Time_Series/Part2_Time_Series_Data_Price_Variation_ShiftingGroupBy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Time Series Data Basics with Pandas Part 2: Price Variation from Pandas groupby </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code demonstrates how to view time series data in pandas as well as shifting dataframe, groupby datetime (daily, weekly, monthly), and price variation by day, month, year etc.  \n",
    "\n",
    "**if this tutorial doesn't cover what you are looking for, please leave a comment below the youtube video and I will try to cover what you are interested in.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Part 1 </b>: Sampling, Rolling Mean (Smoothing), Linear Regression, Filtering, Join, plotting of a Time Series Pandas DataFrame <br>\n",
    "https://www.youtube.com/watch?v=OwnaUVt6VVE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Part 2 </b>: Price Variation from Pandas GroupBy <br>\n",
    "https://www.youtube.com/watch?v=1S5UKLqe-gg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Importing Libraries</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Getting Data and Viewing with Pandas </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-16</th>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>165.67</td>\n",
       "      <td>167.83</td>\n",
       "      <td>163.53</td>\n",
       "      <td>164.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume\n",
       "Date                                              \n",
       "2009-03-16  162.83  164.70  159.14  159.69     NaN\n",
       "2009-03-17  159.93  167.50  159.39  167.50     NaN\n",
       "2009-03-18  167.24  169.83  163.86  166.38     NaN\n",
       "2009-03-19  165.67  167.83  163.53  164.81     NaN\n",
       "2009-03-20  164.98  166.33  163.01  164.91     NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n",
    "google = web.DataReader('GOOG', data_source = 'google', start = '3/14/2009', end = '4/14/2016')\n",
    "google.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Calculate Daily Price Variation </h3>\n",
    "<br> Daily price variation of a stock is the difference between the highest and lowest values on a given trading day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Price Variation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-16</th>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily Price Variation\n",
       "Date                             \n",
       "2009-03-16                   5.56\n",
       "2009-03-17                   8.11\n",
       "2009-03-18                   5.97\n",
       "2009-03-19                   4.30\n",
       "2009-03-20                   3.32"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_shift = (google['High'] - google['Low'])\n",
    "daily_shift = pd.DataFrame(data = daily_shift, columns = ['Daily Price Variation'])\n",
    "daily_shift.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Calculate Daily Price Variation </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daily price variation may also refer to the difference between one day's opening price and the next day's opening price. \n",
    "\n",
    "This is according to: http://finance.zacks.com/calculate-daily-price-variation-stocks-8299.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2009-03-16       NaN\n",
       "2009-03-17    162.83\n",
       "2009-03-18    159.93\n",
       "2009-03-19    167.24\n",
       "2009-03-20    165.67\n",
       "Name: Open, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that the first output is a nan because shift moves \n",
    "# the original dataframe down by one index\n",
    "google['Open'].shift(1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Price Variation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>-2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>-1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily Price Variation\n",
       "Date                             \n",
       "2009-03-16                    NaN\n",
       "2009-03-17                  -2.90\n",
       "2009-03-18                   7.31\n",
       "2009-03-19                  -1.57\n",
       "2009-03-20                  -0.69"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndaily_shift = (google['Open'] - google['Open'].shift(1))\n",
    "ndaily_shift = pd.DataFrame(data = ndaily_shift)\n",
    "ndaily_shift.columns = ['Daily Price Variation'] # renaming column\n",
    "ndaily_shift.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Price Variation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-17</th>\n",
       "      <td>-2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-18</th>\n",
       "      <td>7.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-19</th>\n",
       "      <td>-1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-20</th>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-23</th>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily Price Variation\n",
       "Date                             \n",
       "2009-03-17                  -2.90\n",
       "2009-03-18                   7.31\n",
       "2009-03-19                  -1.57\n",
       "2009-03-20                  -0.69\n",
       "2009-03-23                   1.63"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing indexes with nan\n",
    "ndaily_shift = ndaily_shift.dropna()\n",
    "ndaily_shift.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Calculate Monthly Price Variation</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>176.39</td>\n",
       "      <td>179.40</td>\n",
       "      <td>174.08</td>\n",
       "      <td>176.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-30</th>\n",
       "      <td>197.68</td>\n",
       "      <td>201.67</td>\n",
       "      <td>197.20</td>\n",
       "      <td>197.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-31</th>\n",
       "      <td>205.85</td>\n",
       "      <td>208.41</td>\n",
       "      <td>205.14</td>\n",
       "      <td>208.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>222.31</td>\n",
       "      <td>223.45</td>\n",
       "      <td>219.51</td>\n",
       "      <td>221.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-31</th>\n",
       "      <td>224.77</td>\n",
       "      <td>226.12</td>\n",
       "      <td>221.08</td>\n",
       "      <td>223.14</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume\n",
       "Date                                              \n",
       "2009-03-31  176.39  179.40  174.08  176.47     NaN\n",
       "2009-04-30  197.68  201.67  197.20  197.79     NaN\n",
       "2009-05-31  205.85  208.41  205.14  208.41     NaN\n",
       "2009-06-30  222.31  223.45  219.51  221.94     NaN\n",
       "2009-07-31  224.77  226.12  221.08  223.14     NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find price max in a given month\n",
