13]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "가문별전쟁참가횟수= Series()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Series([], dtype: object)"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "for col in house_col: # ['attacker_1', 'attacker_2', ... 'defender_4']\n",
+ " 가문별전쟁참가횟수 = 가문별전쟁참가횟수.add(battles[col].value_counts, fill_value = 0)\n",
+ "가문별전쟁참가횟수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "Empty 'DataFrame': no numeric data to plot",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# sorting 시 한개의 열을 갖고 잇는 시리즈라서 by 인자 필요없음\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0m가문별전쟁참가횟수\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m 2739\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2740\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2741\u001b[1;33m **kwds)\n\u001b[0m\u001b[0;32m 2742\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2743\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[1;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m 2000\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2001\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2002\u001b[1;33m **kwds)\n\u001b[0m\u001b[0;32m 2003\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[0;32m 1802\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1803\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1804\u001b[1;33m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1805\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1806\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 256\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 259\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 260\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 372\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[1;32m--> 373\u001b[1;33m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[0;32m 374\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 375\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
+ ]
+ }
+ ],
+ "source": [
+ "# sorting 시 한개의 열을 갖고 잇는 시리즈라서 by 인자 필요없음\n",
+ "가문별전쟁참가횟수.sort_values(ascending = False).head(5).plot(kind='bar')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Stark 8\n",
+ "Lannister 8\n",
+ "Greyjoy 7\n",
+ "Baratheon 6\n",
+ "Frey 2\n",
+ "Bolton 2\n",
+ "Brave Companions 1\n",
+ "Darry 1\n",
+ "Brotherhood without Banners 1\n",
+ "Bracken 1\n",
+ "Free folk 1\n",
+ "Name: attacker_1, dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "battles.attacker_1.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Tully 3\n",
+ "Karstark 2\n",
+ "Frey 1\n",
+ "Lannister 1\n",
+ "Thenns 1\n",
+ "Greyjoy 1\n",
+ "Bolton 1\n",
+ "Name: attacker_2, dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "battles.attacker_2.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Mormont 2\n",
+ "Giants 1\n",
+ "Name: attacker_3, dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "battles.attacker_3.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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BHcC9ACJyGjAe6Gy/5j8iEuy2aJXyVeWllJ1HQ7cTrs6pXCVilavesBLiO1jVTe/dAAWHnY7MMTUmemPMCuDQcfsWG2NK7IdrgfIZ8UcBbxtjCo0xP2EtEn6mG+NVyvcU5llzrUcnwkXPaSllQ2meDFcvssZCNs2x7qjd85XTUTnCHX301wAf29utgD2Vnku39ykVuD6+G35N01JKJwSHwKD7rMXVy8pgxlBY/hSUltT8Wj/iUqIXkfuBEuCt8l3VHFbtIo8iMllE1ovI+kBdHkwFgC3vwTf/hf5/0VJKJ7XtY91Re/poWP4EzBwOv/7sdFQNpt6JXkQmABcBl5tjK/amA20qHdYaqHbFX2PMNGNMqjEmNT4+vr5hKOW9svfAwj9bpZQD73E6GhXRFMa8Zq3Du38LvNwfNv3P6agaRL0SvYgMA+4GRhpjjlZ6agEwXkTCRSQZSAG+dD1MpXxMWak1AKillN6n6zirdd+ikzV2Mm8yFOQ4HZVH1aa8cjawBugoIukiMgl4CYgGlojINyLyMoAxZgvwDrAVWATcZIzRuxZU4Fn9L/h5lZZSeqtmSTDxIxh4L3z3P6t1v3ud01F5jBzrdXFOamqqWb9eb1tWfmLvBuvuzE4jrMm3tMrGu+1eB/Oug5w91lQKZ//VZ9YFEJENxpjUmo7TO2OVcqfCPHj3Wog6SUspfUXb3tYdtV3GwedPwusXWFVSfkQTvVLutKhyKWUzp6NRtRXRBEa/Yt1Vm7XduqP22zlOR+U2muiVcpct78PXdillUj+no1H10eVSq3V/0unw3mTr25kfDNRqolfKHXLSYeGt0KqnllL6umYnw4QPYND91pTSU/vDz2ucjsolmuiVclVZKcyzZ6UcraWUfiE4BM65C675BIKCYOaF8NnjPntHrSZ6pVy1+nmrlPKCpyH2FKejUe7UphdcvxK6jocVT8Prw+DQj05HVWea6JVyxd4NsOxx6HyJtbSd8j8RTeCSqXDpDMjaYa1i9c1s8ILS9NrSRK9UfRXmwbvXaSlloDh9DNy4Gk7qCu/fAHOvgfxsp6OqFU30StXXorutr/GjX9FSykAR0wYmfgDnPghb51t31KatdjqqGmmiV6o+ykspB/wFkvo7HY1qSEHBcPadMGkJBIXArItg6WNQWux0ZL9LE71SdZWTbs1K2bKHNVeKCkyte1qrWHX7I6x8xprr/uAPTkdVLU30StVF+ayUpcXWlLdaShnYwqPh4n/D2JlwcBe8cjZ8/ZbXDdRqoleqLlY/D2kr4UItpVSVdL4EbvwCErvD/D/B/yZC/q9OR1VBE71StbV3o1VKedrF0P1yp6NR3qZpa5iwAM57GLZ/AFP7Qdoqp6MCNNErVTsVs1ImwIh/aSmlql5QsDVAP2kxhETAzIvg00ccH6jVRK9UbSy6xy6l1FkpVS206gnXr4AzroBVz8L0IY4O1GqiV6omW+fD129C/9u1lFLVXngUjHoJxr0Bh36y7qjd+KYjA7W1WUpwhohkisjmSvuai8gSEdlp/2xm7xcReUFEdonIJhHp4cnglfK4nL2w4FarlHLQfU5Ho3zRaaOsgdpWPWDBzfC/CXD0UIOGUJsW/Uxg2HH77gGWGmNSgKX2Y4ALsBYETwEmA1PdE6ZSDigrhfeu11JK5bqmreCq+TD4Edj+oXVH7U8rG+zja0z0xpgVwPGXn1HALHt7FnBxpf1vGMtaIEZEEt0VrFIN6osXrFLKC57SUkrluqBg6H8bXPsphDaCWSNgycNQUuT5j67n6xKMMRkA9s8W9v5WwJ5Kx6Xb+5TyLXs3wmd/s752n3GF09Eof9LyDGugtsdVsPpf1kC/h7l7qfPqas6qHXkQkclY3Tu0bdvWzWEo5YKiI5VKKZ/XUkrlfmGRMPIFSBkCid08/nH1bdHvL++SsX9m2vvTgTaVjmsN7KvuDYwx04wxqcaY1Pj4+HqGoZQHlJdSXqKzUioP6zQCYjzf0K1vol8ATLC3JwDzK+2/yq6+6QPklHfxKOUTts6HjW9YfanJA5yORim3qLHrRkRmAwOBOBFJBx4GngTeEZFJwG5grH34R8CFwC7gKHC1B2JWyjMqSinPgIFaSqn8R42J3hhz2e88dV41xxrgJleDUqrBlZVVKqWcDiFhTkeklNu4ezBWKd9UXko58iUtpVR+R6dAUGrf1/DZY9BppJZSKr+kiV4FNi2lVAFAu25UYFt0rzWr4ISF0Li509Eo5RHaoleBa9tC2DhLSymV39NErwLT4X2w4BZr6TctpVR+ThO9CjzlpZQlhVpKqQKC9tGrwLPmRfhpBYx8EeLaOx2NUh6nLXoVWPZ9A0vLSymvdDoapRqEJnoVOIqOwLuTIDJeSylVQNGuGxU4KkopF2gppQoo2qJXgaG8lLLfnyH5bKejUapBaaJX/q9yKeWg+52ORqkGp4le+beyMnjvBruU8jUtpVQBSfvolX9b8yL89DmMeAHiUpyORilHaIte+a+KUsoR1kLMSgUoTfTKP5XPShkZb7XmtZRSBTCXEr2I3C4iW0Rks4jMFpEIEUkWkXUislNE5oiIdoqqhvfJfXBwF1zyspZSqoBX70QvIq2AW4FUY8zpQDAwHngKeM4YkwL8CkxyR6BK1dq2hbBhJvS7Fdqd43Q0SjnO1a6bEKCRiIQAjYEM4Fxgrv38LOBiFz9DqdqrUkr5gNPRKOUV6p3ojTF7gWeA3VgJPgfYAGQbY0rsw9KBVtW9XkQmi8h6EVmflZVV3zCUOkZLKZWqlitdN82AUUAy0BKIBC6o5lBT3euNMdOMManGmNT4+Pj6hqHUMWteskoph/1dSymVqsSVrpvBwE/GmCxjTDEwDzgLiLG7cgBaA/tcjFGpmu37BpY+CqdeBD0mOB2NUl7FlUS/G+gjIo1FRIDzgK3AMuBS+5gJwHzXQlSqBpVLKUe+qKWUSh3HlT76dViDrhuB7+z3mgbcDfxFRHYBscB0N8Sp1O/75H4tpVTqBFyaAsEY8zDw8HG7fwTOdOV9laq1bR/AhtfhLC2lVOr36J2xyncdzrBLKbvBuQ86HY1SXksTvfJNFQt8F+gC30rVQGevVL5p7b/tWSmf11JKpWqgLXrlezK+hU8f0VJKpWpJE73yLUVH7VLKOC2lVKqWtOtG+ZZP7oMDO+Gq97WUUqla0ha98h3bP7RLKW+BdgOdjkYpn6GJXvmGwxkw/2YtpVSqHjTRK+9XVgbv3wDF+TBaZ6VUqq60j155v7X/hh+Xw0X/gvgOTkejlM/RFr3ybhmbjpVS9pzodDRK+SRN9Mp7FR2FdydpKaVSLtKuG+W9Ft8PB3bAlVpKqZQrtEWvvNP2D2H9DKuU8pRBTkejlE/TRK+8T+4vVinlSV3h3IecjkYpn6eJXnmX8gW+i/N1Vkql3ET76JV3Wfsf+HEZXPScllIq5SYutehFJEZE5orIdhHZJiJ9RaS5iCwRkZ32z2buClb5uYxNsPQR6Dgcel7tdDRK+Q1Xu26eBxYZY04FugHbgHuApcaYFGCp/VipEyuflbJRcy2lVMrN6p3oRaQJcDb24t/GmCJjTDYwCphlHzYLuNjVIFUAWPwAHPjeWuA7MtbpaJTyK6606NsBWcDrIvK1iLwmIpFAgjEmA8D+2aK6F4vIZBFZLyLrs7KyXAhD+bztH8H66dD3Zi2lVMoDXEn0IUAPYKox5gzgCHXopjHGTDPGpBpjUuPj410IQ/m03F9gwc1wUhc4T0splfIEVxJ9OpBujFlnP56Llfj3i0gigP0z07UQld8qK4P3b7T658fMgJBwpyNSyi/VO9EbY34B9ohIR3vXecBWYAFQvpDnBGC+SxEq/7VuKvzwGQx7QksplfIgV+vobwHeEpEw4EfgaqyLxzsiMgnYDYx18TOUP8rYBJ9O0VJKpRqAS4neGPMNkFrNU+e58r7Kz2kppVINSu+MVQ1vyYNWKeWV72kppVINQOe6UQ3r+4/hq9fsUspznY5GqYCgiV41nNxfYP5NWkqpVAPTRK8aRpVSyulaSqlUA9I+etUw1r1slVIOfxbiO9Z8vFLKbbRFrzzvl+/g04eh44WQeo3T0SgVcDTRK88qzrdLKZtpKaVSDtGuG+VZix+ArO12KWWc09EoFZC0Ra885/tFWkqplBfQRK88I3c/zP8TJGgppVJO00Sv3K+ilPIIjHlNSymVcpj20Sv3Mga+eAF+WArD/wktTnU6IqUCniZ65T57N8KShyBtpTUrZeokpyNSSqGJXrnDr2mw9DHYPBcax8GFz0DPiVpKqZSX0ESv6u/oIVjxDHw5DYJCYMCd0O/PENHE6ciUUpVoold1V5wP616Blc9CUS50vxwG3QdNWjodmVKqGi4nehEJBtYDe40xF4lIMvA20BzYCFxpjCly9XOUFygrg01z4LO/weF0SDkfBj8CCac5HZlS6gTcUV75Z2BbpcdPAc8ZY1KAXwEdkfMHP3wGr5wN799g3eF61QK4/H+a5JXyAS4lehFpDQwHXrMfC3AuMNc+ZBZwsSufoRz2y3fw5iXWn8Ica4rh65ZBu3OcjkwpVUuudt38C7gLiLYfxwLZxpgS+3E60MrFz1BOyEm3umi+fRsimsLQJ6DXtXrzk1I+qN6JXkQuAjKNMRtEZGD57moONb/z+snAZIC2bdvWNwzlbvnZsOo5WDvVetzvVuh/uzX7pFLKJ7nSou8HjBSRC4EIoAlWCz9GRELsVn1rYF91LzbGTAOmAaSmplZ7MVANqKQQvpoOK562kn3XP8C590OMXoSV8nX1TvTGmHuBewHsFv2dxpjLReR/wKVYlTcTgPluiFN5SlkZbH0PPn0Esn+GdgNhyKOQ2M3pyJRSbuKJOvq7gbdF5G/A18B0D3yGcoe0VbD4Qdi3ERJOhyvmQfvznI5KKeVmbkn0xpjlwHJ7+0fgTHe8r/KQzG3w6RTYsQiatIKLp1pdNUHBTkemlPIAvTM2kBzOgOVPwNf/hbAoGDwFet8AoY2cjkwp5UGa6ANBYS6sfgHWvASlxVZyH3AnRMY6HZlSqgFoovdnpcWwYSYsfxKOHoDOo+G8B6F5O6cjU0o1IE30/sgY2P6B1Q9/cBec3A+GvAOtezodmVLKAZro/c3udbDkQdizDuI6wmVzoMNQnRteqQCmid5fHNgFS6fAtoUQlQAjnofuV0CwnmKlAp1mAV+XlwWfPwnrX7eqZwbdD31vgrBIpyNTSnkJTfS+qugIrPkPrP6XtRBI6tVwzt0Q1cLpyJRSXkYTva8pLYFv3oJlT0DeL3DqRVY9fFyK05EppbyUJnpfYQzs+AQ+fRiytkPrM2HcLGjbx+nIlFJeThO9L9i7AZY8DGkrofkpMO5N6DRCK2mUUrWiid6bHfoJPnsMNr8LjePgwmeg50QIDnU6MqWUD9FE742OHoIV/4AvX4WgEDj7r3DWrRDRxOnIlFI+SBO9NynOh3Uvw8rnoCgXzrgCBt4LTVo6HZlSyodpovcGZaWw6R1rjdbD6ZAy1KqkSTjN6ciUUn5AE73Tdi21Blr3fweJ3eGSqZB8ttNRKaX8iCZ6p2Rsskolf/jMWpd1zHRrdsmgIKcjU0r5mXonehFpA7wBnASUAdOMMc+LSHNgDpAEpAHjjDG/uh6qn8jeA8seh2/fhoimMPQJ6HUthIQ7HZlSyk+50qIvAe4wxmwUkWhgg4gsASYCS40xT4rIPcA9WOvIBrb8bFj1LKx92Xrc71bofzs0auZsXEopv1fvRG+MyQAy7O1cEdkGtAJGAQPtw2ZhrSUbuIm+pBC+mg4rnraSfbfx1sRjMW2cjkwpFSDc0kcvIknAGcA6IMG+CGCMyRCRwJxlq6wMtsyDpY9C9s/QbhAMeRQSuzodmQoAhSWl/Jh1hJ2Zeezcn8vO/Xlk5RXSIjqcxKaNaBkTQcuYRtafphHERYUTFKR3WvsrlxO9iEQB7wK3GWMOSy1vyxeRycBkgLZt27oahnf5aaW1+Me+ryHhdLhiHrQ/z+molB8qKC5P6FYy37E/l12ZeaQdPEKZsY4JEkiKjaRFk3C+35/L8u+zyC8urfI+ocHCSU0jrItAU+sikBhzbLtl00Y0aRRCbf9/K+/iUqIXkVCsJP+V1W24AAAMlklEQVSWMWaevXu/iCTarflEILO61xpjpgHTAFJTU40rcXiNzG3W8n07FkGT1nDxy9B1HAQFOx2Z8nEFxaXsysxjV6aVzMtb6rsPHa1I6MFBwsmxjemQEM3wrom0bxFFh4RokuMiiQg99m/QGENOfjF7s/PJyC4gIyefvfbPjOwCvkr7lf2HMygpq/rfsnFYsHUBaBpBy6aN7ItB+bZ1Qaj8Ocp7uFJ1I8B0YJsx5tlKTy0AJgBP2j/nuxShLzicAcufgK//C2FR1s1OvW+wFgJRqg7yi6yEvjPzWDLfmZnH7kNHMXbeDQkSkuIi6ZTYhJHdW5HSIoqUhCiS4yIJD6k50YoIMY3DiGkcRueWTas9prTMcCCv8LiLwbHtbRm5HMgr/M3rmjUOtS8Gx5J/YtNj3UQJ0eGEBGsJcUMTY+rXmBaR/sBK4Dus8kqA+7D66d8B2gK7gbHGmEMneq/U1FSzfv36esXhqILD8MUL8MVLUFYCZ14HA+6EyFinI1Ne7khhCT9k5bFjf15Ft8vOzFzSf82vSOihwUJyXCQpLaJJSYiq+JkUG0lYiPPJsrCklP059sUgJ5+MnAL7YnBsO7egpMprggQSmkSQ2DSCxJhGtLIvBIlN7e2YCGIjw7SLqJZEZIMxJrXG4+qb6N3J5xJ9aTFsmAnLn4SjB+D0MXDug9A82enIlJfJKyyxWuiVult27M9jb3Z+xTFhwUG0i4+kfQsrmXdIsFroJ8dGEurjrd+8whIysu1vAzkF9nZBlQtDUUlZldeEhQTR0k7+iTER9sWg8nYE0RE6gyvUPtHrnbF1YYy1+PanU+DQD3Byfzj/UWjV0+nIlMNyC4rZmZnHLrtlvmO/1Z9eXULveXIzxvdqY7XSE6I5uXljv+3OiAoPISUhmpSE6GqfN8Zw6EhRtd8GMnIKWPvDQX45XMBxwwVEh4dUjBFY3waqXhhOahpRq26sQKGJvrZ2r4XFD0L6lxDXES6bAx2G6uIfASYnv7hqC93ezsgpqDgmPCSIU+KjSE1qxh8T2lYMirZp1shvE3p9iQixUeHERoVzeqvqxwtKSsvIzC1kX3Y+++xvBRXbOflsSs/h0JGi37wuLiqsYoygctdQ+XZ8dDjBAVJSqom+Jgd2Wi347R9A1Ekw4gXofjkE61+dP8s5WlwxIFpesrhjfy77Dx8bgIwIDaJ9iyj6tIs91ofeIoo2zRsHTAJpCCHBQRWDub+noLiUjJwC6wJgfxsovxj8mHWEVTsPcKSoaklpSJCQ0CSClnbytz4j4thActNGxDQO9YvxAs1WvycvEz5/Cta/blXPDLof+t4EYZFOR6bcKPto0W8GRHfuzyMz91hCbxQaTEpCFP3axx3rQ28RTatmjTShe4mI0GCS4yJJjqv+/6cxhsMFJWTk2N8G7HGCfdnWBeGbPdl8vDmD4lJz3PsGVdxHcKx6qOqFoXGY96dR74+woRUdgTX/htXPWwuBpF4N59wNUYF5g6+/OHSkyBoIzcxj1/7ylnpelRLBxmHBpLSI4uwO8RUliyktomkV00jvGvVxIkLTRqE0bRTKqSdVv1JbWZnhwJFCMrILqnYT2ReEFTuzyMwt5Pj6laaNQivuME6MifjNheGkphGOD6proi9XWgLf/BeW/R3yfrEW3z7vYYhLcToyVQcH8grZuT+PXfaAaHkL/WClPtyo8BDat4hiUMd4OiRE0z7B6kNPbBKhCT2ABQUJLaIjaBEdQbc2MdUeU1RSxv7DBZW6hvKrXBg27P6V7KPFVV4jAvFR4b/9NmBfCE6ObUxM4zCP/m6a6I2BHZ9Yc8NnbYfWZ8K4WdC2j9ORqd9hjOFAXlGlAdFjVS6VB+Wiw0NISYhicKeEigqXlBZRJDaN8It+V9XwwkKCaNO8MW2aN/7dY44WlVTqGqraTbT9l1yWba86