benson_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benson exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from dateutil.parser import parse\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# various options in pandas\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 25)\n",
    "pd.set_option('display.precision', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_mta(file_nums):\n",
    "    path ='http://web.mta.info/developers/data/nyct/turnstile/turnstile_'\n",
    "    allFiles = []\n",
    "    for i in file_nums:\n",
    "        allFiles.append(path + i + \".txt\")\n",
    "    df_list = [pd.read_csv(file) for file in allFiles]\n",
    "    df = pd.concat(df_list)\n",
    "    df.columns = df.columns.str.strip()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# March data\n",
    "df = read_mta(['170325', '170318', '170311', '170304'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C/A', 'UNIT', 'SCP', 'STATION', 'LINENAME', 'DIVISION', 'DATE', 'TIME',\n",
       "       'DESC', 'ENTRIES', 'EXITS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ENTRIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>02/25/2017</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517918</th>\n",
       "      <td>R143</td>\n",
       "      <td>R032</td>\n",
       "      <td>02-03-01</td>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>03/11/2017</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517909</th>\n",
       "      <td>R143</td>\n",
       "      <td>R032</td>\n",
       "      <td>02-03-01</td>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>03/09/2017</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517910</th>\n",
       "      <td>R143</td>\n",
       "      <td>R032</td>\n",
       "      <td>02-03-01</td>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>03/10/2017</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517911</th>\n",
       "      <td>R143</td>\n",
       "      <td>R032</td>\n",
       "      <td>02-03-01</td>\n",
       "      <td>TIMES SQ-42 ST</td>\n",
       "      <td>03/10/2017</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C/A  UNIT       SCP         STATION        DATE      TIME  ENTRIES\n",
       "0       A002  R051  02-00-00           59 ST  02/25/2017  03:00:00        1\n",
       "517918  R143  R032  02-03-01  TIMES SQ-42 ST  03/11/2017  15:00:00        1\n",
       "517909  R143  R032  02-03-01  TIMES SQ-42 ST  03/09/2017  23:00:00        1\n",
       "517910  R143  R032  02-03-01  TIMES SQ-42 ST  03/10/2017  03:00:00        1\n",
       "517911  R143  R032  02-03-01  TIMES SQ-42 ST  03/10/2017  07:00:00        1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove duplicates\n",
    "df = df[df.DESC != 'RECOVR AUD']\n",
    "df = df[df.TIME != '04:01:13']\n",
    "# Sanity check to verify that \"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\" is unique\n",
    "(df\n",
    " .groupby(['C/A', 'UNIT', 'SCP', 'STATION', 'DATE', 'TIME'])\n",
    " .ENTRIES.count()\n",
    " .reset_index()\n",
    " .sort_values(\"ENTRIES\", ascending=False)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6095917</td>\n",
       "      <td>2065975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6095980</td>\n",
       "      <td>2065977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6096012</td>\n",
       "      <td>2066004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6096165</td>\n",
       "      <td>2066103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6096540</td>\n",
       "      <td>2066168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION LINENAME DIVISION        DATE      TIME  \\\n",
       "0  A002  R051  02-00-00   59 ST  NQR456W      BMT  03/18/2017  00:00:00   \n",
       "1  A002  R051  02-00-00   59 ST  NQR456W      BMT  03/18/2017  04:00:00   \n",
       "3  A002  R051  02-00-00   59 ST  NQR456W      BMT  03/18/2017  08:00:00   \n",
       "4  A002  R051  02-00-00   59 ST  NQR456W      BMT  03/18/2017  12:00:00   \n",
       "5  A002  R051  02-00-00   59 ST  NQR456W      BMT  03/18/2017  16:00:00   \n",
       "\n",
       "      DESC  ENTRIES    EXITS  \n",
       "0  REGULAR  6095917  2065975  \n",
       "1  REGULAR  6095980  2065977  \n",
       "3  REGULAR  6096012  2066004  \n",
       "4  REGULAR  6096165  2066103  \n",
       "5  REGULAR  6096540  2066168  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data is at a turnstile level\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['DATETIME'] = pd.to_datetime(df.DATE + ' ' + df.TIME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.set_index(['DATETIME'])\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-18 04:00:00</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6095980</td>\n",
       "      <td>2065977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 08:00:00</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6096012</td>\n",
       "      <td>2066004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 12:00:00</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6096165</td>\n",
       "      <td>2066103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-19 04:00:00</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/19/2017</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6097250</td>\n",
