Report01_Exploring_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis on the Citibike Data\n",
    "Analyze the (pre-processed) Citibike Data and examine basic ridership pattern. Following things were explored here\n",
    "- Seasonal Patterns\n",
    "- Trip Durations\n",
    "- Age Distribution of the riders\n",
    "- Gender Distribution\n",
    "- Subscribers vs non-subscribers\n",
    "- Hourly Ridership Pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as scs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Method for printing the result of a normality test\n",
    "def normal_or_not(normality_test_result, siglevel=0.01):\n",
    "    if normality_test_result.pvalue >= siglevel:\n",
    "        return 'Yes'\n",
    "    else:\n",
    "        return 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Method for printing the result of a significance test\n",
    "def significantly_different_or_not(test_result, siglevel=0.01):\n",
    "    if test_result.pvalue >= siglevel:\n",
    "        return 'No'\n",
    "    else:\n",
    "        return 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import two sets of data separately\n",
    "d1 = pd.read_csv('./data/processed/extended2/p_s_alltime.csv')\n",
    "d2 = pd.read_csv('./data/processed/extended2/p_ns_alltime.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combined the imported data and drop a column\n",
    "daily = pd.concat([d1, d2], axis=0)\n",
    "del daily['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create datetime-related columns\n",
    "daily['datetime'] = pd.to_datetime(daily['date'], format='%Y%m%d')\n",
    "daily['year'] = daily.datetime.dt.year\n",
    "daily['month'] = daily.datetime.dt.month\n",
    "daily['day'] = daily.datetime.dt.day\n",
    "daily['dayofweek'] = daily.datetime.dt.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Seasonal Patterns</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>date</th>\n",
       "      <th>pickups</th>\n",
       "      <th>st_id</th>\n",
       "      <th>datetime</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</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 rowspan=\"6\" valign=\"top\">2013</th>\n",
       "      <th>7</th>\n",
       "      <td>19828</td>\n",
       "      <td>19828</td>\n",
       "      <td>19828</td>\n",
       "      <td>19828</td>\n",
       "      <td>19828</td>\n",
       "      <td>19828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18817</td>\n",
       "      <td>18817</td>\n",
       "      <td>18817</td>\n",
       "      <td>18817</td>\n",
       "      <td>18817</td>\n",
       "      <td>18817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18915</td>\n",
       "      <td>18915</td>\n",
       "      <td>18915</td>\n",
       "      <td>18915</td>\n",
       "      <td>18915</td>\n",
       "      <td>18915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17033</td>\n",
       "      <td>17033</td>\n",
       "      <td>17033</td>\n",
       "      <td>17033</td>\n",
       "      <td>17033</td>\n",
       "      <td>17033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15490</td>\n",
       "      <td>15490</td>\n",
       "      <td>15490</td>\n",
       "      <td>15490</td>\n",
       "      <td>15490</td>\n",
       "      <td>15490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12848</td>\n",
       "      <td>12848</td>\n",
       "      <td>12848</td>\n",
       "      <td>12848</td>\n",
       "      <td>12848</td>\n",
       "      <td>12848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11095</td>\n",
       "      <td>11095</td>\n",
       "      <td>11095</td>\n",
       "      <td>11095</td>\n",
       "      <td>11095</td>\n",
       "      <td>11095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15687</td>\n",
       "      <td>15687</td>\n",
       "      <td>15687</td>\n",
       "      <td>15687</td>\n",
       "      <td>15687</td>\n",
       "      <td>15687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16955</td>\n",
       "      <td>16955</td>\n",
       "      <td>16955</td>\n",
       "      <td>16955</td>\n",
       "      <td>16955</td>\n",
       "      <td>16955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18842</td>\n",
       "      <td>18842</td>\n",
       "      <td>18842</td>\n",
       "      <td>18842</td>\n",
       "      <td>18842</td>\n",
       "      <td>18842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18591</td>\n",
       "      <td>18591</td>\n",
       "      <td>18591</td>\n",
       "      <td>18591</td>\n",
       "      <td>18591</td>\n",
       "      <td>18591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19224</td>\n",
       "      <td>19224</td>\n",
       "      <td>19224</td>\n",
       "      <td>19224</td>\n",
       "      <td>19224</td>\n",
       "      <td>19224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "      <td>19303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18531</td>\n",
       "      <td>18531</td>\n",
       "      <td>18531</td>\n",
       "      <td>18531</td>\n",
       "      <td>18531</td>\n",
       "      <td>18531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18473</td>\n",
       "      <td>18473</td>\n",
       "      <td>18473</td>\n",
       "      <td>18473</td>\n",
       "      <td>18473</td>\n",
       "      <td>18473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15984</td>\n",
       "      <td>15984</td>\n",
       "      <td>15984</td>\n",
       "      <td>15984</td>\n",
       "      <td>15984</td>\n",
       "      <td>15984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15070</td>\n",
       "      <td>15070</td>\n",
       "      <td>15070</td>\n",
       "      <td>15070</td>\n",
       "      <td>15070</td>\n",
       "      <td>15070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>12609</td>\n",
       "      <td>12609</td>\n",
       "      <td>12609</td>\n",
       "      <td>12609</td>\n",
       "      <td>12609</td>\n",
       "      <td>12609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10378</td>\n",
       "      <td>10378</td>\n",
       "      <td>10378</td>\n",
       "      <td>10378</td>\n",
       "      <td>10378</td>\n",
       "      <td>10378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14314</td>\n",
       "      <td>14314</td>\n",
       "      <td>14314</td>\n",
       "      <td>14314</td>\n",
       "      <td>14314</td>\n",
       "      <td>14314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17483</td>\n",
       "      <td>17483</td>\n",
       "      <td>17483</td>\n",
       "      <td>17483</td>\n",
       "      <td>17483</td>\n",
       "      <td>17483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19380</td>\n",
       "      <td>19380</td>\n",
       "      <td>19380</td>\n",
       "      <td>19380</td>\n",
       "      <td>19380</td>\n",
       "      <td>19380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18651</td>\n",
       "      <td>18651</td>\n",
       "      <td>18651</td>\n",
       "      <td>18651</td>\n",
       "      <td>18651</td>\n",
       "      <td>18651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19780</td>\n",
       "      <td>19780</td>\n",
       "      <td>19780</td>\n",
       "      <td>19780</td>\n",
       "      <td>19780</td>\n",
       "      <td>19780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21929</td>\n",
       "      <td>21929</td>\n",
       "      <td>21929</td>\n",
       "      <td>21929</td>\n",
       "      <td>21929</td>\n",
       "      <td>21929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25049</td>\n",
       "      <td>25049</td>\n",
       "      <td>25049</td>\n",
       "      <td>25049</td>\n",
       "      <td>25049</td>\n",
       "      <td>25049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26054</td>\n",
       "      <td>26054</td>\n",
       "      <td>26054</td>\n",
       "      <td>26054</td>\n",
       "      <td>26054</td>\n",
       "      <td>26054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25110</td>\n",
       "      <td>25110</td>\n",
       "      <td>25110</td>\n",
       "      <td>25110</td>\n",
       "      <td>25110</td>\n",
       "      <td>25110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24694</td>\n",
       "      <td>24694</td>\n",
       "      <td>24694</td>\n",
       "      <td>24694</td>\n",
       "      <td>24694</td>\n",
       "      <td>24694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>18597</td>\n",
       "      <td>18597</td>\n",
       "      <td>18597</td>\n",
       "      <td>18597</td>\n",
       "      <td>18597</td>\n",
       "      <td>18597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20092</td>\n",
       "      <td>20092</td>\n",
       "      <td>20092</td>\n",
       "      <td>20092</td>\n",
       "      <td>20092</td>\n",
