20_time/20_02_rossman.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Rossmann Drug Store Sales Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv('rossmann.csv', skipinitialspace=True, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  2015-07-31   5263        555     1      1            0   \n",
       "1      2          5  2015-07-31   6064        625     1      1            0   \n",
       "2      3          5  2015-07-31   8314        821     1      1            0   \n",
       "3      4          5  2015-07-31  13995       1498     1      1            0   \n",
       "4      5          5  2015-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "      <td>1017209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>558.429727</td>\n",
       "      <td>3.998341</td>\n",
       "      <td>5773.818972</td>\n",
       "      <td>633.145946</td>\n",
       "      <td>0.830107</td>\n",
       "      <td>0.381515</td>\n",
       "      <td>0.178647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>321.908651</td>\n",
       "      <td>1.997391</td>\n",
       "      <td>3849.926175</td>\n",
       "      <td>464.411734</td>\n",
       "      <td>0.375539</td>\n",
       "      <td>0.485759</td>\n",
       "      <td>0.383056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3727.000000</td>\n",
       "      <td>405.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>558.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5744.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>838.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7856.000000</td>\n",
       "      <td>837.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1115.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>41551.000000</td>\n",
       "      <td>7388.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Store       DayOfWeek           Sales       Customers  \\\n",
       "count  1017209.000000  1017209.000000  1017209.000000  1017209.000000   \n",
       "mean       558.429727        3.998341     5773.818972      633.145946   \n",
       "std        321.908651        1.997391     3849.926175      464.411734   \n",
       "min          1.000000        1.000000        0.000000        0.000000   \n",
       "25%        280.000000        2.000000     3727.000000      405.000000   \n",
       "50%        558.000000        4.000000     5744.000000      609.000000   \n",
       "75%        838.000000        6.000000     7856.000000      837.000000   \n",
       "max       1115.000000        7.000000    41551.000000     7388.000000   \n",
       "\n",
       "                 Open           Promo   SchoolHoliday  \n",
       "count  1017209.000000  1017209.000000  1017209.000000  \n",
       "mean         0.830107        0.381515        0.178647  \n",
       "std          0.375539        0.485759        0.383056  \n",
       "min          0.000000        0.000000        0.000000  \n",
       "25%          1.000000        0.000000        0.000000  \n",
       "50%          1.000000        0.000000        0.000000  \n",
       "75%          1.000000        1.000000        0.000000  \n",
       "max          1.000000        1.000000        1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are most interested in the `Date` column that contains the date of sales for each store, we will make sure to process that as a `DateTime` type, and make that the index of our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data.set_index('Date', inplace=True)\n",
    "\n",
    "data['Year'] = data.index.year\n",
    "data['Month'] = data.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store1_data = data[data.Store == 1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
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       "      <th>Month</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
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       "      <th>2015-07-30</th>\n",
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       "      <td>5020</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4782</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5011</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6102</td>\n",
       "      <td>612</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-26</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4364</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3706</td>\n",
       "      <td>459</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3769</td>\n",
       "      <td>503</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3464</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3558</td>\n",
       "      <td>469</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4395</td>\n",
       "      <td>526</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-19</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4406</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4852</td>\n",
       "      <td>519</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4427</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4767</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5042</td>\n",
       "      <td>544</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5054</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-12</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3530</td>\n",
       "      <td>441</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3808</td>\n",
       "      <td>449</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3897</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3797</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4359</td>\n",
       "      <td>540</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-05</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4797</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4665</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5558</td>\n",
       "      <td>573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-30</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4601</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3725</td>\n",
       "      <td>522</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4055</td>\n",
       "      <td>549</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-27</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-26</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5598</td>\n",
       "      <td>640</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-25</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5586</td>\n",
       "      <td>676</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-24</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5195</td>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5578</td>\n",
       "      <td>627</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5720</td>\n",
       "      <td>667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-21</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5394</td>\n",
       "      <td>607</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-20</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-19</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5182</td>\n",
       "      <td>688</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-18</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4127</td>\n",
       "      <td>568</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-17</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4044</td>\n",
       "      <td>503</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4008</td>\n",
       "      <td>530</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-15</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3900</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4717</td>\n",
       "      <td>616</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-13</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-12</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4952</td>\n",
       "      <td>646</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-11</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4881</td>\n",
       "      <td>592</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-10</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4892</td>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-09</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5471</td>\n",
       "      <td>626</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-08</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5580</td>\n",
       "      <td>654</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-07</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7176</td>\n",
       "      <td>785</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4997</td>\n",
       "      <td>635</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4486</td>\n",
       "      <td>619</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4327</td>\n",
       "      <td>578</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5530</td>\n",
       "      <td>668</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "Date                                                                       \n",
       "2015-07-31      1          5   5263        555     1      1            0   \n",
       "2015-07-30      1          4   5020        546     1      1            0   \n",
       "2015-07-29      1          3   4782        523     1      1            0   \n",
       "2015-07-28      1          2   5011        560     1      1            0   \n",
       "2015-07-27      1          1   6102        612     1      1            0   \n",
       "2015-07-26      1          7      0          0     0      0            0   \n",
       "2015-07-25      1          6   4364        500     1      0            0   \n",
       "2015-07-24      1          5   3706        459     1      0            0   \n",
       "2015-07-23      1          4   3769        503     1      0            0   \n",
       "2015-07-22      1          3   3464        463     1      0            0   \n",
       "2015-07-21      1          2   3558        469     1      0            0   \n",
       "2015-07-20      1          1   4395        526     1      0            0   \n",
       "2015-07-19      1          7      0          0     0      0            0   \n",
       "2015-07-18      1          6   4406        512     1      0            0   \n",
       "2015-07-17      1          5   4852        519     1      1            0   \n",
       "2015-07-16      1          4   4427        517     1      1            0   \n",
       "2015-07-15      1          3   4767        550     1      1            0   \n",
       "2015-07-14      1          2   5042        544     1      1            0   \n",
       "2015-07-13      1          1   5054        553     1      1            0   \n",
       "2015-07-12      1          7      0          0     0      0            0   \n",
       "2015-07-11      1          6   3530        441     1      0            0   \n",
       "2015-07-10      1          5   3808        449     1      0            0   \n",
       "2015-07-09      1          4   3897        480     1      0            0   \n",
       "2015-07-08      1          3   3797        485     1      0            0   \n",
       "2015-07-07      1          2   3650        485     1      0            0   \n",
       "2015-07-06      1          1   4359        540     1      0            0   \n",
       "2015-07-05      1          7      0          0     0      0            0   \n",
       "2015-07-04      1          6   4797        560     1      0            0   \n",
       "2015-07-03      1          5   4665        538     1      1            0   \n",
       "2015-07-02      1          4   5558        573     1      1            0   \n",
       "...           ...        ...    ...        ...   ...    ...          ...   \n",