       "      <td>2066240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-19 08:00:00</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>NQR456W</td>\n",
       "      <td>BMT</td>\n",
       "      <td>03/19/2017</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>6097260</td>\n",
       "      <td>2066259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      C/A  UNIT       SCP STATION LINENAME DIVISION  \\\n",
       "DATETIME                                                              \n",
       "2017-03-18 04:00:00  A002  R051  02-00-00   59 ST  NQR456W      BMT   \n",
       "2017-03-18 08:00:00  A002  R051  02-00-00   59 ST  NQR456W      BMT   \n",
       "2017-03-18 12:00:00  A002  R051  02-00-00   59 ST  NQR456W      BMT   \n",
       "2017-03-19 04:00:00  A002  R051  02-00-00   59 ST  NQR456W      BMT   \n",
       "2017-03-19 08:00:00  A002  R051  02-00-00   59 ST  NQR456W      BMT   \n",
       "\n",
       "                           DATE      TIME     DESC  ENTRIES    EXITS  \n",
       "DATETIME                                                              \n",
       "2017-03-18 04:00:00  03/18/2017  04:00:00  REGULAR  6095980  2065977  \n",
       "2017-03-18 08:00:00  03/18/2017  08:00:00  REGULAR  6096012  2066004  \n",
       "2017-03-18 12:00:00  03/18/2017  12:00:00  REGULAR  6096165  2066103  \n",
       "2017-03-19 04:00:00  03/19/2017  04:00:00  REGULAR  6097250  2066240  \n",
       "2017-03-19 08:00:00  03/19/2017  08:00:00  REGULAR  6097260  2066259  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter dataset to 4am to noon\n",
    "morning = df.between_time('04:00:00', '12:00:00')\n",
    "morning.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# subtract max from min for each turnstile to get exits\n",
    "morning_turn = (morning.groupby(['C/A', 'UNIT', 'SCP', 'STATION', 'DATE'])['EXITS'].max()\\\n",
    "           -morning.groupby(['C/A', 'UNIT', 'SCP', 'STATION', 'DATE'])['EXITS'].min()).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.309e+05\n",
       "mean     7.928e+02\n",
       "std      8.658e+04\n",
       "min      0.000e+00\n",
       "25%      1.700e+01\n",
       "50%      9.600e+01\n",
       "75%      2.760e+02\n",
       "max      1.685e+07\n",
       "Name: EXITS, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore outliers\n",
    "morning_turn['EXITS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60327        2892\n",
       "60332        2895\n",
       "60325        2943\n",
       "60330        2947\n",
       "60361        2971\n",
       "60333        2994\n",
       "60354        3312\n",
       "60326        3471\n",
       "60941    15447834\n",
       "60913    15448026\n",
       "Name: EXITS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oddly high value for 47-50\n",
    "morning_turn.loc[morning_turn['STATION'] == '47-50 STS ROCK']['EXITS'].sort_values(ascending = True).tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101558        5627\n",
       "101561        5629\n",
       "101554        5632\n",
       "101553        5791\n",
       "101559        5803\n",
       "14729        11936\n",
       "25330        13257\n",
       "75281        14315\n",
       "75975        26042\n",
       "80779        30254\n",
       "81094        31664\n",
       "78655        37722\n",
       "            ...   \n",
       "73918        89231\n",
       "36326       108121\n",
       "78461       246263\n",
       "73632       822530\n",
       "79152       933403\n",
       "127543     1374207\n",
       "78488      1573110\n",
       "100987     7503729\n",
       "100978    12562196\n",
       "60941     15447834\n",
       "60913     15448026\n",
       "68904     16850624\n",
       "Name: EXITS, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morning_turn['EXITS'].sort_values(ascending = True).tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# removing values that appear to be counter resets\n",
    "morning_turn = morning_turn[morning_turn.EXITS <= 11000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>02/25/2017</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>02/26/2017</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>02/27/2017</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>02/28/2017</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>03/01/2017</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    C/A  UNIT       SCP STATION        DATE  EXITS\n",
       "0  A002  R051  02-00-00   59 ST  02/25/2017    103\n",
       "1  A002  R051  02-00-00   59 ST  02/26/2017     65\n",
       "2  A002  R051  02-00-00   59 ST  02/27/2017    255\n",
       "3  A002  R051  02-00-00   59 ST  02/28/2017    386\n",
       "4  A002  R051  02-00-00   59 ST  03/01/2017    346"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "morning_turn.head()\n",
    "#morning_turn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 AV</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>2499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 AV</td>\n",
       "      <td>2017-02-26</td>\n",