       "      <td>20092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25693</td>\n",
       "      <td>25693</td>\n",
       "      <td>25693</td>\n",
       "      <td>25693</td>\n",
       "      <td>25693</td>\n",
       "      <td>25693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25414</td>\n",
       "      <td>25414</td>\n",
       "      <td>25414</td>\n",
       "      <td>25414</td>\n",
       "      <td>25414</td>\n",
       "      <td>25414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26887</td>\n",
       "      <td>26887</td>\n",
       "      <td>26887</td>\n",
       "      <td>26887</td>\n",
       "      <td>26887</td>\n",
       "      <td>26887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26118</td>\n",
       "      <td>26118</td>\n",
       "      <td>26118</td>\n",
       "      <td>26118</td>\n",
       "      <td>26118</td>\n",
       "      <td>26118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27732</td>\n",
       "      <td>27732</td>\n",
       "      <td>27732</td>\n",
       "      <td>27732</td>\n",
       "      <td>27732</td>\n",
       "      <td>27732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30325</td>\n",
       "      <td>30325</td>\n",
       "      <td>30325</td>\n",
       "      <td>30325</td>\n",
       "      <td>30325</td>\n",
       "      <td>30325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32457</td>\n",
       "      <td>32457</td>\n",
       "      <td>32457</td>\n",
       "      <td>32457</td>\n",
       "      <td>32457</td>\n",
       "      <td>32457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32936</td>\n",
       "      <td>32936</td>\n",
       "      <td>32936</td>\n",
       "      <td>32936</td>\n",
       "      <td>32936</td>\n",
       "      <td>32936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29166</td>\n",
       "      <td>29166</td>\n",
       "      <td>29166</td>\n",
       "      <td>29166</td>\n",
       "      <td>29166</td>\n",
       "      <td>29166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26590</td>\n",
       "      <td>26590</td>\n",
       "      <td>26590</td>\n",
       "      <td>26590</td>\n",
       "      <td>26590</td>\n",
       "      <td>26590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>25048</td>\n",
       "      <td>25048</td>\n",
       "      <td>25048</td>\n",
       "      <td>25048</td>\n",
       "      <td>25048</td>\n",
       "      <td>25048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23137</td>\n",
       "      <td>23137</td>\n",
       "      <td>23137</td>\n",
       "      <td>23137</td>\n",
       "      <td>23137</td>\n",
       "      <td>23137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22747</td>\n",
       "      <td>22747</td>\n",
       "      <td>22747</td>\n",
       "      <td>22747</td>\n",
       "      <td>22747</td>\n",
       "      <td>22747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  pickups  st_id  datetime    day  dayofweek\n",
       "year month                                                   \n",
       "2013 7      19828    19828  19828     19828  19828      19828\n",
       "     8      19303    19303  19303     19303  19303      19303\n",
       "     9      18817    18817  18817     18817  18817      18817\n",
       "     10     18915    18915  18915     18915  18915      18915\n",
       "     11     17033    17033  17033     17033  17033      17033\n",
       "     12     15490    15490  15490     15490  15490      15490\n",
       "2014 1      12848    12848  12848     12848  12848      12848\n",
       "     2      11095    11095  11095     11095  11095      11095\n",
       "     3      15687    15687  15687     15687  15687      15687\n",
       "     4      16955    16955  16955     16955  16955      16955\n",
       "     5      18842    18842  18842     18842  18842      18842\n",
       "     6      18591    18591  18591     18591  18591      18591\n",
       "     7      19224    19224  19224     19224  19224      19224\n",
       "     8      19303    19303  19303     19303  19303      19303\n",
       "     9      18531    18531  18531     18531  18531      18531\n",
       "     10     18473    18473  18473     18473  18473      18473\n",
       "     11     15984    15984  15984     15984  15984      15984\n",
       "     12     15070    15070  15070     15070  15070      15070\n",
       "2015 1      12609    12609  12609     12609  12609      12609\n",
       "     2      10378    10378  10378     10378  10378      10378\n",
       "     3      14314    14314  14314     14314  14314      14314\n",
       "     4      17483    17483  17483     17483  17483      17483\n",
       "     5      19380    19380  19380     19380  19380      19380\n",
       "     6      18651    18651  18651     18651  18651      18651\n",
       "     7      19780    19780  19780     19780  19780      19780\n",
       "     8      21929    21929  21929     21929  21929      21929\n",
       "     9      25049    25049  25049     25049  25049      25049\n",
       "     10     26054    26054  26054     26054  26054      26054\n",
       "     11     25110    25110  25110     25110  25110      25110\n",
       "     12     24694    24694  24694     24694  24694      24694\n",
       "2016 1      18597    18597  18597     18597  18597      18597\n",
       "     2      20092    20092  20092     20092  20092      20092\n",
       "     3      25693    25693  25693     25693  25693      25693\n",
       "     4      25414    25414  25414     25414  25414      25414\n",
       "     5      26887    26887  26887     26887  26887      26887\n",
       "     6      26118    26118  26118     26118  26118      26118\n",
       "     7      27732    27732  27732     27732  27732      27732\n",
       "     8      30325    30325  30325     30325  30325      30325\n",
       "     9      32457    32457  32457     32457  32457      32457\n",
       "     10     32936    32936  32936     32936  32936      32936\n",
       "     11     29166    29166  29166     29166  29166      29166\n",
       "     12     26590    26590  26590     26590  26590      26590\n",
       "2017 1      25048    25048  25048     25048  25048      25048\n",
       "     2      23137    23137  23137     23137  23137      23137\n",
       "     3      22747    22747  22747     22747  22747      22747"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by year and month\n",
    "daily.groupby(['year', 'month']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f+zBn5l8j4hrgJRGxFnBeOZx3Diw7SZKkETJ7zvy+zpu7/6wBZyJJGi/6XfSL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104927c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the monthly usage since the inception\n",
    "usage_by_month = daily.groupby(['year', 'month']).count()\n",
    "plt.figure(figsize=(16, 7))\n",
    "ax = usage_by_month['pickups'].plot(kind='bar')\n",
    "ax.set_xlabel('Year, Month', fontsize=16)\n",
    "ax.set_ylabel('Number of Rides', fontsize=16)\n",
    "ax.set_title('Monthly Ridership since July 2013', fontsize=18)\n",
    "for tick in ax.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2Fvfu3UNwcDDCw8OFK+tmZmbw8PDAqVOnsHDhQqiqqmLw4MEoKyuDkpL0loNK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1049b9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same plot as above but with a different grouping of bars\n",
    "usage_by_month2 = daily.groupby(['year', 'month']).count()['pickups'].unstack('year')\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "ax2 = usage_by_month2.plot(kind='bar', figsize=(12,6), width=0.8)# colormap='Set3')\n",
    "ax2.legend(loc='upper left', fontsize=14)\n",
    "ax2.set_xlabel('Month', fontsize=16)\n",
    "ax2.set_ylabel('Number of Rides', fontsize=16)\n",
    "ax2.set_title('Monthly Ridership Over the Years', fontsize=18)\n",
    "ax2.set_ylim([0, 38000])\n",
    "for tick in ax2.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax2.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Overall Trip Durations</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Read in another set of data for trip duration analysis\n",
    "d2013 = pd.read_csv('./data/for_predictions/2013_membership_pred.csv')\n",
    "d2013 = d2013[['tripduration', 'birth year', 'gender', 'usertype', 'start_year', 'start_month', 'start_day', 'start_dayofweek', 'start_hour']]\n",
    "\n",
    "d2014 = pd.read_csv('./data/for_predictions/2014_membership_pred.csv')\n",
    "d2014 = d2014[['tripduration', 'birth year', 'gender', 'usertype', 'start_year', 'start_month', 'start_day', 'start_dayofweek', 'start_hour']]\n",
    "\n",
    "d2015 = pd.read_csv('./data/for_predictions/2015_membership_pred.csv')\n",
    "d2015 = d2015[['tripduration', 'birth year', 'gender', 'usertype', 'start_year', 'start_month', 'start_day', 'start_dayofweek', 'start_hour']]\n",
    "\n",