BcV1A5K5f7hnV5ML7ES/dwMsfgh+XgXNT4Fxb1oteU0CXsEYQ1ZuYcWAaHn54o7M3CqtpiYRIXRIiGZo5wTaV+pDT2gSrgldNbjGYdY3xvYtoqp93hhD9tHiim8DrZp5/g76wEz0h36yZpXcMg8ax8GFz0DPiRCsN2E4wRjD/sOFvxkQ3ZmZR07+sYTetFEoHRKiuLBLotWHbif1+GhN6Mp3iAjNIsNoFvn7U1C4W2Al+qOHYMU/4MtXISgEzv4rnHUrRFQ/OKPcyxjDL4cLrL7zSiWLOzPzqtwq36xxKCkJ0YzollhRspiSEE1clN4ar1R9BEaiL86HdS/DyuegKBfOuAIG3gdNEp2OzC8ZY9iXU1AxD3p5Pfqu/XnkFh5L6LGRYaQkRHFx91Z0SIiivT2XS1yULquolDv5d6IvK4VNc+Czv8HhvdBhmDWzZItOTkfm1/o/tazKrf9xUeF0SIhidI9WtE+IpoPdfxmrCV2pBuG/iX7XUljyMOz/DlqeAZe8DMlnOx1VQLi6XxKNwoKt0sX4KJpFerZ0TCl1Yv6X6DM2wZKH4MdlEHMyjJkOnUdDkM4x0lCuHdDO6RCUUpX4T6LP3mN10WyaA41iYOjfodckCNHuAaVUYPP9RJ+fDauehbUvW4/7/Rn6324le6WUUj6e6Hd8Au9dbyX7buOticdi2jgdlVJKeRWPdVyLyDAR+V5EdonIPR75kNj20CoVrl9hDbZqkldKqd/wSIteRIKBfwNDgHTgKxFZYIzZ6tYPij0Frpjr1rdUSil/46kW/ZnALmPMj8aYIuBtYJSHPksppdQJeCrRtwL2VHqcbu9TSinVwDyV6KubkKTKdP0iMllE1ovI+qysLA+FoZRSylOJPh2oPDLaGthX+QBjzDRjTKoxJjU+Pt5DYSillPJUov8KSBGRZBEJA8YDCzz0WUoppU7AI1U3xpgSEbkZ+AQIBmYYY7Z44rOUUkqdmMdumDLGfAR85Kn3V0opVTs605dSSvk5McbUfJSngxDJAn6u58vjgANuDEe5h54X76PnxDu5cl5ONsbUWM3iFYneFSKy3hiT6nQcqio9L95Hz4l3aojzol03Sinl5zTRK6WUn/OHRD/N6QBUtfS8eB89J97J4+fF5/volVJKnZg/tOiVUkqdgNcnehFpIyLLRGSbiGwRkT/b+7uJyBoR+U5EFopIE3v/5SLyTaU/ZSLS3dnfwr/U45yEisgse/82EbnX2d/AP9XjvISJyOv2/m9FZKCjv4AfEpEIEfnS/vvdIiKP2PuTRWSdiOwUkTn2VDGISLj9eJf9fJJbAjHGePUfIBHoYW9HAzuA07Dm0znH3n8N8Fg1r+0C/Oj07+Bvf+p6ToA/Am/b242BNCDJ6d/D3/7U47zcBLxub7cANgBBTv8e/vQHaybfKHs7FFgH9AHeAcbb+18GbrS3/wS8bG+PB+a4Iw6vb9EbYzKMMRvt7VxgG9bc9h2BFfZhS4Ax1bz8MmB2Q8QZSOpxTgwQKSIhQCOgCDjcoEEHgHqcl9OApfbxmUA2oHX2bmQsefbDUPuPAc4FypfHmwVcbG+Psh9jP3+eiFQ37XudeH2ir8z+GnMG1lVxMzDSfmosVadFLvcHNNF7VC3PyVzgCJAB7AaeMcYcatBAA0wtz8u3wCgRCRGRZKAn1f8/Ui4QkWAR+QbIxLrQ/gBkG2NK7EMqL8xUsWiT/XwOEOtqDD6T6EUkCngXuM0YcxjrK+hNIrIB62tq0XHH9waOGmM2N3iwAaIO5+RMoBRoCSQDd4hIOwdCDgh1OC8zsJLMeuBfwBdAyW/fUbnCGFNqjOmOtS7HmUCn6g6zf9a4aFN9eGz2SncSkVCsf7hvGWPmARhjtgPn2893AIYf97LxaGveY+p4Tv4ILDLGFAOZIrIaq4vgxwYP3M/V5bzYLcbbK732C2BnQ8ccKIwx2SKyHKuPPkZEQuxzUHlhpvJFm9Ltrs6mgMvffr2+RW/3T00Hthljnq20v4X9Mwh4AGtAg0r7xmItSq7crB7nZDdwrlgisf6hb2/YqP1fXc+LiDS2zwciMgQoMcZsbfDA/ZiIxItIjL3dCBiMNXayDLjUPmwCMN/eXmA/xn7+M2OPzLoUhxvew6NEpD+wEvgOKLN33wekYFUNAMwD7i3/C7HLxJ40xvRp2GgDQ13Pid2V8DrW4J9gVXr8o2Gj9n/1OC9JWIsDlQF7gUnGmPrOIquqISJdsQZXg7Ea1u8YYx61uy7fBpoDXwNXGGMKRSQCeBNrfOUQVmWOy998vT7RK6WUco3Xd90opZRyjSZ6pZTyc5rolVLKz2miV0opP6eJXiml/JwmeqWU8nOa6JVSys9poldKKT/3/9DktCzzP+dIAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "#### 색상: https://matplotlib.org/examples/color/named_colors.html\n",
+ "#### 기타 스타일: https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 실습. 아래 그림처럼 연도별 지역별 인구수 그래프 그리기\n",
+ "df.T.plot(kind='bar', subplots = True, figsize=(8,10))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# matplotlib 기반 최신 시각화 라이브러리 소개\n",
+ "1. seaborn\n",
+ " - matplotlib을 기반으로 만들어진 시각화 라이브러리\n",
+ " - 디자인적으로 훨씬 세련됨. \n",
+ " - matplotlib와 사용방식이 유사하므로 쉽고 빠르게 습득할 수 있음.\n",
+ " - https://seaborn.pydata.org/\n",
+ "\n",
+ "2. bokeh\n",
+ " - 웹브라우저 상에서의 시각화에 효과적인 파이썬 인터랙티브 시각화 라이브러리\n",
+ " - 플롯을 html 파일로 export하여 이를 웹브라우저를 통해 확인할 수 있음.\n",
+ " - matplotlib와 비슷, jupyter와 호환이 잘 됨. \n",
+ " - https://bokeh.pydata.org/en/latest/\n",
+ "\n",
+ "3. Folium \n",
+ " - 지리적 데이터 시각화에 특화된 라이브러리 (leaflet.js 기반)\n",
+ " - 웹브라우저에서 확인 가능\n",
+ " - 지도 데이터 사용을 위해 선행되어야 하는 작업이 원래 매우 많은데, 이러한 선행작업을 간단화함.\n",
+ " - https://github.com/python-visualization/folium\n",
+ " - http://python-visualization.github.io/folium/docs-v0.5.0/\n",
+ " - http://pinkwink.kr/971"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. 왕좌의 게임 데이터 분석 및 시각화 실습\n",
+ "### 1) 데이터 적재"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "battles = pd.read_csv('data/data-society-game-of-thrones/battles.csv')\n",
+ "death = pd.read_csv('data/data-society-game-of-thrones/character-deaths.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2) 책의 권별로 사망자 수 추이 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "death_count_book_chapter = death.pivot_table(index = 'Book of Death', values = 'Name', aggfunc='count')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graph1 = death_count_book_chapter.plot(style = 'k--o')\n",
+ "# ==(color= 'k', linestyle= '--', marker = 'o')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph1.set_title('권별 사망자수')\n",
+ "graph1.set_xticks([1,2,3,4,5])\n",
+ "graph1.set_xlabel('book chapter')\n",
+ "graph1.set_ylabel('# of Death')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graph1.figure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# figure : 도화지\n",
+ "graph1.figure.savefig('book_chapter.jpg')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3) 대규모 전쟁의 공격진영과 수비 진영의 숫자 시각화\n",
+ "#### 대규모 전쟁 = 공격과 수비 모두 합쳐서 10,000명 이상 참가한 전쟁"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = battles[battles['attacker_size']+battles['defender_size']>=10000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = big_battles[['name','attacker_size','defender_size']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles.plot(kind = 'barh', stacked = True, figsize = (8,8))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles = big_battles.set_index('name')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "big_battles.T.plot(kind= 'pie', subplots = True, layout = (5,2), figsize = (8,15))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4) (실습) 가문 별로 전쟁을 벌인 횟수 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#컬럼별 이름 출력\n",
+ "battles.columns\n",
+ "# house_col = attacker_1~defender4\n",
+ "house_col = battles.columns[5:13]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "가문별전쟁참가횟수= Series()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Series([], dtype: object)"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "for col in house_col: # ['attacker_1', 'attacker_2', ... 'defender_4']\n",
+ " 가문별전쟁참가횟수 = 가문별전쟁참가횟수.add(battles[col].value_counts, fill_value = 0)\n",
+ "가문별전쟁참가횟수"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "Empty 'DataFrame': no numeric data to plot",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " name year battle_number \\\n",
+ "0 Battle of the Golden Tooth 298 1 \n",
+ "1 Battle at the Mummer's Ford 298 2 \n",
+ "2 Battle of Riverrun 298 3 \n",
+ "3 Battle of the Green Fork 298 4 \n",
+ "4 Battle of the Whispering Wood 298 5 \n",
+ "\n",
+ " attacker_king defender_king attacker_1 attacker_2 \\\n",
+ "0 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n",
+ "1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n",
+ "2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n",
+ "3 Robb Stark Joffrey/Tommen Baratheon Stark NaN \n",
+ "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n",
+ "\n",
+ " attacker_3 attacker_4 defender_1 ... major_death major_capture \\\n",
+ "0 NaN NaN Tully ... 1.0 0.0 \n",
+ "1 NaN NaN Baratheon ... 1.0 0.0 \n",
+ "2 NaN NaN Tully ... 0.0 1.0 \n",
+ "3 NaN NaN Lannister ... 1.0 1.0 \n",
+ "4 NaN NaN Lannister ... 1.0 1.0 \n",
+ "\n",
+ " attacker_size defender_size \\\n",
+ "0 15000.0 4000.0 \n",
+ "1 NaN 120.0 \n",
+ "2 15000.0 10000.0 \n",
+ "3 18000.0 20000.0 \n",
+ "4 1875.0 6000.0 \n",
+ "\n",
+ " attacker_commander \\\n",
+ "0 Jaime Lannister \n",
+ "1 Gregor Clegane \n",
+ "2 Jaime Lannister, Andros Brax \n",
+ "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n",
+ "4 Robb Stark, Brynden Tully \n",
+ "\n",
+ " defender_commander summer location \\\n",
+ "0 Clement Piper, Vance 1.0 Golden Tooth \n",
+ "1 Beric Dondarrion 1.0 Mummer's Ford \n",
+ "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n",
+ "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n",
+ "4 Jaime Lannister 1.0 Whispering Wood \n",
+ "\n",
+ " region note \n",
+ "0 The Westerlands NaN \n",
+ "1 The Riverlands NaN \n",
+ "2 The Riverlands NaN \n",
+ "3 The Riverlands NaN \n",
+ "4 The Riverlands NaN \n",
+ "\n",
+ "[5 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# attacker_1~4, defender_1~4의 가문들 뽑기\n",
+ "battles.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Lannister\n",
+ "1 Lannister\n",
+ "2 Lannister\n",
+ "3 Stark\n",
+ "4 Stark\n",
+ "5 Stark\n",
+ "6 Lannister\n",
+ "7 Greyjoy\n",
+ "8 Greyjoy\n",
+ "9 Greyjoy\n",
+ "10 Stark\n",
+ "11 Greyjoy\n",
+ "12 Greyjoy\n",
+ "13 Bolton\n",
+ "14 Stark\n",
+ "15 Baratheon\n",
+ "16 Lannister\n",
+ "17 Stark\n",
+ "18 Stark\n",
+ "19 Baratheon\n",
+ "20 Darry\n",
+ "21 Stark\n",
+ "22 Brotherhood without Banners\n",
+ "23 Lannister\n",
+ "24 Lannister\n",
+ "25 Frey\n",
+ "26 Frey\n",
+ "27 Free folk\n",
+ "28 Bolton\n",
+ "29 Brave Companions\n",
+ "30 Baratheon\n",
+ "31 Greyjoy\n",
+ "32 Greyjoy\n",
+ "33 Baratheon\n",
+ "34 Baratheon\n",
+ "35 Lannister\n",
+ "36 Bracken\n",
+ "37 Baratheon\n",
+ "Name: attacker_1, dtype: object"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "battles['attacker_1'].dropna()\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 NaN\n",
+ "1 NaN\n",
+ "2 NaN\n",
+ "3 NaN\n",
+ "4 NaN\n",
+ "5 NaN\n",
+ "6 NaN\n",
+ "7 NaN\n",
+ "8 NaN\n",
+ "9 NaN\n",
+ "10 NaN\n",
+ "11 NaN\n",
+ "12 NaN\n",
+ "13 NaN\n",
+ "14 NaN\n",
+ "15 NaN\n",
+ "16 NaN\n",
+ "17 NaN\n",
+ "18 NaN\n",
+ "19 NaN\n",
+ "20 NaN\n",
+ "21 NaN\n",
+ "22 NaN\n",
+ "23 NaN\n",
+ "24 NaN\n",
+ "25 NaN\n",
+ "26 NaN\n",
+ "27 NaN\n",
+ "28 NaN\n",
+ "29 NaN\n",
+ "30 NaN\n",
+ "31 NaN\n",
+ "32 NaN\n",
+ "33 NaN\n",
+ "34 NaN\n",
+ "35 NaN\n",
+ "36 NaN\n",
+ "37 NaN\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "battles['attacker_1']+battles['attacker_2']+battles['attacker_3']+battles['attacker_4']+battles['defender_1']+battles['defender_2']+battles['defender_3']+battles['defender_4']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "1 Lannister,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "2 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "3 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "4 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "5 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "6 Lannister,nan,nan,nan,Darry,nan,nan,nan\n",
+ "7 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "8 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "9 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "10 Stark,nan,nan,nan,Greyjoy,nan,nan,nan\n",
+ "11 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "12 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "13 Bolton,Greyjoy,nan,nan,Stark,nan,nan,nan\n",
+ "14 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "15 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "16 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "17 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "18 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "19 Baratheon,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "20 Darry,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "21 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "22 Brotherhood without Banners,nan,nan,nan,Brave ...\n",
+ "23 Lannister,nan,nan,nan,Stark,nan,nan,nan\n",
+ "24 Lannister,nan,nan,nan,Brave Companions,nan,nan...\n",
+ "25 Frey,Bolton,nan,nan,Stark,nan,nan,nan\n",
+ "26 Frey,nan,nan,nan,Mallister,nan,nan,nan\n",
