plot_transit_per_dow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot of transit per day of the week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a plot of transit vs. day of the week, based on data from the 20 busiest stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step one \n",
    "\n",
    "* read in data\n",
    "* Select only those rows with the busiest stations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = '/Users/aleksandra/Desktop/output_data/Data/data_daily_weather.csv'\n",
    "with open(path) as f:\n",
    "    weather = pd.read_csv(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>C/A</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>SCP</th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>ENTRIES</th>\n",
       "      <th>PREV_DATE</th>\n",
       "      <th>PREV_ENTRIES</th>\n",
       "      <th>EXITS</th>\n",
       "      <th>PREV_EXITS</th>\n",
       "      <th>SALDO_ENTRIES</th>\n",
       "      <th>SALDO_EXITS</th>\n",
       "      <th>TRANSITING</th>\n",
       "      <th>PRCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>5714706</td>\n",
       "      <td>06/26/2016</td>\n",
       "      <td>5714130.0</td>\n",
       "      <td>1938907</td>\n",
       "      <td>1938649.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>834.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-00-01</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>5258231</td>\n",
       "      <td>06/26/2016</td>\n",
       "      <td>5257426.0</td>\n",
       "      <td>1153431</td>\n",
       "      <td>1153236.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-03-00</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>724086</td>\n",
       "      <td>06/26/2016</td>\n",
       "      <td>723741.0</td>\n",
       "      <td>2733057</td>\n",
       "      <td>2731725.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1677.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-03-01</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>5247019</td>\n",
       "      <td>06/26/2016</td>\n",
       "      <td>5246055.0</td>\n",
       "      <td>8295526</td>\n",
       "      <td>8294063.0</td>\n",
       "      <td>964.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>A002</td>\n",
       "      <td>R051</td>\n",
       "      <td>02-03-02</td>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>4955367</td>\n",
       "      <td>06/26/2016</td>\n",
       "      <td>4954510.0</td>\n",
       "      <td>6859058</td>\n",
       "      <td>6857968.0</td>\n",
       "      <td>857.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   C/A  UNIT       SCP STATION  UN_STATION        DATE  ENTRIES  \\\n",
       "0           0  A002  R051  02-00-00   59 ST  59ST456NQR  2016-06-27  5714706   \n",
       "1           1  A002  R051  02-00-01   59 ST  59ST456NQR  2016-06-27  5258231   \n",
       "2           2  A002  R051  02-03-00   59 ST  59ST456NQR  2016-06-27   724086   \n",
       "3           3  A002  R051  02-03-01   59 ST  59ST456NQR  2016-06-27  5247019   \n",
       "4           4  A002  R051  02-03-02   59 ST  59ST456NQR  2016-06-27  4955367   \n",
       "\n",
       "    PREV_DATE  PREV_ENTRIES    EXITS  PREV_EXITS  SALDO_ENTRIES  SALDO_EXITS  \\\n",
       "0  06/26/2016     5714130.0  1938907   1938649.0          576.0        258.0   \n",
       "1  06/26/2016     5257426.0  1153431   1153236.0          805.0        195.0   \n",
       "2  06/26/2016      723741.0  2733057   2731725.0          345.0       1332.0   \n",
       "3  06/26/2016     5246055.0  8295526   8294063.0          964.0       1463.0   \n",
       "4  06/26/2016     4954510.0  6859058   6857968.0          857.0       1090.0   \n",
       "\n",
       "   TRANSITING  PRCP  \n",
       "0       834.0  0.45  \n",
       "1      1000.0  0.45  \n",
       "2      1677.0  0.45  \n",
       "3      2427.0  0.45  \n",
       "4      1947.0  0.45  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select only the relevant columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TRANSITING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>834.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>1677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59 ST</td>\n",
       "      <td>59ST456NQR</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>1947.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  STATION  UN_STATION        DATE  TRANSITING\n",
       "0   59 ST  59ST456NQR  2016-06-27       834.0\n",
       "1   59 ST  59ST456NQR  2016-06-27      1000.0\n",
       "2   59 ST  59ST456NQR  2016-06-27      1677.0\n",
       "3   59 ST  59ST456NQR  2016-06-27      2427.0\n",
       "4   59 ST  59ST456NQR  2016-06-27      1947.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = weather[['STATION', 'UN_STATION', 'DATE', 'TRANSITING']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select 20 busiest stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transit_station = df.groupby('UN_STATION', as_index=False).sum()\n",
    "df_busiest_stations = transit_station.sort_values('TRANSITING', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>TRANSITING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>20580477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>34STHERALDSQBDFMNQR</td>\n",
       "      <td>16952616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>TIMESSQ42ST1237ACENQRS</td>\n",
       "      <td>14461685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14STUNIONSQ456LNQR</td>\n",
       "      <td>14398597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>FULTONST2345ACJZ</td>\n",
       "      <td>11495271.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 UN_STATION  TRANSITING\n",
       "300       GRDCNTRL42ST4567S  20580477.0\n",
       "83      34STHERALDSQBDFMNQR  16952616.0\n",
       "435  TIMESSQ42ST1237ACENQRS  14461685.0\n",
       "29       14STUNIONSQ456LNQR  14398597.0\n",
       "291        FULTONST2345ACJZ  11495271.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_busiest_stations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This df does not have the 'STATION' or the 'DATE' column anymore.\n",
    "\n",
    "Take 'UN_STATION' series and select rows from df based on this series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "busiest_stations = df_busiest_stations.UN_STATION\n",
    "\n",
    "busy_df = pd.DataFrame()\n",
    "\n",
    "for station in busiest_stations:\n",
    "    busy_df = busy_df.append(df.loc[df['UN_STATION'] == station])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TRANSITING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3299</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3300</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3301</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>488.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              STATION         UN_STATION        DATE  TRANSITING\n",
       "3299  GRD CNTRL-42 ST  GRDCNTRL42ST4567S  2016-06-27      2946.0\n",
       "3300  GRD CNTRL-42 ST  GRDCNTRL42ST4567S  2016-06-27      2315.0\n",
       "3301  GRD CNTRL-42 ST  GRDCNTRL42ST4567S  2016-06-27      2238.0\n",
       "3302  GRD CNTRL-42 ST  GRDCNTRL42ST4567S  2016-06-27       473.0\n",
       "3303  GRD CNTRL-42 ST  GRDCNTRL42ST4567S  2016-06-27       488.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step two\n",