    "d2016 = pd.read_csv('./data/for_predictions/2016_membership_pred.csv')\n",
    "d2016 = d2016[['tripduration', 'birth year', 'gender', 'usertype', 'start_year', 'start_month', 'start_day', 'start_dayofweek', 'start_hour']]\n",
    "\n",
    "d2017 = pd.read_csv('./data/for_predictions/2017_membership_pred.csv')\n",
    "d2017 = d2017[['tripduration', 'birth year', 'gender', 'usertype', 'start_year', 'start_month', 'start_day', 'start_dayofweek', 'start_hour']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine data\n",
    "data = pd.concat([d2013, d2014, d2015, d2016, d2017], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert Trip duration to minutes\n",
    "data['tripduration'] /= 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>tripduration</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>usertype</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>start_dayofweek</th>\n",
       "      <th>start_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.566667</td>\n",
       "      <td>\\N</td>\n",
       "      <td>0</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.783333</td>\n",
       "      <td>\\N</td>\n",
       "      <td>0</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.966667</td>\n",
       "      <td>1980</td>\n",
       "      <td>2</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.333333</td>\n",
       "      <td>\\N</td>\n",
       "      <td>0</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.616667</td>\n",
       "      <td>1986</td>\n",
       "      <td>1</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration birth year  gender    usertype  start_year  start_month  \\\n",
       "0     10.566667         \\N       0    Customer        2013            7   \n",
       "1     25.783333         \\N       0    Customer        2013            7   \n",
       "2      2.966667       1980       2  Subscriber        2013            7   \n",
       "3     26.333333         \\N       0    Customer        2013            7   \n",
       "4     12.616667       1986       1  Subscriber        2013            7   \n",
       "\n",
       "   start_day  start_dayofweek  start_hour  \n",
       "0          1                0           0  \n",
       "1          1                0           0  \n",
       "2          1                0           0  \n",
       "3          1                0           0  \n",
       "4          1                0           0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "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>tripduration</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>start_dayofweek</th>\n",
       "      <th>start_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "      <td>35873893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9%</th>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.99%</th>\n",
       "      <td>1663</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>148892</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tripduration    gender  start_year  start_month  start_day  \\\n",
       "count       35873893  35873893    35873893     35873893   35873893   \n",
       "mean              13         1        2014            7         15   \n",
       "std              100         0           1            3          8   \n",
       "min                1         0        2013            1          1   \n",
       "25%                6         1        2014            5          8   \n",
       "50%                9         1        2015            8         16   \n",
       "75%               16         1        2016           10         23   \n",
       "95%               30         2        2017           12         29   \n",
       "99%               46         2        2017           12         31   \n",
       "99.9%            238         2        2017           12         31   \n",
       "99.99%          1663         2        2017           12         31   \n",
       "max           148892         2        2017           12         31   \n",
       "\n",
       "        start_dayofweek  start_hour  \n",
       "count          35873893    35873893  \n",
       "mean                  2          13  \n",
       "std                   1           4  \n",
       "min                   0           0  \n",
       "25%                   1          10  \n",
       "50%                   3          15  \n",
       "75%                   4          18  \n",
       "95%                   6          21  \n",
       "99%                   6          23  \n",
       "99.9%                 6          23  \n",
       "99.99%                6          23  \n",
       "max                   6          23  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99, 0.999, 0.9999]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min 39s, sys: 6.04 s, total: 6min 45s\n",
      "Wall time: 6min 45s\n"
     ]
    },
    {
     "data": {
      "image/png": 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5saYDAACYgtABAABMQegAAACmYE0HAAAwBTMdAADAFNwyW8pSUpz7XI2C1K5dXefO5X1+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\nELmgoIMQQgghcvF/BEnMbbyfNW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f289b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Plot frequency distribution of trip duration\n",
    "# Based on the above, remove any entry with duration more than 2hours. This would easily cover 99% of the rides\n",
    "duration = data[data.tripduration <= 2* 60]['tripduration']\n",
    "ax3 = duration.plot(kind='hist', bins=120, figsize=(8,8))\n",
    "ax3.set_title('Distribution of Trip Duration', fontsize=18)\n",
    "ax3.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax3.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax3.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax3.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tigsXLqBXr16aKJM+UDdu3NB0CR8Fte+5XLhwAQcPHsSECRMU2lNTUwvM1FtZWSl1GUki\nKn/UGi4ymQyTJ0/GpEmTCpyp+fLlS+HzK/n09fULnNlYmNzckp8OT0TiUuuwaOnSpahdu7ZwItrb\nDAwMCgSJTCYr8mS3t6WnFzypjLSHVGqG1FTlPhRK5YtUalbkMrWGS0xMDFJTU+Hk5ATgzaUOX79+\nDScnJ3Tq1KnAx/nT0tKEU+CJSLuoNVw2bNig8FmIdevW4cqVKwgLC8PZs2exbNkyyOVySCQSyOVy\nnD9/HsOGDVNniUSkImoNl3dP/65YsSIMDQ1Ru3ZtWFlZCaev9+/fH9u2bcOLFy/w1VdfqbNEIlKR\ncvOpaFNTU6xYsQIXLlxA9+7dcf78eURGRsLU1FTTpRFRKXwQny3iZKB244Su9ipuQrfc7LkQ0YeF\n4UJEomC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC\n4UJEomC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC\n4UJEomC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCrWHS1JS\nEgYPHgwnJyd4enpi1apVwrKpU6fC1tZW4WfdunXqLpGIVEBXnZ3l5ORg2LBhcHV1xYwZM3D79m0E\nBASgSpUq6NKlCxITExEYGIguXboI9zE1NVVniUSkImoNl5SUFDRp0gTTpk2DoaEhateujebNm+Ps\n2bPo0qULbt++DTs7O0ilUnWWRUQiUOuw6JNPPsHChQthaGgIuVyO+Ph4nD17Fm5ubkhNTUVGRgbq\n1KmjzpKISCQam9B1d3dH//794eTkhPbt2+PWrVvQ1dXFokWL0KpVK3Tp0gU7d+7UVHlEVEZqHRa9\nLSIiAo8ePcL06dMREhIi7LE0aNAA3t7eiIuLw9SpU2FkZIQOHToUuy0LC2Po6uqoo2wSiVRqpukS\nSMUkcrlcrskC9u3bhwkTJuD8+fPIysqCubm5sGzWrFlITEzE+vXri91GaupzscskEUmlZvwbaqni\n3hTUOixKSUnB0aNHFdpsbGyQk5ODzMxMhWABgLp16yIlJUWdJRKRiqg1XJKSkjBq1Cg8fvxYaLt6\n9SosLS0RGRkJX19fhfUTEhJQt25ddZZIRCqi1nBxcXGBjY0NgoKCkJSUhNjYWMybNw9+fn7w9PTE\niRMnsH79eiQnJ2Pjxo3YvXs3hgwZos4SiUhF1D7n8t9//2HGjBmIi4uDiYkJvvnmGwwfPhwSiQT7\n9+9HREQE7t69C2tra4wdOxbt2rV77zY5XtdunHPRXsXNuWh8QlcV+MLUbgwX7VVuJnSJ6OPBcCEi\nUTBciEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEi\nUTBciEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEi\nUTBciEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBS6mi6APmzu7q64fj2h\nTNto0KAhTpw4o6KKSF0kcrlcrs4Ok5KSMGvWLFy6dAnm5uYYMGAAhg4dCgB48OABfvzxR5w/fx7V\nq1dHUFAQPDw83rvN1NTnYpdNIvIJPYY1Qa01XQaVglRqVuQytQ6LcnJyMGzYMFSvXh27d+/G1KlT\nERERgV9//RVyuRz+/v4wNzdHdHQ0unfvjtGjR+PevXvqLJGIVEStw6KUlBQ0adIE06ZNg6GhIWrX\nro3mzZvj7NmzkEqluHPnDjZt2gRTU1PUq1cPp06dQnR0NMaNG6fOMolIBdS65/LJJ59g4cKFMDQ0\nhFwuR3x8PM6ePQs3NzdcunQJjRo1gqmpqbC+s7MzLl68qM4SiUhFNHa0yN3dHf3794eTkxPat2+P\n1NRUVKlSRWEdKysrPHz4UEMVElFZaOxoUUREBB49eoTp06cjJCQEL1++hJ6ensI6+vr6yMnJee+2\nLCyMoaurI1appAbFTQySdtJYuNjb2wMAXr16hQkTJqBnz57IzMxUWEcmk8HQ0PC920pPzxKlRlIf\nHvHTTuXmaFFKSgqOHj2q0GZjY4OcnBxIpVKkpqYqLEtLS4NUKlVniUSkImoNl6SkJIwaNQqPHz8W\n2q5evQpLS0s4Ozvj+vXryMr6v72Q+Ph4ODo6qrNEIlIRtYaLi4sLbGxsEBQUhKSkJMTGxmLevHnw\n8/PD559/jho1aiAoKAiJiYmIjIzEpUuX0KtXL3WWSEQqotZw0dPTQ2RkJHR0dNCrVy9MnToVgwYN\nwsCBA6Gjo4OIiAg8efIEPXr0wJ49e7BkyRJ88skn6iyRiFRE7af/i4GTgdqNp/9rr3IzoUtEHw+G\nCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJEomC4EJEoGC5EJAqG\nCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJEomC4EJEoGC5EJAqG\nCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJEomC4EJEoGC5EJAq1\nh0tycjL8/Pzg4uICd3d3hIaGIjs7GwAwdepU2NraKvysW7dO3SUSkQroqrMzmUwGPz8/1KtXD7/8\n8gseP36MSZMmAQCCgoKQmJiIwMBAdOnSRbiPqampOkskIhVR657L5cuXkZycjJCQENjY2ODzzz/H\nmDFjEBMTAwC4ffs27OzsIJVKhR8jIyN1lkhEKqLWcKlbty4iIyNhYmIitEkkEjx79gypqanIyMhA\nnTp11FkSEYlEreFiaWmJ5s2bC7fz8vKwceNGNG/eHLdu3YKuri4WLVqEVq1aoUuXLti5c6c6yyMi\nFVLrnMu7QkJCkJCQgOjoaMTFxQEAGjRoAG9vb8TFxWHq1KkwMjJChw4dit2OhYUxdHV11FEyiUQq\nNdN0CaRiGgkXuVyO4OBgbNmyBYsWLUL9+vVRr149dOzYEebm5gDehMzdu3exZcuW94ZLenqWOsom\nEaWmPtd0CVQKxb0pqP1QdF5eHiZNmoRffvkFCxYsgJeXF4A3cy/5wZKvbt26SElJUXeJRKQCag+X\n0NBQxMTEIDw8HO3atVNo9/X1VVg3ISEBdevWVXeJRKQCah0WXbx4EVFRUQgICICdnR1SU1OFZZ6e\nnoiKisL69evx5Zdf4sSJE9i9ezdPoiPSUmoNl0OHDgEA5s2bh3nz5iksu3r1KubNm4eIiAjMnTsX\n1tbWmD9/Ppo1a6bOEolIRSRyuVyu6SLKipOB2s0n9BjWBLXWdBlUCuVqQpeIPg4MFyISBcOFiETB\ncCEiUTBciEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhJFqcPl0aNHuHLlCl6/fq3KeojoA6FUuDx9\n+hQBAQFYv349AGD37t1o3bo1evXqhQ4dOuDevXuiFklE2kepcJk9ezZOnTqFGjVqIC8vD2FhYXB0\ndMT69ethYWGB2bNni10nEWkZpcLl+PHjmDhxIry8vHDu3DmkpaVh6NChcHFxgZ+fH06fPi12nUSk\nZZQKl6ysLFSvXh3Am6AxMDCAm5sbAEBfX1+86ohIaykVLjY2Njhy5AjS0tKwf/9+uLm5wcDAAK9f\nv8bmzZtRv359seskIi2j1JXoRo8ejVGjRmH9+vXQ09MTrnXbrl07PH78GMuWLRO1SCLSPkqFi4eH\nB/bu3YsrV67A3t4e1tbWACB8ofynn34qZo1EpIWUvoZurVq1UKtWLTx48AAXL17EZ599hq+++krh\nq1mJiPIpHS6HDh3CvHnzkJycjAoVKmD79u1YsmQJTExMEBISAj09PTHrJCIto9SE7v79+zF27Fi4\nuLhg4cKFyMvLAwC0bdsWR44cwdKlS0Utkoi0j1J7LhERERg4cCAmTpyocLp/jx498OzZM2zYsAFj\nx44VrUgi0j5K7bncvXsXHh4ehS5r2LChwpebEREBSoZLjRo1EB8fX+iyy5cvCyfYERHlU2pYNGDA\nAMyZMwdyuRweHh6QSCRISUnBtWvXsHz5cvj7+4tdJxFpGaXCZeDAgXj27BlWrlyJZcuWQS6Xw9/f\nH7q6uvD29saQIUPErpOItIzSh6JHjhyJQYMG4cKFC3j69CnMzMzQpEkTWFpailkfEWmpEn0RvZmZ\nGdzd3cWqhYg+IEWGS+fOnUu0oZiYmDIXQ0QfjiLDpXHjxpBIJACA169fY//+/ahUqRLc3d0hlUqR\nkZGBP//8E2lpaejTp4/aCiYi7VBkuISGhir83qxZM0RGRipcv+X169cYNWoUnj17Jm6VRKR1lDrP\nJTo6Gt9++22BC0Pp6OigX79+OHTokCjFEZH2UipcDA0NkZycXOiyhIQEVKpUSaVFEZH2U+poUbdu\n3TB//nzIZDK0atUKFhYWePz4MX777TdERkZi1KhRYtdJRFpGqXAZN24csrKyMH/+fISFhQntenp6\nGDp0KIYPHy5agUSknZQKFx0dHUydOhVjxozBxYsX8fz5c5ibm8PR0RGmpqZi10hEWqjIOZeMjAzh\nui0ZGRnIyMiAXC6Hg4MDWrZsCTs7O+Tm5grLlJWcnCxcHtPd3R2hoaHIzs4GADx48AA+Pj5wdHRE\nhw4d8Pvvv5fx4RGRphS55+Lm5oatW7eiSZMm+OKLL4RzXoqSkJDw3s5kMhn8/PxQr149/PLLL3j8\n+DEmTZoEAJgwYQL8/f1hY2OD6OhoHDt2DKNHj8bevXuFa/YSkfYoMlx+/vln4Z86JCREJZ1dvnwZ\nycnJ2L59O0xMTGBjY4MxY8YgNDQUHh4euHPnDjZt2gRTU1PUq1cPp06dQnR0NMaNG6eS/olIfYoM\nl+7duwu/X716Fd26dYOdnV2ZOqtbty4iIyMVLuotkUjw7NkzXLp0CY0aNVKYw3F2dsa5c+fK1CcR\naYZS57ls375dJWfhWlpaonnz5sLtvLw8bNy4Ec2bN0dqaiqqVKmisL6VlRUePnxY5n6JSP2UOlrU\nrFkzHD9+XCEYVCEkJAQJCQmIjo7G2rVrC3yDgL6+PnJyct67HQsLY+jq6qi0NlIvqdRM0yWQiikV\nLjVr1sTmzZuxZ88e1K5du8A1XCQSSYm+dVEulyM4OBhbtmzBokWLUL9+fRgYGCAzM1NhPZlMBkND\nw/duLz09S+m+qXxKTX2u6RKoFIp7U1AqXO7cuQMnJyfh9osXL0pdTF5eHiZPnoyYmBgsWLAAXl5e\nAICqVavi+vXrCuumpaVBKpWWui8i0hylwmXDhg0q6zA0NBQxMTEIDw+Hp6en0O7g4IAVK1YgKysL\nxsbGAID4+Hg4OjqqrG8iUh+lJnRV5eLFi4iKisLo0aNhZ2eH1NRU4efzzz9HjRo1EBQUhMTERERG\nRuLSpUvo1auXOkskIhUp0WUuyyr/0gzz5s3DvHnzFJZdvXoVERERmDx5Mnr06IFatWphyZIl+OST\nT9RZIhGpiEQul8s1XURZcTJQu/mEHsOaoNaaLoNKobgJ3SKHRf/9958oxRDRx6HIcOnWrZvwLYsT\nJ07EvXv31FYUEWm/IudcZDIZLl68iLp162LXrl3o3LkzzMyK3gUyNzcXpUAi0k5FhkubNm0wd+5c\nhIWFQSKRvPdbFZX5VDQRfTyKvfp/x44dkZGRgYkTJ2LEiBGoVauWOmsjIi1WZLjo6uoKJ7nFxcWh\nR48evK4KESlNqfNc8q/ncvz4ccTFxSEzMxMWFhZo2rQp3N3d33shKSL6+CgVLllZWfD19cXZs2dR\nqVIlWFpaIi0tDStWrEDTpk2xatUq4ZR9IiJAydP/582bh5s3b2LVqlU4c+YMDhw4gLNnz2LlypW4\nffs2FixYIHadRKRllAqXgwcP4vvvv0fLli0V2lu1aoVx48bh4MGDohRHRNpLqXB59eoVatSoUeiy\nGjVq4OnTpyotioi0n1Lh0rBhQ+zevbvQZTt37kT9+vVVWhQRaT+lJnTHjBmDQYMG4d9//0X79u1R\nuXJlpKVGe3tKAAAfqElEQVSl4eDBg7h8+TKWLl0qdp1EpGWUChcXFxcsW7YMixYtwuzZsyGXyyGR\nSNCwYUMsXbpU4aJPRERACa7n4uHhAQ8PD2RlZeH58+cwMzPj4WciKlKJLxZlbGzMUCGi91LrZS6J\n6OPBcCEiUTBciEgUSoVLjx498Pvvv4tdCxF9QJQKl7t378LAwEDsWojoA6JUuHz99ddYvnw5rl+/\njlevXoldExF9AJQ6FH3u3DncuHED3bt3BwAYGRkpLJdIJMLFvImIACXDxdPTk2fhElGJKBUuI0eO\nFLsOIvrAKH2G7vPnz7Fhwwb89ddfSEtLw+LFixEbG4uGDRuiVatWYtZIRFpIqQnd+/fvo3Pnzli3\nbh1MTU3xzz//QCaT4caNG/Dz8+NhaiIqQKk9l+DgYEilUqxbtw4GBgaws7MD8Obyl7m5uYiIiICH\nh4eohRKRdlFqz+X06dPw9fWFiYlJgSv99+3bFzdv3hSlOCLSXkqFi76+PrKzswtdlpGRAX19fZUW\nRUTaT6lw8fDwwMKFC/HPP/8IbRKJBBkZGYiMjCxw4W4iIqXCJSgoCPr6+ujUqRO6dOkCAJg8eTLa\ntm2L58+fIzAwUNQiiUj7SORyuVyZFbOzs7Fr1y7ExcUhIyMDpqamcHZ2Rs+ePWFqaip2ncVKTX2u\n0f4/ZqMWnsCLV7karcHEUBfhY901WsPHSio1K3KZ0ue5GBgYoG/fvujbt69KiqIPw4tXuVgT1LpM\n25BKzcr0BuETeqxM/ZM4lA6XhIQErF27FvHx8cJ3Rbu6umLo0KH8gnoiKkCpcDl16hSGDx+OypUr\no2XLlsJ3RR89ehT79u3Dhg0b0LBhQ7FrJSItolS4hIWFoWXLlggPD4eenp7Q/urVK/j6+uKnn37C\npk2bStSxTCZDjx49MGnSJDRv3hwAsGLFCsyfP19hvYEDB2Ly5Mkl2jYRaZ5S4XLr1i2MGzdOIVgA\nwNDQEEOGDMGoUaNK1Gl2djYCAgKQmJio0J6YmAhvb2/4+voKbe9e3oGItINSh6Lr1auHa9euFbrs\nwYMHqFWrltId3rp1C71790ZycnKBZUlJSWjUqBGkUqnwo+kjUURUOkXuuVy9elX4vU+fPggJCYFM\nJkPbtm1RuXJlPH36FH/88QciIyMxY8YMpTuMi4uDq6srxo0bB0dHR6E9Ly8Pd+7cQZ06dUr5UIio\nPCkyXHr27KnwOSK5XI6lS5ciIiJCoQ0ARo8ejYSEBKU67N+/f6HtDx48wMuXL7Ft2zZ8//33MDQ0\nRM+ePeHj44MKFfglBUTapshwWb9+vTrrQFJSEgCgatWqWL58Oa5du4bg4GAAwNChQ4u9r4WFMXR1\ndUSvkQpX3IlU6tqGKmog1SoyXD7//HN11oEvv/wSp0+fhoWFBQDA1tYW6enp2LRp03vDJT09Sx0l\nUhHKeoZ0WU+iU0UNVDplPkM3OzsbUVFRuHz5Mp49e1ZguUQiQVRUVOkr/P/ygyWfjY0NHj16VObt\nEpH6KTWZMWXKFCxYsADp6ekwMzMr8KOKIzpRUVHo3LmzQtu1a9c4wUukpZTac4mNjcXEiRMxcOBA\n0Qpp1aoV5s2bh3nz5uHrr7/G5cuXsXLlyhIdiSKi8kOpcDEzM0Pt2rVFLaRu3bpYsWIFwsLCsH79\nelSuXBnjx48vsDdDRNpBqXDx9fVFREQEbG1tUa1aNZV1fuPGDYXbbm5u2LFjh8q2T0Sao1S4tGnT\nBmvXroWnpycsLS1haGiosFwikeDIkSOiFEhE2kmpcAkMDMSjR4/w1VdfwcrKSuyaiOgDoFS4nD9/\nHsHBwejUqZPY9RDRB0KpQ9FVq1blp5OJqESUChd/f38sXLgQf//9N5S85C4RfeSUGhZt2LABDx48\nQO/evSGRSAqd0I2PjxelQCLSTkqFi6enJzw9PcWuhYg+IEqFy8iRI8Wug4g+MEqFy+7du9+7Trdu\n3cpcDBF9OJQKl6CgoELbJRIJ9PX1YWxszHAhIgVKhcvZs2cLtGVlZeHs2bOYP38+5s6dq/LCiEi7\nKf3BxcLaOnXqhJcvXyI4OBg7d+5UeXFEpL3KfHHamjVr4tatW6qohYg+IErtuWRkZBRoy8vLw6NH\nj7Bs2bISfbUIEX0clAqXL774QuGbAN6mr6+PRYsWqbQoItJ+SoXLzz//XCBcJBIJTE1N4erqWuic\nDBF93JQKlx49eohdBxF9YIoMl8IOPxfHxcWlzMUQ0YejyHDx9vaGRCIp9lPQbw+VlP3GRSL6OBQZ\nLu875f/8+fOYP38+MjMz0bNnT5UXRkTarchwadCgQaHtL168wPz587FlyxZYW1tj6dKlcHV1Fa1A\nItJOSk3o5ouNjcXMmTORmpqKYcOG4bvvvoO+vr5YtRGRFlMqXB4/foxZs2bh0KFDsLe3x4oVK/DZ\nZ5+JXRsRabH3hkt0dDTmzp2LnJwcTJo0Cd98802RJ9QREeUrMlySk5Px448/Ii4uDl9++SWmTZum\n0i9EI6IPW5Hh0rlzZ8hkMpiZmUEul2P69OlFbkQikWDZsmVi1EdEWqrIcGnSpInw+4sXL9RSDBF9\nOIoMlw0bNqizDiL6wJT5ei5ERIVhuBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJE\notBYuMhkMnTq1AmnTp0S2jIyMjB69Gg0bdoUrVu3xq5duzRVHhGVUYkuFqUq2dnZCAgIQGJiokJ7\nUFAQsrKysGXLFvz999+YOnUqateujaZNm2qiTCIqA7WHy61btxAQEFDgwt/JycmIjY3F4cOHUbt2\nbdja2uLChQvYvHkzw4VIC6l9WBQXFwdXV1ds3bpVof3SpUuQSqWoXbu20Obs7IyLFy+qu0QiUgG1\n77n079+/0PbU1FRUqVJFoc3KygoPHz5UR1lEpGIamXMpzMuXLwtc7FtfXx85OTmQy+XFXlrTwsIY\nuro6YpdIRZBKy/51vmXdhipqINUqN+FiYGAAmUym0CaTyWBoaPjea/amp2eJWRoVY0jyr/iz63rN\n1qBvjtTU1hqt4WNVXKiXm3CpWrUq0tLSFNrS0tIglUo1VBEpY3WtLlgTVLZ/bKnUDKmpz0t9/9DQ\nY2hRpgpIDOXmJDpHR0ekpKTg/v37Qlt8fDwcHBw0WBURlVa5CRdra2u0bNkSEyZMwPXr17Fjxw7E\nxMTgm2++0XRpRFQK5SZcAGDOnDkwMzND7969sXTpUvz0009wcnLSdFlEVAoanXO5ceOGwm0rKyss\nX75cQ9UQkSqVqz0XIvpwMFyISBQMFyISBcOFiETBcCEiUTBciEgUDBciEgXDhYhEwXAhIlEwXIhI\nFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEiUTBciEgUDBciEgXDhYhEwXAhIlEwXIhI\nFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEiUTBciEgUupougLSfT+gxjfZvYsiXcXkk\nkcvlck0XUVapqc81XQKVgU/oMawJaq3pMqgUpFKzIpdxWEREomC4EJEoGC5EJAqGCxGJotyFy969\ne2Fra6vw4+/vr+myiKiEyt0xvMTERLRt2xbTpk0T2gwMDDRYERGVRrkLl6SkJNja2kIqlWq6FCIq\ng3I3LLp16xbq1Kmj6TKIqIzKVbjIZDLcu3cPsbGxaNu2Lby8vBAWFgaZTKbp0oiohMrVsOju3bvI\nzc2FsbExwsPDkZycjODgYLx48UJhDuZdFhbG0NXVUWOlpGrFnelJ2qlchUv9+vVx+vRpWFhYAAAa\nNGgAuVyOgIAATJ48Gbq6hZebnp6lzjJJBPwIh3bSqtP/84Mln42NDXJycvDkyRMNVUREpVGuwuXw\n4cNo3ry5whzLtWvXULFiRR49ItIy5SpcXFxcIJfLMXXqVNy5cwfHjx/HnDlzMGTIEEgkEk2XR0Ql\nUK7mXCwsLLB69WqEhISgR48eMDU1Rd++feHr66vp0oiohMpVuABAo0aNsGHDBk2XQURlVK6GRUT0\n4WC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJE\nomC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiYLhQkSiYLgQkSgYLkQkCoYLEYmC4UJE\nomC4EJEoGC5EJAqGCxGJguFCRKJguBCRKBguRCQKhgsRiUIil8vlmi6irFJTn2u6BCqCu7srrl9P\nKNM2GjRoiBMnzqioIlIlqdSsyGUMF9I4qdSMf0MtVVy4cFhERKJguBCRKMpduMhkMvz4449wcXFB\nixYtsHLlSk2XRESloKvpAt41Z84cXLx4EWvXrsXDhw8RGBiIGjVqoGPHjpoujYhKoFztuWRlZWHb\ntm2YOHEi7Ozs4OXlhaFDh2Ljxo2aLo2ISqhchcv169chk8ng7OwstDk7O+Pvv//G69evNVgZEZVU\nuQqX1NRUVKpUCQYGBkJb5cqVkZOTg8ePH2uwMiIqqXI15/Ly5Uvo6+srtOXflslkRd7PwsIYuro6\notZG4irufAnSTuUqXAwMDAqESP5tIyOjIu+Xnp4lal0kLp5Ep7205iS6qlWr4tmzZwoBk5qaCn19\nfVSqVEmDlRFRSZWrcGnYsCH09PRw4cIFoS0+Ph6NGzeGrm652skiovcoV+FiZGSEbt26YcaMGbh8\n+TKOHj2KNWvWYODAgZoujYhKqNx9cPHly5eYPn06Dh8+DBMTE/j4+MDHx6fY+3C8rt0456K9+Klo\nKtcYLtpLayZ0iejDwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEiUTBc\niEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEiUTBc\niEgUDBciEgXDhYhEwXAhIlEwXIhIFAwXIhIFw4WIRMFwISJRMFyISBQMFyISBcOFiETBcCEiUTBc\niEgUDBciEgXDhYhEoavpAt524cIF9O3bV6GtQYMG2LNnj4YqIqLSKlfhcuvWLdjZ2WH58uVCm65u\nuSqRiJRUrv5zb926hfr160MqlWq6FCIqo3I153Lr1i3UqVNH02UQkQqUqz2XpKQkGBkZoVOnTsjM\nzIS7uzt++OEHmJmZabo0IiohiVwul6urs+zsbDx8+LDQZVZWVnB2dkabNm3g7++PjIwMhIaGokaN\nGoiMjFRXiUSkImoNl3PnzmHAgAGFLgsJCYGXlxdMTEygo6MDALhy5Qp69uyJ2NhY1KhRQ11lEpEK\nqHVY1KxZM9y4cUPp9W1sbAAAKSkpDBciLVNuJnQvX74MJycnpKSkCG3Xrl2Djo4OateurcHKiKg0\nyk24NGjQAFWrVsXkyZORmJiIs2fPYsqUKejZsycsLS01XR4RlZBa51ze5969ewgODsa5c+ego6OD\nzp07IzAwEPr6+poujYhKqFyFCxF9OMrNsIjULyEhAefOnSt0WVBQEMaPH1+q7d69exe2tra4f/9+\nWcor0uPHj7F//37