+ "27 Free folk,Thenns,Giants,nan,Night's Watch,Bara...\n",
+ "28 Bolton,nan,nan,nan,Greyjoy,nan,nan,nan\n",
+ "29 Brave Companions,nan,nan,nan,nan,nan,nan,nan\n",
+ "30 Baratheon,Karstark,Mormont,Glover,Greyjoy,nan,...\n",
+ "31 Greyjoy,nan,nan,nan,Tyrell,nan,nan,nan\n",
+ "32 Greyjoy,nan,nan,nan,Tyrell,nan,nan,nan\n",
+ "33 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "34 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "35 Lannister,Frey,nan,nan,Tully,nan,nan,nan\n",
+ "36 Bracken,Lannister,nan,nan,Blackwood,nan,nan,nan\n",
+ "37 Baratheon,Karstark,Mormont,Glover,Bolton,Frey,...\n",
+ "Name: 가문, dtype: object"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ " battles['가문'] = battles.attacker_1.astype(str).str.cat([battles.attacker_2.astype(str),battles.attacker_3.astype(str),battles.attacker_4.astype(str),battles.defender_1.astype(str),battles.defender_2.astype(str),battles.defender_3.astype(str),battles.defender_4.astype(str)], sep=',')\n",
+ "battles.가문"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "1 Lannister,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "2 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "3 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "4 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "5 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "6 Lannister,nan,nan,nan,Darry,nan,nan,nan\n",
+ "7 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "8 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "9 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "10 Stark,nan,nan,nan,Greyjoy,nan,nan,nan\n",
+ "11 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "12 Greyjoy,nan,nan,nan,Stark,nan,nan,nan\n",
+ "13 Bolton,Greyjoy,nan,nan,Stark,nan,nan,nan\n",
+ "14 Stark,Tully,nan,nan,Lannister,nan,nan,nan\n",
+ "15 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "16 Lannister,nan,nan,nan,Tully,nan,nan,nan\n",
+ "17 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "18 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "19 Baratheon,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "20 Darry,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "21 Stark,nan,nan,nan,Lannister,nan,nan,nan\n",
+ "22 Brotherhood without Banners,nan,nan,nan,Brave ...\n",
+ "23 Lannister,nan,nan,nan,Stark,nan,nan,nan\n",
+ "24 Lannister,nan,nan,nan,Brave Companions,nan,nan...\n",
+ "25 Frey,Bolton,nan,nan,Stark,nan,nan,nan\n",
+ "26 Frey,nan,nan,nan,Mallister,nan,nan,nan\n",
+ "27 Free folk,Thenns,Giants,nan,Night's Watch,Bara...\n",
+ "28 Bolton,nan,nan,nan,Greyjoy,nan,nan,nan\n",
+ "29 Brave Companions,nan,nan,nan,nan,nan,nan,nan\n",
+ "30 Baratheon,Karstark,Mormont,Glover,Greyjoy,nan,...\n",
+ "31 Greyjoy,nan,nan,nan,Tyrell,nan,nan,nan\n",
+ "32 Greyjoy,nan,nan,nan,Tyrell,nan,nan,nan\n",
+ "33 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "34 Baratheon,nan,nan,nan,Baratheon,nan,nan,nan\n",
+ "35 Lannister,Frey,nan,nan,Tully,nan,nan,nan\n",
+ "36 Bracken,Lannister,nan,nan,Blackwood,nan,nan,nan\n",
+ "37 Baratheon,Karstark,Mormont,Glover,Bolton,Frey,...\n",
+ "Name: 가문, dtype: object"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "가문별전쟁 = battles.가문.replace(\",nan\", \"\")\n",
+ "가문별전쟁"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5) (실습) 년도별로 사망자의 숫자와 전쟁이 벌어진 횟수 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "battle_count = battles.pivot_table(index='year', values='name', aggfunc = 'count')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "death_count = death.pivot_table(index = 'Death Year', values = 'Name', aggfunc = 'count')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "battle_death_count = pd.concat([battle_count, death_count], axis = 1)\n",
+ "battle_death_count.columns = ['# of Battles','']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | name | \n", + "year | \n", + "battle_number | \n", + "attacker_king | \n", + "defender_king | \n", + "attacker_1 | \n", + "attacker_2 | \n", + "attacker_3 | \n", + "attacker_4 | \n", + "defender_1 | \n", + "... | \n", + "major_death | \n", + "major_capture | \n", + "attacker_size | \n", + "defender_size | \n", + "attacker_commander | \n", + "defender_commander | \n", + "summer | \n", + "location | \n", + "region | \n", + "note | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Battle of the Golden Tooth | \n", + "298 | \n", + "1 | \n", + "Joffrey/Tommen Baratheon | \n", + "Robb Stark | \n", + "Lannister | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "Tully | \n", + "... | \n", + "1.0 | \n", + "0.0 | \n", + "15000.0 | \n", + "4000.0 | \n", + "Jaime Lannister | \n", + "Clement Piper, Vance | \n", + "1.0 | \n", + "Golden Tooth | \n", + "The Westerlands | \n", + "NaN | \n", + "
| 1 | \n", + "Battle at the Mummer's Ford | \n", + "298 | \n", + "2 | \n", + "Joffrey/Tommen Baratheon | \n", + "Robb Stark | \n", + "Lannister | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "Baratheon | \n", + "... | \n", + "1.0 | \n", + "0.0 | \n", + "NaN | \n", + "120.0 | \n", + "Gregor Clegane | \n", + "Beric Dondarrion | \n", + "1.0 | \n", + "Mummer's Ford | \n", + "The Riverlands | \n", + "NaN | \n", + "
| 2 | \n", + "Battle of Riverrun | \n", + "298 | \n", + "3 | \n", + "Joffrey/Tommen Baratheon | \n", + "Robb Stark | \n", + "Lannister | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "Tully | \n", + "... | \n", + "0.0 | \n", + "1.0 | \n", + "15000.0 | \n", + "10000.0 | \n", + "Jaime Lannister, Andros Brax | \n", + "Edmure Tully, Tytos Blackwood | \n", + "1.0 | \n", + "Riverrun | \n", + "The Riverlands | \n", + "NaN | \n", + "
| 3 | \n", + "Battle of the Green Fork | \n", + "298 | \n", + "4 | \n", + "Robb Stark | \n", + "Joffrey/Tommen Baratheon | \n", + "Stark | \n", + "NaN | \n", + "NaN | \n", + "NaN | \n", + "Lannister | \n", + "... | \n", + "1.0 | \n", + "1.0 | \n", + "18000.0 | \n", + "20000.0 | \n", + "Roose Bolton, Wylis Manderly, Medger Cerwyn, H... | \n", + "Tywin Lannister, Gregor Clegane, Kevan Lannist... | \n", + "1.0 | \n", + "Green Fork | \n", + "The Riverlands | \n", + "NaN | \n", + "
| 4 | \n", + "Battle of the Whispering Wood | \n", + "298 | \n", + "5 | \n", + "Robb Stark | \n", + "Joffrey/Tommen Baratheon | \n", + "Stark | \n", + "Tully | \n", + "NaN | \n", + "NaN | \n", + "Lannister | \n", + "... | \n", + "1.0 | \n", + "1.0 | \n", + "1875.0 | \n", + "6000.0 | \n", + "Robb Stark, Brynden Tully | \n", + "Jaime Lannister | \n", + "1.0 | \n", + "Whispering Wood | \n", + "The Riverlands | \n", + "NaN | \n", + "
5 rows × 25 columns
\n", + ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "battle_death_count.plot(xticks = battle_death_count.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.ipynb" "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.ipynb"
new file mode 100644
index 0000000..0920174
--- /dev/null
+++ "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.ipynb"
@@ -0,0 +1,5412 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame\n",
+ "import numpy as np\n",
+ "import matplotlib\n",
+ "matplotlib.rcParams['font.family'].insert(0, 'Malgun Gothic') \n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. openAPI 활용 및 json 데이터 읽기\n",
+ "## API : Application Programming Interface\n",
+ "## 서울시 데이터 포털에서 공공 API 활용하기\n",
+ "### 서울시 동별 인구통계\n",
+ "### http://data.seoul.go.kr/dataList/datasetView.do?infId=10043&srvType=S&serviceKind=2¤tPageNo=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests # api 사용시 필요"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "http://openapi.seoul.go.kr:8088/sample/json/octastatapi10043/1/5\n"
+ ]
+ }
+ ],
+ "source": [
+ "url = 'http://openapi.seoul.go.kr:8088'\n",
+ "api_key = 'sample' #개인인증키 발급받은 후, 입력\n",
+ "return_format = 'json'\n",
+ "service_id = 'octastatapi10043'\n",
+ "start_idx = 1\n",
+ "end_idx = 5\n",
+ "\n",
+ "request_url = '/'.join([url, api_key, return_format, service_id, str(start_idx), str(end_idx)])\n",
+ "print(request_url)\n",
+ "# 'http://openapi.seoul.go.kr:8088/sample/json/octastatapi10043/1/500/' \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = requests.get(request_url)\n",
+ "# 주소창에 주소를 치고 엔터치는 격"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "map1 = folium.Map(location = [37.5, 126.9], zoom_start = 10)\n",
+ "map1\n",
+ "# 서울시청을 지도의 중심으로, 지도를 그려줌. \n",
+ "# zoom_start는 지도를 그릴 때 얼마나 zoom 해서 보여줄 지를 결정. 값이 클수록, zoom in"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Marker 추가하기 (지도에 지점 추가하기)\n",
+ "folium.Marker([37.3, 126], popup = '테스트지점').add_to(map1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "map1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "map1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "map1.save('map_img1.html')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. 서울시 유동인구 분석"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 데이터 적재"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
ERROR-335\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " DONG GIGAN GYE_1 GYE_2 GYE_3 JACHIGU NAMJA_1 NAMJA_2 NAMJA_3 \\\n",
+ "0 합계 2016 10204057 9930616 273441 합계 5007379 4876789 130590 \n",
+ "1 소계 2016 161922 152737 9185 종로구 79322 75201 4121 \n",
+ "2 사직동 2016 10013 9695 318 종로구 4605 4429 176 \n",
+ "3 삼청동 2016 3118 2903 215 종로구 1491 1377 114 \n",
+ "4 부암동 2016 11044 10616 428 종로구 5349 5160 189 \n",
+ "\n",
+ " N_65SEISANGGORYEONGJA SEDAE SEDAEDANGINGU YEOJA_1 YEOJA_2 YEOJA_3 \n",
+ "0 1300877 4189839 2.37 5196678 5053827 142851 \n",
+ "1 25091 72118 2.12 82600 77536 5064 \n",
+ "2 1707 4399 2.20 5408 5266 142 \n",
+ "3 624 1403 2.07 1627 1526 101 \n",
+ "4 1723 4371 2.43 5695 5456 239 "
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "jsondata = jsondata['octastatapi10043']['row']\n",
+ "동별인구통계 = DataFrame(jsondata)\n",
+ "동별인구통계"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. folium 을 활용한 지도 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#folium 라이브러리 설치\n",
+ "# anaconda prompt에서 'pip install folium' 실행\n",
+ "import folium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | DONG | \n", + "GIGAN | \n", + "GYE_1 | \n", + "GYE_2 | \n", + "GYE_3 | \n", + "JACHIGU | \n", + "NAMJA_1 | \n", + "NAMJA_2 | \n", + "NAMJA_3 | \n", + "N_65SEISANGGORYEONGJA | \n", + "SEDAE | \n", + "SEDAEDANGINGU | \n", + "YEOJA_1 | \n", + "YEOJA_2 | \n", + "YEOJA_3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "합계 | \n", + "2016 | \n", + "10204057 | \n", + "9930616 | \n", + "273441 | \n", + "합계 | \n", + "5007379 | \n", + "4876789 | \n", + "130590 | \n", + "1300877 | \n", + "4189839 | \n", + "2.37 | \n", + "5196678 | \n", + "5053827 | \n", + "142851 | \n", + "
| 1 | \n", + "소계 | \n", + "2016 | \n", + "161922 | \n", + "152737 | \n", + "9185 | \n", + "종로구 | \n", + "79322 | \n", + "75201 | \n", + "4121 | \n", + "25091 | \n", + "72118 | \n", + "2.12 | \n", + "82600 | \n", + "77536 | \n", + "5064 | \n", + "
| 2 | \n", + "사직동 | \n", + "2016 | \n", + "10013 | \n", + "9695 | \n", + "318 | \n", + "종로구 | \n", + "4605 | \n", + "4429 | \n", + "176 | \n", + "1707 | \n", + "4399 | \n", + "2.20 | \n", + "5408 | \n", + "5266 | \n", + "142 | \n", + "