       "2013-01-30      1          3   4601        560     1      0            0   \n",
       "2013-01-29      1          2   3725        522     1      0            0   \n",
       "2013-01-28      1          1   4055        549     1      0            0   \n",
       "2013-01-27      1          7      0          0     0      0            0   \n",
       "2013-01-26      1          6   5598        640     1      0            0   \n",
       "2013-01-25      1          5   5586        676     1      1            0   \n",
       "2013-01-24      1          4   5195        645     1      1            0   \n",
       "2013-01-23      1          3   5578        627     1      1            0   \n",
       "2013-01-22      1          2   5720        667     1      1            0   \n",
       "2013-01-21      1          1   5394        607     1      1            0   \n",
       "2013-01-20      1          7      0          0     0      0            0   \n",
       "2013-01-19      1          6   5182        688     1      0            0   \n",
       "2013-01-18      1          5   4127        568     1      0            0   \n",
       "2013-01-17      1          4   4044        503     1      0            0   \n",
       "2013-01-16      1          3   4008        530     1      0            0   \n",
       "2013-01-15      1          2   3900        512     1      0            0   \n",
       "2013-01-14      1          1   4717        616     1      0            0   \n",
       "2013-01-13      1          7      0          0     0      0            0   \n",
       "2013-01-12      1          6   4952        646     1      0            0   \n",
       "2013-01-11      1          5   4881        592     1      1            0   \n",
       "2013-01-10      1          4   4892        615     1      1            0   \n",
       "2013-01-09      1          3   5471        626     1      1            0   \n",
       "2013-01-08      1          2   5580        654     1      1            0   \n",
       "2013-01-07      1          1   7176        785     1      1            0   \n",
       "2013-01-06      1          7      0          0     0      0            0   \n",
       "2013-01-05      1          6   4997        635     1      0            0   \n",
       "2013-01-04      1          5   4486        619     1      0            0   \n",
       "2013-01-03      1          4   4327        578     1      0            0   \n",
       "2013-01-02      1          3   5530        668     1      0            0   \n",
       "2013-01-01      1          2      0          0     0      0            a   \n",
       "\n",
       "            SchoolHoliday  Year  Month  \n",
       "Date                                    \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-30              1  2015      7  \n",
       "2015-07-29              1  2015      7  \n",
       "2015-07-28              1  2015      7  \n",
       "2015-07-27              1  2015      7  \n",
       "2015-07-26              0  2015      7  \n",
       "2015-07-25              0  2015      7  \n",
       "2015-07-24              0  2015      7  \n",
       "2015-07-23              0  2015      7  \n",
       "2015-07-22              0  2015      7  \n",
       "2015-07-21              0  2015      7  \n",
       "2015-07-20              0  2015      7  \n",
       "2015-07-19              0  2015      7  \n",
       "2015-07-18              0  2015      7  \n",
       "2015-07-17              0  2015      7  \n",
       "2015-07-16              0  2015      7  \n",
       "2015-07-15              0  2015      7  \n",
       "2015-07-14              0  2015      7  \n",
       "2015-07-13              0  2015      7  \n",
       "2015-07-12              0  2015      7  \n",
       "2015-07-11              0  2015      7  \n",
       "2015-07-10              0  2015      7  \n",
       "2015-07-09              0  2015      7  \n",
       "2015-07-08              0  2015      7  \n",
       "2015-07-07              0  2015      7  \n",
       "2015-07-06              0  2015      7  \n",
       "2015-07-05              0  2015      7  \n",
       "2015-07-04              0  2015      7  \n",
       "2015-07-03              0  2015      7  \n",
       "2015-07-02              0  2015      7  \n",
       "...                   ...   ...    ...  \n",
       "2013-01-30              0  2013      1  \n",
       "2013-01-29              0  2013      1  \n",
       "2013-01-28              0  2013      1  \n",
       "2013-01-27              0  2013      1  \n",
       "2013-01-26              0  2013      1  \n",
       "2013-01-25              0  2013      1  \n",
       "2013-01-24              0  2013      1  \n",
       "2013-01-23              0  2013      1  \n",
       "2013-01-22              0  2013      1  \n",
       "2013-01-21              0  2013      1  \n",
       "2013-01-20              0  2013      1  \n",
       "2013-01-19              0  2013      1  \n",
       "2013-01-18              0  2013      1  \n",
       "2013-01-17              0  2013      1  \n",
       "2013-01-16              0  2013      1  \n",
       "2013-01-15              0  2013      1  \n",
       "2013-01-14              0  2013      1  \n",
       "2013-01-13              0  2013      1  \n",
       "2013-01-12              0  2013      1  \n",
       "2013-01-11              1  2013      1  \n",
       "2013-01-10              1  2013      1  \n",
       "2013-01-09              1  2013      1  \n",
       "2013-01-08              1  2013      1  \n",
       "2013-01-07              1  2013      1  \n",
       "2013-01-06              1  2013      1  \n",
       "2013-01-05              1  2013      1  \n",
       "2013-01-04              1  2013      1  \n",
       "2013-01-03              1  2013      1  \n",
       "2013-01-02              1  2013      1  \n",
       "2013-01-01              1  2013      1  \n",
       "\n",
       "[942 rows x 10 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "store1_data = store1_data.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d07ed90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LrufDxcz6thnj0CoO1mrkejwrQiA2+gOMsTM4588AuAjAk+LvUgCfEv9vEY/8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c1ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store1_data[ store1_data.Open == 1 ].Sales.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration and MINEing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare sales on holidays, we can compare the sales using box-plots, which allows us to compare the distribution of sales on holidays against all other days. On state holidays the store is closed (and as a nice sanity check there are 0 sales), and on school holidays the sales are relatively similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x114bb4690>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M7q+MO+MNHhgAYNxRY5+vlbXB/f0w8cjXo5CRitfUNDXrJhSt5+W9ADRNfH3GLSnCxNL8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\nEZGoFDIiEpVCRkSi+n/66c84rB15wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103480d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "sb.factorplot(\n",
    "    x='SchoolHoliday',\n",
    "    y='Sales',\n",
    "    data=store1_data, \n",
    "    kind='box'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Check: See if there is a difference affecting sales on promotion days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10cd90650>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0PMju3bvK3m6sm4wtWNBGe/sNZW2zXBQyIimaOPGirLuQOoWMSBHt7TdU7Mig2mjiV0Si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\nRkSi+n8ulc8ITrYpwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130898d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO\n",
    "sb.factorplot(\n",
    "    x='Promo',\n",
    "    y='Sales',\n",
    "    data=store1_data,\n",
    "    kind='box', \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5530</td>\n",
       "      <td>668</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4327</td>\n",
       "      <td>578</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4486</td>\n",
       "      <td>619</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4997</td>\n",
       "      <td>635</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "Date                                                                       \n",
       "2013-01-01      1          2      0          0     0      0            a   \n",
       "2013-01-02      1          3   5530        668     1      0            0   \n",
       "2013-01-03      1          4   4327        578     1      0            0   \n",
       "2013-01-04      1          5   4486        619     1      0            0   \n",
       "2013-01-05      1          6   4997        635     1      0            0   \n",
       "\n",
       "            SchoolHoliday  Year  Month  \n",
       "Date                                    \n",
       "2013-01-01              1  2013      1  \n",
       "2013-01-02              1  2013      1  \n",
       "2013-01-03              1  2013      1  \n",
       "2013-01-04              1  2013      1  \n",
       "2013-01-05              1  2013      1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x114c1a490>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcb4890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO\n",
    "sb.factorplot(\n",
    "    x='Promo',\n",
    "    y='Sales',\n",
    "    data=store1_data[ store1_data.Open == 1 ],\n",
    "    kind='box', \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Check: See if there is a difference affecting sales based on the day of week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113237c10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y4CyKbyvjqoihD5XHERHPAl6ZmTdCMeVBZv6i4rAAzgR+kJk/OeSebZKZ3wA2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f827a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO\n",
    "sb.factorplot(col='Open',\n",
    "              x='DayOfWeek',\n",
    "              y='Sales',\n",
    "              data=store1_data,\n",
    "              kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we want to identify larger-scale trends in our data. How did sales change from 2014 to 2015? Were there any particularly interesting outliers in terms of sales or customer visits?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123fdf450>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bG+xVz1xDWhvBQZQIRscr6BksYQXv26mgqvSP63nBRh+nDRH3KZFwa7yTBCel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\ngu/oHhHL1vaT7PywwmzBLqfRFMgGj2sCPu39vV1sPmEzjluaziTadSU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lAAAJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123fd0ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter to days store 1 was open\n",
    "store1_open_data = store1_data[store1_data.Open==1]\n",
    "store1_open_data[['Sales']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123feedd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X9k2tMfz04Q+lD5B5iJRy6QxkMwAWbBXRdV8Dk217JzsmBmQxoQ542slnWxrc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\nKmjRj83sFcJHdpNOB3ifgXgKYWedkgbVZcBOkpYvNarUn+WIgJl/KB+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x123fd0810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store1_open_data[['Customers']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-06-05</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5278</td>\n",
       "      <td>585</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-06</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4957</td>\n",
       "      <td>575</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-07</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4923</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4607</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-11</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4207</td>\n",
