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add notebook
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‎week7-1-co2-emissions-analysis/挑战 25：各国历年二氧化碳 CO2 排放量统计分析.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 挑战 25：各国历年二氧化碳 CO2 排放量统计分析"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. 数据清洁"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 读取数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# 读取数据文件\n",
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"df_data = pd.read_excel(\"ClimateChange.xlsx\", sheet_name='Data')\n",
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"df_country = pd.read_excel(\"ClimateChange.xlsx\", sheet_name='Country')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 处理 data 数据表"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 选取 EN.ATM.CO2E.KT 数据，并将国家代码设置为索引\n",
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"\n",
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"df_data_reindex = df_data[df_data['Series code']== 'EN.ATM.CO2E.KT'].set_index('Country code')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 剔除不必要的数据列\n",
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"df_data_drop = df_data_reindex.drop(labels=['Country name', 'Series code', 'Series name', 'SCALE', 'Decimals'], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_data_nan = df_data_drop.replace({'..': pd.np.NaN})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# 对 NaN 空值进行向前和向后填充\n",
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"df_data_fill = df_data_nan.fillna(method='ffill', axis=1).fillna(method='bfill', axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# 对填充后依旧全部为空值的数据行进行剔除\n",
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"df_data_dropna = df_data_fill.dropna(how='all')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"scrolled": true
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},
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"source": [
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"### 处理 Country 数据表"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 将国家代码设置为索引\n",
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"df_country_reindex = pd.DataFrame(df_country).set_index('Country code')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 剔除不必要的数据列\n",
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"df_country_drop = df_country_reindex.drop(labels=['Capital city', 'Region', 'Lending category'], axis=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"scrolled": true
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},
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"source": [
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"### 合并数据表"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 对 Data 和 Country 表按照索引进行合并\n",
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"df_combine = pd.concat([df_data_dropna, df_country_drop], axis=1, sort=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 对合并后数据集进行求和得到各国排放总量\n",
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"df_combine['Sum emissions'] = df_combine[list(df_combine)[:-2]].sum(axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# 对合并后存在空值的数据行进行剔除，得到清洁后的数据集\n",
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"df_clean = df_combine.dropna(thresh=10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. 数据求和整理"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 按收入群体对数据进行求和"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 按收入群体对数据进行求和\n",
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"sum_by_groups = df_clean.groupby('Income group')['Sum emissions'].sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 按要求整理 DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 按要求整理 DataFrame\n",
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"item_high_list = []\n",
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"item_low_list = []\n",
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"\n",
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"for group_name in list(sum_by_groups.index):\n",
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"\n",
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" # 得到各收入群体最高排放量数据\n",
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" item_high = df_clean[df_clean['Income group'] == group_name].sort_values(\n",
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" by='Sum emissions', ascending=False).iloc[0]\n",
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"\n",
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" # 将最高排放量数据存入相应列表方便生成最终 DataFrame\n",
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" item_high_list.append(\n",
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" (item_high['Income group'], item_high['Country name'], item_high['Sum emissions']))\n",
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"\n",
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" # 得到各收入群体最低排放量数据\n",
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" item_low = df_clean[df_clean['Income group'] ==\n",
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" group_name].sort_values(by='Sum emissions').iloc[0]\n",
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"\n",
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" # 将最低排放量数据存入相应列表方便生成最终 DataFrame\n",
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" item_low_list.append(\n",
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" (item_low['Income group'], item_low['Country name'], item_low['Sum emissions']))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 合并输出"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Sum emissions</th>\n",
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" <th>Highest emission country</th>\n",
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" <th>Highest emissions</th>\n",
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" <th>Lowest emission country</th>\n",
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" <th>Lowest emissions</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Income group</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>High income: OECD</th>\n",
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" <td>2.588373e+08</td>\n",
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" <td>United States</td>\n",
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" <td>1.179918e+08</td>\n",
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" <td>Iceland</td>\n",
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" <td>46944.934</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>High income: nonOECD</th>\n",
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" <td>1.581112e+07</td>\n",
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" <td>Saudi Arabia</td>\n",
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" <td>7.009760e+06</td>\n",
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" <td>Turks and Caicos Islands</td>\n",
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" <td>1503.470</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Low income</th>\n",
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" <td>5.485641e+06</td>\n",
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" <td>Korea, Dem. Rep.</td>\n",
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" <td>3.104479e+06</td>\n",
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" <td>Comoros</td>\n",
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" <td>2068.188</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Lower middle income</th>\n",
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" <td>6.272726e+07</td>\n",
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" <td>India</td>\n",
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" <td>2.681828e+07</td>\n",
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" <td>Kiribati</td>\n",
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" <td>601.388</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Upper middle income</th>\n",
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" <td>2.100775e+08</td>\n",
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" <td>China</td>\n",
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" <td>9.809777e+07</td>\n",
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" <td>Niue</td>\n",
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" <td>80.674</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Sum emissions Highest emission country \\\n",
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"Income group \n",
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"High income: OECD 2.588373e+08 United States \n",
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"High income: nonOECD 1.581112e+07 Saudi Arabia \n",
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"Low income 5.485641e+06 Korea, Dem. Rep. \n",
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"Lower middle income 6.272726e+07 India \n",
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"Upper middle income 2.100775e+08 China \n",
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"\n",
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" Highest emissions Lowest emission country \\\n",
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"Income group \n",
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"High income: OECD 1.179918e+08 Iceland \n",
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"High income: nonOECD 7.009760e+06 Turks and Caicos Islands \n",
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"Low income 3.104479e+06 Comoros \n",
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"Lower middle income 2.681828e+07 Kiribati \n",
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"Upper middle income 9.809777e+07 Niue \n",
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"\n",
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" Lowest emissions \n",
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"Income group \n",
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"High income: OECD 46944.934 \n",
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"High income: nonOECD 1503.470 \n",
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"Low income 2068.188 \n",
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"Lower middle income 601.388 \n",
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"Upper middle income 80.674 "
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 设置 DataFrame 标签\n",
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"high_labels = ['Income group', 'Highest emission country', 'Highest emissions']\n",
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"low_labels = ['Income group', 'Lowest emission country', 'Lowest emissions']\n",
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"\n",
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"# 生成并合并目标 DataFrame\n",
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"highest_df = pd.DataFrame.from_records(item_high_list, columns=high_labels).set_index('Income group')\n",
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"lowest_df = pd.DataFrame.from_records(item_low_list, columns=low_labels).set_index('Income group')\n",
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"\n",
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"results = pd.concat([sum_by_groups, highest_df, lowest_df], axis=1)\n",
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"results"
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]
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}
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],
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"nbformat": 4,
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