       "      <td>1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 AV</td>\n",
       "      <td>2017-02-27</td>\n",
       "      <td>6897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1 AV</td>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1 AV</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>7222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATION       DATE  EXITS\n",
       "0    1 AV 2017-02-25   2499\n",
       "1    1 AV 2017-02-26   1817\n",
       "2    1 AV 2017-02-27   6897\n",
       "3    1 AV 2017-02-28   7145\n",
       "4    1 AV 2017-03-01   7222"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add up total morning exits per station\n",
    "morning_station = morning_turn.groupby(['STATION', 'DATE']).sum().reset_index()\n",
    "morning_station['DATE'] = pd.to_datetime(morning_station['DATE'])\n",
    "morning_station.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# summing for whole month\n",
    "morning_month = morning_station.groupby(['STATION'])['EXITS'].sum().reset_index()\n",
    "morning_month = morning_month.set_index('STATION')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a055e48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BRyXdlEcbL08aMX1SE65ZtC1pVPW3C+26AtgduNL2jNzlfhlz3v3O0f0sqbvt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\nIgCHEEIINfD/AYxLWpby3BfYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a0555f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# determine busiest stations\n",
    "topsts = morning_month.sort_values('EXITS', ascending=False).head(15)\n",
    "topsts.head()\n",
    "topsts.plot(kind='barh', title='Morning Exits by Station')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grand Central Station analysis\n",
    "\n",
    "Station chosen because it has the greatest number of morning exits. What do exit patterns look like over the course of the month (at four hour increments). Weekly patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# limit to Grand Central at turnstile level\n",
    "grdcentral = df[df['STATION'] == 'GRD CNTRL-42 ST']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10144, 11)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grdcentral.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-18 01:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747519</td>\n",
       "      <td>2221855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 05:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747528</td>\n",
       "      <td>2221890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 09:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747552</td>\n",
       "      <td>2222107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 13:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747659</td>\n",
       "      <td>2222859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 17:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747888</td>\n",
       "      <td>2223831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      C/A  UNIT       SCP          STATION LINENAME DIVISION  \\\n",
       "DATETIME                                                                       \n",
       "2017-03-18 01:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 05:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 09:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 13:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 17:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "\n",
       "                           DATE      TIME     DESC  ENTRIES    EXITS  \n",
       "DATETIME                                                              \n",
       "2017-03-18 01:00:00  03/18/2017  01:00:00  REGULAR   747519  2221855  \n",
       "2017-03-18 05:00:00  03/18/2017  05:00:00  REGULAR   747528  2221890  \n",
       "2017-03-18 09:00:00  03/18/2017  09:00:00  REGULAR   747552  2222107  \n",
       "2017-03-18 13:00:00  03/18/2017  13:00:00  REGULAR   747659  2222859  \n",
       "2017-03-18 17:00:00  03/18/2017  17:00:00  REGULAR   747888  2223831  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grdcentral.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "turnstiles = grdcentral['SCP'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/braeburn/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "masked_dfs = []\n",
    "for i in turnstiles:\n",
    "    mask = grdcentral[((grdcentral[\"C/A\"] == \"R236\") & \n",
    "    (grdcentral[\"UNIT\"] == \"R045\") & \n",
    "    (grdcentral[\"SCP\"] ==  i) & \n",
    "    (grdcentral[\"STATION\"] == \"GRD CNTRL-42 ST\"))]\n",
    "            \n",
    "    mask['DIFFS'] = mask['EXITS'].diff()\n",
    "    masked_dfs.append(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exits_turn = pd.concat(masked_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DIFFS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-18 01:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747519</td>\n",
       "      <td>2221855</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 05:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747528</td>\n",
       "      <td>2221890</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 09:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747552</td>\n",