       "      <td>535</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-12</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3702</td>\n",
       "      <td>489</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-13</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3901</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-14</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4757</td>\n",
       "      <td>562</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-16</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4788</td>\n",
       "      <td>529</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5770</td>\n",
       "      <td>594</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-18</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5256</td>\n",
       "      <td>567</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-20</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6362</td>\n",
       "      <td>709</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-21</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3982</td>\n",
       "      <td>496</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3755</td>\n",
       "      <td>456</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-24</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3351</td>\n",
       "      <td>444</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-25</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3727</td>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-26</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2749</td>\n",
       "      <td>368</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-27</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4019</td>\n",
       "      <td>501</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-28</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5162</td>\n",
       "      <td>566</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5738</td>\n",
       "      <td>641</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5138</td>\n",
       "      <td>533</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5161</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4756</td>\n",
       "      <td>514</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-04</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4025</td>\n",
       "      <td>450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-05</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5176</td>\n",
       "      <td>589</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4223</td>\n",
       "      <td>505</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4081</td>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-09</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3547</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-10</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3741</td>\n",
       "      <td>472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-11</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4020</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-27</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4019</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5197</td>\n",
       "      <td>541</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5735</td>\n",
       "      <td>568</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5223</td>\n",
       "      <td>562</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5558</td>\n",
       "      <td>573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4665</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4797</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4359</td>\n",
       "      <td>540</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3650</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3797</td>\n",
       "      <td>485</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3897</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3808</td>\n",
       "      <td>449</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3530</td>\n",
       "      <td>441</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5054</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5042</td>\n",
       "      <td>544</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4767</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4427</td>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4852</td>\n",
       "      <td>519</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4406</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4395</td>\n",
       "      <td>526</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3558</td>\n",
       "      <td>469</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3464</td>\n",
       "      <td>463</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3769</td>\n",
       "      <td>503</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3706</td>\n",
       "      <td>459</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4364</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6102</td>\n",
       "      <td>612</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5011</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4782</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5020</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "Date                                                                       \n",
       "2014-06-05      1          4   5278        585     1      1            0   \n",
       "2014-06-06      1          5   4957        575     1      1            0   \n",
       "2014-06-07      1          6   4923        555     1      0            0   \n",
       "2014-06-10      1          2   4607        560     1      0            0   \n",
       "2014-06-11      1          3   4207        535     1      0            0   \n",
       "2014-06-12      1          4   3702        489     1      0            0   \n",
       "2014-06-13      1          5   3901        498     1      0            0   \n",
       "2014-06-14      1          6   4757        562     1      0            0   \n",
       "2014-06-16      1          1   4788        529     1      1            0   \n",
       "2014-06-17      1          2   5770        594     1      1            0   \n",
       "2014-06-18      1          3   5256        567     1      1            0   \n",
       "2014-06-20      1          5   6362        709     1      1            0   \n",
       "2014-06-21      1          6   3982        496     1      0            0   \n",
       "2014-06-23      1          1   3755        456     1      0            0   \n",
       "2014-06-24      1          2   3351        444     1      0            0   \n",
       "2014-06-25      1          3   3727        440     1      0            0   \n",
       "2014-06-26      1          4   2749        368     1      0            0   \n",
       "2014-06-27      1          5   4019        501     1      0            0   \n",
       "2014-06-28      1          6   5162        566     1      0            0   \n",
       "2014-06-30      1          1   5738        641     1      1            0   \n",
       "2014-07-01      1          2   5138        533     1      1            0   \n",
       "2014-07-02      1          3   5161        560     1      1            0   \n",
       "2014-07-03      1          4   4756        514     1      1            0   \n",
       "2014-07-04      1          5   4025        450     1      1            0   \n",
       "2014-07-05      1          6   5176        589     1      0            0   \n",
       "2014-07-07      1          1   4223        505     1      0            0   \n",
       "2014-07-08      1          2   4081        510     1      0            0   \n",
       "2014-07-09      1          3   3547        485     1      0            0   \n",
       "2014-07-10      1          4   3741        472     1      0            0   \n",
       "2014-07-11      1          5   4020        463     1      0            0   \n",
       "...           ...        ...    ...        ...   ...    ...          ...   \n",
       "2015-06-27      1          6   4019        463     1      0            0   \n",
       "2015-06-29      1          1   5197        541     1      1            0   \n",
       "2015-06-30      1          2   5735        568     1      1            0   \n",
       "2015-07-01      1          3   5223        562     1      1            0   \n",
       "2015-07-02      1          4   5558        573     1      1            0   \n",
       "2015-07-03      1          5   4665        538     1      1            0   \n",
       "2015-07-04      1          6   4797        560     1      0            0   \n",
       "2015-07-06      1          1   4359        540     1      0            0   \n",
       "2015-07-07      1          2   3650        485     1      0            0   \n",
       "2015-07-08      1          3   3797        485     1      0            0   \n",
       "2015-07-09      1          4   3897        480     1      0            0   \n",
       "2015-07-10      1          5   3808        449     1      0            0   \n",
       "2015-07-11      1          6   3530        441     1      0            0   \n",
       "2015-07-13      1          1   5054        553     1      1            0   \n",
       "2015-07-14      1          2   5042        544     1      1            0   \n",
       "2015-07-15      1          3   4767        550     1      1            0   \n",
       "2015-07-16      1          4   4427        517     1      1            0   \n",
       "2015-07-17      1          5   4852        519     1      1            0   \n",
       "2015-07-18      1          6   4406        512     1      0            0   \n",
       "2015-07-20      1          1   4395        526     1      0            0   \n",
       "2015-07-21      1          2   3558        469     1      0            0   \n",
       "2015-07-22      1          3   3464        463     1      0            0   \n",
       "2015-07-23      1          4   3769        503     1      0            0   \n",
       "2015-07-24      1          5   3706        459     1      0            0   \n",
       "2015-07-25      1          6   4364        500     1      0            0   \n",
       "2015-07-27      1          1   6102        612     1      1            0   \n",
       "2015-07-28      1          2   5011        560     1      1            0   \n",
       "2015-07-29      1          3   4782        523     1      1            0   \n",
       "2015-07-30      1          4   5020        546     1      1            0   \n",
       "2015-07-31      1          5   5263        555     1      1            0   \n",
       "\n",
       "            SchoolHoliday  Year  Month  \n",
       "Date                                    \n",
       "2014-06-05              0  2014      6  \n",
       "2014-06-06              0  2014      6  \n",
       "2014-06-07              0  2014      6  \n",
       "2014-06-10              0  2014      6  \n",
       "2014-06-11              0  2014      6  \n",
       "2014-06-12              0  2014      6  \n",
       "2014-06-13              0  2014      6  \n",
       "2014-06-14              0  2014      6  \n",
       "2014-06-16              0  2014      6  \n",
       "2014-06-17              0  2014      6  \n",
       "2014-06-18              0  2014      6  \n",
       "2014-06-20              0  2014      6  \n",
       "2014-06-21              0  2014      6  \n",
       "2014-06-23              0  2014      6  \n",
       "2014-06-24              0  2014      6  \n",
       "2014-06-25              0  2014      6  \n",
