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time series with pandas part 2 rough draft
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‎Time_Series/.ipynb_checkpoints/Part2_Time_Series_Data_Nonlinear_Regression-checkpoint.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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"<h1 align=\"center\"> Part 2: Time Series Data Nonlinear Regression, Shifting Dataframes, Daily Growth,</h1>"
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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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"This code demonstrates how to view time series data in pandas as well as nonlinear regression, shifting dataframe, apply by month, \n",
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"\n",
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"**if this tutorial doesn't cover what you are looking for, please leave a comment on the youtube video and I will try to cover what you are interested in.**"
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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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"<b> Part 1 </b>: https://www.youtube.com/watch?v=OwnaUVt6VVE"
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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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"<h3 align='Left'> Importing Libraries</h3>"
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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": 26,
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"metadata": {
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"collapsed": false
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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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"import pandas_datareader.data as web\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.linear_model import LinearRegression\n",
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"%matplotlib inline"
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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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"<h3 align='Left'> Getting Data and Viewing with Pandas </h3>"
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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": 40,
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"metadata": {
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"collapsed": false
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},
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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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"<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>Open</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</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>2009-03-16</th>\n",
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" <td>162.83</td>\n",
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" <td>164.70</td>\n",
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" <td>159.14</td>\n",
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" <td>159.69</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-17</th>\n",
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" <td>159.93</td>\n",
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" <td>167.50</td>\n",
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" <td>159.39</td>\n",
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" <td>167.50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-18</th>\n",
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" <td>167.24</td>\n",
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" <td>169.83</td>\n",
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" <td>163.86</td>\n",
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" <td>166.38</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-19</th>\n",
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" <td>165.67</td>\n",
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" <td>167.83</td>\n",
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" <td>163.53</td>\n",
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" <td>164.81</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-20</th>\n",
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" <td>164.98</td>\n",
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" <td>166.33</td>\n",
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" <td>163.01</td>\n",
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" <td>164.91</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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" Open High Low Close\n",
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"Date \n",
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"2009-03-16 162.83 164.70 159.14 159.69\n",
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"2009-03-17 159.93 167.50 159.39 167.50\n",
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"2009-03-18 167.24 169.83 163.86 166.38\n",
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"2009-03-19 165.67 167.83 163.53 164.81\n",
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"2009-03-20 164.98 166.33 163.01 164.91"
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]
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},
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"execution_count": 40,
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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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"# https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n",
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"google = web.DataReader('GOOG', data_source = 'google', start = '3/14/2009', end = '4/14/2016')\n",
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"google = google.drop('Volume', axis = 1 )\n",
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"google.head()"
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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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"<h3 align='Left'> Calculate Daily Price Variation </h3>"
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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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"shift in pandas\n",
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"http://www.productiveegg.com/productive-egg-blog/how-python-pandas-makes.html"
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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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"daily price variation:\n",
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"http://finance.zacks.com/calculate-daily-price-variation-stocks-8299.html"
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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": 52,
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"metadata": {
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"collapsed": false
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},
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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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"<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>Open</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</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>2009-03-16</th>\n",
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" <td>-2.90</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-17</th>\n",
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" <td>7.31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-18</th>\n",
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" <td>-1.57</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-19</th>\n",
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" <td>-0.69</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-20</th>\n",
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" <td>1.63</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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" Open\n",
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"Date \n",
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"2009-03-16 -2.90\n",
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"2009-03-17 7.31\n",
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"2009-03-18 -1.57\n",
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"2009-03-19 -0.69\n",
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"2009-03-20 1.63"
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]
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},
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"execution_count": 52,
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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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"daily_shift = (google['Open'].shift(-1) - google['Open'])\n",
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"pd.DataFrame(data = daily_shift.head())"
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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": 53,
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"metadata": {
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"collapsed": false
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},
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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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"<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>0</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</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>2009-03-16</th>\n",
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" <td>5.56</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-17</th>\n",
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" <td>8.11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-18</th>\n",
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" <td>5.97</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-19</th>\n",
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" <td>4.30</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2009-03-20</th>\n",
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" <td>3.32</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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" 0\n",
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"Date \n",
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"2009-03-16 5.56\n",
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"2009-03-17 8.11\n",
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"2009-03-18 5.97\n",
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"2009-03-19 4.30\n",
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"2009-03-20 3.32"
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]
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},
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"execution_count": 53,
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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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"# Difference between two columns\n",
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"difference = google['High'] - google['Low']\n",
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"pd.DataFrame(data = difference.head())"
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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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"<h3 align='Left'> Calculating Monthly Price Variation using a GroupBy </h3>"
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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": 55,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# First link is great others not so much\n",
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"# http://stackoverflow.com/questions/24082784/pandas-dataframe-groupby-datetime-month\n",
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"# http://stackoverflow.com/questions/30405413/python-pandas-extract-year-from-datetime-dfyear-dfdate-year-is-not"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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"codemirror_mode": {
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"name": "ipython",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.12"
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}
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"nbformat": 4,
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