htq2tLU6dOiVKX1R65eokOlKv7777DiNGjECzZs0KLJs8ebIGKlJOWFgYcnJy\n8NVXXwEATp48iUqVKmm4KnoXw4UKVZ7Pin53JM/PopVPHBZ9pLy9vfHgwQNMmTIFrVu3hru7O2bO\nnAlnZ2eEh4crDIvCw8MxevRoTJo0CQ4ODmjfvj2OHDkibCsnJwczZ85Es2bN4OHhgT/++EOhr3eH\nLTt37oS7uzsA4MyZMwX6zsnJwezZs+Hu7o7GjRvD09MTmzdvFmrZtWsXYmJi0Lp16wLbz87ORlhY\nGDw8PODo6Ag/Pz88ePAAAHD//n3Y2tri0KFDaNu2Lezt7TFs2DA8efJEpGf548Zw+UiFh4ejWrVq\nCAoKwqRJk5CSkoLMzEzs2rUL3bt3L7D+sWPH8Pr1a+zcuRNff/01Ro8eLZwQGR4ejuPHj2PZsmVY\nuHAhNmzYUKJa3u175cqVOHbsGBYvXoyDBw+ie/fuCA4ORkpKCnx8fNChQwe0b98e0dHRBbY1bdo0\nHD58GLNnz8bWrVuRm5uLESNG4PXr18I6K1asQFhYGDZu3IirV69i9erVJXz2SBkMl4+Uubk5dHR0\nYGpqKgyBhg4dilq1auGTTz4psH7FihUxa9Ys2NjYYNiwYWjatCmio6Mhl8uxfft2jBw5Ei4uLnBy\nckJQUFCJ63m7788++wzBwcFwdHSEtbU1/Pz8kJubizt37sDExASGhobQ19cvcP7T06dPsWfPHkye\nPBlffPEFbG1tERYWhuTkZIW9qZEjR8LBwQEODg7o3Lkz/v777xLXS+/HORcS1KxZs8hljRo1Ujjf\nyM7ODomJiUhPT8eTJ0/QoEEDhWVl6dvLywt//vknQkNDcfv2bVy7dg0AkJeXV+w2/vnnH+Tl5cHB\nwUFoMzc3R506dZCUlIR69eoBAGrVqiUsNzU1RW5ubonrpffjngsJDAwMilz27hUBX79+DYlEItx+\ne5L1fVcPfHuIUljfCxYsQEBAAHR0dNC1a1ds3br1vbW/u413+3u7Tz09PYXlPNVLHAwXUsrNmzcV\n9hyuXLkCW1tbWFhYoHLlygpDi4SEBIX76unp4cWLF8Lte/fuFdvXL7/8gilTpuCHH35Ax44d8fLl\nSwD/FwJvh9rbatWqBV1dXVy6dEloS09Px927d1G3bl0lHympCodFHzETExPcvn1bqXNEHjx4gNDQ\nUPTt2xeHDx/GlStXEBISAolEgv79+2PJkiWwtraGmZkZQkNDFe5rb2+PTZs2oX79+rhz5w527tyJ\nChWKfl8zNzdHbGwsHBwckJKSgp9//hkAIJPJAADGxsZISEhASkoKqlatKtzP2NgYffv2RXBwMAwM\nDGBhYYGwsDBUrVoVrVq1QmpqammeJiol7rl8xAYMGCDsJbyPnZ0dnj9/ju7du2P//v2IjIzEp59+\nCgAYMWIEunfvju+//x5+fn7o06ePwn1//PFHPHv2DJ06dcKKFSswZsyYYvv6+eefcfPmTXTs2BFB\nQUH43//+B0dHR2HupWvXrkhOTkaXLl0KDGl++OEHtGzZEmPGjEHfvn2hr6+PqKioYod8JA5+toje\nKzw8HKdOncKWLVs0XQppEe65EJEoGC5EJAoOi4hIFNxzISJRMFyISBQMF1IpjrIL9zE+LwyXMggK\nCoKtrW2xP+Hh4UXe39vbG76+viqtoWHDhmjWrBm++eYbHDhwoEzbLqkjR45g2rRpwu3w8HA4OTmp\nrf/jx4/D29u71PfPvyTDwYMHVVjVm+/rGj16tEq3GRsbi0GDBql0m6rGM3TLwN/fH3379hVuT5gw\nAbVr14a/v7/QVq1atSLvP23atGLPVFWWtbU1wsLCAAC5ublIT09HTEwMxo4di3v37mH48OFl7kMZ\nUVFRMDY2Fm736tULHh4eauk7MzMT06dPLzbM36dKlSrYunWrcHKgqkRHR+POnTsq3aanpyfWrFmD\nbdu2oXfv3irdtqowXMqgVq1aCp+wNTQ0hKWlJRwdHZW6f/6ndMvK0NCwQJ9t27ZFYGAgFi5ciHbt\n2qn8H0YZ1apVKzZcVWndunWoU6cO7O3tS70NfX19pf925cHQoUMxadIkdOvWrVx+QwaHRWqQv7sd\nFRWF1q1bw9nZGefOnVMYFp05cwa2trY4efIkunfvjiZNmqBHjx7466+/St3vqFGjhAs8vd3Hu9cv\nadasmfCOv3PnTri6umLVqlVwdXWFh4cHsrKykJmZiZ9++gmenp6ws7PDF198gQkTJuDZs2cA3gzx\n4uLicPz4ceHi3O8Oi3JychAZGYn27dvD3t4enTt3RkxMTIHn6dixYxgyZAgcHBzQqlUrLFu2rNjH\nmZ2djc2bNwvX1H37cZw4cQKdO3eGvb09vv76ayQlJeG3335D+/bt4eTkBF9fXzx+/Fih//xhUVBQ\nEEaPHo2oqCh4enqiSZMm8Pb2RlJSktBPYUPbdevWwdbWVtjGrl27kJiYCFtbW5w5cwbAm4uMBwYG\n4vPPP4eTkxP8/PwUPtD5+vVrzJkzB19++SXs7Ozw1VdfFThDukWLFsjNzcXu3buLfX40heGiRhER\nERg/fjx+/PFHNGnSpNB1AgIC0KZNG4SHh8PS0hLDhg3DzZs3S9WftbU1atasiQsXLpTofs+fP0dM\nTAzCwsIwceJEGBsbIyAgAMeOHUNAQABWr14NHx8f7N27FxEREQDeDPEaNWqEpk2bYuvWrahSpUqB\n7U6YMAERERHo3bs3li1bBicnJ4wfPx7bt29XWG/ixIlwcHDA8uXL4enpiYULF+L3338vst7Tp0/j\nyZMnaNu2rUL7ixcvMH36dAwfPhwLFizAw4cPhd+///57TJs2DadOncKiRYuK3PapU6ewe/duTJ48\nGXPnzsXdu3dLdDEsf39/eHh4wNraGlu3bkXjxo3x6tUrDBw4EPHx8ZgyZQrmzJmDtLQ0fPPNN3j6\n9CmAN1fL27FjB8aOHYvVq1ejVatWmD59usJFr3R1ddG6dWvs27dP6XrUicMiNercubPCu2thevXq\nhZEjRwIA3Nzc4OXlhbVr1yIkJKRUfVpZWSEtLa1E93n9+jVGjhyJVq1aAXizZ5CTk4Pp06cL1751\ndXXFhQsXEBcXB+DNEM/U1BTGxsaFDi1u3LiBffv2YcaMGcI8VcuWLZGZmYn58+ejR48ewrodOnQQ\nJkBdXV1x6NAhnDhxosj5m9OnT6NGjRowNzdXaM/JycH48eOF5/zSpUuIjIzExo0b4eLiAgD4888/\nFS7R8K4XL15gxYoVQlimpKQgODgY6enpsLCweM8z+WbobGlpiX///Vd4Xn755RfcuXMHMTExwveh\nu7m5wdPTExs2bMDIkSMRHx8POzs7dOvWTXgeDA0NYWRkpLD9Ro0aYe/evZDJZOVuaMQ9FzWqU6fO\ne9fp2LGj8Lu+vj5atWpV5HcLientWg0MDLBmzRq4u7vj/v37OHnyJNauXYukpCTk5OQotb38x/C/\n//1Pof2rr77CkydPFIYab4dThQoVUKVKFWRlZRW57QcPHqB69eqFLnt7DsbKygqA4pXyzM3N8fz5\n8yK3XaNGDYW9sPw5pPxrzJTGmTNnULt2bdSuXRu5ubnIzc2FoaEhnJ2dcfr0aQBvhqonT56Et7c3\noqKicO/ePYwbN67A18DUqFEDMpmsxG8g6sA9FzXKf3EX592vybC0tBR2lUsjJSWlVBdKevf6tEeP\nHkVISAju3bsHCwsL2NnZwdDQ8L2Xnsz39OlT6OrqFti7qFy5MoA3R3vyjzQZGhoqrFOhQoVizxPJ\nzMwscJ98JiYmBdreffcvzrvr5h/dU/ZxFyYjIwO3b99G48aNCyzLn3gfPnw4jIyMEB0djZ9//hk/\n//wznJ2dERoaqnAQIb++4gJSUxgu5UxGRobwDwe8mfh79x9dWf/88w9SUlLQq1cvAP93Bbe3/1Hl\ncvl734X/+ecfjBkzBt27d8fGjRuFd+8xY8Yo7HEUp1KlSsjNzUVGRoZCwOS/474bOiVhbm4ufH2I\nJrwbNMXtZQFvvhOqQYMG+Omnnwosyx/a6OjoYPDgwRg8eDD+/fdfHDlyBOHh4Zg5cyZWrVolrJ//\nxlOW508sHBaVM7GxscLvMpkMJ06cgKura6m2tWLFCujp6QnjdlNTUwDAo0ePhHUuXrz43gtUX7t2\nDTk5ORg+fLgQLFlZWYiPj1cIquLO2XF2dgaAAieo7d+/H1ZWVmU6VF6tWjU8fPiw1PcvC1NTU4Xn\nEwDi4+MVbr/7vDRt2hT3799HzZo1YW9vD3t7e9jZ2WHdunU4fvw4AMDHx0eYZ6tRowYGDhwILy8v\n/PfffwrbSklJgb6+vsIbUnnBPZdyJiIiAnp6eqhTpw7Wr1+PrKwsDB06tNj7vHr1ChcvXgTwZjL2\n8ePH2LdvHw4ePIgJEybA2toawJsvD6tatSoWLVoEXV1dZGZmYvHixe/9dsWGDRtCR0cHc+fORb9+\n/ZCeno41a9YgLS1NYRKxYsWKSEhIwJkzZxSuwA8ADRo0QPv27REaGooXL17A1tYWR48exb59+zB1\n6tQynUzo5uaG1atXF7jspTq4u7sLJ++5uLjg0KFDuHLlisI6FStWxMOHD/Hnn3/Czs4OX3/9NTZs\n2AAfHx8MHz4c5ubm2Lp1Kw4fPowuXboAeBPGy5Ytg1Qqhb29PZKSknDw4MECZ+VevHgRrq6u0NHR\nUdtjVhb3XMqZwMBAbN++HaNHj4ZMJsOmTZuEcCjKvXv30KdPH/Tp0wcDBgzA5MmT8fTpU0RERMDH\nx0dYT0dHBwsXLoS+vj5GjhyJJUuW4IcfflAYwxemTp06mD17Nm7cuIHhw4cjLCwM9vb2mDZtGv77\n7z+kpKQAAAYPHgyZTIahQ4cKl6R8W1hYGAYMGIB169ZhxIgROH/+PObOnYsBAwaU4pn6P66urqhU\nqVKBb3pUh169emHQoEHYuHEjRowYgczMTEyaNElhnT59+sDKygq+vr74888/YWpqik2bNqFu3bqY\nPn06/P398e+//yIiIkI4Iubn5wdfX19s2bIFQ4YMwcqVKzFo0CDhSCLw5mjYmTNn0K5dO7U+ZmXx\nei7lxJkzZzBw4EBER0eX6SzTj9XHeCnOw4cPY+bMmTh69Gi5vEYw91zogzBo0CAkJycXe87Kh2bt\n2rUYMWJEuQwWgOFCH4iKFStixowZmDt3rqZLUYsjR45AV1cX/fv313QpReKwiIhEwT0XIhIFw4WI\nRMFwISJRMFyISBQMFyISBcOFiETx/wBf/qwVWWRRkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f2907a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot the same thing\n",
    "ax3_1 = duration.plot(kind='box', figsize=(4,8))\n",
    "ax3_1.set_title('Distribution of Trip Duration', fontsize=18)\n",
    "ax3_1.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax3_1.set_ylabel('Number of rides', fontsize=16)\n",
    "ax3_1.set_ylim([-5, 40])\n",
    "for tick in ax3_1.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax3_1.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Test for normality for trip duration distribution -----\n",
      "Is the trip duration normally distributed?: No (p-value=0.000000)\n"
     ]
    }
   ],
   "source": [
    "# Normality test here\n",
    "duration_normaltest_ressult = scs.normaltest(duration)\n",
    "\n",
    "print('----- Test for normality for trip duration distribution -----')\n",
    "print('Is the trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(duration_normaltest_ressult), duration_normaltest_ressult.pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Age Distributions</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop entries with invalid 'birth year' field\n",
    "age = data[data['birth year'] != '\\\\N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:2: 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",
      "  \n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Calculate the age of the rider\n",
    "age['birth year'] = age['birth year'].astype(float).astype(int)\n",
    "age['start_year'] = age['start_year'].astype(int)\n",
    "age['age'] = age['start_year'] - age['birth year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>tripduration</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>usertype</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>start_dayofweek</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.966667</td>\n",
       "      <td>1980</td>\n",
       "      <td>2</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.616667</td>\n",
       "      <td>1986</td>\n",
       "      <td>1</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.350000</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.766667</td>\n",
       "      <td>1980</td>\n",
       "      <td>2</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration  birth year  gender    usertype  start_year  start_month  \\\n",
       "2      2.966667        1980       2  Subscriber        2013            7   \n",
       "4     12.616667        1986       1  Subscriber        2013            7   \n",
       "5     14.350000        1988       1  Subscriber        2013            7   \n",
       "7      4.800000        1985       2  Subscriber        2013            7   \n",
       "8     12.766667        1980       2  Subscriber        2013            7   \n",
       "\n",
       "   start_day  start_dayofweek  start_hour  age  \n",
       "2          1                0           0   33  \n",
       "4          1                0           0   27  \n",
       "5          1                0           0   25  \n",
       "7          1                0           0   28  \n",
       "8          1                0           0   33  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     34626249\n",
       "mean          1976\n",
       "std             11\n",
       "min           1885\n",
       "25%           1969\n",
       "50%           1979\n",
       "75%           1986\n",
       "95%           1992\n",
       "99%           1995\n",