       "2014-06-26              0  2014      6  \n",
       "2014-06-27              0  2014      6  \n",
       "2014-06-28              0  2014      6  \n",
       "2014-06-30              0  2014      6  \n",
       "2014-07-01              0  2014      7  \n",
       "2014-07-02              0  2014      7  \n",
       "2014-07-03              0  2014      7  \n",
       "2014-07-04              0  2014      7  \n",
       "2014-07-05              0  2014      7  \n",
       "2014-07-07              0  2014      7  \n",
       "2014-07-08              0  2014      7  \n",
       "2014-07-09              0  2014      7  \n",
       "2014-07-10              0  2014      7  \n",
       "2014-07-11              0  2014      7  \n",
       "...                   ...   ...    ...  \n",
       "2015-06-27              0  2015      6  \n",
       "2015-06-29              0  2015      6  \n",
       "2015-06-30              0  2015      6  \n",
       "2015-07-01              0  2015      7  \n",
       "2015-07-02              0  2015      7  \n",
       "2015-07-03              0  2015      7  \n",
       "2015-07-04              0  2015      7  \n",
       "2015-07-06              0  2015      7  \n",
       "2015-07-07              0  2015      7  \n",
       "2015-07-08              0  2015      7  \n",
       "2015-07-09              0  2015      7  \n",
       "2015-07-10              0  2015      7  \n",
       "2015-07-11              0  2015      7  \n",
       "2015-07-13              0  2015      7  \n",
       "2015-07-14              0  2015      7  \n",
       "2015-07-15              0  2015      7  \n",
       "2015-07-16              0  2015      7  \n",
       "2015-07-17              0  2015      7  \n",
       "2015-07-18              0  2015      7  \n",
       "2015-07-20              0  2015      7  \n",
       "2015-07-21              0  2015      7  \n",
       "2015-07-22              0  2015      7  \n",
       "2015-07-23              0  2015      7  \n",
       "2015-07-24              0  2015      7  \n",
       "2015-07-25              0  2015      7  \n",
       "2015-07-27              1  2015      7  \n",
       "2015-07-28              1  2015      7  \n",
       "2015-07-29              1  2015      7  \n",
       "2015-07-30              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "\n",
       "[350 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1_open_data['2014-06-05':'201']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Check: Use the index filtering to filter to just 2015. Zoom in on changes over time.\n",
    "\n",
    "> Is it easier to identify the holiday sales bump?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fbecc90>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4FZsNrrpwOe9XNQEnGfIbszp3LP+OJiNe5YL4lc0KuSxVEKYCqFVKlWutDwOX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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc37450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store1_open_data['2014-12':'2015-01'][['Sales']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In pandas we can compute rolling average using the `pd.rolling_mean` or `pd.rolling_median` functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data REFINING Using Time Series Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To measure how much the sales are correlated with each other, we want to compute the _autocorrelation_ of the 'Sales' column. In pandas, we do this we with the `autocorr` function.\n",
    "\n",
    "`autocorr` takes one argument, the `lag` - which is how many prior data points should be used to compute the correlation. If we set the `lag` to 1, we compute the correlation between every point and the point directly preceding it, while setting `lag` to 10, computes the correlation between every point and the point 10 days earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th>2015-07-31</th>\n",
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       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
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       "      <td>2015</td>\n",
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       "            Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "Date                                                                       \n",
       "2015-07-31      1          5   5263        555     1      1            0   \n",
       "2015-07-31      2          5   6064        625     1      1            0   \n",
       "2015-07-31      3          5   8314        821     1      1            0   \n",
       "2015-07-31      4          5  13995       1498     1      1            0   \n",
       "2015-07-31      5          5   4822        559     1      1            0   \n",
       "\n",
       "            SchoolHoliday  Year  Month  \n",
       "Date                                    \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
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   },
   "outputs": [
    {
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       "      <th>Sales</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>2013-01-01</th>\n",
       "      <td>87.284560</td>\n",
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       "      <th>2013-01-02</th>\n",
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       "      <th>2013-01-03</th>\n",
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       "      <th>2013-01-05</th>\n",
       "      <td>5337.751570</td>\n",
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       "      <th>2013-01-06</th>\n",
       "      <td>129.061883</td>\n",
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       "      <th>2013-01-07</th>\n",
       "      <td>9710.177578</td>\n",
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       "      <th>2013-01-08</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-12</th>\n",
       "      <td>5396.852915</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-13</th>\n",
       "      <td>129.194619</td>\n",
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       "      <th>2013-01-14</th>\n",
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       "      <th>2013-01-15</th>\n",
       "      <td>4944.027803</td>\n",
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       "      <th>2013-01-16</th>\n",
       "      <td>4747.103139</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-17</th>\n",
       "      <td>4830.999103</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-18</th>\n",
       "      <td>5504.044843</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-19</th>\n",
       "      <td>5390.306726</td>\n",
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       "      <th>2013-01-20</th>\n",
       "      <td>135.964126</td>\n",
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       "      <th>2013-01-21</th>\n",
       "      <td>7857.465471</td>\n",
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       "      <th>2013-01-22</th>\n",
       "      <td>7462.142601</td>\n",
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       "      <th>2013-01-23</th>\n",
       "      <td>6947.590135</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-24</th>\n",
       "      <td>6672.498655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-25</th>\n",
       "      <td>6978.286099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-26</th>\n",
       "      <td>5245.533632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-27</th>\n",
       "      <td>128.114798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>5272.378475</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>4890.285202</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-30</th>\n",
       "      <td>5300.250224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>7575.150673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>7650.572197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>5429.843946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-05</th>\n",
       "      <td>241.478027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>6762.756054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>5733.653812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>6202.354260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>6222.584753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>6336.241256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>5616.163229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-12</th>\n",
       "      <td>248.472646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>9723.109417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>8377.044843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>7853.037668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>7510.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>7129.450224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>5783.840359</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-19</th>\n",
       "      <td>267.639462</td>\n",
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       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>6582.350673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>5700.880717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>5480.168610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>5997.370404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>5920.470852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>5400.597309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-26</th>\n",
       "      <td>256.381166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>9602.952466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>8174.953363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>7623.284305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30</th>\n",
       "      <td>7891.348879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>9067.033184</td>\n",
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       "  </tbody>\n",
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       "<p>942 rows × 1 columns</p>\n",
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      "text/plain": [
       "                  Sales\n",
       "Date                   \n",
       "2013-01-01    87.284560\n",
       "2013-01-02  6233.030493\n",
       "2013-01-03  5693.112108\n",
       "2013-01-04  5954.218834\n",
       "2013-01-05  5337.751570\n",
       "2013-01-06   129.061883\n",
       "2013-01-07  9710.177578\n",
       "2013-01-08  7847.028700\n",
       "2013-01-09  6947.626009\n",
       "2013-01-10  6952.004484\n",
       "2013-01-11  7210.139910\n",
       "2013-01-12  5396.852915\n",
       "2013-01-13   129.194619\n",
       "2013-01-14  5279.630493\n",
       "2013-01-15  4944.027803\n",
       "2013-01-16  4747.103139\n",
       "2013-01-17  4830.999103\n",
       "2013-01-18  5504.044843\n",
       "2013-01-19  5390.306726\n",
       "2013-01-20   135.964126\n",
       "2013-01-21  7857.465471\n",
       "2013-01-22  7462.142601\n",
       "2013-01-23  6947.590135\n",
       "2013-01-24  6672.498655\n",
       "2013-01-25  6978.286099\n",
       "2013-01-26  5245.533632\n",
       "2013-01-27   128.114798\n",
       "2013-01-28  5272.378475\n",
       "2013-01-29  4890.285202\n",
       "2013-01-30  5300.250224\n",
       "...                 ...\n",
       "2015-07-02  7575.150673\n",
       "2015-07-03  7650.572197\n",
       "2015-07-04  5429.843946\n",
       "2015-07-05   241.478027\n",
       "2015-07-06  6762.756054\n",
       "2015-07-07  5733.653812\n",
       "2015-07-08  6202.354260\n",
       "2015-07-09  6222.584753\n",
       "2015-07-10  6336.241256\n",
       "2015-07-11  5616.163229\n",
       "2015-07-12   248.472646\n",
       "2015-07-13  9723.109417\n",
       "2015-07-14  8377.044843\n",
       "2015-07-15  7853.037668\n",
       "2015-07-16  7510.200000\n",
       "2015-07-17  7129.450224\n",
       "2015-07-18  5783.840359\n",
       "2015-07-19   267.639462\n",
       "2015-07-20  6582.350673\n",
       "2015-07-21  5700.880717\n",
       "2015-07-22  5480.168610\n",
       "2015-07-23  5997.370404\n",
       "2015-07-24  5920.470852\n",
       "2015-07-25  5400.597309\n",
       "2015-07-26   256.381166\n",
       "2015-07-27  9602.952466\n",
       "2015-07-28  8174.953363\n",
       "2015-07-29  7623.284305\n",
       "2015-07-30  7891.348879\n",
       "2015-07-31  9067.033184\n",
       "\n",
       "[942 rows x 1 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Sales']].resample('D', how='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02585827600638356"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sales'].resample('D', how='mean').autocorr(lag=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-01-01      87.284560\n",
       "2013-01-02    6233.030493\n",
       "2013-01-03    5693.112108\n",
       "2013-01-04    5954.218834\n",
       "2013-01-05    5337.751570\n",
       "Freq: D, Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sales'].resample('D', how='mean').head()\n",
    "#data[['Sales']].resample('D', how='mean').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Check: What do the autocorrelation values for Sales and Customers say about shopping habits? Is this true for 1, 5, 10 day autocorrelations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0951315839098\n",
      "-0.189215471313\n",
      "-0.0737077010343\n",
      "-0.0258582760064\n",
      "-0.192355899471\n",
      "-0.0697875468489\n"
     ]
    }
   ],
   "source": [
    "print data['Customers'].resample('D', how='mean').autocorr(lag=1)\n",
    "print data['Customers'].resample('D', how='mean').autocorr(lag=2)\n",
    "print data['Customers'].resample('D', how='mean').autocorr(lag=3)\n",
    "\n",
    "\n",
    "print data['Sales'].resample('D', how='mean').autocorr(lag=1)\n",
    "print data['Sales'].resample('D', how='mean').autocorr(lag=5)\n",
    "print data['Sales'].resample('D', how='mean').autocorr(lag=10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling Averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to investigate trends over time in sales, as always, we will start by computing simple aggregates.  We want to know what the mean and median sales were for each month and year.\n",
    "\n",
    "In Pandas, this is performed using the `resample` command, which is very similar to the `groupby` command. It allows us to group over different time intervals.\n",
    "\n",
    "We can use `data.resample` and provide as arguments:\n",
    "    - The level on which to roll-up to, 'D' for day, 'W' for week, 'M' for month, 'A' for year\n",
    "    - What aggregation to perform: 'mean', 'median', 'sum', etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>5194.0</td>\n",
       "      <td>5211.555578</td>\n",
       "      <td>180132207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>5421.5</td>\n",
       "      <td>5494.371397</td>\n",
       "      <td>171534275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>5844.0</td>\n",
       "      <td>5820.349168</td>\n",
       "      <td>201180369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>5464.0</td>\n",
       "      <td>5483.749836</td>\n",
       "      <td>183431432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>5585.0</td>\n",
       "      <td>5364.127383</td>\n",
       "      <td>185411063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sales                        \n",
       "            median         mean        sum\n",
       "Date                                      \n",
       "2013-01-31  5194.0  5211.555578  180132207\n",
       "2013-02-28  5421.5  5494.371397  171534275\n",
       "2013-03-31  5844.0  5820.349168  201180369\n",
       "2013-04-30  5464.0  5483.749836  183431432\n",
       "2013-05-31  5585.0  5364.127383  185411063"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Sales']].resample('M', how=['median', 'mean','sum']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While identifying the monthly averages are useful, we often want to compare the sales data of a date to a smaller window. To understand holidays sales, we don't want to compare late December with the entire month, but perhaps a few days surrounding it. We can do this using rolling averages.\n",
    "\n",
    "In pandas, we can compute rolling average using the `pd.rolling_mean` or `pd.rolling_median` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>2013-01-01</th>\n",
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       "                  Sales\n",
       "Date                   \n",
       "2013-01-01          NaN\n",
       "2013-01-02  4004.475720\n",
       "2013-01-03  5960.120478\n",
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   "source": [
    "pd.rolling_mean(data[['Sales']], window=3, center=True, freq='D').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rolling_mean` (as well as `rolling_median`) takes these important parameters:\n",
    "    - the first is the series to aggregate\n",
    "    - `window` is the number of days to include in the average\n",
    "    - `center` is whether the window should be centered on the date or use data prior to that date\n",
    "    - `freq` is on what level to roll-up the averages to (as used in `resample`). Either `D` for day, `M` for month or `A` for year, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>5303.2</td>\n",
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       "      <th>2013-01-05</th>\n",
       "      <td>5313.2</td>\n",
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       "      <th>2013-01-07</th>\n",
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       "      <th>2013-01-11</th>\n",
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       "      <th>2013-01-12</th>\n",
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       "      <th>2013-01-14</th>\n",
       "      <td>4491.6</td>\n",
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       "      <th>2013-01-15</th>\n",
       "      <td>4324.2</td>\n",
       "    </tr>\n",
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       "      <th>2013-01-16</th>\n",
       "      <td>4159.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-17</th>\n",
       "      <td>4252.2</td>\n",
       "    </tr>\n",
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       "      <th>2013-01-18</th>\n",
       "      <td>4551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-19</th>\n",
       "      <td>4893.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-21</th>\n",
       "      <td>5200.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-22</th>\n",
       "      <td>5413.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-23</th>\n",
       "      <td>5494.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-24</th>\n",
       "      <td>5535.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-25</th>\n",
       "      <td>5202.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-26</th>\n",
       "      <td>4831.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>4713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>4537.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-30</th>\n",
       "      <td>4544.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>4927.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>5589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-02</th>\n",
       "      <td>5878.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-04</th>\n",
       "      <td>6164.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-05</th>\n",
       "      <td>6138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-27</th>\n",
       "      <td>4360.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>4698.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>5146.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>5275.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>5195.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>4920.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>4605.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>4253.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>4100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>3902.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>3736.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>4017.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>4266.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>4440.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>4564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>4828.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>4698.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>4569.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>4327.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>4135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>3918.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>3778.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>3772.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>4281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>4590.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>4793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>5055.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>5235.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>781 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sales\n",
       "Date              \n",
       "2013-01-02     NaN\n",
       "2013-01-03     NaN\n",
       "2013-01-04  5303.2\n",
       "2013-01-05  5313.2\n",
       "2013-01-07  5542.0\n",
       "2013-01-08  5623.2\n",
       "2013-01-09  5600.0\n",
       "2013-01-10  5155.2\n",
       "2013-01-11  4982.6\n",
       "2013-01-12  4668.4\n",
       "2013-01-14  4491.6\n",
       "2013-01-15  4324.2\n",
       "2013-01-16  4159.2\n",
       "2013-01-17  4252.2\n",
       "2013-01-18  4551.0\n",
       "2013-01-19  4893.4\n",
       "2013-01-21  5200.2\n",
       "2013-01-22  5413.8\n",
       "2013-01-23  5494.6\n",
       "2013-01-24  5535.4\n",
       "2013-01-25  5202.4\n",
       "2013-01-26  4831.8\n",
       "2013-01-28  4713.0\n",
       "2013-01-29  4537.6\n",
       "2013-01-30  4544.6\n",
       "2013-01-31  4927.6\n",
       "2013-02-01  5589.0\n",
       "2013-02-02  5878.6\n",
       "2013-02-04  6164.8\n",
       "2013-02-05  6138.0\n",
       "...            ...\n",
       "2015-06-27  4360.2\n",
       "2015-06-29  4698.2\n",
       "2015-06-30  5146.4\n",
       "2015-07-01  5275.6\n",
       "2015-07-02  5195.6\n",
       "2015-07-03  4920.4\n",
       "2015-07-04  4605.8\n",
       "2015-07-06  4253.6\n",
       "2015-07-07  4100.0\n",
       "2015-07-08  3902.2\n",
       "2015-07-09  3736.4\n",
       "2015-07-10  4017.2\n",
       "2015-07-11  4266.2\n",
       "2015-07-13  4440.2\n",
       "2015-07-14  4564.0\n",
       "2015-07-15  4828.4\n",
       "2015-07-16  4698.8\n",
       "2015-07-17  4569.4\n",
       "2015-07-18  4327.6\n",
       "2015-07-20  4135.0\n",
       "2015-07-21  3918.4\n",
       "2015-07-22  3778.4\n",
       "2015-07-23  3772.2\n",
       "2015-07-24  4281.0\n",
       "2015-07-25  4590.4\n",
       "2015-07-27  4793.0\n",
       "2015-07-28  5055.8\n",
       "2015-07-29  5235.6\n",
       "2015-07-30     NaN\n",
       "2015-07-31     NaN\n",
       "\n",