| 3 | \n", + "삼청동 | \n", + "2016 | \n", + "3118 | \n", + "2903 | \n", + "215 | \n", + "종로구 | \n", + "1491 | \n", + "1377 | \n", + "114 | \n", + "624 | \n", + "1403 | \n", + "2.07 | \n", + "1627 | \n", + "1526 | \n", + "101 | \n", + "
| 4 | \n", + "부암동 | \n", + "2016 | \n", + "11044 | \n", + "10616 | \n", + "428 | \n", + "종로구 | \n", + "5349 | \n", + "5160 | \n", + "189 | \n", + "1723 | \n", + "4371 | \n", + "2.43 | \n", + "5695 | \n", + "5456 | \n", + "239 | \n", + "
"
+ ],
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 조사지점코드 조사지점명 구코드 동코드 주번지 부번지 도로명 보도너비 \\\n",
+ "0 01-003 신흥모피명품전문크리닝. 11010.0 1101055.0 127 11 NaN 3.0 \n",
+ "1 01-004 GS25 11010.0 1101055.0 94 2 세검정로 230 3.0 \n",
+ "2 01-005 세검정정류장 11010.0 1101055.0 92 0 세검정길 4.0 \n",
+ "3 01-008 안성타워內 굿모닝파워공인중개사. 11010.0 1101056.0 72 72 NaN 4.0 \n",
+ "4 01-009 복실 손뜨기. 11010.0 1101056.0 88 46 NaN 2.0 \n",
+ "\n",
+ " 차선수 버스차로유무 ... 지구중심상세내용 도심부도심지역명 용도구분 거주유형구분 입지유형명 X좌표 \\\n",
+ "0 8.0 유 ... NaN NaN 2종주거 NaN NaN 196423.97707 \n",
+ "1 7.0 유 ... NaN NaN 2종주거 NaN NaN 196315.80243 \n",
+ "2 5.0 유 ... NaN NaN 1종주거 NaN NaN 196357.17125 \n",
+ "3 4.0 유 ... NaN NaN 2종주거 NaN NaN 197904.19277 \n",
+ "4 7.0 유 ... NaN NaN 2종주거 NaN NaN 196360.44943 \n",
+ "\n",
+ " Y좌표 집계구코드 년도 조사구분 \n",
+ "0 455511.52968 1.101055e+12 2015 본조사 \n",
+ "1 455621.38262 1.101055e+12 2015 본조사 \n",
+ "2 455680.82580 1.101055e+12 2015 본조사 \n",
+ "3 456718.34996 1.101056e+12 2015 본조사 \n",
+ "4 456405.89296 1.101056e+12 2015 본조사 \n",
+ "\n",
+ "[5 rows x 37 columns]"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 조사지점 위치 읽어오기\n",
+ "조사지점 = pd.read_excel('data/서울시유동인구/4_유동인구_조사지점정보_2015.xlsx', \n",
+ " skiprows = [0,1,3])\n",
+ "측정위치.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 조사지점코드 | \n", + "조사지점명 | \n", + "구코드 | \n", + "동코드 | \n", + "주번지 | \n", + "부번지 | \n", + "도로명 | \n", + "보도너비 | \n", + "차선수 | \n", + "버스차로유무 | \n", + "... | \n", + "지구중심상세내용 | \n", + "도심부도심지역명 | \n", + "용도구분 | \n", + "거주유형구분 | \n", + "입지유형명 | \n", + "X좌표 | \n", + "Y좌표 | \n", + "집계구코드 | \n", + "년도 | \n", + "조사구분 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "01-003 | \n", + "신흥모피명품전문크리닝. | \n", + "11010.0 | \n", + "1101055.0 | \n", + "127 | \n", + "11 | \n", + "NaN | \n", + "3.0 | \n", + "8.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "2종주거 | \n", + "NaN | \n", + "NaN | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "1.101055e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
| 1 | \n", + "01-004 | \n", + "GS25 | \n", + "11010.0 | \n", + "1101055.0 | \n", + "94 | \n", + "2 | \n", + "세검정로 230 | \n", + "3.0 | \n", + "7.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "2종주거 | \n", + "NaN | \n", + "NaN | \n", + "196315.80243 | \n", + "455621.38262 | \n", + "1.101055e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
| 2 | \n", + "01-005 | \n", + "세검정정류장 | \n", + "11010.0 | \n", + "1101055.0 | \n", + "92 | \n", + "0 | \n", + "세검정길 | \n", + "4.0 | \n", + "5.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "1종주거 | \n", + "NaN | \n", + "NaN | \n", + "196357.17125 | \n", + "455680.82580 | \n", + "1.101055e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
| 3 | \n", + "01-008 | \n", + "안성타워內 굿모닝파워공인중개사. | \n", + "11010.0 | \n", + "1101056.0 | \n", + "72 | \n", + "72 | \n", + "NaN | \n", + "4.0 | \n", + "4.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "2종주거 | \n", + "NaN | \n", + "NaN | \n", + "197904.19277 | \n", + "456718.34996 | \n", + "1.101056e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
| 4 | \n", + "01-009 | \n", + "복실 손뜨기. | \n", + "11010.0 | \n", + "1101056.0 | \n", + "88 | \n", + "46 | \n", + "NaN | \n", + "2.0 | \n", + "7.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "2종주거 | \n", + "NaN | \n", + "NaN | \n", + "196360.44943 | \n", + "456405.89296 | \n", + "1.101056e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
5 rows × 37 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 조사지점명 X좌표 Y좌표\n",
+ "0 신흥모피명품전문크리닝. 196423.97707 455511.52968\n",
+ "1 GS25 196315.80243 455621.38262\n",
+ "2 세검정정류장 196357.17125 455680.82580\n",
+ "3 안성타워內 굿모닝파워공인중개사. 197904.19277 456718.34996\n",
+ "4 복실 손뜨기. 196360.44943 456405.89296\n",
+ "5 24시 동대문 설렁탕. 200966.43423 452483.73642\n",
+ "6 서울전문학교. 201686.66781 452747.41594\n",
+ "7 서울 종묘농약 200231.75517 452355.91115\n",
+ "8 서울 종묘농약 200231.75517 452355.91115\n",
+ "9 삼성카드빌딩 199935.57893 452934.78947\n",
+ "10 TIFS(성우빌딩) 199800.95512 453650.39376\n",
+ "11 TIFS(성우빌딩) 199800.95512 453650.39376\n",
+ "12 KFC 200202.62257 453589.66617\n",
+ "13 영안빌딩(미샤) 198688.03274 452134.47214\n",
+ "14 영안빌딩(미샤) 198688.03274 452134.47214\n",
+ "15 우리은행 198735.59303 452269.98495\n",
+ "16 종묘공원 199613.61204 452366.33351\n",
+ "17 U+ 199171.23557 452305.62273\n",
+ "18 U+ 199171.23557 452305.62273\n",
+ "19 성종사 198489.34152 452574.37932\n",
+ "20 금호미술관 198191.61215 453299.24582\n",
+ "21 남원추어탕 맞은편. 197468.53801 453960.58038\n",
+ "22 대영빌딩 197440.64238 452917.23467\n",
+ "23 픽스딕스. 198188.23447 452301.16163\n",
+ "24 세실치과의원 196630.61453 452352.58803\n",
+ "25 금호아시아나 1관. 197617.63370 452261.08752\n",
+ "26 lg광화문 빌딩 197472.52625 452270.54377\n",
+ "27 안경마을 콘텍트 198082.11244 452594.04638\n",
+ "28 세란병원. 196239.36250 452844.44602\n",
+ "29 에이스조명 199797.32477 452206.28026\n",
+ "30 매헌빌딩 199870.61751 452367.06857\n",
+ "31 경복궁 돌담길 경복궁동문 정류장에서 춘추관앞 방향으로 60M 198167.84898 453088.98273\n",
+ "32 버스정류장에서 도로 바라보고 좌측20미터 일성툴 199417.15706 452126.23062\n",
+ "33 우리은행 198720.17896 452308.82284\n",
+ "34 Tworld 대리점 199080.59246 452271.65642\n",
+ "35 준코 뮤직타운 198721.18954 452307.83355\n",
+ "36 준코 뮤직타운 198721.18954 452307.83355\n",
+ "37 광화문 빌딩 197893.71130 452255.02517\n",
+ "38 서울보증보험 조형물 200053.53059 452658.03034\n",
+ "39 효제초교 지하주차장입구 200248.77419 452636.10706\n",
+ "40 대일빌딩 200525.50255 452569.04854\n",
+ "41 운촌빌딩 200456.34661 452677.31833\n",
+ "42 국일관 199089.97499 452235.75446\n",
+ "43 성제종합휘장타운 199087.99675 452139.72858\n",
+ "44 안국역 6번 출구 건너편 198593.37554 452947.50485\n",
+ "45 농협중앙회 앞 198572.80445 452531.26545\n",
+ "46 SINDO 앞 197602.32079 453323.97458\n",
+ "47 감사원뒷길 198670.87338 454122.38772\n",
+ "48 서울 닭 문화관 198675.39755 453697.41488\n",
+ "49 애지중지 198826.12373 453442.00663"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "측정위치50 = 측정위치[['조사지점명', 'X좌표', 'Y좌표']].iloc[:50]\n",
+ "측정위치50\n",
+ "# 50개의 측정 위치에 대해 필요한 정보만 저장"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 위치 정보 변환을 위한 라이브러리 설치\n",
+ "# pip install pyproj\n",
+ "#conda install pyproj\n",
+ "from pyproj import Proj, transform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# X좌표, Y좌표를 위도와 경도로 변환하는 함수 선언\n",
+ "def convert(x,y):\n",
+ " inProj = Proj(init = 'epsg:5181')\n",
+ " outProj = Proj(init = 'epsg:4326')\n",
+ " x2,y2 = transform(inProj, outProj, x, y)\n",
+ " return [y2, x2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 조사지점명 | \n", + "X좌표 | \n", + "Y좌표 | \n", + "
|---|---|---|---|
| 0 | \n", + "신흥모피명품전문크리닝. | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "
| 1 | \n", + "GS25 | \n", + "196315.80243 | \n", + "455621.38262 | \n", + "
| 2 | \n", + "세검정정류장 | \n", + "196357.17125 | \n", + "455680.82580 | \n", + "
| 3 | \n", + "안성타워內 굿모닝파워공인중개사. | \n", + "197904.19277 | \n", + "456718.34996 | \n", + "
| 4 | \n", + "복실 손뜨기. | \n", + "196360.44943 | \n", + "456405.89296 | \n", + "
| 5 | \n", + "24시 동대문 설렁탕. | \n", + "200966.43423 | \n", + "452483.73642 | \n", + "
| 6 | \n", + "서울전문학교. | \n", + "201686.66781 | \n", + "452747.41594 | \n", + "
| 7 | \n", + "서울 종묘농약 | \n", + "200231.75517 | \n", + "452355.91115 | \n", + "
| 8 | \n", + "서울 종묘농약 | \n", + "200231.75517 | \n", + "452355.91115 | \n", + "
| 9 | \n", + "삼성카드빌딩 | \n", + "199935.57893 | \n", + "452934.78947 | \n", + "
| 10 | \n", + "TIFS(성우빌딩) | \n", + "199800.95512 | \n", + "453650.39376 | \n", + "
| 11 | \n", + "TIFS(성우빌딩) | \n", + "199800.95512 | \n", + "453650.39376 | \n", + "
| 12 | \n", + "KFC | \n", + "200202.62257 | \n", + "453589.66617 | \n", + "
| 13 | \n", + "영안빌딩(미샤) | \n", + "198688.03274 | \n", + "452134.47214 | \n", + "
| 14 | \n", + "영안빌딩(미샤) | \n", + "198688.03274 | \n", + "452134.47214 | \n", + "
| 15 | \n", + "우리은행 | \n", + "198735.59303 | \n", + "452269.98495 | \n", + "
| 16 | \n", + "종묘공원 | \n", + "199613.61204 | \n", + "452366.33351 | \n", + "
| 17 | \n", + "U+ | \n", + "199171.23557 | \n", + "452305.62273 | \n", + "
| 18 | \n", + "U+ | \n", + "199171.23557 | \n", + "452305.62273 | \n", + "
| 19 | \n", + "성종사 | \n", + "198489.34152 | \n", + "452574.37932 | \n", + "
| 20 | \n", + "금호미술관 | \n", + "198191.61215 | \n", + "453299.24582 | \n", + "
| 21 | \n", + "남원추어탕 맞은편. | \n", + "197468.53801 | \n", + "453960.58038 | \n", + "
| 22 | \n", + "대영빌딩 | \n", + "197440.64238 | \n", + "452917.23467 | \n", + "
| 23 | \n", + "픽스딕스. | \n", + "198188.23447 | \n", + "452301.16163 | \n", + "
| 24 | \n", + "세실치과의원 | \n", + "196630.61453 | \n", + "452352.58803 | \n", + "
| 25 | \n", + "금호아시아나 1관. | \n", + "197617.63370 | \n", + "452261.08752 | \n", + "
| 26 | \n", + "lg광화문 빌딩 | \n", + "197472.52625 | \n", + "452270.54377 | \n", + "
| 27 | \n", + "안경마을 콘텍트 | \n", + "198082.11244 | \n", + "452594.04638 | \n", + "
| 28 | \n", + "세란병원. | \n", + "196239.36250 | \n", + "452844.44602 | \n", + "
| 29 | \n", + "에이스조명 | \n", + "199797.32477 | \n", + "452206.28026 | \n", + "
| 30 | \n", + "매헌빌딩 | \n", + "199870.61751 | \n", + "452367.06857 | \n", + "
| 31 | \n", + "경복궁 돌담길 경복궁동문 정류장에서 춘추관앞 방향으로 60M | \n", + "198167.84898 | \n", + "453088.98273 | \n", + "
| 32 | \n", + "버스정류장에서 도로 바라보고 좌측20미터 일성툴 | \n", + "199417.15706 | \n", + "452126.23062 | \n", + "
| 33 | \n", + "우리은행 | \n", + "198720.17896 | \n", + "452308.82284 | \n", + "
| 34 | \n", + "Tworld 대리점 | \n", + "199080.59246 | \n", + "452271.65642 | \n", + "
| 35 | \n", + "준코 뮤직타운 | \n", + "198721.18954 | \n", + "452307.83355 | \n", + "
| 36 | \n", + "준코 뮤직타운 | \n", + "198721.18954 | \n", + "452307.83355 | \n", + "
| 37 | \n", + "광화문 빌딩 | \n", + "197893.71130 | \n", + "452255.02517 | \n", + "
| 38 | \n", + "서울보증보험 조형물 | \n", + "200053.53059 | \n", + "452658.03034 | \n", + "
| 39 | \n", + "효제초교 지하주차장입구 | \n", + "200248.77419 | \n", + "452636.10706 | \n", + "
| 40 | \n", + "대일빌딩 | \n", + "200525.50255 | \n", + "452569.04854 | \n", + "
| 41 | \n", + "운촌빌딩 | \n", + "200456.34661 | \n", + "452677.31833 | \n", + "
| 42 | \n", + "국일관 | \n", + "199089.97499 | \n", + "452235.75446 | \n", + "
| 43 | \n", + "성제종합휘장타운 | \n", + "199087.99675 | \n", + "452139.72858 | \n", + "
| 44 | \n", + "안국역 6번 출구 건너편 | \n", + "198593.37554 | \n", + "452947.50485 | \n", + "
| 45 | \n", + "농협중앙회 앞 | \n", + "198572.80445 | \n", + "452531.26545 | \n", + "
| 46 | \n", + "SINDO 앞 | \n", + "197602.32079 | \n", + "453323.97458 | \n", + "
| 47 | \n", + "감사원뒷길 | \n", + "198670.87338 | \n", + "454122.38772 | \n", + "
| 48 | \n", + "서울 닭 문화관 | \n", + "198675.39755 | \n", + "453697.41488 | \n", + "
| 49 | \n", + "애지중지 | \n", + "198826.12373 | \n", + "453442.00663 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 조사지점명 X좌표 Y좌표 \\\n",
+ "0 신흥모피명품전문크리닝. 196423.97707 455511.52968 \n",
+ "1 GS25 196315.80243 455621.38262 \n",
+ "2 세검정정류장 196357.17125 455680.82580 \n",
+ "\n",
+ " 위경도 \n",
+ "0 [37.5991696223151, 126.95950527754184] \n",
+ "1 [37.60015896037769, 126.95827976042075] \n",
