python-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.16.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http:https://pbpython.com/simple-graphing-pandas.html\n",
    "# http:https://www.randalolson.com/2014/06/28/how-to-make-beautiful-data-visualizations-in-python-with-matplotlib/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food_item</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>onion</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-06-18 20:35:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-09-20 12:37:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tomato</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-08-09 18:31:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>onion</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-10 18:35:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tomato</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-25 12:55:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      food_item  count                date\n",
       "index                                     \n",
       "1         onion      8 2015-06-18 20:35:54\n",
       "2        tomato      5 2015-09-20 12:37:03\n",
       "3        tomato      2 2015-08-09 18:31:22\n",
       "4         onion      2 2015-10-10 18:35:24\n",
       "5        tomato      1 2015-07-25 12:55:21"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the data\n",
    "file_name = 'data/panera-data-zach.txt'\n",
    "raw = pd.read_table(file_name, index_col = 0, parse_dates = ['date'])\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "food_item            object\n",
       "count                 int64\n",
       "date         datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# exract hours data for grouping\n",
    "# http:https://stackoverflow.com/a/32366268/1927178\n",
    "hours = pd.DatetimeIndex(raw['date']).hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food_item</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>onion</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-06-18 20:35:54</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-09-20 12:37:03</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tomato</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-08-09 18:31:22</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>onion</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-10 18:35:24</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tomato</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-25 12:55:21</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      food_item  count                date  hour\n",
       "index                                           \n",
       "1         onion      8 2015-06-18 20:35:54    20\n",
       "2        tomato      5 2015-09-20 12:37:03    12\n",