    "google.groupby(pd.TimeGrouper(freq='M')).max().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>159.93</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-30</th>\n",
       "      <td>171.72</td>\n",
       "      <td>177.44</td>\n",
       "      <td>170.13</td>\n",
       "      <td>176.87</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-31</th>\n",
       "      <td>194.21</td>\n",
       "      <td>195.91</td>\n",
       "      <td>192.15</td>\n",
       "      <td>193.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>203.12</td>\n",
       "      <td>204.29</td>\n",
       "      <td>200.74</td>\n",
       "      <td>202.64</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-31</th>\n",
       "      <td>199.80</td>\n",
       "      <td>202.80</td>\n",
       "      <td>197.79</td>\n",
       "      <td>198.12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume\n",
       "Date                                              \n",
       "2009-03-31  159.93  164.70  159.14  159.69     NaN\n",
       "2009-04-30  171.72  177.44  170.13  176.87     NaN\n",
       "2009-05-31  194.21  195.91  192.15  193.56     NaN\n",
       "2009-06-30  203.12  204.29  200.74  202.64     NaN\n",
       "2009-07-31  199.80  202.80  197.79  198.12     NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find price min in a given month\n",
    "google.groupby(pd.TimeGrouper(freq='M')).min().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly Price Variation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-30</th>\n",
       "      <td>25.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-31</th>\n",
       "      <td>11.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>19.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-31</th>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Monthly Price Variation\n",
       "Date                               \n",
       "2009-03-31                    16.46\n",
       "2009-04-30                    25.96\n",
       "2009-05-31                    11.64\n",
       "2009-06-30                    19.19\n",
       "2009-07-31                    24.97"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_groupby = google.groupby(pd.TimeGrouper(freq='M'))\n",
    "month_difference = (month_groupby['Open'].max() - month_groupby['Open'].min())\n",
    "month_difference = pd.DataFrame(data = month_difference)\n",
    "month_difference.columns = ['Monthly Price Variation']\n",
    "month_difference.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yearly Price Variation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>152.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>95.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>84.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>105.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>207.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Yearly Price Variation\n",
       "Date                              \n",
       "2009-12-31                  152.13\n",
       "2010-12-31                   95.74\n",
       "2011-12-31                   84.25\n",
       "2012-12-31                  105.08\n",
       "2013-12-31                  207.63"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_groupby = google.groupby(pd.TimeGrouper(freq='A'))\n",
    "year_difference = (year_groupby['Open'].max() - year_groupby['Open'].min())\n",
    "year_difference = pd.DataFrame(data = year_difference)\n",
    "year_difference.columns = ['Yearly Price Variation']\n",
    "year_difference.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Volume by Month </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fM2eOzVM47TTrmJ42rbjWQEmnXz8bunv77d6M4Q+DI46weQ1bt3p/raLvxKzN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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119957f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Annual time grouper ('A'). We could also use monthly (M), quarterly (Q), or weekly (W).\n",
    "#google.groupby(pd.TimeGrouper(freq='M'))\n",
    "month_volume = google['Volume'].groupby(pd.TimeGrouper(freq='M')).sum().dropna()\n",
    "month_volume = pd.DataFrame(data = month_volume)\n",
    "month_volume = month_volume.reset_index()\n",
    "month_volume.plot('Date', 'Volume', legend = None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'> Plotting Price Variation by Day, Month, Year </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-03-16</td>\n",
       "      <td>162.83</td>\n",
       "      <td>164.70</td>\n",
       "      <td>159.14</td>\n",
       "      <td>159.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-03-17</td>\n",
       "      <td>159.93</td>\n",
       "      <td>167.50</td>\n",
       "      <td>159.39</td>\n",
       "      <td>167.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>167.24</td>\n",
       "      <td>169.83</td>\n",
       "      <td>163.86</td>\n",
       "      <td>166.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-03-19</td>\n",
       "      <td>165.67</td>\n",
       "      <td>167.83</td>\n",
       "      <td>163.53</td>\n",
       "      <td>164.81</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-03-20</td>\n",
       "      <td>164.98</td>\n",
       "      <td>166.33</td>\n",
       "      <td>163.01</td>\n",
       "      <td>164.91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close  Volume\n",
       "0 2009-03-16  162.83  164.70  159.14  159.69     NaN\n",
       "1 2009-03-17  159.93  167.50  159.39  167.50     NaN\n",
       "2 2009-03-18  167.24  169.83  163.86  166.38     NaN\n",
       "3 2009-03-19  165.67  167.83  163.53  164.81     NaN\n",
       "4 2009-03-20  164.98  166.33  163.01  164.91     NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a Date Column\n",
    "google = google.reset_index()\n",
    "daily_shift = daily_shift.reset_index()\n",
    "month_difference = month_difference.reset_index()\n",
    "year_difference = year_difference.reset_index()\n",
    "google.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TaUdSnBIykRamxEqk9UTXkKlkUUTqEZYrtsPxQpbLFpWQiYiI5Eh0DZlmyESk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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119caf9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (15,5));\n",
    "fig.suptitle('Price Variation', size = 15, x = .515, y=1.02)\n",
    "axes[0].plot('Date', 'Daily Price Variation', data = daily_shift);\n",
    "axes[0].set_title('Daily');\n",
    "axes[0].set_xlabel('Year', fontsize=10);\n",
    "axes[1].plot('Date', 'Monthly Price Variation', data = month_difference);\n",
    "axes[1].set_title('Monthly');\n",
    "axes[1].set_xlabel('Year', fontsize=10);\n",
    "axes[2].plot('Date', 'Yearly Price Variation', data = year_difference);\n",
    "axes[2].set_title('Yearly');\n",
    "axes[2].set_xlabel('Year', fontsize=10);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}