+ "2 [37.60069470246659, 126.95874792945385] "
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#측정위치50에 위도와 경도 정보를 추가 # row 단위 # lambda : row의 갯수만큼 반복하게 도와줌\n",
+ "측정위치50['위경도'] = 측정위치50.apply(lambda 지점: convert(지점['X좌표'], 지점['Y좌표']), axis = 1)\n",
+ "측정위치50.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#측정위치 50개를 지도에 표시\n",
+ "for row in 측정위치50.iterrows():\n",
+ " folium.Marker(row[1]['위경도'], popup = row[1]['조사지점명']).add_to(map1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 조사지점명 | \n", + "X좌표 | \n", + "Y좌표 | \n", + "위경도 | \n", + "
|---|---|---|---|---|
| 0 | \n", + "신흥모피명품전문크리닝. | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "[37.5991696223151, 126.95950527754184] | \n", + "
| 1 | \n", + "GS25 | \n", + "196315.80243 | \n", + "455621.38262 | \n", + "[37.60015896037769, 126.95827976042075] | \n", + "
| 2 | \n", + "세검정정류장 | \n", + "196357.17125 | \n", + "455680.82580 | \n", + "[37.60069470246659, 126.95874792945385] | \n", + "
"
+ ],
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID유동인구조사 조사지점코드 조사구분 조사요일 시간대 유동인구수 년도\n",
+ "0 1 30-001 지하철 금 07시-08시 129.0 2015\n",
+ "1 2 30-001 지하철 금 08시-09시 112.5 2015\n",
+ "2 3 30-001 지하철 금 09시-10시 108.0 2015\n",
+ "3 4 30-001 지하철 금 10시-11시 94.5 2015\n",
+ "4 5 30-001 지하철 금 11시-12시 87.0 2015"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 데이터 적재\n",
+ "유동인구 = pd.read_excel('data/서울시유동인구/0_유동인구_유동인구기본_2015.xlsx', \n",
+ " skiprows = [0,1,3])\n",
+ "유동인구.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = pd.read_excel('data/서울시유동인구/4_유동인구_조사지점정보_2015.xlsx', \n",
+ " sheet_name = None, skiprows = [0,1,3])\n",
+ "# 구코드, 동코드, 구명, 동명 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "odict_keys(['유동인구_조사지점정보_2015', 'FTPTH_STLE_CN', 'GU_CD', 'DONG_CD', 'SM_GU_CD'])"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.keys()\n",
+ "# 5개의 시트정보"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "조사지점 = data.get('유동인구_조사지점정보_2015')\n",
+ "구코드 = data.get('GU_CD')\n",
+ "동코드 = data.get('DONG_CD')\n",
+ "집계구코드 = data.get('SM_GU_CD')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | ID유동인구조사 | \n", + "조사지점코드 | \n", + "조사구분 | \n", + "조사요일 | \n", + "시간대 | \n", + "유동인구수 | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "07시-08시 | \n", + "129.0 | \n", + "2015 | \n", + "
| 1 | \n", + "2 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "08시-09시 | \n", + "112.5 | \n", + "2015 | \n", + "
| 2 | \n", + "3 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "09시-10시 | \n", + "108.0 | \n", + "2015 | \n", + "
| 3 | \n", + "4 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "10시-11시 | \n", + "94.5 | \n", + "2015 | \n", + "
| 4 | \n", + "5 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "11시-12시 | \n", + "87.0 | \n", + "2015 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " ID유동인구조사 조사지점코드 조사구분 조사요일 시간대 유동인구수 년도\n",
+ "0 1 30-001 지하철 금 07시-08시 129.0 2015"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | ID유동인구조사 | \n", + "조사지점코드 | \n", + "조사구분 | \n", + "조사요일 | \n", + "시간대 | \n", + "유동인구수 | \n", + "년도 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "30-001 | \n", + "지하철 | \n", + "금 | \n", + "07시-08시 | \n", + "129.0 | \n", + "2015 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " 집계구코드 구코드 동코드 구명 동명 연도 집계구명칭\n",
+ "0 1104070010005 1104 1104070 성동구 용답동 2013 영해약국"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "집계구코드.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 집계구코드 | \n", + "구코드 | \n", + "동코드 | \n", + "구명 | \n", + "동명 | \n", + "연도 | \n", + "집계구명칭 | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "1104070010005 | \n", + "1104 | \n", + "1104070 | \n", + "성동구 | \n", + "용답동 | \n", + "2013 | \n", + "영해약국 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " 조사지점코드 조사지점명 구코드 동코드 주번지 부번지 도로명 보도너비 차선수 버스차로유무 \\\n",
+ "0 01-003 신흥모피명품전문크리닝. 11010.0 1101055.0 127 11 NaN 3.0 8.0 유 \n",
+ "\n",
+ " ... 지구중심상세내용 도심부도심지역명 용도구분 거주유형구분 입지유형명 X좌표 Y좌표 \\\n",
+ "0 ... NaN NaN 2종주거 NaN NaN 196423.97707 455511.52968 \n",
+ "\n",
+ " 집계구코드 년도 조사구분 \n",
+ "0 1.101055e+12 2015 본조사 \n",
+ "\n",
+ "[1 rows x 37 columns]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "조사지점.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 조사지점[['조사지점코드', '조사지점명', '구코드', '동코드', 'X좌표', 'Y좌표', '집계구코드']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 구코드[['구코드', '구명']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 동코드[['동코드', '동명']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 조사지점코드 | \n", + "조사지점명 | \n", + "구코드 | \n", + "동코드 | \n", + "주번지 | \n", + "부번지 | \n", + "도로명 | \n", + "보도너비 | \n", + "차선수 | \n", + "버스차로유무 | \n", + "... | \n", + "지구중심상세내용 | \n", + "도심부도심지역명 | \n", + "용도구분 | \n", + "거주유형구분 | \n", + "입지유형명 | \n", + "X좌표 | \n", + "Y좌표 | \n", + "집계구코드 | \n", + "년도 | \n", + "조사구분 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "01-003 | \n", + "신흥모피명품전문크리닝. | \n", + "11010.0 | \n", + "1101055.0 | \n", + "127 | \n", + "11 | \n", + "NaN | \n", + "3.0 | \n", + "8.0 | \n", + "유 | \n", + "... | \n", + "NaN | \n", + "NaN | \n", + "2종주거 | \n", + "NaN | \n", + "NaN | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "1.101055e+12 | \n", + "2015 | \n", + "본조사 | \n", + "
1 rows × 37 columns
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " ID유동인구조사 조사지점코드 조사구분 조사요일 시간대 유동인구수 년도 조사지점명 구코드 \\\n",
+ "0 3067 01-003 본조사 금 07시-08시 21.0 2015 신흥모피명품전문크리닝. 11010.0 \n",
+ "1 3068 01-003 본조사 금 08시-09시 36.0 2015 신흥모피명품전문크리닝. 11010.0 \n",
+ "\n",
+ " 동코드 X좌표 Y좌표 집계구코드 구명 동명 \n",
+ "0 1101055.0 196423.97707 455511.52968 1.101055e+12 종로구 부암동 \n",
+ "1 1101055.0 196423.97707 455511.52968 1.101055e+12 종로구 부암동 "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "유동인구.head(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = 유동인구[유동인구.조사구분=='본조사']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 1. 유동인구를 요일과 시간대 별로 분석하여, 유동인구가 가장 많은 상위 10개의 요일과 시간대를 출력. (e.g., 금요일 18시-19시가 유동인구가 가장 많음.)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구.pivot_table(index = ['조사요일', '시간대'], values = '유동인구수', aggfunc = 'sum') \\\n",
+ " .sort_values(by = '유동인구수', ascending = False).head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 2\t. 서울시 각 구별 전체 유동인구 분석"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "구별유동인구 = 유동인구.pivot_table(index = '구명', values = '유동인구수', aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | ID유동인구조사 | \n", + "조사지점코드 | \n", + "조사구분 | \n", + "조사요일 | \n", + "시간대 | \n", + "유동인구수 | \n", + "년도 | \n", + "조사지점명 | \n", + "구코드 | \n", + "동코드 | \n", + "X좌표 | \n", + "Y좌표 | \n", + "집계구코드 | \n", + "구명 | \n", + "동명 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "3067 | \n", + "01-003 | \n", + "본조사 | \n", + "금 | \n", + "07시-08시 | \n", + "21.0 | \n", + "2015 | \n", + "신흥모피명품전문크리닝. | \n", + "11010.0 | \n", + "1101055.0 | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "1.101055e+12 | \n", + "종로구 | \n", + "부암동 | \n", + "
| 1 | \n", + "3068 | \n", + "01-003 | \n", + "본조사 | \n", + "금 | \n", + "08시-09시 | \n", + "36.0 | \n", + "2015 | \n", + "신흥모피명품전문크리닝. | \n", + "11010.0 | \n", + "1101055.0 | \n", + "196423.97707 | \n", + "455511.52968 | \n", + "1.101055e+12 | \n", + "종로구 | \n", + "부암동 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 유동인구수\n",
+ "구명 \n",
+ "중구 7783989.0\n",
+ "종로구 5587683.0\n",
+ "서초구 4392018.0\n",
+ "강남구 3831897.0\n",
+ "구로구 2333352.0\n",
+ "관악구 2295348.0\n",
+ "영등포구 2200728.0\n",
+ "노원구 2119686.0\n",
+ "동작구 2104242.0\n",
+ "강동구 2007342.0\n",
+ "용산구 1964184.0\n",
+ "송파구 1786455.0\n",
+ "마포구 1724937.0\n",
+ "양천구 1633581.0\n",
+ "광진구 1472685.0\n",
+ "서대문구 1452975.0\n",
+ "은평구 1348008.0\n",
+ "강북구 1228965.0\n",
+ "동대문구 1185888.0\n",
+ "성동구 1180284.0\n",
+ "성북구 1176366.0\n",
+ "강서구 1075908.0\n",
+ "중랑구 957777.0\n",
+ "금천구 862296.0\n",
+ "도봉구 742461.0"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "구별유동인구.sort_values(by = '유동인구수', ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | 유동인구수 | \n", + "
|---|---|
| 구명 | \n", + "\n", + " |
| 중구 | \n", + "7783989.0 | \n", + "
| 종로구 | \n", + "5587683.0 | \n", + "
| 서초구 | \n", + "4392018.0 | \n", + "
| 강남구 | \n", + "3831897.0 | \n", + "
| 구로구 | \n", + "2333352.0 | \n", + "
| 관악구 | \n", + "2295348.0 | \n", + "
| 영등포구 | \n", + "2200728.0 | \n", + "
| 노원구 | \n", + "2119686.0 | \n", + "
| 동작구 | \n", + "2104242.0 | \n", + "
| 강동구 | \n", + "2007342.0 | \n", + "
| 용산구 | \n", + "1964184.0 | \n", + "
| 송파구 | \n", + "1786455.0 | \n", + "
| 마포구 | \n", + "1724937.0 | \n", + "
| 양천구 | \n", + "1633581.0 | \n", + "
| 광진구 | \n", + "1472685.0 | \n", + "
| 서대문구 | \n", + "1452975.0 | \n", + "
| 은평구 | \n", + "1348008.0 | \n", + "
| 강북구 | \n", + "1228965.0 | \n", + "
| 동대문구 | \n", + "1185888.0 | \n", + "
| 성동구 | \n", + "1180284.0 | \n", + "
| 성북구 | \n", + "1176366.0 | \n", + "
| 강서구 | \n", + "1075908.0 | \n", + "
| 중랑구 | \n", + "957777.0 | \n", + "
| 금천구 | \n", + "862296.0 | \n", + "
| 도봉구 | \n", + "742461.0 | \n", + "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "구별유동인구.plot(kind = 'barh',figsize = (10,8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 3. 서울시 각 구별로 출퇴근 시간의 유동인구 분석 (출퇴근 시간은 6시-10시, 18시-21시로 가정함)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "구별시간대별유동인구 = 유동인구.pivot_table(index = ['구명', '시간대'], values = '유동인구수', aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대 = list(np.unique(유동인구['시간대'])[0:3]) + list(np.unique(유동인구['시간대'])[-3:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구 = 구별시간대별유동인구[구별시간대별유동인구.index.get_level_values('시간대').isin(출퇴근시간대)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구 = 출퇴근시간대구별유동인구.unstack()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구.plot(kind = 'barh', stacked = True, figsize = (10,10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 4. 평일 유동인구와 주말 유동인구를 분석하여, 그 차이가 가장 많이 나는 상위 5개 구 정보를 출력\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "조사요일 금 목 수 월 토 화\n",
+ "구명 \n",
+ "강남구 831537.0 NaN 786945.0 763788.0 678015.0 771612.0\n",
+ "강동구 415683.0 NaN 400101.0 387411.0 422109.0 382038.0\n",
+ "강북구 248163.0 NaN 246363.0 241245.0 252609.0 240585.0\n",
+ "강서구 216459.0 NaN 219156.0 217440.0 204612.0 218241.0\n",
+ "관악구 483966.0 NaN 473145.0 462741.0 414660.0 460836.0\n",
+ "광진구 296775.0 NaN 288054.0 281484.0 311508.0 294864.0\n",
+ "구로구 480456.0 NaN 474879.0 469695.0 441702.0 466620.0\n",
+ "금천구 179799.0 NaN 182733.0 180936.0 131775.0 187053.0\n",
+ "노원구 436677.0 NaN 426096.0 416238.0 421353.0 419322.0\n",
+ "도봉구 158115.0 NaN 153099.0 150927.0 134208.0 146112.0\n",
+ "동대문구 246735.0 717.0 240081.0 238452.0 222105.0 237798.0\n",
+ "동작구 435240.0 5889.0 421491.0 418782.0 411021.0 411819.0\n",
+ "마포구 364620.0 15873.0 337584.0 329019.0 360222.0 317619.0\n",
+ "서대문구 295785.0 NaN 300354.0 294792.0 250437.0 311607.0\n",
+ "서초구 924369.0 3150.0 872241.0 854952.0 864129.0 873177.0\n",
+ "성동구 241830.0 NaN 240942.0 237183.0 217677.0 242652.0\n",
+ "성북구 249603.0 NaN 238350.0 234726.0 224070.0 229617.0\n",
+ "송파구 360699.0 NaN 350838.0 338676.0 389028.0 347214.0\n",
+ "양천구 339801.0 NaN 335472.0 324081.0 304281.0 329946.0\n",
+ "영등포구 463551.0 3552.0 441756.0 431580.0 422748.0 437541.0\n",
+ "용산구 408657.0 NaN 384765.0 366411.0 431826.0 372525.0\n",