       "99.9%         1999\n",
       "99.99%        2000\n",
       "max           2001\n",
       "Name: birth year, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age['birth year'].describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99, 0.999, 0.9999]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     34626249\n",
       "mean            38\n",
       "std             11\n",
       "min             16\n",
       "25%             29\n",
       "50%             36\n",
       "75%             46\n",
       "95%             59\n",
       "99%             67\n",
       "99.9%           77\n",
       "99.99%         116\n",
       "max            132\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age['age'].describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99, 0.999, 0.9999]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Rides by people above 100 years old 13496 ???!!!!\n"
     ]
    }
   ],
   "source": [
    "# Check how many rider were made by people above \"100\" years old\n",
    "print('# Rides by people above 100 years old %d ???!!!!' % len(age[age.age > 100]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min 36s, sys: 9.53 s, total: 6min 45s\n",
      "Wall time: 6min 46s\n"
     ]
    },
    {
     "data": {
      "image/png": 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rI0aMMC4vXC8z+vi1V155hZ07dzJnzhweeOCBYueM/OMf/6B27dqsWrWKr7/+mrvuuosF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3284661d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Plot age distribution of riders. Only include ones whose age is less than or equal to 100\n",
    "# Based on the above, remove any entry with duration more than 4hours. This would cover 99.9% of the rides\n",
    "age_distribution = age[age.age <= 100]['age']\n",
    "ax4 = age_distribution.plot(kind='hist', bins=28, figsize=(8,8))\n",
    "ax4.set_title('Distribution of Rider\\'s Age', fontsize=18)\n",
    "ax4.set_xlabel('Rider\\'s Age', fontsize=16)\n",
    "ax4.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax4.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax4.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KlCkqWrSoWrZsqSFDhlinau7V/VjHP73xxhvavn275syZo1atWmV7DGXUqFEqWbKkli1b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\nEIIDAGAIwQEAMITgAAAYQnAAAAz5fxQqv1eZ0S3KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3672ac630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same thing plotted in boxplot\n",
    "ax4_1 = age_distribution.plot(kind='box', figsize=(6,8))\n",
    "ax4_1.set_title('Distribution of Rider\\'s Age', fontsize=18)\n",
    "ax4_1.set_xlabel('Rider\\'s Age', fontsize=16)\n",
    "ax4_1.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax4_1.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax4_1.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Test for normality for age distribution -----\n",
      "Is the rider age normally distributed?: No (p-value=0.000000)\n"
     ]
    }
   ],
   "source": [
    "# Normality test here\n",
    "age_normaltest_ressult = scs.normaltest(age_distribution)\n",
    "\n",
    "print('----- Test for normality for age distribution -----')\n",
    "print('Is the rider age normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(age_normaltest_ressult), age_normaltest_ressult.pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Gender Distribution</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Unknown, 1: Male, 2: Female\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gender\n",
       "0     1302575\n",
       "1    26433121\n",
       "2     8138197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the gender size\n",
    "print('0: Unknown, 1: Male, 2: Female')\n",
    "data.groupby('gender').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male: 73.7%\n",
      "Female: 22.7%\n",
      "Gender Unknown: 3.6%\n"
     ]
    }
   ],
   "source": [
    "# Convert into percentage\n",
    "total_rider = len(data)\n",
    "unknowns, num_males, num_females = data.groupby('gender').size()\n",
    "print('Male: %.1f%%' % (100 * num_males / total_rider))\n",
    "print('Female: %.1f%%' % (100 * num_females / total_rider))\n",
    "print('Gender Unknown: %.1f%%' % (100 * unknowns / total_rider))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create separate datasets for males and females\n",
    "males = data[data.gender == 1]\n",
    "females = data[data.gender == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get trip durations\n",
    "m_dur = males[males.tripduration <= 120]['tripduration']\n",
    "f_dur = females[females.tripduration <= 120]['tripduration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Li+OBBx7A0dHxhuoWERERERGR4rNpMnrgwAFWrFhBeHg43t7epKSkGI/WrVtT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\nMCoiIiIiIiIJ9w+vfyUhpA0b3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x329b39860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot trip duration distribution for males and females\n",
    "fig, (ax5, ax6) = plt.subplots(1, 2, figsize=(15, 7), sharey=False, sharex=True)\n",
    "ax5.hist(m_dur, bins=120, color='b', alpha=0.4)\n",
    "ax5.set_title('Distribution of Trip Duration: Males', fontsize=18)\n",
    "ax5.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax5.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax5.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax5.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "    \n",
    "ax6.hist(f_dur, bins=120, color='r', alpha=0.3)\n",
    "ax6.set_title('Distribution of Trip Duration: Females', fontsize=18)\n",
    "ax6.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax6.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax6.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax6.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 60)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PTWFhETIyyna/YW1StWrlT74d2AZsA4BtALANAMDK6u23Rv/YfHJd9Hp66t1e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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e842a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This time, plot them on the same graph after normalization\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.hist(m_dur, bins=120, normed=True, label='Male', color='b', alpha=0.4)\n",
    "plt.hist(f_dur, bins=120, normed=True, label='Female', color='r', alpha=0.3)\n",
    "plt.title('Distribution of Trip Duration: Males vs Females (Normalized)', fontsize=18)\n",
    "plt.xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "plt.ylabel('Fraction of total rides', fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "plt.xlim([0, 60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5, 40)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KB+FsbGyQkJCArVu3onXr1oL1DRo0gL+/P9atW4cvv/wSvr6+yMrKwrp162BpaSl3JUxZ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\nbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12522ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the above with boxplot\n",
    "plt.figure(figsize=(6, 7))\n",
    "\n",
    "m_f = [m_dur, f_dur]\n",
    "plt.boxplot(m_f)\n",
    "plt.title('Distribution of Trip Duration: Males & Females', fontsize=18)\n",
    "#plt.xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "plt.ylabel('Number of rides', fontsize=16)\n",
    "plt.xticks([1, 2], ['Male', 'Female'], fontsize=16)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylim([-5, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26392521\n",
       "mean           11\n",
       "std             8\n",
       "min             1\n",
       "25%             5\n",
       "50%             9\n",
       "75%            14\n",
       "max           120\n",
       "Name: tripduration, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_dur.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8119883\n",
       "mean          13\n",
       "std            9\n",
       "min            1\n",
       "25%            6\n",
       "50%           10\n",
       "75%           17\n",
       "max          120\n",
       "Name: tripduration, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_dur.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Test for normality and test for differences between subs and non-subs -----\n",
      "Is male trip duration normally distributed?: No (p-value=0.000000)\n",
      "Is female trip duration normally distributed?: No (p-value=0.000000)\n",
      "----- Are male and femal trip duration pattern different? -----\n",
      "Mann-Whitney U test: Yes (p-value=0.000000)\n",
      "T test: Yes (p-value=0.000000)\n"
     ]
    }
   ],
   "source": [
    "# Normality Test & Test for significant difference\n",
    "male_normaltest_ressult = scs.normaltest(m_dur)\n",
    "female_normaltest_ressult = scs.normaltest(f_dur)\n",
    "\n",
    "gender_MWtest_result = scs.mannwhitneyu(m_dur, f_dur)\n",