       "[781 rows x 1 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.rolling_mean(store1_open_data[['Sales']], window=5, center=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of plotting the full timeseries, we can plot the rolling mean instead, which smooths random changes in sales as well as removing outliers, helping us identify larger trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1129ac0d0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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bG+xVz1xDWhvBQZQIRscr6BksYQXv26mgqvSP63nBRh+nDRH3KZFwa7yTBCel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\ngu/oHhHL1vaT7PywwmzBLqfRFMgGj2sCPu39vV1sPmEzjluaziTadSU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lAAAJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f432790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store1_open_data[['Sales']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1127e56d0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EBK9pb82z7PNQ4ZfRvQPDv6ENw3DULMqp4ZeDDd+jNjqjGDZ83TDw55e3O/ZX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\ntAe9hFoMvmJhgT8T3si+RBdpBjFB5X4inWxTopTq2sAHgdm2L6vrAUs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127f34d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_std(store1_open_data[['Sales']], window=10, center=True).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example comparing 2015 vs rolling means of 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'FigureCanvasAgg' object has no attribute 'invalidate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-87-036f7f02c51a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'd'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2015'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'2015'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   3669\u001b[0m                           \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3670\u001b[0m                           \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3671\u001b[0;31m                           sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m   3672\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   2554\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2555\u001b[0m                  \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2556\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   2557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   2382\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2384\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2385\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2386\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    985\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    988\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1662\u001b[0m                              \u001b[0mstacking_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                              \u001b[0mis_errorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1664\u001b[0;31m                              **kwds)\n\u001b[0m\u001b[1;32m   1665\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_ts_plot\u001b[0;34m(cls, ax, x, data, style, **kwds)\u001b[0m\n\u001b[1;32m   1701\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1702\u001b[0m         \u001b[0;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1703\u001b[0;31m         \u001b[0mformat_dateaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1704\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tseries/plotting.pyc\u001b[0m in \u001b[0;36mformat_dateaxis\u001b[0;34m(subplot, freq)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \"y = {1:8f}\".format(Period(ordinal=int(t), freq=freq), y))\n\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m     \u001b[0mpylab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/IPython/utils/decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_macosx.pyc\u001b[0m in \u001b[0;36mdraw_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0mfigManager\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfigManager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m             \u001b[0mfigManager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'FigureCanvasAgg' object has no attribute 'invalidate'"
     ]
    },
    {
     "data": {
      "image/png": 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f/HRZQ/6KYNUVLHazhlRFwabhsr1BRyr9oJPBMC2srXciaWOyYplOCeFNw2Wc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\n+H+YQZRdzMJA9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4ec3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Sales']].resample('d','mean')['2015'].plot()\n",
    "pd.rolling_mean(data[['Sales']],window=3,center=True,freq='D')['2015'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Check: Compare rolling_mean, rolling_median, and ewma using the same windows for store1_data. How do they compare graphically? (EWMA takes in an argument `span`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store1_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a82a390>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bG+xVz1xDWhvBQZQIRscr6BksYQXv26mgqvSP63nBRh+nDRH3KZFwa7yTBCel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\ngu/oHhHL1vaT7PywwmzBLqfRFMgGj2sCPu39vV1sPmEzjluaziTadSU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lAAAJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bc93e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OM4E/PFGbRlnV9OoxPpPd0G6GcjflFpmRAj/qoeE7lM5aEZ5qfMFa2ZWcalQ1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\nD8C8kNcd9OagAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bc93f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M3wkxfF0tc/Vjvnkqy8rwndXB8Ns561Gv46hlbwS+IqUsSSkPAweAPX4bxF4p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f91bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nMr/6y7vwV5/9OWZXuJVwMhCSTi6jI581eJSOqIcfJXz2910PHMJf/uv9Kjxz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132045fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store1_open_data[['Sales']].plot()\n",
    "pd.rolling_mean( store1_open_data[['Sales']],window=10).plot()\n",
    "pd.rolling_median( store1_open_data[['Sales']],window=10).plot()\n",
    "pd.ewma( store1_open_data[['Sales']],span=10).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas Window functions\n",
    "Pandas `rolling_mean` and `rolling_median` are only two examples of Pandas window function capabilities. Window functions are operate on a set of N consecutive rows (a window) and produce an output.\n",
    "\n",
    "In addition to `rolling_mean` and `rolling_median`, there are `rolling_sum`, `rolling_min`, `rolling_max`... and many more.\n",
    "\n",
    "Another common one is `diff`, which takes the difference over time. `pd.diff` takes one argument, `periods`, which is how many prior rows to use for the difference.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-01-02    5530\n",
       "2013-01-03    4327\n",
       "2013-01-04    4486\n",
       "2013-01-05    4997\n",
       "2013-01-07    7176\n",
       "2013-01-08    5580\n",
       "2013-01-09    5471\n",
       "2013-01-10    4892\n",
       "2013-01-11    4881\n",
       "2013-01-12    4952\n",
       "2013-01-14    4717\n",
       "2013-01-15    3900\n",
       "2013-01-16    4008\n",
       "2013-01-17    4044\n",
       "2013-01-18    4127\n",
       "2013-01-19    5182\n",
       "2013-01-21    5394\n",
       "2013-01-22    5720\n",
       "2013-01-23    5578\n",
       "2013-01-24    5195\n",
       "2013-01-25    5586\n",
       "2013-01-26    5598\n",
       "2013-01-28    4055\n",
       "2013-01-29    3725\n",
       "2013-01-30    4601\n",
       "2013-01-31    4709\n",
       "2013-02-01    5633\n",
       "2013-02-02    5970\n",
       "2013-02-04    7032\n",
       "2013-02-05    6049\n",
       "              ... \n",
       "2015-06-27    4019\n",
       "2015-06-29    5197\n",
       "2015-06-30    5735\n",
       "2015-07-01    5223\n",
       "2015-07-02    5558\n",
       "2015-07-03    4665\n",
       "2015-07-04    4797\n",
       "2015-07-06    4359\n",
       "2015-07-07    3650\n",
       "2015-07-08    3797\n",
       "2015-07-09    3897\n",
       "2015-07-10    3808\n",
       "2015-07-11    3530\n",
       "2015-07-13    5054\n",
       "2015-07-14    5042\n",
       "2015-07-15    4767\n",
       "2015-07-16    4427\n",
       "2015-07-17    4852\n",
       "2015-07-18    4406\n",
       "2015-07-20    4395\n",
       "2015-07-21    3558\n",
       "2015-07-22    3464\n",
       "2015-07-23    3769\n",
       "2015-07-24    3706\n",
       "2015-07-25    4364\n",
       "2015-07-27    6102\n",
       "2015-07-28    5011\n",
       "2015-07-29    4782\n",
       "2015-07-30    5020\n",
       "2015-07-31    5263\n",
       "Name: Sales, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store1_open_data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-07</th>\n",
       "      <td>2849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-08</th>\n",
       "      <td>1094</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-09</th>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-10</th>\n",
       "      <td>-2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-11</th>\n",
       "      <td>-699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-12</th>\n",
       "      <td>-519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sales\n",
       "Date             \n",
       "2013-01-02    NaN\n",
       "2013-01-03    NaN\n",
       "2013-01-04    NaN\n",
       "2013-01-05   -533\n",
       "2013-01-07   2849\n",
       "2013-01-08   1094\n",
       "2013-01-09    474\n",
       "2013-01-10  -2284\n",
       "2013-01-11   -699\n",
       "2013-01-12   -519"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "store1_open_data[['Sales']].diff(periods=3).head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas expanding functions\n",
    "\n",
    "In addition to the set of `rolling_*` functions, Pandas also provides a similar collection of `expanding_*` functions, which, instead of using a window of N values, use all values up until that time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-01-01      87.284560\n",
       "2013-01-02    3160.157527\n",
       "2013-01-03    4004.475720\n",
       "2013-01-04    4491.911499\n",
       "2013-01-05    4661.079513\n",
       "Freq: D, Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computes the average sales, from the first date _until_ the date specified.\n",
    "pd.expanding_mean(data['Sales'], freq='d').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "      <th>2013-01-12</th>\n",
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       "      <th>2013-01-13</th>\n",
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       "      <th>2013-01-14</th>\n",
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       "      <th>2013-01-15</th>\n",
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       "      <th>2013-01-18</th>\n",
       "      <td>6137010</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-19</th>\n",
       "      <td>6010192</td>\n",
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       "      <th>2013-01-21</th>\n",
       "      <td>8761074</td>\n",
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       "      <th>2013-01-22</th>\n",
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       "      <th>2013-01-23</th>\n",
       "      <td>7746563</td>\n",
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       "      <th>2013-01-24</th>\n",
       "      <td>7439836</td>\n",
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       "      <th>2013-01-25</th>\n",
       "      <td>7780789</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-26</th>\n",
       "      <td>5848770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-27</th>\n",
       "      <td>142848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>5878702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>5452668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-30</th>\n",
       "      <td>5909779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>8446293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>8530388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>6054276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-05</th>\n",