       "3        tomato      2 2015-08-09 18:31:22    18\n",
       "4         onion      2 2015-10-10 18:35:24    18\n",
       "5        tomato      1 2015-07-25 12:55:21    12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in hour data to new DataFrame\n",
    "df = raw\n",
    "df['hour'] = hours\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_item</th>\n",
       "      <th>onion</th>\n",
       "      <th>pineapple</th>\n",
       "      <th>tomato</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>88</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>72</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>176</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count                 \n",
       "food_item onion pineapple tomato\n",
       "hour                            \n",
       "6           NaN        45     17\n",
       "7           NaN        48     26\n",
       "8             3        88     15\n",
       "9           NaN        64     34\n",
       "10           20        40     32\n",
       "11           22        14     39\n",
       "12           72         8     57\n",
       "13          176         4     57\n",
       "14          165         1     87\n",
       "15          221       NaN     75\n",
       "16          327       NaN     63\n",
       "17          347       NaN     60\n",
       "18          364       NaN     69\n",
       "19          240       NaN     84\n",
       "20          205       NaN     31\n",
       "21          205       NaN     46"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group the data\n",
    "df_group = df.groupby(['hour', 'food_item'])\n",
    "df_group.sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f76438>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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UHilXfS3hu9/9bkuXsJFf/OIXLV1CSZTim+awYcMYNmxYCaqRJElbsrIF6JTS\n+xFxVErp3YhoD/wuIh4i96vjD1NK6/UdRMRA4N+AgcCuwKMRsVdKae1GJ1eb0tTc1+v2kSRJag7N\n0sIREdsCTwJfBE4AVqaUJmywzzhgbUrp6vzyNGB8SunpDfar2BYOVS7fX5LU8mzhUHNrrIWjrLNw\nRES7iHgeWAI8klJ6Nr/pwoj4c0RMjIh1E+5WA/PrHT6f3Ei0JEmS1GqUdRaOfPvFkIjYHvhVROwD\n/BS4Mr/Lt4EJwJjGTtHQyvHjxxce19bWUltbW6KKJUmS1FbV1dVRV1fX5H7NNgtHRFwOvFu/dSMi\naoCpKaX9ImIsQErpqvy2acAVKaVnNjiPLRxqdr6/JKnl2cKh5tbsLRwRseO69oyI2AY4FpgRET3r\n7fZJ4MX84/uAERHRMSJ2B/YEnkWSJElqRcrZwrELcHNEbEUuqN+VUnowIm6JiCHkfoV8Dfg8QErp\npYi4G3gJ+BC4oMGhZkmSJKkFeSOVZjRu3Dh69uzZqm6ksiX52te+Rr9+/fjCF75Q8nNXwvtLkjZX\nU1OBtpbvf7ZwqLk11sKxRQfo5pgbuNjX74033mDo0KHMmjWLTp06lbmqhtXV1TFy5EjmzZtX9DE1\nNTXcdNNNLXInwtGjR9OrVy++/e1vF7X/4sWLOeigg5g1axYdOnQoaS0GaElbskoJppVSp7YcLTKN\nXWuQyviVxeTJkznppJNaLDxvrkoKjj179mTAgAHcd999LV2KJEnagm3xAbq1mDZtGkceeeR6637z\nm98wZMgQtt9+e/r168fDDz8MwMKFCznllFPYYYcd2HPPPbnxxhsLx4wePZrLL7+8sFxXV0evXr0K\nyzU1NUyYMIHBgwfTrVs3RowYwerVq1m1ahUnnHACCxcupEuXLnTt2pXFixdvsuaRI0cyd+5chg0b\nRpcuXbj22msBuO+++9hnn32oqqriqKOO4uWXX17v+tdeey2DBw9mu+2247zzzmPJkiWccMIJdO3a\nlWOPPZa33367sP9nPvMZdtllF7p168aRRx7JSy+9BMDPf/5z7rjjDq655hq6dOnCqaeeCsCMGTOo\nra2lqqqKfffdl6lTp65Xc21tLQ888EDT/yGSJEmbK6VUUV+5kjfW0HogpTJ+NVZLQ3baaaf0xz/+\nsbD8zDPPpO233z49+uijKaWUFixYkF5++eWUUkpHHHFE+tKXvpRWr16dnn/++bTTTjul6dOnp5RS\nGj16dLo42H+4AAAgAElEQVT88ssL53nsscfSbrvtVliuqalJH//4x9OiRYvSsmXL0t57751+9rOf\npZRSqqurW2/fYtTU1KTf/va3heWZM2emzp07p0cffTR9+OGH6Zprrkn9+vVLa9asKex/yCGHpNdf\nfz0tWLAg7bzzzmn//fdPzz//fHr//ffT0Ucfnb71rW8Vzjdp0qS0cuXK9MEHH6SvfOUraciQIYVt\nGz7XDz74IO2xxx7p+9//flqzZk2aPn166tKlS5o5c2Zhn3vvvTftv//+mZ5jMbL8X0tSpQE28eOu\n9Xz/q5Q6teXIv682yqOOQDeTt99+my5duhSWJ06cyJgxYzjmmGMAqK6upn///sybN4+nnnqKq6++\nmo4dOzJ48GDOO+88brnllsKxqYmWiosuuoiePXtSVVXFsGHDeP7554s6rhh33XUXJ598Mscccwxb\nbbUVX/va13jvvfd46qmnCvtceOGF7LTTTlRXV3PEEUdw8MEHM3jwYDp16sQnP/lJnnvuucK+o0eP\npnPnznTo0IErrriCP//5z6xYsaLB5/r000+zatUqxo4dS/v27TnqqKM4+eSTmTJlSmGfLl26rDfC\nLUmSVGoG6GZSVVW1XjCcP38+e+yxx0b7LVy4kO7du9O5c+fCut69e7NgwYKir9Wz5z+n2t5mm21Y\nuXLlZla9sUWLFtG7d+/CckTQq1ev9err0aPHetevv7z11lsX6vnHP/7B2LFj6devH9tvvz277747\nAG+++WaD1164cOF67SoAffr0We/aK1asoFu3bhseKkmSVDIG6GYyaNAgZs6cWVju1asXr7766kb7\nVVdXs2zZsvVC79y5c9ltt90A6Ny5M++++25hW1N9zPVtzqwkGx5TXV3NnDlzCsspJebNm8euu+7a\n6DkaG/m+4447uO+++/jtb3/LO++8w2uvvbbe/g1de968eeudb86cOYXXBnI90kOGDCny2UmSJGVn\ngG4mJ554Io8//nhhecyYMUyaNInp06ezdu1aFixYwMyZM+nVqxeHHnoo48aNY/Xq1bzwwgvcdNNN\nnHXWWQAMGTKEBx98kLfeeovFixdz/fXXF11Djx49WLp0KcuXLy+sq6uro127xt8GPXr0YNasWYXl\n4cOH88ADDzB9+nTWrFnDhAkT2HrrrTn00EOzvBwArFy5kk6dOtG9e3dWrVrFpZdeutG1//73vxeW\nDz74YLbddluuueYa1qxZQ11dHffffz8jRowo7PP4449zwgknZK5FkiSpWFt8gI4yfmUxatQoHnzw\nQd5//30ADjzwQCZNmsQll1xCt27dqK2tZe7cuQBMmTKF2bNnU11dzemnn86VV15ZmId55MiRDB48\nmJqaGo4//nhGjBixyZHliChsHzBgAGeccQZ9+/ale/fuLFq0iHnz5nHYYYc1evy4ceP4zne+Q1VV\nFT/84Q/Za6+9uO222wp9zg888ABTp06lffvGb2pZv7769YwaNYo+ffqw6667su+++3LIIYest++Y\nMWN46aWXqKqq4vTTT6dDhw5MnTqVhx56iJ122okvf/nL3Hrrrey1115Arr1kxowZnHbaaZv8v5Ak\nSfootugbqbQ2l112GTvvvHOruhPh+eefz/Dhwzn22GNbupSPzDsRStLmqZQblFRKndpytMk7EUql\n4vtL0pasUoJppdSpLUebvROhJEmSVEoGaEmSJCkDA7QkSZKUgQFakiRJysAALUmSJGXQ+OS9FWhz\n7rQnSZIkZbHFBGinrpEkSVJzsIVDkiRJysAALUmSJGVggJYkSZIyMEBLkiRJGRigJUmSpAwM0JIk\nSVIGW8w0dpIktTZN3Z/AKVilymSAliSprBoLyd78S6pUtnBIkiRJGRigJUmSpAwM0JIkSVIGBmhJ\nkiQpAwO0JEmSlIEBWpIkScrAAC1JkiRlYICWJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIk\nKQMDtCRJkpSBAVqSJEnKwAAtSZIkZWCAliRJkjIwQEuSJEkZGKAlSZKkDAzQkiRJUgYGaEmSJCkD\nA7QkSZKUgQFakiRJysAALUmSJGVggJYkSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpg7IF\n6IjYOiKeiYjnI+IvETE+v757RPxPRLwSEY9ERLd6x4yLiL9FxMsRcVy5apMkSZI2V6SUynfyiG1T\nSu9GRHvgd8DFwKeAN1NK10TEN4CqlNLYiBgI3AEcCOwKPArslVJau8E5UzlrliSpVCICaOxnVtBa\nfp5Zp9SwiCClFBuuL2sLR0rp3fzDjkAHcu/6U4Cb8+tvBk7LPz4VmJJSWpNSmg28ChxUzvokSZKk\nrMoaoCOiXUQ8DywBHkkpPQv0SCktye+yBOiRf1wNzK93+HxyI9GSJElSq9G+nCfPt18MiYjtgV9F\nxL4bbE8Rsam/tzS4bfz48YXHtbW11NbWfvRiJUmS1KbV1dVRV1fX5H5l7YFe70IRlwPvAucDtSml\nxRGxC/BYSmlARIwFSCldld9/GnBFSumZDc5jD7QkqSJUSs+udUoNa/Ye6IjYcd0MGxGxDXAsMAO4\nDzg7v9vZwK/zj+8DRkREx4jYHdgTeLZc9UmSJEmbo5wtHLsAN0fEVuSC+l0ppQcj4mng7ogYA8wG\nhgOklF6KiLuBl4APgQscapYkSVJr02wtHKViC4ckqVJUSsuBdUoNa5Fp7CRJkqQtjQFakiRJysAA\nLUmSJGVggJYkSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrAAC1J\nkiRlYICWJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJkpSBAVqSJEnKwAAtSZIk\nZWCAliRJkjIwQEuSJEkZGKAlSZKkDAzQkiRJUgYGaEmSJCkDA7QkSZKUgQFakiRJysAALUmSJGVg\ngJYkSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrAAC1JkiRlYICW\nJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJkpSBAVqSJEnKwAAtSZIkZWCAliRJ\nkjIwQEuSJEkZGKAlSZKkDAzQkiRJUgYGaEmSJCkDA7QkSZKUgQFakiRJysAALUmSJGVggJYkSZIy\nMEBLkiRJGRigJUmSpAzKGqAjoldEPBYRf42Iv0TERfn14yNifkQ8l/86od4x4yLibxHxckQcV876\nJEmSpKwipVS+k0f0BHqmlJ6PiO2APwGnAcOBFSmlH26w/0DgDuBAYFfgUWCvlNLaevukctYsSVKp\nRATQ2M+soLX8PLNOqWERQUopNlxf1hHolNLilNLz+ccrgRnkgjHARsUApwJTUkprUkqzgVeBg8pZ\noyRJkpRFs/VAR0QNMBR4Or/qwoj4c0RMjIhu+XXVwPx6h83nn4FbkiRJanHtm+Mi+faNXwIXp5RW\nRsRPgSvzm78NTADGNHL4Rn+PGT9+fOFxbW0ttbW1pSxXkiRJbVBdXR11dXVN7lfWHmiAiOgA3A88\nlFK6voHtNcDUlNJ+ETEWIKV0VX7bNOCKlNIz9fa3B1qSVBEqpWfXOqWGtUgPdOTe6ROBl+qH54jY\npd5unwRezD++DxgRER0jYndgT+DZctYoSZIkZVHuFo7DgLOAFyLiufy6S4EzImIIuV8jXwM+D5BS\neiki7gZeAj4ELnC4WZIkSa1J2Vs4Ss0WDklSpaiUlgPrlBrWIi0ckiRJ0pbGAC1JkiRlYICWJEmS\nMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKYNy30hFkiSpTcnNV90456uufAZoSZKkkmv8hi+q\nfLZwSJIkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJkpSBAVqSJEnKwAAtSZIkZdDoPNAR8eIm\njksppUFlqEeSJElq1TZ1I5Vh+X8vyP97K7nZv88sa0WSJElSKxZN3U4yIp5PKQ3ZYN1zKaWhZa2s\n8XqSt8CUJFWC3C2dG78jXWv5eWadpVUpdappEUFKaaPbRxbTAx0RcXi9hcPwPpSSJElqozbVwrHO\nucCkiNg+v/w2cE75SpIkSZJaryZbOAo75gJ0pJTeLm9JTdZhC4ckqSJUyp/yrbO0KqVONW2zWzgi\nomdETATuSim9HREDI2JMWaqUJEmSWrlieqAnA48A1fnlvwGXlKsgSZIkqTUrJkDvmFK6C/gHQEpp\nDfBhWauSJEmSWqliAvTKiNhh3UJEHAy8U76SJEmSpNarmFk4vgpMBfpGxFPATsCny1qVJEmS1Ept\nMkBHxFbAJ/JfA8jN/zwzpfRBM9QmSZIktTrF3InwDymlA5upniY5jZ0kqVJUynRm1llalVKnmtbY\nNHbFBOjrgA7AXcAqcqPQKaX0f+UotCkGaElSpaiUIGWdpVUpdappHyVA19HAuyCldFTJqsvAAC1J\nqhSVEqSss7QqpU41bbMDdGtjgJYk5QJK41rLz4lKCVLWWVqVUqea1liALmYWDiLiZGAgsPW6dSml\nK0tXniRJWTUeUCSpnIq5lfd/A8OBi8h9VxoO9ClzXZIkSVKrVMyNVA5NKY0ClqWUvgUcDPQvb1mS\nJElS61RMgH4v/++7EbErudt49yxfSZIkSVLrVUwP9NSIqAJ+APwfuaazX5S1KkmSJKmVyjQLR0R0\nArZOKb1TvpKarMFZOCSpjauUWQ6ss7SsU81ts2fhiIhtgAuAw8m9G56MiJ+mlN4vfZmSJElS61bM\njVTuAZYDt5GbheOzwPYppc+Uv7wG63EEWpLauEoZ4bPO0rJONbePMg/0PimlgfWWp0fES6UrTZIk\nSaocxczC8X8Rcci6hYg4GPhT+UqSJEmSWq9iWjheBvYC5pH7e0RvYCa56exSSmlQuYvcoB5bOCSp\njauUP5FbZ2lZp5rbR2nhOL4M9UiSJEkVqckWjpTSbKAXcFT+8SpyI9ez88uSJElSm9FkgI6I8cDX\ngXH5VR3JzcghSZIktTnFfIjwk8Cp5EaeSSktALqUsyhJkiSptSomQK9OKa1dtxARnctYjyRJktSq\nFROg74mI/wa6RcTngN8CN5a3LEmSJKl12uQ0dpGbh6UXMAA4Lr/64ZTS/zRDbY3V5DR2ktTGVco0\nYdZZWtap5tbYNHbFBOgXU0r7lrO4LAzQkqRKCSjWWVrWqebWWIDeZAtHPqn+KSIOKltlkiRJanYR\n0ehXa7GpGluyzmLuRDgT6AfMIT8TBy1wB8J69TgCLUltXKWM8FlnaVlnaTVeZyXUCM1R50e5E+G6\n3ufMMT8iegG3ADuTe/Y/Tyn9OCK6A3cBfYDZwPCU0tv5Y8YB5wL/AC5KKT2S9bqSJElSuTQ6Ah0R\nPYBLyY0+vwB8P6W0PNPJI3oCPVNKz0fEdsCfgNOAc4A3U0rXRMQ3gKqU0tiIGAjcARwI7Ao8Cuy1\nwTR6jkBLUhvX0qNSxbLO0rLO0nIEurjrZ+2BvgVYCdxA7sYpP8560ZTS4pTS8/nHK4EZ5ILxKcDN\n+d1uJheqIXfDlikppTX524S/Cth/LUmSpFZjUy0cPVNKl+UfT4uI5z7KhSKiBhgKPAP0SCktyW9a\nAvTIP64Gnq532HxygVuSJElqFTYVoCPfqwy5/uet6i2TUlpW7EXy7Rv3AhenlFbU/9RkSilFxKbG\n3zfaNn78+MLj2tpaamtriy1FkiRJalBdXR11dXVN7repHujZNN50klJKfYspJCI6APcDD6WUrs+v\nexmoTSktjohdgMdSSgMiYmz+5Ffl95sGXJFSeqbe+eyBlqQ2rqX7IotlnaVlnaVlD3Rx1898I5US\nXDTI9TgvTSldUm/9Nfl1V+dDc7cNPkR4EP/8EGG/+onZAC1JaukfqsWyztKyztIyQBd3/ZYI0IcD\nT5CbxWPdhcYBzwJ3A73ZeBq7S8lNY/chuZaPhzc4pwFaktq4lv6hWizrLC3rLC0DdHHXb/YAXQ4G\naElSS/9QLZZ1lpZ1lpYBurjrZ76VtyRJkqT1bTJAR0T7/K28JUmSJNFEgE4pfQi8HBF9mqkeSZIk\nqVXb1DzQ63QH/hoRzwKr8utSSumU8pUlSZIktU7FBOjLG1jXOjrLJUmSpGbWZIBOKdXlb8PdL6X0\naERsW8xxkiRJ0paoyVk4IuJzwD3Af+dX7Qb8qpxFSZIkSa1VMdPYfQk4HFgOkFJ6Bdi5nEVJkiRJ\nrVUxAXp1Smn1uoWIaI890JIkSWqjignQj0fEZcC2EXEsuXaOqeUtS5IkSWqdmryVd0RsBYwBjsuv\nehi4saXup+2tvCVJLX1732JZZ2lZZ2l5K+/irt/QrbybDND5gzsBA8g9g5dTSh+UvsTiGKAlSS39\nQ7VY1lla1llaBujirt9QgG5yOrqIOAn4GfD3/Kq+EfH5lNKDJa5RkiRJavWKaeGYCZyUUno1v7wH\n8GBKqX8z1NdQPY5AS1Ib19KjUsWyztKyztJyBLq46zc0Al3MhwiXrwvPeX8nP6WdJEmS1NY02sIR\nEZ/KP/xjRDwI3J1f/gzwx3IXJkmSJLVGm+qBHsY/x8xfB47MP34D2LqcRUmSJEmtVVGzcLQm9kBL\nklq6L7JY1lla1lla9kAXd/3NnYWjL3AhUFNv/5RSOqWkFUqSJEkVoMkADfwauJHc3QfX5te1jl9L\nJEmSpGZWTIB+L6X047JXIkmSJFWAYuaBPhPoBzwCrF63PqX0f+UtrdF67IGWpDaupfsii2WdpWWd\npWUPdHHX36weaGBfYCRwNP9s4QA4qkS1SZIkSRWjmAA9HOibUvqg3MVIkiRJrV0xdyJ8EagqdyGS\nJElSJShmBLoKeDki/sA/e6Cdxk6SJEltUjEB+oqyVyFJbUDuwzCNay0f2pEkbVqTATqlVNcMdUhS\nG9H4p8klSZWhmDsRruSf3/E7Ah2AlSmlruUsTJIkSWqNihmB3m7d44hoB5wCHFzOoiRJkqTWqphZ\nOApSSmtTSr8Gji9TPZIkSVKrVkwLx6fqLbYDDgDeK1tFkiRJUitWzCwcw/hnD/SHwGzg1HIVJEmS\nJLVmUWnTJkVEqrSaJQnWTWPX+Cwcfm8rXqW8ltZZWtZZWo3XWQk1QnPUGRGklDaaJqnREeiIaGz+\n5wSQUrqyRLVJkiRJFWNTLRyr2DjydwbGADsCBmhJkiS1OUW1cEREV+AicuH5bmBCSun1MtfWWC22\ncEiqSC39p8gtSaW8ltZZWtZZWrZwFHf9TC0c+YN2AC4BzgRuAfZPKb1VnhIlSZKk1m9TPdDXAp8E\nfg4MSimtaLaqJEmSpFaq0RaOiFgLfACsaWBzaqlbedvCIalStfSfIouRq7FxraFGqIzXEqyz1Kyz\ntGzhKO76mVo4UkqZ7lIoSdpSNP7DSpKU8VbekiRJUltngJYkSZIyMEBLkiRJGRigJUmSpAwM0JIk\nSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrAAC1JkiRlYICWJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElS\nBmUN0BFxU0QsiYgX660bHxHzI+K5/NcJ9baNi4i/RcTLEXFcOWuTJEmSNke5R6AnAcdvsC4BP0wp\nDc1/PQQQEQOBfwMG5o/5SUQ4Qi5JkqRWpawBNaX0JPBWA5uigXWnAlNSSmtSSrOBV4GDylieJEmS\nlFlLjfBeGBF/joiJEdEtv64amF9vn/nArs1fmiRJktS49i1wzZ8CV+YffxuYAIxpZN/U0Mrx48cX\nHtfW1lJbW1u66iRJktQm1dXVUVdX1+R+kVKDGbVkIqIGmJpS2m9T2yJiLEBK6ar8tmnAFSmlZzY4\nJpW7Zkkqh4igkXEBIGgN39sqoUawzlKzztKq/DoroUZojjojgpTSRq3Hzd7CERG71Fv8JLBuho77\ngBER0TEidgf2BJ5t7vokSZKkTSlrC0dETAGOBHaMiHnAFUBtRAwh9+vEa8DnAVJKL0XE3cBLwIfA\nBQ41S5IkqbUpewtHqdnCIalStfSfIotRCTWCdZaadZZW5ddZCTVCm2rhkCRJkiqZAVqSJEnKwAAt\nSZIkZWCAliRJkjIwQEuSJEkZGKAlSZKkDAzQkiRJUgYGaEmSJCkDA7QkSZKUgQFakiRJysAALUmS\nJGVggJYkSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrAAC1JkiRl\nYICWJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJkpSBAVqSJEnKwAAtSZIkZWCA\nliRJkjIwQEuSJEkZGKAlSZKkDAzQkiRJUgYGaEmSJCkDA7QkSZKUgQFakiRJysAALUmSJGVggJYk\nSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrAAC1JkiRlYICWJEmS\nMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJkpSBAVqSJEnKwAAtSZIkZWCAliRJkjIw\nQEuSJEkZGKAlSZKkDMoaoCPipohYEhEv1lvXPSL+JyJeiYhHIqJbvW3jIuJvEfFyRBxXztokSZKk\nzVHuEehJwPEbrBsL/E9KaS/gt/llImIg8G/AwPwxP4kIR8glSZLUqpQ1oKaUngTe2mD1KcDN+cc3\nA6flH58KTEkprUkpzQZeBQ4qZ32SJElSVi0xwtsjpbQk/3gJ0CP/uBqYX2+/+cCuzVmYJEmS1JT2\nLXnxlFKKiLSpXRpaOX78+MLj2tpaamtrS1uYJEmS2py6ujrq6uqa3C9S2lR+/egiogaYmlLaL7/8\nMlCbUlocEbsAj6WUBkTEWICU0lX5/aYBV6SUntngfKncNUtSOUQEjYwLAEFr+N5WCTWCdZaadZZW\n5ddZCTVCc9QZEaSUYsP1LdHCcR9wdv7x2cCv660fEREdI2J3YE/g2RaoT5IkSWpUWVs4ImIKcCSw\nY0TMA74JXAXcHRFjgNnAcICU0ksRcTfwEvAhcIFDzZIkSWptyt7CUWq2cEiqVC39p8hiVEKNYJ2l\nZp2lVfl1VkKN0NZaOCRJkqSKZYCWJEmSMjBAS5IkSRkYoCVJkqQMDNCSJElSBgZoSZIkKQMDtCRJ\nkpSBAVqSJEnKwAAtSZIkZWCAliRJkjJo39IFqPLlbrPZuNZyO1BJkqRSMECrNMZnXC9pi+Iv0pLa\nEgO0JKkkGovIm47WklR57IGWJEmSMjBAS5IkSRnYwiFJktQK+FmCymGAliRJaiX8LEFlsIVDkiRJ\nysAALUmSJGVggJYkSZIyMEBLkiRJGRigJUmSpAwM0JIkSVIGBmhJkiQpAwO0JEmSlIEBWpIkScrA\nOxFKktoMb5UsqRQM0JKkNsVbJUv6qGzhkCRJkjIwQEuSJEkZ2MIhqeLZ1ypJak4GaElbCDtbJUnN\n4/9v796jZa3rOo6/P4KKF5IFC0jLxEWi5kIBlUWZeryUx7wgFiK58gBKmYWmrmWkLd2aKZioaGrh\nBUilRPBWpnmkc0AzQRLwyEUzOSKo4AVQ0IzLtz+eZ8Owb2c/MHueZ/Z+v9aadZ79PLNnPmf2npnv\n/s3393ts4ZAkSZI6sICWJEmSOrCFQ5IkrWrOk9C4WUBLkqRVz1kSGidbOCRJkqQOLKAlSZKkDiyg\nJUmSpA4soCVJkqQOnESoNcNZ2JIkaRwsoLW2zHTcL0k98A9+adgsoCVJGiCXXZOGyx5oSZIkqQML\naEmSJKkDWzgkSdLtYq+21ioLaEmSdLvZq621yBYOSZIkqQMLaEmSJKkDWzgkrVnT0r+5VM6hZJSk\ntcQCWtKaNi39mwvlHFpGSVorbOGQJEmSOrCAliRJkjqwgJYkSZI66K0HOslW4MfATcANVbV/kp2B\nDwH3A7YCz6qqa/rKKEmSJM3V5wh0Aeuqat+q2r/ddzSwsar2As5ov5YkSZIGo+8WjrmTyJ8OnNxu\nnww8Y7JxJEmSpKX1PQL92STnJjmy3bd7VV3Zbl8J7N5PNEmSJGlhfa4D/aiq+m6SXYGNSS4ZPVhV\nlWTBJVpnZmZu2V63bh3r1q1byZySJElaAzZv3szmzZu3eb0M4SxWSV4NXAccSdMX/b0k9wY2VdWD\n5ly3hpBZt0oCM4scnBnOmdKmJae6a87Ut/gpURb72SZZ8kQq4/6dGHfOacjYfJc5F7tHc3Zjzknl\nXDzjpN3ex3Kc919V885b1UsLR5K7J9mx3b4H8NvAFuATwIb2ahuAj/WRbyiSLHmRJEnS5PXVwrE7\n8NG2CNwe+GBVfSbJucCpSZ5Hu4xdT/mGY6bjfkmSJK2oXgroqroU2GeB/T8Cnjj5RJIkSdLy9DmJ\nUJIkSRq7bbW63tHe6TVbQK/0AytJkqT+LDUh845aswU0YH+xJEmSOuv7TISSJEnSVLGAliRJkjqw\ngJYkSZI6sICWJEmSOljbkwilAXKFGEmShs0CWhqimY77JUnSxFhASxo7R9ElSauZBbSkFbGSC9hL\nktQnJxFKkiRJHVhAS5IkSR1YQEuSJEkdWEBLkiRJHTiJUGottXKEq0ZIkqRZFtBSy1UjJEnSclhA\nS1PGkXJJkvplAS1NGUfKJUnqlwW0JEmSlsUzzTYsoCVJkrRsfhLqMnaSJElSJxbQkiRJUgcW0JIk\nSVIHFtCSJElSB04inGKuByxJkjR5FtBTzFmwkiRJk2cLhyRJktSBBbQkSZLUgQW0JEmS1IE90JIW\n5SlbJUmazwJa0jY4XVWSpFEW0FIPtjWyK0mShssCWuqNI7uSJE0jJxFKkiRJHVhAS5IkSR3YwrEI\nT5MtSZKkhVhAL8LuVEmSJC3EFg5JkiSpA0egteJsh5EkSauJBbRWnO0wq5frWUuS1iILaEm330zH\n/ZIkrQL2QEuSJEkdWEBLkiRJHVhAS5IkSR1YQEuSJEkdWEBLkiRJHVhAS5IkSR24jJ2kVc/1qiVJ\n47jgMOoAAAtFSURBVGQBLWn1m+m4X5KkJdjCIUmSJHVgAS1JkiR1YAEtSZIkdTC4AjrJ+iSXJPnv\nJH/edx5JmpQkC14kScMyqAI6yXbA3wLrgV8DDk3y4BW/40tX/B7Go8eci72xL/jm7uM5PtOQEcw5\nLjPtZcPI9kxfYbZt8+bNfUdYFnOOzzRkBHOOmznnG1QBDewPfKOqtlbVDcA/AQeu+L1uXfF7GI+t\nfQeoRS5zbJ1kpjtga98BlmFr3wGWaWvfAZZpa98Blmlr3wGWxzfV8ZqGnNOQEcw5buacb2gF9C8B\n3x75+vJ2nyRpAEY/fXrNa15jm4mkNWlo60AvMJwpSRqMmZHtTcDjFtgvSatcqoZTsyY5AJipqvXt\n138B3FxVx45cZziBJUmStKpV1byP2YZWQG8PfA14AvAd4Bzg0Kq6uNdgkiRJUmtQLRxVdWOSPwX+\nDdgOeK/FsyRJkoZkUCPQkiRJ0tANagR6pbU91hdX1bVJ7g4cDewHXAi8vqqu7TVgK8mLgI9W1be3\neeUeJbkr8Gzgiqr6bJLnAL8BXASc0C5FOAhJ9gSeCfwycDNNq9ApVfXjXoNJkqSps6ZGoJNcBDy0\nbRV5N3A9cBrwxHb/M3sN2EpyLfBT4H+AU4APV9X3+001X5JTaFpt7g5cA9wT+AjN40lVbegv3a2S\nvBh4KnAm8BTgPJq8BwEvrKpNPcaTplKS3arqqr5zrBZJdqmqH/adQ5p2ST5VVU9e6fsZ2jrQKy1V\ndWO7/fCq+rOq+nxVzQB79phrrm/SrH/9WuARwEVJPp1kQ5Id+412G3tX1SE0heiTgIOr6v3AYTQj\n+0NxJLC+ql5HM0H1IVX1CprMb+k1WSvJTkmOaU9jf3WSH7XbxyTZqe98y5HkU31nmJXkXu1j94Ek\nvz/n2Dv7yjVXknsneVeSdyTZJclMki1JTk1y777zzUqy85zLLsA5s1/3nW9WkvUj2zsleW/7eJ6S\nZPc+s41KcmySXdvtRyT5JnB2ksuSrOs3XSPJeUn+sv30brCSPDLJpva5ft8kG5Ncm+RLSfbtO9+s\nJDsmeW2SC5P8OMkPkpyd5LC+s42alveiJPstcnk4MJGf+1oroC9MckS7fUGSRwIk2Qv4v/5izVdV\nN1fVZ6rqCJpi+l3AkxnWyYjTtnHsCNwNuFe7fweG1R5UwJ3b7R2AewBU1WUj+/t2KnA1sA7Yuap2\npllh95r22CAM4UVrmU5s/z0dODTJ6Ul2aPf9ek+ZFnISTcvT5cBm4H9pPiX5HPB3vaWa7wfAf41c\nzqV5XZrdHoo3jGwfB3wXeBrwJeDve0m0sKeMfKr4JuCQqvpVmk/vjusv1m3s1F42tcXoS5Lcp+9Q\nC3gn8Ebgk8B/AifQ5D66PTYUH6R5/15Ps2r624A/AB6f5PU95pprKt6LaJ7Txy1weRO31iIraq21\ncOwEHA88Gvg+zSjp5TRnPzyqqi7oMd4tkpxXVQsWI0nuUVXXTzrTQpK8BDiKplg+Dng6zQvEAcBp\n7ch+79oWjucDZ9P87I+tqvcl2Y0m52N6DQgk+XpV7dX12KQluQk4a5HDB1TV3SaZZzFJLqiqh418\n/Urgd4ADgY2LPb8mbfS5nuSyqvqVkWO3+T/0KcnLgN8CXl5VX2n3XVpV9+832W3NeTwvAPap9k1u\nYI/nxTRtgzck+WJVHTBybEtV7d1jvNkc51XVvklC87p5KM2njRcD/1hVJ/QasLWN59D5VbVPf+lu\nleQrVfXQka/PrapHJLkTzdysB/YY7xZT9F50IXBQVX19gWPfrqr7rnSGIY0SrriqugbYkORewP1p\n/v+XV9X3+k02z7MXOzCU4hmgqt6S5NR2+4ok/0AzgnJCVZ3Tb7pbVdXxSc4AHgS8qaouafdfBfRe\nPLe+leTlwMlVdSVAkl8ENgCX9Zrsti4B/mixF60e8izmLknuVFU3A1TVXye5gqYP/p79RruN0U8B\n37/EsV5V1XHtc/3NSS4HXt13pkXsmuSlQJg/CjWk842/E/jXJG8APp3keJr5I48Hzu812RztHyBn\nAWclOYrmNf4QmpHeIfh5kifR/LwryUFV9dEkjwVu3Mb3TtL1SR5dVZ9LciDwQ2g+bW7+RhmMaXkv\nmmHx18ijJhFgTRXQs9rVNgb1IjWqqr7Wd4blqqorRravBj7cY5xFVdVXga/2nWMJh9B85HjmSK/m\nlcAngGf1lmq+GXp+0Vqmf6Hpd984u6OqTkryPeDtvaWa7xNJdqyqn1TVK2d3JnkAzUoxg9GuCnRw\n++a/kWby8NC8h6alDJo2nl2Bq9L0kw/mNb+q3p7kq8AfAw+geS/eC/gY8Lo+s42Y9/vXziH6dHsZ\nihfQtHDcTDOv5YVJTqQ5GduRfQab4wXAe9rn9oXAEQBtL/w7+gw2x1S8F1XVh5M8OMkTgLOr6rqR\nwz+fRIY11cIhTaMkh1fVidu+Zr+SHFFV7+s7x7aY845LswzonlW1xd/P8ZqGx3MaMsJU5RzU72aS\nB9PMcTi7qn4ysn99VQ3ij6c0y/3+CU1L0b7Ai6vqY+2xRdtgx5rBAloatkn1c91R5hwvc46XOcdn\nGjKCOW+PIRSmy9F+gnNAVV2XZA+aJYk/UFVvnVTONdnCIQ1Nki1LHB7S8lvmHKNVknO3iQXZBh/P\n8ZmGjLBqfuaDyQn8Ic0yv7OF6elJ9qiqt/Yba57Mtm1U1dY0yz+enuR+TGi+gwW0NAy70SxvdPUC\nx74w4SxLMed4mXO8zDk+05ARzDlucwvTxzLhwnSZrkqyT1WdD9AW/E8F3gs8dOlvHQ8LaGkYPgnc\ns6rOm3sgyZk95FmMOcfLnONlzvGZhoxgznHrvTBdpucCN4zuaJeF3MCEVoixB1qSJEkkuS9ww9zl\nfdv1wB9VVZ/vJ9nwWEBLkiRJHQxmoX5JkiRpGlhAS5IkSR1YQEuSJEkdWEBL0oQluW7O14clmegp\nxpMcnOSiJGfM2b9Hkp8l+XJ7/Ox2ZrskqeUydpI0eXNnb49lNneS7avqxmVe/XnA86tqoTVov1FV\n+7W3eX/gI0lSVSeNI6ckTTtHoCWpf7ecoKAdAf73JBck+Wy7rBRJTkryuyPXu679d12SzyX5OHDh\nvBtODk3ylSRbkhzT7nsV8CjgfUneuFSwqroUeCnwovZ790/yhXaE+j+S7NXuPzPJw0bu9/NJ9r7d\nj4gkDZgj0JI0eXdLMnpChZ2Bj7fbbwdOrKr3JzkceBtwEEuPWu8LPKSqvjV6hST3AY4B9gOuAT6T\n5MCqem2SxwEvq6ovLyPvecCD2u2LgUdX1U1Jngi8Hvg9mhMtHAa8pC2q71pVS52+WJKmliPQkjR5\nP6uqfWcvwKu4dRT6AOCUdvsDwG8u4/bOmVs8tx4JbKqqH1bVTcAHgceMHF/uqXlHr7cTcFqSLcCb\ngYe0+08Dnppke+AI4MRl3rYkTR0LaEnq39xCdqHC9kba1+wkdwLuMnLs+kVut+bcVrjtyPVye6/3\nBS5qt/8KOKOq9gaeBuwAUFU/BTYCzwAOpinWJWlVsoCWpGH5AvDsdvs5wFnt9lbg4e3204E7L+O2\nvgQ8NskuSbZrb/fMLmGS7AH8DU1rCcAvAN9ptw+fc/X30LScnFNV13a5H0maJhbQkjR5C/Uzz+47\nCjg8yQU0BfSL2/3vpimGz6dp87huzvfPv5Oq7wJHA5uA84Fzq+qfl5Fvz9ll7IAPAcdX1cntsTcC\nb0jyZWC70ftu+6mvxfYNSatcqsayepIkaY1rJy1uqqoH9p1FklaSI9CSpDssyXOBLwKv6DuLJK00\nR6AlSZKkDhyBliRJkjqwgJYkSZI6sICWJEmSOrCAliRJkjqwgJYkSZI6+H8J0O/suNwG7gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5a785f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_plot = df_group.sum().unstack().plot(kind = 'bar', \n",
    "                                        stacked = False, \n",
    "                                        title = \"Total Prepared by Food Item\",\n",
    "                                        figsize = (12, 9))\n",
    "sum_plot.set_xlabel(\"Hour of Day\")\n",
    "sum_plot.set_ylabel(\"Number Prepared\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x8b6a160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BsVRPGN2rsphnWcyzHGZZFvMsLUUHaAZfm7FYqgEAAICe6FQDADBhdKqB1W22U71tK8MA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\nQqe6D7Llqzkf2fLUnE2qOx/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders = df[['food_item', 'hour']]\n",
    "order_plot = orders['hour'].hist(bins = 16,\n",
    "                                 figsize = (12, 9))\n",
    "order_plot.set_title(\"Distribution of Orders\")\n",
    "order_plot.set_xlabel(\"Hour of Day\")\n",
    "order_plot.set_ylabel(\"# of Orders\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}