+ "은평구 274662.0 NaN 272100.0 266055.0 272460.0 262731.0\n",
+ "종로구 1179027.0 NaN 1094118.0 1039425.0 1221630.0 1053483.0\n",
+ "중구 1563354.0 NaN 1496586.0 1426587.0 1783842.0 1513620.0\n",
+ "중랑구 191439.0 NaN 188961.0 187746.0 203190.0 186441.0"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "요일별구별유동인구 = 유동인구.pivot_table(index = '구명', columns = '조사요일', values = '유동인구수', aggfunc = 'sum')\n",
+ "요일별구별유동인구"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주중'] = (요일별구별유동인구['월'] + 요일별구별유동인구['화'] + \n",
+ " 요일별구별유동인구['수']+요일별구별유동인구['목']+요일별구별유동인구['금'])/5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주말'] = 요일별구별유동인구['토']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주중주말편차'] = (요일별구별유동인구['주중']-요일별구별유동인구['주말']).abs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "조사요일 금 목 수 월 토 화 주중 주말 \\\n",
+ "구명 \n",
+ "강남구 831537.0 NaN 786945.0 763788.0 678015.0 771612.0 NaN 678015.0 \n",
+ "강동구 415683.0 NaN 400101.0 387411.0 422109.0 382038.0 NaN 422109.0 \n",
+ "\n",
+ "조사요일 주중주말편차 \n",
+ "구명 \n",
+ "강남구 NaN \n",
+ "강동구 NaN "
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "요일별구별유동인구.head(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구 = 요일별구별유동인구[['주중','주말', '주중주말편차']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| 조사요일 | \n", + "금 | \n", + "목 | \n", + "수 | \n", + "월 | \n", + "토 | \n", + "화 | \n", + "주중 | \n", + "주말 | \n", + "주중주말편차 | \n", + "
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+ "조사요일 주중 주말 주중주말편차\n",
+ "구명 \n",
+ "서초구 705577.8 864129.0 158551.2\n",
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+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "요일별구별유동인구.sort_values(by = '주중주말편차', ascending= False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "요일별구별유동인구.plot(kind = 'barh', figsize = (10,10))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "요일별구별유동인구.주중주말편차.plot(kind = 'bar')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 5. 유동인구의 수와 보행환경 정보를 분석하여, 보행 환경 개선이 시급한 곳 10군데를 선정. (보행환경 정보는 유동인구상세로그_2015.xlsx의 보행환경 컬럼 참고. 매우불만족(1), 약간불만족(2), 보통(3), 약간만족(4), 매우만족(5) )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 보행평균점수 - [1,5]\n",
+ "#불만족비율 - 전체 응답 중에 매우불만족과 약간불만족의 비율 [0,1]\n",
+ "#유동인구등급(유동인구수 많은 곳을 더 시급하게 개선이 필요하다고 생각) - 유동인구수를 10등급으로 나눔, [0,9]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구_속성조사 = pd.read_excel('data/서울시유동인구/2_유동인구_속성조사_2015.xlsx', \n",
+ " skiprows = [0,1,3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def 보행환경(만족도):\n",
+ " if 만족도 == '매우만족':\n",
+ " return 5\n",
+ " elif 만족도 == '약간만족':\n",
+ " return 4\n",
+ " elif 만족도 == '보통':\n",
+ " return 3\n",
+ " elif 만족도 == '약간불만족':\n",
+ " return 2\n",
+ " elif 만족도 == '매우불만족':\n",
+ " return 1\n",
+ " else:\n",
+ " return 0\n",
+ " \n",
+ "지점별보행점수 = 유동인구_속성조사.pivot_table(index = '조사지점코드', values = '보행점수',aggfunc = 'mean')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#2\n",
+ "보행환경매트릭스=유동인구_속성조사.pivot_table(index = '조사지점코드', columns = '보행환경', values = 'ID속성조사', aggfunc = 'count',fill_value= 0 )\n",
+ "보행환경매트릭스['불만족비율'] = (보행환경매트릭스.매우불만족+보행환경매트릭스.약간불만족)/보행환경매트릭스.sum(axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ", line 1)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m 환경개선지점50['위경도'] = 환경개선지점50.apply(lamda 지점 : convert_locatin(지점.X좌표, 지점.Y좌표), axis=1)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
+ ],
+ "source": [
+ "환경개선지점50['위경도'] = 환경개선지점50.apply(lamda 지점 : convert_locatin(지점.X좌표, 지점.Y좌표), axis=1)\n",
+ "# X좌표, Y좌표를 위도와 경도로 변환하는 함수 선언\n",
+ "def convert(x,y):\n",
+ " inProj = Proj(init = 'epsg:5181')\n",
+ " outProj = Proj(init = 'epsg:4326')\n",
+ " x2,y2 = transform(inProj, outProj, x, y)\n",
+ " return [y2, x2]\n",
+ "map2 = foluim.Map(location = [37.56535,126.977], zoom_start = 15)\n",
+ "for row in 환경개선지점50.interrows():\n",
+ " folium.Marker(row[1]['위경도'], popup =row[1]['조사지점명'])add_to(map2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_msg(x):\n",
+ " string = '조사지점명: {0.조사지점명}\\n개선시급도:{0.개선시급도:.2f}'.format()\n",
+ " return string"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'map2' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmap2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m: name 'map2' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "map2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#ClusterMarker 실습\n",
+ "map3 = folium.Map(location = [37.56535,126.977], zoom_start = 15)\n",
+ "from folium.plugins import MarkerCluster\n",
+ "mc = MarkerCluster().add_to(map3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "SyntaxError",
+ "evalue": "invalid syntax (, line 3)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m folium.Marker(row[1]['위경도'], popup =row[1]['조사지점명'])add_to(mc)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 각각의 마커를 클러스터 마커에 추가\n",
+ "for row in 환경개선지점50.interrows():\n",
+ " folium.Marker(row[1]['위경도'], popup =row[1]['조사지점명'])add_to(mc)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'map3' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmap3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m: name 'map3' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "map3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def 연령대(나이):\n",
+ " if 나이 == '65세이상':\n",
+ " return 3\n",
+ " else : \n",
+ " return 0\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def 방문횟수(횟수):\n",
+ " if 횟수 =='매일':\n",
+ " return 6\n",
+ " elif 횟수 == '주3~5회':\n",
+ " return 5\n",
+ " elif 횟수 == '주1~2회':\n",
+ " return 4\n",
+ " elif 횟수 == '월1~2회':\n",
+ " return 3\n",
+ " elif 횟수 == '6개월1~3회':\n",
+ " return 2\n",
+ " elif 횟수 =='오늘처음':\n",
+ " return 1\n",
+ " else:\n",
+ " return 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구_속성조사['보행점수'] = 유동인구_속성조사.보행환경.apply(보행환경)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구_속성조사['연령대점수'] = 유동인구_속성조사.연령대.apply(연령대)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#folium 라이브러리 설치\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# anaconda prompt에서 'pip install folium' 실행\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'folium'"
+ ]
+ }
+ ],
+ "source": [
+ "#folium 라이브러리 설치\n",
+ "# anaconda prompt에서 'pip install folium' 실행\n",
+ "import folium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "map1 = folium.Map(location = [37.566535, 126.97796919999996], zoom_start = 15)\n",
+ "map1\n",
+ "# 서울시청을 지도의 중심으로, 지도를 그려줌. \n",
+ "# zoom_start는 지도를 그릴 때 얼마나 zoom 해서 보여줄 지를 결정. 값이 클수록, zoom in"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Marker 추가하기 (지도에 지점 추가하기)\n",
+ "folium.Marker([37, 126], popup = '테스트지점').add_to(map1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "map1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "측정위치 = pd.read_excel('data/서울시유동인구/4_유동인구_조사지점정보_2015.xlsx', \n",
+ " skiprows = [0,1,3])\n",
+ "측정위치.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "측정위치50 = 측정위치[['조사지점명', 'X좌표', 'Y좌표']].iloc[:50]\n",
+ "측정위치50\n",
+ "# 50개의 측정 위치에 대해 필요한 정보만 저장"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 위치 정보 변환을 위한 라이브러리 설치\n",
+ "# pip install pyproj\n",
+ "#conda install pyproj\n",
+ "from pyproj import Proj, transform"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# X좌표, Y좌표를 위도와 경도로 변환하는 함수 선언\n",
+ "def convert(x,y):\n",
+ " inProj = Proj(init = 'epsg:5181')\n",
+ " outProj = Proj(init = 'epsg:4326')\n",
+ " x2,y2 = transform(inProj, outProj, x, y)\n",
+ " return [y2, x2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#측정위치50에 위도와 경도 정보를 추가\n",
+ "측정위치50['위경도'] = 측정위치50.apply(lambda 지점: convert(지점['X좌표'], 지점['Y좌표']), axis = 1)\n",
+ "측정위치50.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#측정위치 50개를 지도에 표시\n",
+ "for row in 측정위치50.iterrows():\n",
+ " folium.Marker(row[1]['위경도'], popup = row[1]['조사지점명']).add_to(map1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "map1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. 서울시 유동인구 분석"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 데이터 적재"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.read_excel('data/서울시유동인구/0_유동인구_유동인구기본_2015.xlsx', \n",
+ " skiprows = [0,1,3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = pd.read_excel('data/서울시유동인구/4_유동인구_조사지점정보_2015.xlsx', \n",
+ " sheetname = None, skiprows = [0,1,3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "조사지점 = data.get('유동인구_조사지점정보_2015')\n",
+ "구코드 = data.get('GU_CD')\n",
+ "동코드 = data.get('DONG_CD')\n",
+ "집계구코드 = data.get('SM_GU_CD')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "집계구코드.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 조사지점[['조사지점코드', '조사지점명', '구코드', '동코드', 'X좌표', 'Y좌표', '집계구코드']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 구코드[['구코드', '구명']])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구 = pd.merge(유동인구, 동코드[['동코드', '동명']])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 1. 유동인구를 요일과 시간대 별로 분석하여, 유동인구가 가장 많은 상위 10개의 요일과 시간대를 출력. (e.g., 금요일 18시-19시가 유동인구가 가장 많음.)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구.pivot_table(index = ['조사요일', '시간대'], values = '유동인구수', aggfunc = 'sum') \\\n",
+ " .sort_values(by = '유동인구수', ascending = False).head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 2\t. 서울시 각 구별 전체 유동인구 분석"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "구별유동인구 = 유동인구.pivot_table(index = '구명', values = '유동인구수', aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "구별유동인구.plot(kind = 'barh')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 3. 서울시 각 구별로 출퇴근 시간의 유동인구 분석 (출퇴근 시간은 6시-10시, 18시-21시로 가정함)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "구별시간대별유동인구 = 유동인구.pivot_table(index = ['구명', '시간대'], values = '유동인구수', aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대 = list(np.unique(유동인구['시간대'])[0:3]) + list(np.unique(유동인구['시간대'])[-3:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구 = 구별시간대별유동인구[구별시간대별유동인구.index.get_level_values('시간대').isin(출퇴근시간대)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구 = 출퇴근시간대구별유동인구.unstack()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "출퇴근시간대구별유동인구.plot(kind = 'barh', stacked = True, figsize = (10,10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 4. 평일 유동인구와 주말 유동인구를 분석하여, 그 차이가 가장 많이 나는 상위 5개 구 정보를 출력\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구 = 유동인구.pivot_table(index = '구명', columns = '조사요일', values = '유동인구수', aggfunc = 'sum')\n",
+ "요일별구별유동인구"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주중'] = (요일별구별유동인구['월'] + 요일별구별유동인구['화'] + \n",
+ " 요일별구별유동인구['수']+요일별구별유동인구['목']+요일별구별유동인구['금'])/5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주말'] = 요일별구별유동인구['토']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구['주중주말편차'] = (요일별구별유동인구['주중']-요일별구별유동인구['주말']).abs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구 = 요일별구별유동인구[['주중','주말', '주중주말편차']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구.sort_values(by = '주중주말편차', ascending= False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "요일별구별유동인구.plot(kind = 'barh', figsize = (10,10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 미션 5. 유동인구의 수와 보행환경 정보를 분석하여, 보행 환경 개선이 시급한 곳 10군데를 선정. (보행환경 정보는 유동인구상세로그_2015.xlsx의 보행환경 컬럼 참고. 매우불만족(1), 약간불만족(2), 보통(3), 약간만족(4), 매우만족(5) )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구_속성조사 = pd.read_excel('data/서울시유동인구/2_유동인구_속성조사_2015.xlsx', \n",