       "      <td>269248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>7540473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>6393024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>6915625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>6938182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>7064909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>6262022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-12</th>\n",
       "      <td>277047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>10841267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>9340405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>8756137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>8373873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>7949337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>6448982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-19</th>\n",
       "      <td>298418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>7339321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>6356482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>6110388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>6687068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>6601325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>6021666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-26</th>\n",
       "      <td>285865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>10707292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>9115073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>8499962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30</th>\n",
       "      <td>8798854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>10109742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>942 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Date                \n",
       "2013-01-01     97235\n",
       "2013-01-02   6949829\n",
       "2013-01-03   6347820\n",
       "2013-01-04   6638954\n",
       "2013-01-05   5951593\n",
       "2013-01-06    143904\n",
       "2013-01-07  10826848\n",
       "2013-01-08   8749437\n",
       "2013-01-09   7746603\n",
       "2013-01-10   7751485\n",
       "2013-01-11   8039306\n",
       "2013-01-12   6017491\n",
       "2013-01-13    144052\n",
       "2013-01-14   5886788\n",
       "2013-01-15   5512591\n",
       "2013-01-16   5293020\n",
       "2013-01-17   5386564\n",
       "2013-01-18   6137010\n",
       "2013-01-19   6010192\n",
       "2013-01-20    151600\n",
       "2013-01-21   8761074\n",
       "2013-01-22   8320289\n",
       "2013-01-23   7746563\n",
       "2013-01-24   7439836\n",
       "2013-01-25   7780789\n",
       "2013-01-26   5848770\n",
       "2013-01-27    142848\n",
       "2013-01-28   5878702\n",
       "2013-01-29   5452668\n",
       "2013-01-30   5909779\n",
       "...              ...\n",
       "2015-07-02   8446293\n",
       "2015-07-03   8530388\n",
       "2015-07-04   6054276\n",
       "2015-07-05    269248\n",
       "2015-07-06   7540473\n",
       "2015-07-07   6393024\n",
       "2015-07-08   6915625\n",
       "2015-07-09   6938182\n",
       "2015-07-10   7064909\n",
       "2015-07-11   6262022\n",
       "2015-07-12    277047\n",
       "2015-07-13  10841267\n",
       "2015-07-14   9340405\n",
       "2015-07-15   8756137\n",
       "2015-07-16   8373873\n",
       "2015-07-17   7949337\n",
       "2015-07-18   6448982\n",
       "2015-07-19    298418\n",
       "2015-07-20   7339321\n",
       "2015-07-21   6356482\n",
       "2015-07-22   6110388\n",
       "2015-07-23   6687068\n",
       "2015-07-24   6601325\n",
       "2015-07-25   6021666\n",
       "2015-07-26    285865\n",
       "2015-07-27  10707292\n",
       "2015-07-28   9115073\n",
       "2015-07-29   8499962\n",
       "2015-07-30   8798854\n",
       "2015-07-31  10109742\n",
       "\n",
       "[942 rows x 1 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Sales']].resample('D',how='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>2013-01-02</th>\n",
       "      <td>7047064</td>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>13394884</td>\n",
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       "      <th>2013-01-04</th>\n",
       "      <td>20033838</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "               Sales\n",
       "Date                \n",
       "2013-01-01     97235\n",
       "2013-01-02   7047064\n",
       "2013-01-03  13394884\n",
       "2013-01-04  20033838\n",
       "2013-01-05  25985431"
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     "execution_count": 109,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.expanding_sum( data[['Sales']].resample('D',how='sum'), freq='d').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Plot the distribution of sales by month and compare the effect of promotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'/05'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-ee8d8eb0057c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstore1_open_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'/05'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1967\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1969\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1971\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1974\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1976\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1977\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1089\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1090\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1092\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1093\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3210\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3211\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3212\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3213\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1757\u001b[0m                                  'backfill or nearest lookups')\n\u001b[1;32m   1758\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         indexer = self.get_indexer([key], method=method,\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3979)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3843)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12265)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12216)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '/05'"
     ]
    }
   ],
   "source": [
    "store1_open_data[''].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13b104b50>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d8ENgj6rOEZEpwIPAOfgFjFT1gN93BbAQiAFLVHWLT5+FW8BoIm4Bo6Wlvo7x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\nJFnwM8ZEkgU/Y0wkWfAzxkTS/wBEbMsXf9VLiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b104a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.factorplot(\n",
    "    col='Open',\n",
    "    hue='Promo',\n",
    "    x='Month',\n",
    "    y='Sales',\n",
    "    data=store1_open_data, \n",
    "    kind='box'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Are sales more correlated with the prior date, a similar date last year, or a similar date last month?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation with last day: -0.0258582760064\n",
      "Correlation with last month: -0.129842458225\n",
      "Correlation with last year: 0.0202385294483\n"
     ]
    }
   ],
   "source": [
    "average_daily_sales = data[['Sales', 'Open']].resample('D', how='mean')\n",
    "\n",
    "print('Correlation with last day: {}'.format(average_daily_sales['Sales'].autocorr(lag=1)))\n",
    "print('Correlation with last month: {}'.format(average_daily_sales['Sales'].autocorr(lag=30)))\n",
    "print('Correlation with last year: {}'.format(average_daily_sales['Sales'].autocorr(lag=365)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Store  DayOfWeek  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "Date                                                                       \n",
       "2015-07-31      1          5   5263        555     1      1            0   \n",
       "2015-07-31      2          5   6064        625     1      1            0   \n",
       "2015-07-31      3          5   8314        821     1      1            0   \n",
       "2015-07-31      4          5  13995       1498     1      1            0   \n",
       "2015-07-31      5          5   4822        559     1      1            0   \n",
       "\n",
       "            SchoolHoliday  Year  Month  \n",
       "Date                                    \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  \n",
       "2015-07-31              1  2015      7  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Plot the 15 day rolling mean of customers in the stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'FigureCanvasAgg' object has no attribute 'invalidate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-150-75c58598513b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrolling_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Customers'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   3669\u001b[0m                           \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3670\u001b[0m                           \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3671\u001b[0;31m                           sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m   3672\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   2554\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2555\u001b[0m                  \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2556\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   2557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   2382\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2384\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2385\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2386\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    985\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    988\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1662\u001b[0m                              \u001b[0mstacking_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                              \u001b[0mis_errorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1664\u001b[0;31m                              **kwds)\n\u001b[0m\u001b[1;32m   1665\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36m_ts_plot\u001b[0;34m(cls, ax, x, data, style, **kwds)\u001b[0m\n\u001b[1;32m   1701\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1702\u001b[0m         \u001b[0;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1703\u001b[0;31m         \u001b[0mformat_dateaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1704\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/pandas/tseries/plotting.pyc\u001b[0m in \u001b[0;36mformat_dateaxis\u001b[0;34m(subplot, freq)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \"y = {1:8f}\".format(Period(ordinal=int(t), freq=freq), y))\n\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m     \u001b[0mpylab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/IPython/utils/decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brianchung/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_macosx.pyc\u001b[0m in \u001b[0;36mdraw_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0mfigManager\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfigManager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m             \u001b[0mfigManager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'FigureCanvasAgg' object has no attribute 'invalidate'"
     ]
    },
    {
     "data": {