    "* Convert 'DATE' to datetime format.\n",
    "* Extract day of the week from 'DATE' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "busy_df['DATE'] = pd.to_datetime(busy_df['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "busy_df['DAY_OF_WEEK'] = busy_df['DATE'].dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TRANSITING</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
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       "      <th>3299</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
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       "      <td>2016-06-27</td>\n",
       "      <td>2946.0</td>\n",
       "      <td>Monday</td>\n",
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       "    <tr>\n",
       "      <th>3300</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3301</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2238.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>473.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>488.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              STATION         UN_STATION       DATE  TRANSITING DAY_OF_WEEK\n",
       "3299  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2946.0      Monday\n",
       "3300  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2315.0      Monday\n",
       "3301  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2238.0      Monday\n",
       "3302  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27       473.0      Monday\n",
       "3303  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27       488.0      Monday"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TRANSITING</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>382018</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>14ST123FLM</td>\n",
       "      <td>2016-09-23</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382019</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>14ST123FLM</td>\n",
       "      <td>2016-09-23</td>\n",
       "      <td>811.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382020</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>14ST123FLM</td>\n",
       "      <td>2016-09-23</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382021</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>14ST123FLM</td>\n",
       "      <td>2016-09-23</td>\n",
       "      <td>2338.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382022</th>\n",
       "      <td>14 ST</td>\n",
       "      <td>14ST123FLM</td>\n",
       "      <td>2016-09-23</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION  UN_STATION       DATE  TRANSITING DAY_OF_WEEK\n",
       "382018   14 ST  14ST123FLM 2016-09-23      1054.0      Friday\n",
       "382019   14 ST  14ST123FLM 2016-09-23       811.0      Friday\n",
       "382020   14 ST  14ST123FLM 2016-09-23      1133.0      Friday\n",
       "382021   14 ST  14ST123FLM 2016-09-23      2338.0      Friday\n",
       "382022   14 ST  14ST123FLM 2016-09-23      1931.0      Friday"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Add a column that has a number for each weekday, this will permit us to sort the columns in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "mapping = {day: i for i, day in enumerate(weekdays)}\n",
    "busy_df['WD_NUM'] = busy_df['DAY_OF_WEEK'].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>UN_STATION</th>\n",
       "      <th>DATE</th>\n",
       "      <th>TRANSITING</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>WD_NUM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3299</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2946.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3300</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3301</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>2238.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>473.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>GRD CNTRL-42 ST</td>\n",
       "      <td>GRDCNTRL42ST4567S</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>488.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              STATION         UN_STATION       DATE  TRANSITING DAY_OF_WEEK  \\\n",
       "3299  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2946.0      Monday   \n",
       "3300  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2315.0      Monday   \n",
       "3301  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27      2238.0      Monday   \n",
       "3302  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27       473.0      Monday   \n",
       "3303  GRD CNTRL-42 ST  GRDCNTRL42ST4567S 2016-06-27       488.0      Monday   \n",
       "\n",
       "      WD_NUM  \n",
       "3299       0  \n",
       "3300       0  \n",
       "3301       0  \n",
       "3302       0  \n",
       "3303       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "per_day = busy_df.groupby('WD_NUM', as_index=True).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a columns with weekdays in string format to be used as x-ticks in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_day['DAY_OF_WEEK'] = weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRANSITING</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WD_NUM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14925948.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29349287.0</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33320081.0</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33975271.0</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34008691.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29883198.0</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16759165.0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TRANSITING DAY_OF_WEEK\n",
       "WD_NUM                        \n",
       "0       14925948.0      Monday\n",
       "1       29349287.0     Tuesday\n",
       "2       33320081.0   Wednesday\n",
       "3       33975271.0    Thursday\n",
       "4       34008691.0      Friday\n",
       "5       29883198.0    Saturday\n",
       "6       16759165.0      Sunday"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34008691.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_day.TRANSITING.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step three\n",
    "\n",
    "__The plot__\n",
    "\n",
    "* Barplot visualizes the fluctuation in transit straightforwardly.\n",
    "* Legend not necessary, there is only one variable.\n",
    "* rotate ticks for readability.\n",
    "* add labels for both axes and a title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113cb6ac8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c519e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6qm33HcCrImIZ4BGy++eokRzfzMzm3WBdQBdK2gS4JCJmV3b1AbMlvWKQ404FJgL7R0Rr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b87e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "pl = per_day.plot(x = 'DAY_OF_WEEK', y= 'TRANSITING', kind='bar'\n",
    "                  , legend=False, title = 'TRANSIT PER DAY OF THE WEEK', rot=45, fontsize=10, colormap = 'ocean')\n",
    "pl.set_xlabel('Weekday')\n",
    "pl.set_ylabel('Transit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = pl.get_figure()\n",
    "fig.savefig('Data/plot_transit_dow.pdf', bbox_inches=\"tight\")"
   ]
  }
 ],
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