+ " skiprows = [0,1,3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def 보행환경(만족도):\n",
+ " if 만족도 == '매우만족':\n",
+ " return 5\n",
+ " elif 만족도 == '약간만족':\n",
+ " return 4\n",
+ " elif 만족도 == '보통':\n",
+ " return 3\n",
+ " elif 만족도 == '약간불만족':\n",
+ " return 2\n",
+ " elif 만족도 == '매우불만족':\n",
+ " return 1\n",
+ " else:\n",
+ " return 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구_속성조사['보행점수'] = 유동인구_속성조사.보행환경.apply(보행환경)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별유동인구 = 유동인구.pivot_table(index = '조사지점코드', values = '유동인구수', aggfunc = 'sum')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별보행점수 = 유동인구_속성조사.pivot_table(index = '조사지점코드', values = '보행점수', aggfunc = 'mean')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별 = pd.concat([지점별유동인구, 지점별보행점수], axis = 1)\n",
+ "지점별"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별 = 지점별.join(조사지점.set_index('조사지점코드')['조사지점명'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별.sort_values(by = '보행점수')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 유동인구수 불만족비율 보행점수 유동인구등급 개선시급도\n",
+ "조사지점코드 \n",
+ "23-076 1928.142857 1.00 2.633333 9 9.00\n",
+ "23-034 1389.728571 1.00 1.927778 9 9.00\n",
+ "23-285 1178.742857 1.00 2.383333 9 9.00\n",
+ "23-075 1236.000000 1.00 2.005556 9 9.00\n",
+ "23-236 1166.228571 0.95 1.983333 9 8.55\n",
+ "01-2095 2193.857143 0.95 1.755556 9 8.55\n",
+ "13-5002 1972.414286 0.90 2.111111 9 8.10\n",
+ "04-2026 980.528571 1.00 2.144444 8 8.00\n",
+ "21-039 1130.057143 0.85 2.450000 9 7.65\n",
+ "17-2055 3242.828571 0.85 1.738889 9 7.65\n",
+ "02-260 1810.328571 0.85 1.944444 9 7.65\n",
+ "14-047 1043.957143 0.85 1.805556 9 7.65\n",
+ "17-082 1572.342857 0.85 2.188889 9 7.65\n",
+ "23-276 706.114286 0.90 2.383333 8 7.20\n",
+ "15-096 822.342857 0.90 2.683333 8 7.20\n",
+ "22-139 723.171429 0.85 2.550000 8 6.80\n",
+ "17-252 1584.814286 0.75 2.138889 9 6.75\n",
+ "01-213 3368.914286 0.75 2.150000 9 6.75\n",
+ "01-084 1831.928571 0.75 1.872222 9 6.75\n",
+ "25-430 1371.985714 0.75 2.700000 9 6.75\n",
+ "01-082 1502.528571 0.75 2.011111 9 6.75\n",
+ "20-1103 555.814286 0.95 2.622222 7 6.65\n",
+ "15-1197 743.314286 0.80 2.538889 8 6.40\n",
+ "15-093 587.142857 0.80 2.466667 8 6.40\n",
+ "16-065 961.542857 0.80 2.522222 8 6.40\n",
+ "12-2024 662.614286 0.80 2.272222 8 6.40\n",
+ "02-074 1834.542857 0.70 1.622222 9 6.30\n",
+ "20-825 1215.300000 0.70 2.427778 9 6.30\n",
+ "06-019 499.328571 0.90 2.272222 7 6.30\n",
+ "19-013 463.585714 0.90 2.338889 7 6.30\n",
+ "09-3146 740.914286 0.75 2.800000 8 6.00\n",
+ "01-066 877.542857 0.75 2.172222 8 6.00\n",
+ "01-2104 880.800000 0.75 1.944444 8 6.00\n",
+ "20-022 487.242857 0.85 2.394444 7 5.95\n",
+ "19-066 542.828571 0.85 2.450000 7 5.95\n",
+ "22-362 413.871429 0.85 2.311111 7 5.95\n",
+ "09-219 1029.857143 0.65 2.261111 9 5.85\n",
+ "11-2044 1083.471429 0.65 2.322222 9 5.85\n",
+ "04-005 1054.714286 0.65 2.522222 9 5.85\n",
+ "01-2127 1873.200000 0.65 2.244444 9 5.85\n",
+ "01-248 336.728571 0.95 2.183333 6 5.70\n",
+ "17-027 372.042857 0.95 2.572222 6 5.70\n",
+ "19-052 503.657143 0.80 2.416667 7 5.60\n",
+ "21-012 745.028571 0.70 2.477778 8 5.60\n",
+ "19-030 897.771429 0.70 2.600000 8 5.60\n",
+ "19-016 729.600000 0.70 2.450000 8 5.60\n",
+ "18-2005 853.071429 0.70 2.372222 8 5.60\n",
+ "23-859 296.142857 0.90 2.466667 6 5.40\n",
+ "01-233 320.314286 0.90 2.427778 6 5.40\n",
+ "19-004 1194.814286 0.60 2.216667 9 5.40"
+ ]
+ },
+ "execution_count": 106,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#3. 유동인구수\n",
+ "지점별유동인구 = 유동인구[유동인구.조사구분 =='본조사'].pivot_table(index = '조사지점코드', values= '유동인구수', aggfunc = 'mean')\n",
+ "지점별유동인구.head()\n",
+ "#1~3에서 구한 항목을 하나의 데이터프레임으로 합치기\n",
+ "지점별통계 = pd.concat([지점별유동인구, 보행환경매트릭스.불만족비율, 지점별보행점수], axis =1, sort = True, join = 'inner')\n",
+ "지점별유동인구.유동인구수.min()\n",
+ "지점별유동인구.유동인구수.max()\n",
+ "경계점 =[]\n",
+ "for i in range(1,10):\n",
+ " 경계점.append(지점별통계.유동인구수.quantile(i/10))\n",
+ "경계점\n",
+ "# 등급 나누기\n",
+ "지점별통계['유동인구등급']=np.digitize(지점별통계.유동인구수,경계점)\n",
+ "지점별통계.head()\n",
+ "#개선시급도\n",
+ "지점별통계['개선시급도'] = 지점별통계.불만족비율*지점별통계.유동인구등급\n",
+ "지점별통계.head(10)\n",
+ "환경개선지점50 = 지점별통계.sort_values(by = '개선시급도', ascending = False).head(50)\n",
+ "환경개선지점50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# 지도에 환경개선지점 시각화\n",
+ "np.unique(조사지점.조사구분)\n",
+ "# index 값이 유일해야 concat 가능\n",
+ "조사지점2 = 조사지점[조사지점.조사구분 =='본조사'].set_index('조사지점코드')[['조사지점명','X좌표','Y좌표']]\n",
+ "환경개선50 =pd.concat([환경개선지점50, 조사지점2], axis = 1, join = 'inner')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 123,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "SyntaxError",
+ "evalue": "invalid syntax (| \n", + " | 유동인구수 | \n", + "불만족비율 | \n", + "보행점수 | \n", + "유동인구등급 | \n", + "개선시급도 | \n", + "
|---|---|---|---|---|---|
| 조사지점코드 | \n", + "\n", + " | \n", + " | \n", + " | \n", + " | \n", + " |
| 23-076 | \n", + "1928.142857 | \n", + "1.00 | \n", + "2.633333 | \n", + "9 | \n", + "9.00 | \n", + "
| 23-034 | \n", + "1389.728571 | \n", + "1.00 | \n", + "1.927778 | \n", + "9 | \n", + "9.00 | \n", + "
| 23-285 | \n", + "1178.742857 | \n", + "1.00 | \n", + "2.383333 | \n", + "9 | \n", + "9.00 | \n", + "
| 23-075 | \n", + "1236.000000 | \n", + "1.00 | \n", + "2.005556 | \n", + "9 | \n", + "9.00 | \n", + "
| 23-236 | \n", + "1166.228571 | \n", + "0.95 | \n", + "1.983333 | \n", + "9 | \n", + "8.55 | \n", + "
| 01-2095 | \n", + "2193.857143 | \n", + "0.95 | \n", + "1.755556 | \n", + "9 | \n", + "8.55 | \n", + "
| 13-5002 | \n", + "1972.414286 | \n", + "0.90 | \n", + "2.111111 | \n", + "9 | \n", + "8.10 | \n", + "
| 04-2026 | \n", + "980.528571 | \n", + "1.00 | \n", + "2.144444 | \n", + "8 | \n", + "8.00 | \n", + "
| 21-039 | \n", + "1130.057143 | \n", + "0.85 | \n", + "2.450000 | \n", + "9 | \n", + "7.65 | \n", + "
| 17-2055 | \n", + "3242.828571 | \n", + "0.85 | \n", + "1.738889 | \n", + "9 | \n", + "7.65 | \n", + "
| 02-260 | \n", + "1810.328571 | \n", + "0.85 | \n", + "1.944444 | \n", + "9 | \n", + "7.65 | \n", + "
| 14-047 | \n", + "1043.957143 | \n", + "0.85 | \n", + "1.805556 | \n", + "9 | \n", + "7.65 | \n", + "
| 17-082 | \n", + "1572.342857 | \n", + "0.85 | \n", + "2.188889 | \n", + "9 | \n", + "7.65 | \n", + "
| 23-276 | \n", + "706.114286 | \n", + "0.90 | \n", + "2.383333 | \n", + "8 | \n", + "7.20 | \n", + "
| 15-096 | \n", + "822.342857 | \n", + "0.90 | \n", + "2.683333 | \n", + "8 | \n", + "7.20 | \n", + "
| 22-139 | \n", + "723.171429 | \n", + "0.85 | \n", + "2.550000 | \n", + "8 | \n", + "6.80 | \n", + "
| 17-252 | \n", + "1584.814286 | \n", + "0.75 | \n", + "2.138889 | \n", + "9 | \n", + "6.75 | \n", + "
| 01-213 | \n", + "3368.914286 | \n", + "0.75 | \n", + "2.150000 | \n", + "9 | \n", + "6.75 | \n", + "
| 01-084 | \n", + "1831.928571 | \n", + "0.75 | \n", + "1.872222 | \n", + "9 | \n", + "6.75 | \n", + "
| 25-430 | \n", + "1371.985714 | \n", + "0.75 | \n", + "2.700000 | \n", + "9 | \n", + "6.75 | \n", + "
| 01-082 | \n", + "1502.528571 | \n", + "0.75 | \n", + "2.011111 | \n", + "9 | \n", + "6.75 | \n", + "
| 20-1103 | \n", + "555.814286 | \n", + "0.95 | \n", + "2.622222 | \n", + "7 | \n", + "6.65 | \n", + "
| 15-1197 | \n", + "743.314286 | \n", + "0.80 | \n", + "2.538889 | \n", + "8 | \n", + "6.40 | \n", + "
| 15-093 | \n", + "587.142857 | \n", + "0.80 | \n", + "2.466667 | \n", + "8 | \n", + "6.40 | \n", + "
| 16-065 | \n", + "961.542857 | \n", + "0.80 | \n", + "2.522222 | \n", + "8 | \n", + "6.40 | \n", + "
| 12-2024 | \n", + "662.614286 | \n", + "0.80 | \n", + "2.272222 | \n", + "8 | \n", + "6.40 | \n", + "
| 02-074 | \n", + "1834.542857 | \n", + "0.70 | \n", + "1.622222 | \n", + "9 | \n", + "6.30 | \n", + "
| 20-825 | \n", + "1215.300000 | \n", + "0.70 | \n", + "2.427778 | \n", + "9 | \n", + "6.30 | \n", + "
| 06-019 | \n", + "499.328571 | \n", + "0.90 | \n", + "2.272222 | \n", + "7 | \n", + "6.30 | \n", + "
| 19-013 | \n", + "463.585714 | \n", + "0.90 | \n", + "2.338889 | \n", + "7 | \n", + "6.30 | \n", + "
| 09-3146 | \n", + "740.914286 | \n", + "0.75 | \n", + "2.800000 | \n", + "8 | \n", + "6.00 | \n", + "
| 01-066 | \n", + "877.542857 | \n", + "0.75 | \n", + "2.172222 | \n", + "8 | \n", + "6.00 | \n", + "
| 01-2104 | \n", + "880.800000 | \n", + "0.75 | \n", + "1.944444 | \n", + "8 | \n", + "6.00 | \n", + "
| 20-022 | \n", + "487.242857 | \n", + "0.85 | \n", + "2.394444 | \n", + "7 | \n", + "5.95 | \n", + "
| 19-066 | \n", + "542.828571 | \n", + "0.85 | \n", + "2.450000 | \n", + "7 | \n", + "5.95 | \n", + "
| 22-362 | \n", + "413.871429 | \n", + "0.85 | \n", + "2.311111 | \n", + "7 | \n", + "5.95 | \n", + "
| 09-219 | \n", + "1029.857143 | \n", + "0.65 | \n", + "2.261111 | \n", + "9 | \n", + "5.85 | \n", + "
| 11-2044 | \n", + "1083.471429 | \n", + "0.65 | \n", + "2.322222 | \n", + "9 | \n", + "5.85 | \n", + "
| 04-005 | \n", + "1054.714286 | \n", + "0.65 | \n", + "2.522222 | \n", + "9 | \n", + "5.85 | \n", + "
| 01-2127 | \n", + "1873.200000 | \n", + "0.65 | \n", + "2.244444 | \n", + "9 | \n", + "5.85 | \n", + "
| 01-248 | \n", + "336.728571 | \n", + "0.95 | \n", + "2.183333 | \n", + "6 | \n", + "5.70 | \n", + "
| 17-027 | \n", + "372.042857 | \n", + "0.95 | \n", + "2.572222 | \n", + "6 | \n", + "5.70 | \n", + "
| 19-052 | \n", + "503.657143 | \n", + "0.80 | \n", + "2.416667 | \n", + "7 | \n", + "5.60 | \n", + "
| 21-012 | \n", + "745.028571 | \n", + "0.70 | \n", + "2.477778 | \n", + "8 | \n", + "5.60 | \n", + "
| 19-030 | \n", + "897.771429 | \n", + "0.70 | \n", + "2.600000 | \n", + "8 | \n", + "5.60 | \n", + "
| 19-016 | \n", + "729.600000 | \n", + "0.70 | \n", + "2.450000 | \n", + "8 | \n", + "5.60 | \n", + "
| 18-2005 | \n", + "853.071429 | \n", + "0.70 | \n", + "2.372222 | \n", + "8 | \n", + "5.60 | \n", + "
| 23-859 | \n", + "296.142857 | \n", + "0.90 | \n", + "2.466667 | \n", + "6 | \n", + "5.40 | \n", + "
| 01-233 | \n", + "320.314286 | \n", + "0.90 | \n", + "2.427778 | \n", + "6 | \n", + "5.40 | \n", + "
| 19-004 | \n", + "1194.814286 | \n", + "0.60 | \n", + "2.216667 | \n", + "9 | \n", + "5.40 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID속성조사 조사지점코드 조사일자 조사요일 남여구분 조사시간대 조사시간대_텍스트 연령대 거주지 통행주목적 \\\n",
+ "0 1 01-003 1016 금 여자 NaN 오전11시~오후2시 50-54세 종로구 업무관련 \n",
+ "1 2 01-003 1016 금 남자 NaN 오전11시~오후2시 60-64세 성북구 업무관련 \n",
+ "2 3 01-003 1016 금 남자 NaN 오전11시~오후2시 65세이상 종로구 업무관련 \n",
+ "3 4 01-003 1016 금 남자 NaN 오전7시30분~11시 45-49세 서대문구 출근 \n",
+ "4 5 01-003 1016 금 남자 NaN 오전7시30분~11시 35-39세 도봉구 출근 \n",
+ "\n",
+ " 방문횟수 동행자명 교통수단 보행환경 직업명 년도 보행점수 연령대점수 방문횟수점수 \n",
+ "0 주1~2회 NaN NaN 약간만족 전문/자유직 2015 4 0 4 \n",
+ "1 주3~5회 NaN NaN 매우만족 일용/작업직 2015 5 0 5 \n",
+ "2 주3~5회 NaN NaN 약간만족 일용/작업직 2015 4 3 5 \n",
+ "3 매일 NaN NaN 약간만족 사무/기술직 2015 4 0 6 \n",
+ "4 매일 NaN NaN 매우만족 사무/기술직 2015 5 0 6 "
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "유동인구_속성조사['방문횟수점수'] = 유동인구_속성조사.방문횟수.apply(방문횟수)\n",
+ "유동인구_속성조사.head()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
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+ "4 5 0 6"
+ ]
+ },
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+ }
+ ],
