tori-read.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>index</th>\n",
       "      <th>food_item</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>onion</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-06-18 20:35:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-09-20 12:37:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>tomato</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-08-09 18:31:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>onion</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-10-10 18:35:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\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": [
       "   index food_item  count                 date\n",
       "0      1     onion      8  2015-06-18 20:35:54\n",
       "1      2    tomato      5  2015-09-20 12:37:03\n",
       "2      3    tomato      2  2015-08-09 18:31:22\n",
       "3      4     onion      2  2015-10-10 18:35:24\n",
       "4      5    tomato      1  2015-07-25 12:55:21"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = 'data/panera-data-zach.txt'\n",
    "df = pd.read_table(file_name)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis \n",
    "\n",
    "How much food is prepped every day?\n",
    "\n",
    " 1. Group the data and sum the count by day of the week and what kind of food\n",
    " 2. average the food for each day of the week"
   ]
  },
  {
   "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>index</th>\n",
       "      <th>food_item</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>1</th>\n",
       "      <td>2</td>\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>2</th>\n",
       "      <td>3</td>\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>3</th>\n",
       "      <td>4</td>\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>4</th>\n",
       "      <td>5</td>\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": [
       "   index food_item  count                date  dayofweek\n",
       "0      1     onion      8 2015-06-18 20:35:54         20\n",
       "1      2    tomato      5 2015-09-20 12:37:03         12\n",
       "2      3    tomato      2 2015-08-09 18:31:22         18\n",
       "3      4     onion      2 2015-10-10 18:35:24         18\n",
       "4      5    tomato      1 2015-07-25 12:55:21         12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['dayofweek'] = df['date'].dt.hour \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grouped = df[['food_item', 'count', 'dayofweek']].groupby(['dayofweek', 'food_item'])\n",
    "average_prepped = grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c8e0b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8b272b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#my_plot = average_prepped.plot(kind='bar')\n",
    "average_prepped.unstack().plot(kind = 'bar', legend = False)"
   ]
  }
 ],
 "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": "3.4.3"
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