       "      <td>2222107</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 13:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>13:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747659</td>\n",
       "      <td>2222859</td>\n",
       "      <td>752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-18 17:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-00-00</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/18/2017</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>747888</td>\n",
       "      <td>2223831</td>\n",
       "      <td>972.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      C/A  UNIT       SCP          STATION LINENAME DIVISION  \\\n",
       "DATETIME                                                                       \n",
       "2017-03-18 01:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 05:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 09:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 13:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-18 17:00:00  R236  R045  00-00-00  GRD CNTRL-42 ST    4567S      IRT   \n",
       "\n",
       "                           DATE      TIME     DESC  ENTRIES    EXITS  DIFFS  \n",
       "DATETIME                                                                     \n",
       "2017-03-18 01:00:00  03/18/2017  01:00:00  REGULAR   747519  2221855    NaN  \n",
       "2017-03-18 05:00:00  03/18/2017  05:00:00  REGULAR   747528  2221890   35.0  \n",
       "2017-03-18 09:00:00  03/18/2017  09:00:00  REGULAR   747552  2222107  217.0  \n",
       "2017-03-18 13:00:00  03/18/2017  13:00:00  REGULAR   747659  2222859  752.0  \n",
       "2017-03-18 17:00:00  03/18/2017  17:00:00  REGULAR   747888  2223831  972.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exits_turn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#remove NaN values and negative values (i.e. counter resets)\n",
    "exits_turn.dropna(inplace=True)\n",
    "exits_turn = exits_turn[exits_turn['DIFFS'] >= 0]"
   ]
  },
  {
   "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>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>LINENAME</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>DESC</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>DIFFS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-03-03 04:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-06-01</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5334942</td>\n",
       "      <td>3221500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03 08:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-06-01</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5334969</td>\n",
       "      <td>3221541</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03 12:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-06-01</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5335160</td>\n",
       "      <td>3221686</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03 16:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-06-01</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5335747</td>\n",
       "      <td>3221811</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03 20:00:00</th>\n",
       "      <td>R236</td>\n",
       "      <td>R045</td>\n",
       "      <td>00-06-01</td>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>4567S</td>\n",
       "      <td>IRT</td>\n",
       "      <td>03/03/2017</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>5337059</td>\n",
       "      <td>3221856</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      C/A  UNIT       SCP          STATION LINENAME DIVISION  \\\n",
       "DATETIME                                                                       \n",
       "2017-03-03 04:00:00  R236  R045  00-06-01  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-03 08:00:00  R236  R045  00-06-01  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-03 12:00:00  R236  R045  00-06-01  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-03 16:00:00  R236  R045  00-06-01  GRD CNTRL-42 ST    4567S      IRT   \n",
       "2017-03-03 20:00:00  R236  R045  00-06-01  GRD CNTRL-42 ST    4567S      IRT   \n",
       "\n",
       "                           DATE      TIME     DESC  ENTRIES    EXITS  DIFFS  \n",
       "DATETIME                                                                     \n",
       "2017-03-03 04:00:00  03/03/2017  04:00:00  REGULAR  5334942  3221500    0.0  \n",
       "2017-03-03 08:00:00  03/03/2017  08:00:00  REGULAR  5334969  3221541   41.0  \n",
       "2017-03-03 12:00:00  03/03/2017  12:00:00  REGULAR  5335160  3221686  145.0  \n",
       "2017-03-03 16:00:00  03/03/2017  16:00:00  REGULAR  5335747  3221811  125.0  \n",
       "2017-03-03 20:00:00  03/03/2017  20:00:00  REGULAR  5337059  3221856   45.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now have turnstile exits per four period (diffs column)\n",
    "exits_turn.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# add exits across turnstiles by time\n",
    "exits_turn = exits_turn.reset_index()\n",
    "ct = exits_turn.groupby(['DATETIME'])['DIFFS'].sum().reset_index()"