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+ "execution_count": 67,
+ "metadata": {},
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+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | 보행점수 | \n", + "연령대점수 | \n", + "방문횟수점수 | \n", + "
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+ " ID속성조사 조사지점코드 조사일자 조사요일 남여구분 조사시간대 조사시간대_텍스트 연령대 거주지 통행주목적 \\\n",
+ "0 1 01-003 1016 금 여자 NaN 오전11시~오후2시 50-54세 종로구 업무관련 \n",
+ "1 2 01-003 1016 금 남자 NaN 오전11시~오후2시 60-64세 성북구 업무관련 \n",
+ "2 3 01-003 1016 금 남자 NaN 오전11시~오후2시 65세이상 종로구 업무관련 \n",
+ "3 4 01-003 1016 금 남자 NaN 오전7시30분~11시 45-49세 서대문구 출근 \n",
+ "4 5 01-003 1016 금 남자 NaN 오전7시30분~11시 35-39세 도봉구 출근 \n",
+ "\n",
+ " 방문횟수 동행자명 교통수단 보행환경 직업명 년도 보행점수 연령대점수 방문횟수점수 \n",
+ "0 주1~2회 NaN NaN 약간만족 전문/자유직 2015 4 0 4 \n",
+ "1 주3~5회 NaN NaN 매우만족 일용/작업직 2015 5 0 5 \n",
+ "2 주3~5회 NaN NaN 약간만족 일용/작업직 2015 4 3 5 \n",
+ "3 매일 NaN NaN 약간만족 사무/기술직 2015 4 0 6 \n",
+ "4 매일 NaN NaN 매우만족 사무/기술직 2015 5 0 6 "
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "유동인구_속성조사.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
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+ "data": {
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+ ],
+ "text/plain": [
+ " ID속성조사 조사지점코드 조사일자 조사요일 남여구분 조사시간대 조사시간대_텍스트 연령대 거주지 통행주목적 \\\n",
+ "0 1 01-003 1016 금 여자 NaN 오전11시~오후2시 50-54세 종로구 업무관련 \n",
+ "1 2 01-003 1016 금 남자 NaN 오전11시~오후2시 60-64세 성북구 업무관련 \n",
+ "2 3 01-003 1016 금 남자 NaN 오전11시~오후2시 65세이상 종로구 업무관련 \n",
+ "3 4 01-003 1016 금 남자 NaN 오전7시30분~11시 45-49세 서대문구 출근 \n",
+ "4 5 01-003 1016 금 남자 NaN 오전7시30분~11시 35-39세 도봉구 출근 \n",
+ "\n",
+ " 방문횟수 동행자명 교통수단 보행환경 직업명 년도 보행점수 연령대점수 방문횟수점수 지점별보행점수 \n",
+ "0 주1~2회 NaN NaN 약간만족 전문/자유직 2015 2.222222 0 4 2.666667 \n",
+ "1 주3~5회 NaN NaN 매우만족 일용/작업직 2015 2.777778 0 5 3.333333 \n",
+ "2 주3~5회 NaN NaN 약간만족 일용/작업직 2015 4.000000 3 5 4.000000 \n",
+ "3 매일 NaN NaN 약간만족 사무/기술직 2015 3.111111 0 6 3.333333 \n",
+ "4 매일 NaN NaN 매우만족 사무/기술직 2015 3.222222 0 6 3.666667 "
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "보행점수 =유동인구_속성조사.iloc[:,16:19].mean(axis=1)\n",
+ "유동인구_속성조사['보행점수']=보행점수\n",
+ "유동인구_속성조사.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
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+ {
+ "data": {
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+ "\n",
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+ "
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+ ],
+ "text/plain": [
+ " 유동인구수\n",
+ "조사지점코드 \n",
+ "01-003 2895.0\n",
+ "01-004 13872.0\n",
+ "01-005 10509.0\n",
+ "01-008 5316.0\n",
+ "01-009 5637.0"
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "지점별유동인구 = 유동인구.pivot_table(index = '조사지점코드', values = '유동인구수', aggfunc = 'sum')\n",
+ "지점별유동인구.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
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+ "0 1 30-001 지하철 금 07시-08시 129.0 2015"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "유동인구.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "지점별 = pd.concat([지점별유동인구, 지점별보행점수], axis = 1)\n",
+ "지점별"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "지점별 = 지점별.join(측정위치.set_index('조사지점코드')['조사지점명'])"
+ ]
+ },
+ {
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+ "metadata": {},
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+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "지점별.sort_values(by = 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.\354\231\204\354\204\261\353\263\270.ipynb" "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.\354\231\204\354\204\261\353\263\270.ipynb"
new file mode 100644
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+++ "b/\353\260\260\355\217\254\354\232\251_\354\213\244\354\212\265 5. openAPI\354\202\254\354\232\251, \354\247\200\353\217\204\354\213\234\352\260\201\355\231\224 \353\260\217 \354\204\234\354\232\270\354\213\234 \354\234\240\353\217\231\354\235\270\352\265\254 \353\215\260\354\235\264\355\204\260 \353\266\204\354\204\235 \353\260\217 \354\213\234\352\260\201\355\231\224.\354\231\204\354\204\261\353\263\270.ipynb"
@@ -0,0 +1,912 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import Series, DataFrame\n",
+ "import numpy as np\n",
+ "import matplotlib\n",
+ "matplotlib.rcParams['font.family'].insert(0, 'Malgun Gothic') \n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1. openAPI 활용 및 json 데이터 읽기\n",
+ "## API : Application Programming Interface\n",
+ "## 서울시 데이터 포털에서 공공 API 활용하기\n",
+ "### 서울시 동별 인구통계\n",
+ "### http://data.seoul.go.kr/dataList/datasetView.do?infId=10043&srvType=S&serviceKind=2¤tPageNo=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "http://openapi.seoul.go.kr:8088/sample/json/octastatapi10043/1/5\n"
+ ]
+ }
+ ],
+ "source": [
+ "url = 'http://openapi.seoul.go.kr:8088'\n",
+ "api_key = 'sample' #개인인증키 발급받은 후, 입력\n",
+ "return_format = 'json'\n",
+ "service_id = 'octastatapi10043'\n",
+ "start_idx = 1\n",
+ "end_idx = 5\n",
+ "\n",
+ "request_url = '/'.join([url, api_key, return_format, service_id, str(start_idx), str(end_idx)])\n",
+ "print(request_url)\n",
+ "# 'http://openapi.seoul.go.kr:8088/sample/json/octastatapi10043/1/500/' \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = requests.get(request_url)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"octastatapi10043\":{\"list_total_count\":450,\"RESULT\":{\"CODE\":\"INFO-000\",\"MESSAGE\":\"정상 처리되었습니다\"},\"row\":[{\"GIGAN\":\"2016\",\"JACHIGU\":\"합계\",\"DONG\":\"합계\",\"SEDAE\":\"4189839\",\"GYE_1\":\"10204057\",\"NAMJA_1\":\"5007379\",\"YEOJA_1\":\"5196678\",\"GYE_2\":\"9930616\",\"NAMJA_2\":\"4876789\",\"YEOJA_2\":\"5053827\",\"GYE_3\":\"273441\",\"NAMJA_3\":\"130590\",\"YEOJA_3\":\"142851\",\"SEDAEDANGINGU\":\"2.37\",\"N_65SEISANGGORYEONGJA\":\"1300877\"},{\"GIGAN\":\"2016\",\"JACHIGU\":\"종로구\",\"DONG\":\"소계\",\"SEDAE\":\"72118\",\"GYE_1\":\"161922\",\"NAMJA_1\":\"79322\",\"YEOJA_1\":\"82600\",\"GYE_2\":\"152737\",\"NAMJA_2\":\"75201\",\"YEOJA_2\":\"77536\",\"GYE_3\":\"9185\",\"NAMJA_3\":\"4121\",\"YEOJA_3\":\"5064\",\"SEDAEDANGINGU\":\"2.12\",\"N_65SEISANGGORYEONGJA\":\"25091\"},{\"GIGAN\":\"2016\",\"JACHIGU\":\"종로구\",\"DONG\":\"사직동\",\"SEDAE\":\"4399\",\"GYE_1\":\"10013\",\"NAMJA_1\":\"4605\",\"YEOJA_1\":\"5408\",\"GYE_2\":\"9695\",\"NAMJA_2\":\"4429\",\"YEOJA_2\":\"5266\",\"GYE_3\":\"318\",\"NAMJA_3\":\"176\",\"YEOJA_3\":\"142\",\"SEDAEDANGINGU\":\"2.20\",\"N_65SEISANGGORYEONGJA\":\"1707\"},{\"GIGAN\":\"2016\",\"JACHIGU\":\"종로구\",\"DONG\":\"삼청동\",\"SEDAE\":\"1403\",\"GYE_1\":\"3118\",\"NAMJA_1\":\"1491\",\"YEOJA_1\":\"1627\",\"GYE_2\":\"2903\",\"NAMJA_2\":\"1377\",\"YEOJA_2\":\"1526\",\"GYE_3\":\"215\",\"NAMJA_3\":\"114\",\"YEOJA_3\":\"101\",\"SEDAEDANGINGU\":\"2.07\",\"N_65SEISANGGORYEONGJA\":\"624\"},{\"GIGAN\":\"2016\",\"JACHIGU\":\"종로구\",\"DONG\":\"부암동\",\"SEDAE\":\"4371\",\"GYE_1\":\"11044\",\"NAMJA_1\":\"5349\",\"YEOJA_1\":\"5695\",\"GYE_2\":\"10616\",\"NAMJA_2\":\"5160\",\"YEOJA_2\":\"5456\",\"GYE_3\":\"428\",\"NAMJA_3\":\"189\",\"YEOJA_3\":\"239\",\"SEDAEDANGINGU\":\"2.43\",\"N_65SEISANGGORYEONGJA\":\"1723\"}]}}'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "response.text\n",
+ "#JSON 형태"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### json (Java Script Object Notation)\n",
+ "\n",
+ "### 1) json 설명: https://ko.wikipedia.org/wiki/JSON\n",
+ "### 2) json online editor: https://jsoneditoronline.org/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample = ' { \\\n",
+ " \"이름\": \"김지아\", \\\n",
+ " \"나이\": 25, \\\n",
+ " \"성별\": \"여\", \\\n",
+ " \"주소\": \"서울특별시 양천구 목동\",\\\n",
+ " \"특기\": [\"농구\", \"도술\"],\\\n",
+ " \"가족관계\": {\"#\": 2, \"아버지\": \"홍판서\", \"어머니\": \"춘섬\"},\\\n",
+ " \"회사\": \"경기 수원시 팔달구 우만동\"\\\n",
+ " }'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jsonsample = json.loads(sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'홍판서'"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# json의 특정 항목에 접근\n",
+ "jsonsample['가족관계']['아버지']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#API 결과를 json 포맷으로 변환\n",
+ "jsondata = response.json()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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| 37-016 | \n", + "5877.0 | \n", + "NaN | \n", + "종로3가역 2-1번출구앞 | \n", + "
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| 40-002 | \n", + "42636.0 | \n", + "NaN | \n", + "에뛰드 하우스 | \n", + "
| 40-003 | \n", + "30255.0 | \n", + "NaN | \n", + "빌리프 삼청동 플래그샾 | \n", + "
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| 40-005 | \n", + "16860.0 | \n", + "NaN | \n", + "스타벅스 삼청동점 앞 | \n", + "
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\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " DONG GIGAN GYE_1 GYE_2 GYE_3 JACHIGU NAMJA_1 NAMJA_2 NAMJA_3 \\\n",
+ "0 합계 2016 10204057 9930616 273441 합계 5007379 4876789 130590 \n",
+ "1 소계 2016 161922 152737 9185 종로구 79322 75201 4121 \n",
+ "2 사직동 2016 10013 9695 318 종로구 4605 4429 176 \n",
+ "3 삼청동 2016 3118 2903 215 종로구 1491 1377 114 \n",
+ "4 부암동 2016 11044 10616 428 종로구 5349 5160 189 \n",
+ "\n",
+ " N_65SEISANGGORYEONGJA SEDAE SEDAEDANGINGU YEOJA_1 YEOJA_2 YEOJA_3 \n",
+ "0 1300877 4189839 2.37 5196678 5053827 142851 \n",
+ "1 25091 72118 2.12 82600 77536 5064 \n",
+ "2 1707 4399 2.20 5408 5266 142 \n",
+ "3 624 1403 2.07 1627 1526 101 \n",
+ "4 1723 4371 2.43 5695 5456 239 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "jsondata = jsondata['octastatapi10043']['row']\n",
+ "동별인구통계 = DataFrame(jsondata)\n",
+ "동별인구통계"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. folium 을 활용한 지도 시각화"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'folium'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m| \n", + " | DONG | \n", + "GIGAN | \n", + "GYE_1 | \n", + "GYE_2 | \n", + "GYE_3 | \n", + "JACHIGU | \n", + "NAMJA_1 | \n", + "NAMJA_2 | \n", + "NAMJA_3 | \n", + "N_65SEISANGGORYEONGJA | \n", + "SEDAE | \n", + "SEDAEDANGINGU | \n", + "YEOJA_1 | \n", + "YEOJA_2 | \n", + "YEOJA_3 | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "합계 | \n", + "2016 | \n", + "10204057 | \n", + "9930616 | \n", + "273441 | \n", + "합계 | \n", + "5007379 | \n", + "4876789 | \n", + "130590 | \n", + "1300877 | \n", + "4189839 | \n", + "2.37 | \n", + "5196678 | \n", + "5053827 | \n", + "142851 | \n", + "
| 1 | \n", + "소계 | \n", + "2016 | \n", + "161922 | \n", + "152737 | \n", + "9185 | \n", + "종로구 | \n", + "79322 | \n", + "75201 | \n", + "4121 | \n", + "25091 | \n", + "72118 | \n", + "2.12 | \n", + "82600 | \n", + "77536 | \n", + "5064 | \n", + "
| 2 | \n", + "사직동 | \n", + "2016 | \n", + "10013 | \n", + "9695 | \n", + "318 | \n", + "종로구 | \n", + "4605 | \n", + "4429 | \n", + "176 | \n", + "1707 | \n", + "4399 | \n", + "2.20 | \n", + "5408 | \n", + "5266 | \n", + "142 | \n", + "
| 3 | \n", + "삼청동 | \n", + "2016 | \n", + "3118 | \n", + "2903 | \n", + "215 | \n", + "종로구 | \n", + "1491 | \n", + "1377 | \n", + "114 | \n", + "624 | \n", + "1403 | \n", + "2.07 | \n", + "1627 | \n", + "1526 | \n", + "101 | \n", + "
| 4 | \n", + "부암동 | \n", + "2016 | \n", + "11044 | \n", + "10616 | \n", + "428 | \n", + "종로구 | \n", + "5349 | \n", + "5160 | \n", + "189 | \n", + "1723 | \n", + "4371 | \n", + "2.43 | \n", + "5695 | \n", + "5456 | \n", + "239 | \n", + "