      "image/png": 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RNsMAwFIAlwP4ufH/k9LxBxljt0E3wcwGsNzrwm1tXV5fZ43W1i5UlJNgzxXJVMqyHVoq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14134c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(data[['Customers']], window=15, freq='D').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Identify the date with largest drop in sales from the same date in the previous month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Sales</th>\n",
       "      <th>Open</th>\n",
       "      <th>DiffVsLastWeek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-24</th>\n",
       "      <td>4693.101345</td>\n",
       "      <td>1</td>\n",
       "      <td>-7014.515695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-24</th>\n",
       "      <td>4933.386096</td>\n",
       "      <td>1</td>\n",
       "      <td>-5209.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-09</th>\n",
       "      <td>6822.938117</td>\n",
       "      <td>1</td>\n",
       "      <td>-4811.537220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-14</th>\n",
       "      <td>5893.636771</td>\n",
       "      <td>1</td>\n",
       "      <td>-4027.798206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>5909.294170</td>\n",
       "      <td>1</td>\n",
       "      <td>-4023.443049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Sales  Open  DiffVsLastWeek\n",
       "Date                                         \n",
       "2013-12-24  4693.101345     1    -7014.515695\n",
       "2014-12-24  4933.386096     1    -5209.272727\n",
       "2013-12-09  6822.938117     1    -4811.537220\n",
       "2013-10-14  5893.636771     1    -4027.798206\n",
       "2015-04-20  5909.294170     1    -4023.443049"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_daily_sales = data[['Sales', 'Open']].resample('D', how='mean')\n",
    "average_daily_sales['DiffVsLastWeek'] = average_daily_sales[['Sales']].diff(periods=7)\n",
    "\n",
    "average_daily_sales.sort_values(by='DiffVsLastWeek').head()\n",
    "\n",
    "# Unsurprisingly, this day is Dec. 25 and Dec. 26 in 2014 and 2015. When the store is closed and there are many sales in the preceding week. How, about when the store is open?\n",
    "\n",
    "average_daily_sales[average_daily_sales.Open == 1].sort_values(by='DiffVsLastWeek').head()\n",
    "\n",
    "# The top values are Dec. 24 and then 2013-12-09 and 2013-10-14 where on average sales were 4k lower than the same day in the previous week.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Compute the total sales up until Dec. 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>4291837359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-02</th>\n",
       "      <td>4300681181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-03</th>\n",
       "      <td>4308939200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-04</th>\n",
       "      <td>4317493242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-05</th>\n",
       "      <td>4327295098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Date                  \n",
       "2014-12-01  4291837359\n",
       "2014-12-02  4300681181\n",
       "2014-12-03  4308939200\n",
       "2014-12-04  4317493242\n",
       "2014-12-05  4327295098"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_daily_sales = data[['Sales']].resample('D', how='sum')\n",
    "pd.expanding_sum(total_daily_sales)['2014-12'].sort_index().head()\n",
    "\n",
    "# Note that this is **not**\n",
    "#\n",
    "#pd.expanding_sum(data['Sales'], freq='D').sort_index()\n",
    "# since we do not, want to first average over stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-01-01       87.284560\n",
       "2013-01-02     6320.315053\n",
       "2013-01-03    12013.427161\n",
       "2013-01-04    17967.645995\n",
       "2013-01-05    23305.397565\n",
       "Freq: D, Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.expanding_sum(data['Sales'], freq='D').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> When were the largest differences between 15-day moving/rolling averages?\n",
    "> HINT: Using `rolling_mean` and `diff`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-773.446039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-668.394774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>-663.000538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>-596.739073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>-585.158326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Date                  \n",
       "2014-01-01 -773.446039\n",
       "2015-01-01 -668.394774\n",
       "2013-12-31 -663.000538\n",
       "2014-01-05 -596.739073\n",
       "2014-05-01 -585.158326"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.rolling_mean(data[['Sales']], window=15, freq='D').diff(1).sort_values(by='Sales').head()\n",
    "\n",
    "# Unsurprisingly, they occur at the beginning of every year after the holiday season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Plot out the (Store Sales - rolling_avg(Store Sales, 63 day)) / rolling_std( Store Sales, 63 day )\n",
    "\n",
    "> This plot will give you a rough \"change detection\" technique that you can use for any variety of applications\n",
    "> The values can be roughly interpreted as standard deviation moves from a running average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f(x) =   ( x - mean(x) ) / std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-05</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-07</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-08</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2013-01-11</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-01-12</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-14</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-15</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-17</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-18</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-19</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-21</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-22</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-23</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-24</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-25</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-26</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-29</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-30</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-27</th>\n",
       "      <td>-0.529266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>0.938480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>1.593460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0.936734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>1.306934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>0.203490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>0.363569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td>-0.168443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-07</th>\n",
       "      <td>-1.034087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>-0.886845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>-0.783828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>-0.930305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-11</th>\n",
       "      <td>-1.309930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>0.662245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>0.665114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-15</th>\n",
       "      <td>0.313863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-16</th>\n",
       "      <td>-0.110286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-17</th>\n",
       "      <td>0.517088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-18</th>\n",
       "      <td>-0.084261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>-0.074266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>-1.283430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>-1.372320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>-0.904054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>-0.966788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>-0.006090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td>2.363945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0.813715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-29</th>\n",
       "      <td>0.475003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-30</th>\n",
       "      <td>0.787918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>1.098915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>781 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Date                \n",
       "2013-01-02       NaN\n",
       "2013-01-03       NaN\n",
       "2013-01-04       NaN\n",
       "2013-01-05       NaN\n",
       "2013-01-07       NaN\n",
       "2013-01-08       NaN\n",
       "2013-01-09       NaN\n",
       "2013-01-10       NaN\n",
       "2013-01-11       NaN\n",
       "2013-01-12       NaN\n",
       "2013-01-14       NaN\n",
       "2013-01-15       NaN\n",
       "2013-01-16       NaN\n",
       "2013-01-17       NaN\n",
       "2013-01-18       NaN\n",
       "2013-01-19       NaN\n",
       "2013-01-21       NaN\n",
       "2013-01-22       NaN\n",
       "2013-01-23       NaN\n",
       "2013-01-24       NaN\n",
       "2013-01-25       NaN\n",
       "2013-01-26       NaN\n",
       "2013-01-28       NaN\n",
       "2013-01-29       NaN\n",
       "2013-01-30       NaN\n",
       "2013-01-31       NaN\n",
       "2013-02-01       NaN\n",
       "2013-02-02       NaN\n",
       "2013-02-04       NaN\n",
       "2013-02-05       NaN\n",
       "...              ...\n",
       "2015-06-27 -0.529266\n",
       "2015-06-29  0.938480\n",
       "2015-06-30  1.593460\n",
       "2015-07-01  0.936734\n",
       "2015-07-02  1.306934\n",
       "2015-07-03  0.203490\n",
       "2015-07-04  0.363569\n",
       "2015-07-06 -0.168443\n",
       "2015-07-07 -1.034087\n",
       "2015-07-08 -0.886845\n",
       "2015-07-09 -0.783828\n",
       "2015-07-10 -0.930305\n",
       "2015-07-11 -1.309930\n",
       "2015-07-13  0.662245\n",
       "2015-07-14  0.665114\n",
       "2015-07-15  0.313863\n",
       "2015-07-16 -0.110286\n",
       "2015-07-17  0.517088\n",
       "2015-07-18 -0.084261\n",
       "2015-07-20 -0.074266\n",
       "2015-07-21 -1.283430\n",
       "2015-07-22 -1.372320\n",
       "2015-07-23 -0.904054\n",
       "2015-07-24 -0.966788\n",
       "2015-07-25 -0.006090\n",
       "2015-07-27  2.363945\n",
       "2015-07-28  0.813715\n",
       "2015-07-29  0.475003\n",
       "2015-07-30  0.787918\n",
       "2015-07-31  1.098915\n",
       "\n",
       "[781 rows x 1 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1_sales = store1_open_data[['Sales']]\n",
    "\n",
    "xx = (s1_sales - pd.rolling_mean(s1_sales,window=63))/ \\\n",
    "pd.rolling_std(s1_sales,window=63)\n",
    "\n",
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-04-16</th>\n",
       "      <td>-2.088865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-18</th>\n",
       "      <td>-2.294824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-26</th>\n",
       "      <td>-2.033303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-25</th>\n",
       "      <td>-2.488433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>-2.002278</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Date                \n",
       "2013-04-16 -2.088865\n",
       "2013-04-18 -2.294824\n",
       "2014-06-26 -2.033303\n",
       "2014-09-25 -2.488433\n",
       "2014-12-31 -2.002278"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "xx[ xx.Sales < -2.0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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