   ]
  },
  {
   "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>DIFFS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-25 04:00:00</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25 08:00:00</th>\n",
       "      <td>438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25 12:00:00</th>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25 16:00:00</th>\n",
       "      <td>2241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-25 20:00:00</th>\n",
       "      <td>2655.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DIFFS\n",
       "DATETIME                   \n",
       "2017-02-25 04:00:00    25.0\n",
       "2017-02-25 08:00:00   438.0\n",
       "2017-02-25 12:00:00  1960.0\n",
       "2017-02-25 16:00:00  2241.0\n",
       "2017-02-25 20:00:00  2655.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.set_index(['DATETIME'], inplace=True)\n",
    "ct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "we1 = ct[(ct.index > '2017-02-25 00:00:00') & (ct.index < '2017-02-27 05:00:00')]\n",
    "wd1 = ct[(ct.index > '2017-02-27 00:00:00') & (ct.index < '2017-03-04 05:00:00')]\n",
    "we2 = ct[(ct.index > '2017-03-04 00:00:00') & (ct.index < '2017-03-06 05:00:00')]\n",
    "wd2 = ct[(ct.index > '2017-03-06 00:00:00') & (ct.index < '2017-03-11 05:00:00')]\n",
    "we3 = ct[(ct.index > '2017-03-11 00:00:00') & (ct.index < '2017-03-13 05:00:00')]\n",
    "wd3 = ct[(ct.index > '2017-03-13 00:00:00') & (ct.index < '2017-03-18 05:00:00')]\n",
    "we4 = ct[(ct.index > '2017-03-18 00:00:00') & (ct.index < '2017-03-20 05:00:00')]\n",
    "wd4 = ct[(ct.index > '2017-03-20 00:00:00') & (ct.index < '2017-03-25 05:00:00')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114d85dd8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YyDBMdlh4M1ZG0dVELYmdRWAoimwnqAsi29hltaWbFsrK0AOa411gN5qyHO+0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\n23Ecx3Ecx3Fy4P8DnXk4iQCvhpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11361a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.xticks(rotation=70)\n",
    "plt.plot(we1, color = 'navy')\n",
    "plt.plot(wd1, color = '#008080')\n",
    "plt.plot(we2, color = 'navy')\n",
    "plt.plot(wd2, color = '#008080')\n",
    "plt.plot(we3, color = 'navy')\n",
    "plt.plot(wd3, color = '#008080')\n",
    "plt.plot(we4, color = 'navy')\n",
    "plt.plot(wd4, color = '#008080')\n",
    "plt.title('Number of People Exiting Grand Central for the Month of March')\n",
    "plt.ylabel('People Exiting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115b5da20>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyQAKb0IIIYQQQjKAwpsQQgghhJAMoPAmhBBCCCEkAyi8CSGEEEIIyQAKb0IIIYQQQjKA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\nZtYHXnibmZmZmfXB/wHWTqFyaibfyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b32b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "\n",
    "plt.xticks(rotation=70)\n",
    "plt.plot(we2, color = 'navy')\n",
    "plt.plot(wd2, color = '#008080')\n",
    "plt.title('Number of People Exiting Grand Central for One Week')\n",
    "plt.ylabel('People Exiting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116af6278>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T+veIyA4R2SYi5zgtTxWRMvO+NSIiI3FSRERERESeZtDCW0SmArgTQJpSajGAQACXArgb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116af6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "\n",
    "plt.xticks(rotation=200)\n",
    "weekday1 = ct[(ct.index > '2017-03-05 21:00:00') & (ct.index < '2017-03-07 03:00:00')]\n",
    "weekday1['DIFFS'].plot(title = 'Number of People Exiting Grand Central for One Day', color = '#008080' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# weekly plot without weekends separated\n",
    "\n",
    "# wk1 = ct[(ct.index > '2017-02-25 00:00:00') & (ct.index < '2017-03-03 23:59:59')]\n",
    "# wk2 = ct[(ct.index > '2017-03-04 00:00:00') & (ct.index < '2017-03-10 23:59:59')]\n",
    "# wk3 = ct[(ct.index > '2017-03-11 00:00:00') & (ct.index < '2017-03-17 23:59:59')]\n",
    "# wk4 = ct[(ct.index > '2017-03-18 00:00:00') & (ct.index < '2017-03-24 23:59:59')]\n",
    "# plt.plot(wk1)\n",
    "# plt.plot(wk2)\n",
    "# plt.plot(wk3)\n",
    "# plt.plot(wk4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# daily plots (too few points to be that useful)\n",
    "\n",
    "# day1 = ct[(ct.index > '2017-02-25 00:00:00') & (ct.index < '2017-02-25 23:59:59')]\n",
    "# day2 = ct[(ct.index > '2017-02-26 00:00:00') & (ct.index < '2017-02-26 23:59:59')]\n",
    "# day3 = ct[(ct.index > '2017-02-27 00:00:00') & (ct.index < '2017-02-27 23:59:59')]\n",
    "# day4 = ct[(ct.index > '2017-02-28 00:00:00') & (ct.index < '2017-02-28 23:59:59')]\n",
    "# day5 = ct[(ct.index > '2017-03-01 00:00:00') & (ct.index < '2017-03-01 23:59:59')]\n",
    "# day6 = ct[(ct.index > '2017-03-02 00:00:00') & (ct.index < '2017-03-02 23:59:59')]\n",
    "# day7 = ct[(ct.index > '2017-03-03 00:00:00') & (ct.index < '2017-03-03 23:59:59')]\n",
    "# plt.close()\n",
    "# day1.plot()\n",
    "# day2.plot()\n",
    "# day3.plot()\n",
    "# day4.plot()\n",
    "# day5.plot()\n",
    "# day6.plot()\n",
    "# day7.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}