    "gender_Ttest_result = scs.ttest_ind(m_dur, f_dur)\n",
    "\n",
    "print('----- Test for normality and test for differences between subs and non-subs -----')\n",
    "print('Is male trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(male_normaltest_ressult), male_normaltest_ressult.pvalue))\n",
    "print('Is female trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(female_normaltest_ressult), female_normaltest_ressult.pvalue))\n",
    "print('----- Are male and femal trip duration pattern different? -----')\n",
    "print('Mann-Whitney U test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(gender_MWtest_result), gender_MWtest_result.pvalue))\n",
    "print('T test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(gender_Ttest_result), gender_Ttest_result.pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Subscribers vs non-subscribers</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tripduration', 'birth year', 'gender', 'usertype', 'start_year',\n",
       "       'start_month', 'start_day', 'start_dayofweek', 'start_hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get subscriber and non-subscriber data\n",
    "daily_subs = data[data['usertype'] == 'Subscriber']\n",
    "daily_nonsubs = data[data['usertype'] == 'Customer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Trip durations data\n",
    "daily_subs_dur = daily_subs[daily_subs.tripduration <=120]['tripduration']\n",
    "daily_nonsubs_dur = daily_nonsubs[daily_nonsubs.tripduration <=120]['tripduration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gYOzZswdXr15FXl4eVFRUMGbMGFy5cgU7duxAfHx8iR23hYUFwsPDsWHDBgQF\nBaGwsBCdO3fG5s2bxV8PK+POnTvw9PTEl19+WWbHra6ujtDQUPFXzx07duDly5fQ0dGBjY0Npk2b\nJjkvO3fujHXr1mHz5s3w8/NDixYtMGXKFLx69Qp+fn6SfX/00UdYvXo1Vq5cid27d6Nt27b47rvv\nMHz4cDHPrFmzoK2tjYMHD2L16tVQUlJC9+7d8d1334mTR6irq2PXrl1Yt24dTpw4gRcvXsDAwADr\n1q0T/2GvrPLOcVVVVezcuRObN2/GqVOncPToUTRr1gxjxoyBh4eHOGy5NH369EFgYCC2bt0q/oPf\nqVMnrF27ttg9S/RhY5/GPq0s7NPk26eVRVdXF3PmzMHSpUtrtK6lUVRUxLp16xAZGYnIyEiEhYXh\nxYsX0NLSgqWlJZYsWQJTU1Mxv46ODrZv3441a9Zgw4YNaNy4MUaNGoUOHTqIz9N80/bt27F8+XIc\nP34cenp68Pb2xoQJE8R0JycnqKioIDQ0FGvXrkVhYSGMjY2xfft28XtFUVERAQEB2Lx5M44dO4aj\nR4+iTZs2WLRoEcaMGVOldvv4+OD06dPw9fWFuro6vvjiC/znP/8RZ/hWUFDA+vXrsWPHDhw5cgQ/\n/fQTdHR0YGdnh9mzZ5d7NdbIyAjBwcHYvHkzgoKCkJWVhXbt2uHbb78t99mv5VEQKjN1FxF98IyM\njMQb/YmIiD5k7NOIPmy8qYaIiIiIiIjkjsEoERERERERyR2DUSIiIiIiIpI73jNKREREREREcsfZ\ndGtYevqL8jOVoHHjBnj6tPh073UN21m3sJ11T31pa3W0U1e3UTXVpn6oav8I8HNZ17CddQvbWbdU\nVztL6yM5TPc9paxc+Wf6fYjYzrqF7ax76ktb60s764r68n6xnXUL21m3sJ3Vg8EoERERERERyR2D\nUSIiIiIiIpI7BqNEREREREQkdwxGiYiIiIiISO4YjBIREREREZHcMRglIiIiIiIiuWMwSkRERERE\nRHLHYJSIiIiIiIjkjsEoERERERERyR2DUSIiIiIiIpI7BqNEREREREQkdwxGiYiIiIiISO4YjBIR\nEREREZHcMRglIiIiIiIiuWMwSkRERERERHLHYJSIiIiIiIjkTrm2K0DVy6+Bqvi3Z7asFmtCRERE\nVZXwLBjJeWp4mZ2LLlputV0dIqIawSujREREREREJHe8MlqH8SopERERERG9r3hllIiIiIiIiOSO\nwSgRERH2RI+JAAAgAElEQVQRERHJHYfpEhEREb0HEp4F13YViIjkildGiYiIiIiISO4YjBIRERER\nEZHccZguERERkZwVDcmtyDNE3xy+y2eOElFdwmC0nuBjXoiIiIiI6H3CYbpEREREREQkd7wySkRE\nRFRLOIMuEdVncr8ympycjOnTp8PKygp9+/aFr68vcnNzAQCLFi2CkZGR5LVz505x2ytXrmDYsGEw\nMzODi4sL7t+/L9n3rl270LdvX1hYWMDb2xvZ2dlimkwmw7fffgsrKyv07t0b27dvl2ybkpKCiRMn\nwtzcHPb29rh48aIkvbyyiYiIiIiIqOLkGozKZDJMnz4dqqqq2Lt3L77//nucPXsW/v7+AICkpCR4\nenri8uXL4mv06NEAgNTUVMyYMQPDhw/HwYMH0bRpU7i7u6OwsBAAcPr0aaxbtw4+Pj4IDQ1FfHw8\nfH19xbL9/PwQGxuL4OBgLFmyBIGBgThx4gQAQBAEuLu7Q1tbGwcOHMDIkSMxa9YsPHjwoEJlExER\nERERUeXINRi9ceMGkpOTsWrVKhgYGKBnz56YPXs2jh07BgC4e/cujI2NoaurK740NDQAABEREejc\nuTOmTJmCjh07YuXKlUhNTcWVK1cAACEhIRg3bhwGDBgAExMTLF68GIcPH8bLly+RnZ2NiIgIeHt7\nw9jYGAMHDsTkyZMRFhYG4PVVz3v37mHp0qXo2LEjpk6dCgsLCxw4cKBCZRMREREREVHlyDUY7dCh\nA7Zt2wZNTU1xnYKCAp4/f4709HRkZmaiffv2JW4bFxcHKysrcVlDQwPdunVDTEwMCgoKEB8fL0k3\nNzdHQUEBEhISkJiYCJlMBktLSzHd0tIS8fHxKCgoQFxcHLp27YqGDRtK0mNjY8stm4iIiIiIiCpP\nrsGojo4ObGxsxOXCwkKEhYXBxsYGt2/fhrKyMtavX4+PP/4Yw4cPx6FDh8S86enpaNasmWR/TZo0\nwcOHD/H8+XPk5uZK0pWVlaGtrY20tDSkp6dDS0sLampqYnrTpk2Rl5eHx48fl7rvtLS0cssmIiIi\nIiKiyqvV2XRXrVqFhIQEHDhwAFevXgUAdO7cGS4uLrh69SoWLVoEDQ0N2NvbIycnB6qqqpLtVVVV\nIZPJ8OrVK3G5pPT8/PwS04DX97Hm5ORARUWlWHpeXh4AlFl2eRo3bgBlZaVy85VEV7dRpbfRLD8L\ndDXVys8kR1Vp54eI7axb6ks7gfrT1vrSTiIiovdFrQSjgiBgxYoVCA8Px/r169GpUyd07NgRQ4YM\ngba2NoDXQen9+/cRHh4Oe3t7qKmpFQv+ZDIZtLW1xSueJaWrq6tDQUGhxDTg9ZBbNTU1ZGVllbgt\ngDLLLs/Tp9nl5imJrm4jpKe/qPR2LxuolpsnPbv8IFpeqtrODw3bWbfUl3YC9aet1dFOBrMkD6U9\nCqaLlpuca0JE9O7k/miXwsJCzJ8/H3v37oW/vz8GDhwI4PW9o28Hdx06dBCHwurp6SE9PV2SnpGR\nAV1dXTEgzcjIENPy8/ORmZmJZs2aQU9PD8+fP5cElOnp6VBVVYWWllaZ+y6vbCIiIiIiIqo8uQej\nvr6+OHbsGDZu3Ag7OzvJ+mnTpknyJiQkoEOHDgAAMzMzXL9+XUzLycnBH3/8AXNzcygqKsLExATR\n0dFiemxsLJSUlNClSxd06dIFKioqkgmHoqOj0a1bNygrK8PMzAyJiYmS55JGR0fD3Ny83LKJiIiI\nSpPwLLjEFxERyTkYjY2NRUhICGbNmgVjY2Okp6eLr08++QQ///wzQkNDkZycjLCwMBw5cgSTJk0C\nADg6OiIuLg6BgYG4ffs2FixYAH19fVhbWwMAxo4di6CgIJw+fRrx8fFYsmQJHB0doampCQ0NDYwY\nMQJLlizBjRs3cO7cOQQFBcHV1RUA0LNnT+jr68PLywtJSUnYtm0b4uLi4OTkVKGyiYiIiIiIqHLk\nes/oqVOnAABr1qzBmjVrJGm3bt3CmjVrEBAQgO+++w6tW7fG2rVr0aNHDwBAq1atsHHjRqxatQpb\ntmyBmZkZAgICoKj4Op4eMmQIUlJSsHjxYshkMnz66afw8vIS9+/t7Y3Fixdj/Pjx0NTUhIeHBwYP\nHgwAUFJSQkBAABYsWAAHBwe0adMGmzZtQqtWrSpUNhEREREREVWOgiAIQm1Xoi6r6oQYVZ1Mw68C\nExh5cgIjuWM765b60k6g/rSVExjJ37sc7w/pc/kuQ3I1G6jhZXZuhfJ+yBMYfUjv57tgO+sWtrPy\n+ykJL+0RERERERGR3DEYJSIiIiIiIrmrleeMEhEREdVVnC2XiKhieGWUiIiIiIiI5I5XRomIiIje\nEa+GEhFVHq+MEhERERERkdwxGCUiIiIiIiK5YzBKREREREREcsdglIiIiIiIiOSOwSgRERERERHJ\nHYNRIiIiIiIikjs+2oWIiIioCvg4FyKid8NglIiIiOgD92Zg3EXLrRZrQkRUcRymS0RERERERHLH\nYJSIiIiIiIjkjsEoERFRHbNw4UK4uLiIy4mJiRg9ejTMzMzg4OCAGzduSPJHRUXh008/hZmZGWbM\nmIHHjx+LaYIgwN/fH9bW1rCyssLq1atRUFAgpmdmZmLWrFno3r07+vfvj8OHD9d8A4mIqE5gMEpE\nRFSH/Pe//8X+/fvF5ezsbEyePBlmZmY4dOgQLC0tMW3aNGRlZQEAbty4AS8vL8yYMQP79u1DVlYW\nPD09xe137tyJw4cPY8OGDdi0aROOHz+OH374QUz38vJCZmYmwsPD4e7ujkWLFuH69evya7CcJTwL\nFl9ERPRuOIFRHeHXQLVKeT2zZTVRHSIiqgXZ2dn49ttv0b17d3FdVFQUVFRU4OXlBUVFRcyfPx8X\nL17EyZMn4eTkhLCwMNjZ2cHBwQEA4Ofnh379+uH+/fto27YtQkJCMHPmTFhZWQEA5s6di7Vr12Lq\n1KlITk7G+fPncfr0abRt2xZGRkaIiYnBnj17JHUgIiIqCa+MEhER1RH+/v7o2bMnevbsKa6Li4tD\n9+7doaj4ustXUFBA9+7dERMTI6YXBZoA0KJFC7Rs2RIxMTF4+PAhUlNT0aNHDzHd0tISaWlpSE1N\nRVxcHHR1ddG2bVtJemxsbE03lYiI6gAGo0RERHVATEwMfvzxR8ybN0+yPj09Hc2aNZOsa9KkCR4+\nfAgAePToUanp6enpACBJb9q0KQAgLS2t1H2npaVVT6OIiKhO4zBdIiKiD5xMJsOCBQswf/58aGlp\nSdJycnKgqiq9lUNVVRUy2evbNF69elVq+qtXr8TlN9OKyixt33l5eRAEAQoKCqXWuXHjBlBWVqpk\nS/9HV7dRlbd9F8l5anItT7NB5currWPzLj7EOlcF21m3sJ3vjsEoERHRB27z5s1o27Yt7O3ti6Wp\nqamJgWcRmUwGdXX1ctPfDDxVVFTEvwFAQ0OjzG3LCkQB4OnT7Eq0UEpXtxHS019Ueft38TI7V25l\naTZQq1J5tXVsqqo23095YjvrFraz8vspCYNRIiKiD9yxY8eQnp4OCwsLAEBeXh4KCgpgYWGBoUOH\nisNti2RkZEBXVxcAoKenh4yMjBLT9fT0xGVNTU0AEPdVlF7atkREROXhPaNEREQfuF27duH48eM4\ncuQIjhw5AicnJxgbG+PIkSMwMzNDTEwMBEEA8Pq5odevX4e5uTkAwMzMDNHR0eK+UlNT8c8//8Dc\n3Bx6enrQ19eXpEdHR6NZs2Zo0aIFzM3N8fDhQ/z999+SdDMzMzm1nIiIPmS8MkpERPSBa9mypWT5\no48+grq6Otq2bYsmTZpgzZo1WLZsGcaOHYuIiAi8fPkSgwcPBgCMGTMGLi4u6N69O8zMzLBixQr0\n7dsX7dq1E9PXrl2LFi1aQElJCWvXroWrqysAoHXr1ujTpw/mzZuHb7/9Frdu3cKxY8cQGhoq1/bX\nND5TlIioZjAYJSIiqsMaNmyIrVu3wsfHB/v374eRkRG2bduGhg0bAgAsLCywbNkybNiwAZmZmbCx\nscGyZcvE7SdNmoQnT55g1qxZUFRUhIODAyZNmiSm+/n5YcGCBRg1ahSaNm2K5cuXi8OFiYiIyqIg\nFI3boRpR1Rt+K3uzsF8D1fIzlcAzW1Z+phrEm7/rFraz7qkvba2OdtaXWRWry7scb3l/LmvrymhV\nJzDqouVWA7WpOfyeqVvYzrqlpicw4j2jREREREREJHcMRomIiIjqkIRnwbzPlYg+CAxGiYiIiIiI\nSO4YjBIREREREZHcMRglIiIiIiIiuWMwSkRERERERHLHYJSIiIiIiIjkTrm2K0BERET0vuFstERE\nNY9XRomIiIiIiEjuGIwSERERERGR3DEYJSIiIiIiIrljMEpERERERERyx2CUiIiIiIiI5I7BKBER\nEREREckdg1EiIiIiIiKSOwajREREREREJHcMRomIiIiIiEjulGu7AlS7/Bqoin97ZstqsSZERERE\nRFSf8MooERERERERyR2DUSIiIiIiIpI7BqNEREREREQkdwxGiYiIiIiISO4YjBIREREREZHcMRgl\nIiIiIiIiueOjXYiIiIjqoIRnweLfXbTcarEmREQl45VRIiIiIiIikjsGo0RERERERCR3HKZLRERE\n9P+9ObSViIhqFq+MEhERERERkdwxGCUiIiIiIiK5k3swmpycjOnTp8PKygp9+/aFr68vcnNzAQAp\nKSmYOHEizM3NYW9vj4sXL0q2vXLlCoYNGwYzMzO4uLjg/v37kvRdu3ahb9++sLCwgLe3N7Kzs8U0\nmUyGb7/9FlZWVujduze2b98u2fZdyyYiIiIiIqKKk2swKpPJMH36dKiqqmLv3r34/vvvcfbsWfj7\n+0MQBLi7u0NbWxsHDhzAyJEjMWvWLDx48AAAkJqaihkzZmD48OE4ePAgmjZtCnd3dxQWFgIATp8+\njXXr1sHHxwehoaGIj4+Hr6+vWLafnx9iY2MRHByMJUuWIDAwECdOnACAdy6biIiIiIiIKkeuweiN\nGzeQnJyMVatWwcDAAD179sTs2bNx7NgxXLlyBffu3cPSpUvRsWNHTJ06FRYWFjhw4AAAICIiAp07\nd8aUKVPQsWNHrFy5Eqmpqbhy5QoAICQkBOPGjcOAAQNgYmKCxYsX4/Dhw3j58iWys7MREREBb29v\nGBsbY+DAgZg8eTLCwsIA4J3LJiIiIiIiosqRazDaoUMHbNu2DZqamuI6BQUFPH/+HHFxcejatSsa\nNmwopllaWiI2NhYAEBcXBysrKzFNQ0MD3bp1Q0xMDAoKChAfHy9JNzc3R0FBARISEpCYmAiZTAZL\nS0vJvuPj41FQUPBOZRMREREREVHlyfXRLjo6OrCxsRGXCwsLERYWBhsbG6Snp6NZs2aS/E2aNEFa\nWhoAlJr+8OFDPH/+HLm5uZJ0ZWVlaGtrIy0tDSoqKtDS0oKampqY3rRpU+Tl5eHx48fvVDYRERER\nERFVXq0+Z3TVqlVISEjAgQMHEBwcDBUVFUm6qqoq8vLyAAA5OTlQVVUtli6TyfDq1StxuaT0/Pz8\nEtOA1/ex5uTkVLns8jRu3ADKykrl5iuJrm6jCufVLD9L+eVpqpWfqQZUpp0fMrazbqkv7QTqT1vr\nSzuJiIjeF7USjAqCgBUrViA8PBzr169Hp06doKamhqysLEk+mUwGdXV1AICamlqx4E8mk0FbW1u8\n4llSurq6OhQUFEpMA14PuX2Xssvz9Gl2uXlKoqvbCOnpLyqc/2UD1fIzlSM9u/zgurpVtp0fKraz\nbqkv7QTqT1uro50MZomIiCpH7o92KSwsxPz587F37174+/tj4MCBAAA9PT2kp6dL8mZkZEBXV7fc\n9KKANCMjQ0zLz89HZmYmmjVrBj09PTx//lwSUKanp0NVVRVaWlrvVDYRERERERFVntyDUV9fXxw7\ndgwbN26EnZ2duN7MzAyJiYmSZ4NGR0fD3NxcTL9+/bqYlpOTgz/++APm5uZQVFSEiYkJoqOjxfTY\n2FgoKSmhS5cu6NKlC1RUVCQTDkVHR6Nbt25QVlZ+p7KJiIiIiIio8uQajMbGxiIkJASzZs2CsbEx\n0tPTxVfPnj2hr68PLy8vJCUlYdu2bYiLi4OTkxMAwNHREXFxcQgMDMTt27exYMEC6Ovrw9raGgAw\nduxYBAUF4fTp04iPj8eSJUvg6OgITU1NaGhoYMSIEViyZAlu3LiBc+fOISgoCK6urgDwzmUTERER\nERFR5cg1GD116hQAYM2aNejTp4/kJQgCAgIC8OTJEzg4OCAyMhKbNm1Cq1atAACtWrXCxo0bERkZ\nCUdHR2RkZCAgIACKiq+bMGTIEMyYMQOLFy+Gm5sbjI2N4eXlJZbt7e0NExMTjB8/Hj4+PvDw8MDg\nwYMBAEpKSu9UNhEREREREVWOgiAIQm1Xoi6r6oQYlZ1Mw68aJjDy5ARGNYbtrFvqSzuB+tNWTmAk\nf+9yvGvyc5nwLLhG9lsVmg3U8DI7t1r21UXLrVr2UxP4PVO3sJ11S3W1s7Q+slYf7UJERERENe/N\nIPt9DkyJqH7hOFMiIiIiIiKSOwajREREREREJHcMRomIiIiIiEjuGIwSERERERGR3HECIyIiIqrX\n3qcZdImI6hNeGSUiIiIiIiK5YzBKREREREREcsdhuiTya6Aq/u2ZLavFmhARERERUV3HK6NERERE\nREQkdwxGiYiIiIiISO4YjBIREREREZHcMRglIiIiIiIiuWMwSkRERERERHLH2XQ/YG/OfktERERE\nRPQh4ZVRIiIiIiIikjsGo0RERERERCR3DEaJiIiIiIhI7hiMEhERERERkdwxGCUiIiIiIiK5YzBK\nREREREREcsdglIiIiIiIiOSOwSgRERERERHJHYNRIiIiIiIikrsqB6OPHj3CzZs3UVBQUJ31ISIi\nqtfYvxIRUX1RoWD02bNn+PrrrxEaGgoAOHLkCPr37w8nJyfY29vjwYMHNVpJIiKiuoj9KxER1WcV\nCkZXr16NX3/9Ffr6+igsLMT3338Pc3NzhIaGonHjxli9enVN15OIiKjOqc7+9c6dO5gwYQIsLCzw\nySefYMeOHWJaSkoKJk6cCHNzc9jb2+PixYuSba9cuYJhw4bBzMwMLi4uuH//viR9165d6Nu3Lyws\nLODt7Y3s7GwxTSaT4dtvv4WVlRV69+6N7du3V/FoEBFRfVOhYPTChQvw9vbGwIEDce3aNWRkZGDy\n5MmwsrLC9OnTceXKlZquJxERUZ1TXf1rXl4epkyZghYtWuDIkSNYtGgRAgICcPToUQiCAHd3d2hr\na+PAgQMYOXIkZs2aJV51TU1NxYwZMzB8+HAcPHgQTZs2hbu7OwoLCwEAp0+fxrp16+Dj44PQ0FDE\nx8fD19dXLNvPzw+xsbEIDg7GkiVLEBgYiBMnTlT/wSIiojqnQsFodnY2WrRoAeB1x6mmpgZra2sA\ngKqqas3VjoiIqA6rrv714cOHMDU1hY+PD9q2bYtPPvkENjY2+P3333HlyhXcu3cPS5cuRceOHTF1\n6lRYWFjgwIEDAICIiAh07twZU6ZMQceOHbFy5UqkpqaKgXBISAjGjRuHAQMGwMTEBIsXL8bhw4fx\n8uVLZGdnIyIiAt7e3jA2NsbAgQMxefJkhIWFVfORIiKiuqhCwaiBgQHOnj2LjIwMREVFwdraGmpq\naigoKMCePXvQqVOnmq4nERFRnVNd/WurVq2wbt06qKurQxAEREdH4/fff4e1tTXi4uLQtWtXNGzY\nUMxvaWmJ2NhYAEBcXBysrKzENA0NDXTr1g0xMTEoKChAfHy8JN3c3BwFBQVISEhAYmIiZDIZLC0t\nJfuOj4/nBExERFQu5YpkmjVrFmbOnInQ0FCoqKhg2rRpAAA7Ozs8fvwYgYGBNVpJIiKiuqgm+te+\nffvi0aNH+OSTTzBo0CCsXLkSzZo1k+Rp0qQJ0tLSAADp6eklpj98+BDPnz9Hbm6uJF1ZWRna2tpI\nS0uDiooKtLS0oKamJqY3bdoUeXl5ePz4cbH9vqlx4wZQVlaqdPuK6Oo2qvK2b0vOUys/Uy3RbFD9\ndavOY1dd3sc61QS2s25hO99dhYJRW1tbHD9+HDdv3oSJiQlat24NAJg+fTqsrKzQrl27GqsgERFR\nXVUT/WtAQAAePXqExYsXY9WqVcjJyYGKiookj6qqKvLy8gAAOTk5xYYEq6qqQiaT4dWrV+JySen5\n+fklpgGvJzYqy9On2WWml0VXtxHS019Uefu3vczOrbZ9VSfNBmo1UrfqPHbVobrfz/cV21m3sJ2V\n309JKhSMAkCbNm3Qpk0bpKSkIDY2FoaGhhg8eDA0NTXfuXJERET1VXX3ryYmJgCAV69eYd68eXB0\ndERWVpYkj0wmg7q6OgBATU2tWOAok8mgra0tXvEsKV1dXR0KCgolpgGvh/sSERGVpUL3jALAqVOn\nYGdnhwEDBmDs2LG4d+8e5s6di7lz54q/rhIREVHlVEf/+vDhQ5w7d06yzsDAAHl5edDV1UV6erok\nLSMjA7q6ugAAPT29UtOLAtKMjAwxLT8/H5mZmWjWrBn09PTw/PlzSUCanp4OVVVVaGlpVeo4EBFR\n/VOhYDQqKgpfffUVrKyssG7dOnG6908//RRnz57F5s2ba7SSREREdVF19a937tzBzJkz8fjxY3Hd\nrVu3oKOjA0tLSyQmJkqeDRodHQ1zc3MAgJmZGa5fvy6m5eTk4I8//oC5uTkUFRVhYmKC6OhoMT02\nNhZKSkro0qULunTpAhUVFcTExEj23a1bNygrV3jwFclZwrNg8UVEVJsqFIwGBATA1dUVK1aswKef\nfiqud3BwwFdffYVjx47VWAWJiIjqqurqX62srGBgYAAvLy/cuXMH58+fx5o1azB9+nT07NkT+vr6\n8PLyQlJSErZt24a4uDg4OTkBABwdHREXF4fAwEDcvn0bCxYsgL6+vviImbFjxyIoKAinT59GfHw8\nlixZAkdHR2hqakJDQwMjRozAkiVLcOPGDZw7dw5BQUFwdXWt/oNFRER1ToWC0fv378PW1rbEtC5d\nuhQb3kNERETlq67+VUVFBdu2bYOSkhKcnJywaNEijB8/Hq6urlBSUkJAQACePHkCBwcHREZGYtOm\nTWjVqhWA14+F2bhxIyIjI+Ho6IiMjAwEBARAUfH1vwhDhgzBjBkzsHjxYri5ucHY2BheXl5i2d7e\n3jAxMcH48ePh4+MDDw8PDB48+B2PDBER1QcVGkOjr6+P6Oho2NjYFEu7ceOG+MBuIiIiqrjq7F9b\ntGiBLVu2lJjWtm1bhIWFlbqtra1tqUExAEydOhVTp04tMU1DQwOrV6/G6tWrK1zX9wGHqBIR1b4K\nBaPOzs7w8/ODIAiwtbWFgoICHj58iD/++ANbtmyBu7t7TdeTiIiozmH/SkRE9VmFglFXV1c8f/4c\n27dvR2BgIARBgLu7O5SVleHi4oJJkybVdD2JiIjqHPavRERUn1V4qrsvv/wS48ePR0xMDJ49e4ZG\njRrB1NQUOjo6NVk/IiKiOo39KxER1VeVmne9UaNG6Nu3b03VhYiIqF5i/0pERPVRqcHosGHDKrUj\nPt6lbvFroCr+7ZktKyMnERFVBvtXIiKi10oNRrt16wYFBQUAQEFBAaKioqClpYW+fftCV1cXmZmZ\n+OWXX5CRkYHRo0fLrcJEREQfMvavREREr5UajPr6+kr+7tGjB7Zt2wZV1f9dMSsoKMDMmTPx/Pnz\nmq0lERFRHcH+lYiI6DXFimQ6cOAA3NzcJB0lACgpKWHMmDE4depUjVSOiIioLmP/SkRE9VmFglF1\ndXUkJyeXmJaQkAAtLa1qrRQREVF9wP6ViIjqswrNpjtixAisXbsWMpkMH3/8MRo3bozHjx/jzJkz\n2LZtG2bOnFnT9SQiIqpz2L8SEVF9VqFgdM6cOcjOzsbatWvx/fffi+tVVFQwefJkTJ06tcYqSERE\nVFexfyUiovqsQsGokpISFi1ahNmzZyM2NhYvXryAtrY2zM3N0bBhw5quIxERUZ3E/pWIiOqzUoPR\nzMxMfPTRR1BUVERmZqa43szMTPw7Pz9fTNPW1q7BahIREdUN7F+JiIheKzUYtba2xr59+2Bqaop/\n/etf4jPRSpOQkFDtlSMiIqpr2L8SERG9VmowunLlSrRu3RoAsGrVKrlViIiIqC5j/0pERPRaqcHo\nyJEjxb9v3bqFESNGwNjYWC6VIiIiqqvYvxIREb1WoeeM7t+/H8+fP6/puhAREdUr7F+JiKg+q1Aw\n2qNHD1y4cKGGq0JERFS/sH8lIqL6rEKPdmnZsiX27NmDyMhItG3bFjo6OpJ0BQUFBAYG1kgFiYiI\n6ir2r0REVJ9VKBi9d+8eLCwsxOWXL1/WWIWIiIjqC/avRERUn1UoGN21a1e1FyyTyeDg4ID58+fD\nxsYGALB161asXbtWks/V1RULFiwAACQmJsLHxweJiYkwMDDA4sWLYWpqKuaNioqCv78/Hj16BBsb\nGyxfvhxNmjQBAAiCgHXr1iEiIgL5+fn4/PPPMXfuXCgpKQF4/dy3RYsW4fLly9DW1sbMmTMlk0yU\nVzYREVFl1UT/SkRE9KGo0D2j1S03Nxf/+c9/kJSUJFmflJQEFxcXXL58WXzNnj0bAJCdnY3JkyfD\nzMwMhw4dgqWlJaZNm4asrCwAwI0bN+Dl5YUZM2Zg3759yMrKgqenp7jvnTt34vDhw9iwYQM2bdqE\n48eP44cffhDTvby8kJmZifDwcLi7u2PRokW4fv16hcomIiIiIiKiypF7MHr79m2MGjUKycnJxdLu\n3LmDrl27QldXV3w1bNgQwOurnioqKvDy8oKBgQHmz5+PRo0a4eTJkwCAsLAw2NnZwcHBAZ07d4af\nnx8uX76M+/fvAwBCQkIwc+ZMWFlZoVevXpg7dy52794NAEhOTsb58+exbNkyGBkZ4fPPP8fw4cOx\nZ7aQck8AACAASURBVM+eCpVNRERERERElSP3YPTq1avo1asX9u3bJ1lfWFiIe/fuoX379iVuFxcX\nh+7du0NR8XWVFRQU0L17d8TExIjpVlZWYv4WLVqgZcuWiImJwcOHD5GamooePXqI6ZaWlkhLS0Nq\nairi4uKgq6uLtm3bStJjY2MrVDYRERERERFVTqn3jKampqJFixbVXuDYsWNLXJ+SkoKcnBxERETg\nP//5D9TV1eHo6IiJEydCUVER6enpxQLVJk2aIDExEQDw6NEjNGvWrFj6w4cPkZ6eDgCS9KZNmwIA\n0tLSkJ6eXuK2aWlpAFBu2WVp3LgBlJWVys1XEl3dRmWma1Zpr1Woh6Zaze6/nHbWFWxn3VJf2gnU\nn7bKq5011b8SVUXCs2Dx7y5abrVYEyKqj0oNRkeMGIGAgABYWlrC29sb7u7uaN26dY1V5M6dOwAA\nPT09bNmyBX/88QdWrFgB4P+1d+dxUZb7/8ffozAgYG4sSanZRibElnIyl8xssexnmFaW2nFNKzsn\n/SJqpoZbuJaJaSmadiwz9RxbzBbrZB2zUNBKyjqVS4JgorLIAN6/P/gyXycBh21gZl7Px4OHzH3d\ny+eaGefDZ+7rvm5pxIgRKigokNlsttnGbDbLYrFIks6ePVth+9mzZ62Pz2+TSidSqmjfRUVFMgzj\noseuzMmT+RddpzwBAU2VlXWm0nXyfMyVtteWrPyL97O67OmnK6CfrsVd+im5T19ro5/2FrOOzq8A\nADRUFRajFotFqampuvLKK7V582b17dtXTZtWnGibN29eo0BuueUW7dq1Sy1atJAkhYSE6OTJk3r9\n9dc1YsQIeXl5XVD8WSwWeXt7S1Kl7ecXnp6entbfJalJkyaVbmsymS56bAAA7OXo/AoAQENVYTHa\nq1cvzZs3T/Pnz5fJZNLw4cMr3dGBAwdqHExZIVrmqquu0vHjxyWVnjEtG25bJjs7WwEBAdb27Ozs\nctuDgoKsj319Swe3lu2rrL2ibe05NgAA9qqP/AoAQENUYTE6d+5c3X333crJydGkSZM0ZswYtW3b\nts4CWbNmjTZu3KitW7dal33//ffWazXDw8O1bNkyGYYhk8kkwzC0Z88ejRw50tqekpKiAQMGSCq9\nJuf3339XRESEgoKCFBwcrJSUFOskRSkpKQoMDFTr1q0VERGhzMxMHTlyRJdffrm1PTw83K5jAwBg\nL0fnVwAAGqoKi1EPDw/17NlTUukMuLGxsXV6TUu3bt20YMECLViwQPfff7/27dunV155RTNmzJAk\n3XnnnVqwYIESEhI0aNAgbdiwQXl5eerTp48k6aGHHtLgwYMVFRWl8PBwzZo1S927d9cVV1xhbV+4\ncKFat26txo0ba+HChRoyZIgkqU2bNuratasmTpyoqVOn6rvvvtPWrVv12muv2XVsAADs5ej8CgBA\nQ1VhMXq+OXPmSJI+/fRT7d69W7m5uWrRooWioqLUvXt3mUymGgdy5ZVXavny5Zo/f75ee+01+fv7\na8KECerbt68kyc/PT8uXL9e0adP01ltvKSQkRCtWrLDehzQyMlIJCQl68cUXlZOToy5duighIcG6\n/+HDh+uPP/7QuHHj1KhRI8XGxtoMjUpMTNSUKVM0cOBA+fv7a+bMmYqMjLTr2AAAVIcj8isAAA2V\nyTAM42Ir5efna/To0fr666/VrFkztWzZUtnZ2Tpz5oyioqL06quvysfHxxHxOp3qzs5oz8yOiQ6a\nTbdMXB3MqstMna6Ffroed+mrI2fTPZ8759eaPN+18Xqdf0uThsrXx0t5+YUOO1593dqFzxnXQj9d\nS231s6Ic2ciejRcsWKAff/xRr776qr766iu9//77+vrrr/XKK6/ov//9rxYtWlTjAAEAcDfkVwCA\nO7OrGN22bZuefvppde3a1WZ5t27d9Pe//13btm2rk+AAAHBl5FcAgDuzqxg9e/asgoODy20LDg7W\nqVOnajUoAADcAfkVAODO7CpGO3TooC1btpTbtmnTJl1zzTW1GhQAAO6A/AoAcGd2zab71FNPaejQ\nofr99991xx13yN/fX9nZ2dq2bZv27dunpUuX1nWcAAC4HPKrYznDpEUA4E7sKkY7deqkZcuW6YUX\nXtDzzz8vwzBkMpnUoUMHLV261Hq/NAAAYD/yKwDAndlVjEpSjx491KNHD+Xn5+vMmTNq2rSpy043\nDwCAo5BfAQDuyu5itIyPjw9JEgCAWkZ+BQC4G7smMAIAAAAAoDZRjAIAAAAAHI5iFAAAAADgcHYV\no7Gxsfrss8/qOhYAANwK+RUA4M7sKkZ/++03eXl51XUsAAC4FfIrAMCd2VWM3n///Xr55ZeVnp6u\ns2fP1nVMAAC4BfIrAMCd2XVrl2+++UY//PCD7rvvPklSkyZNbNpNJpNSUlJqPzoAAFwY+RUA4M7s\nKkZ79uypnj171nUsAAC4FfIrAMCd2VWMPvHEE3UdBwAAbof8CgBwZ3YVo5J05swZrV27Vv/5z3+U\nnZ2tF198UTt27FCHDh3UrVu3uowRAACXRX4FALgruyYwOnLkiPr27avVq1fLz89Pv/76qywWi374\n4Qc99thjTEsPAEA1kF8BAO7MrjOjs2bNUkBAgFavXi0vLy+FhoZKkhYsWKDi4mIlJSWpR48edRoo\nAACuhvwKAHBndp0Z3bVrl0aPHi1fX1+ZTCabtgcffFA//vhjnQQHAIArI78CANyZXWdGzWazCgsL\ny23LycmR2Wyu1aDQMCX6/N/rHJdvqcdIAMA1kF8BAO7MrjOjPXr00OLFi/Xrr79al5lMJuXk5GjF\nihXq2rVrXcUHAIDLIr8CANyZXcVofHy8zGaz7rnnHt17772SpClTpqh37946c+aM4uLi6jRIAABc\nEfkVAODO7Bqm27JlS23atEmbN2/W7t27FRQUJD8/P/Xr10/9+/eXn59fXccJAIDLIb8CANyZ3fcZ\n9fLy0oMPPqgHH3ywLuMBAMCtkF8BAO7K7mL0wIEDSk5OVkpKinJzc9WiRQvFxMRoxIgRatOmTV3G\nCACAyyK/AgDclV3F6JdffqlRo0bJ399fXbt2VcuWLZWdna2PP/5Y7777rtauXasOHTrUdawAALgU\n8isAwJ3ZVYzOnz9fXbt21ZIlS+Tp6WldfvbsWY0ePVozZ87U66+/XmdBAgDgisivaEgOnEq2/t6h\n2V/rMRIA7sKu2XR/+uknPfzwwzaJUpK8vb01fPhwffvtt3USHAAAroz8CgBwZ3YVo1dffbW+//77\nctuOHj2qtm3b1mpQAAC4A/IrAMCdVThM97vvvrP+/sADD2jOnDmyWCzq3bu3/P39derUKX3++eda\nsWKFZsyY4ZBgAQBwduRXAABKVViM9u/fXyaTyfrYMAwtXbpUSUlJNsskady4cTpw4EAdhgkAgGsg\nvwIAUKrCYvS1115zZBwAALgF8isAAKUqLEY7d+7syDgAAHAL5FcAAErZdWuXwsJCrVmzRvv27dPp\n06cvaDeZTFqzZk2tBwcAgCsjvwIA3Jlds+k+88wzWrRokU6ePKmmTZte8OPn51fXcQIA4HJqM78e\nOnRIjz32mDp16qTu3btr7ty5KiwslFQ6M++wYcMUERGhu+66S5999pnNtrt27VLfvn0VHh6uwYMH\n67fffrNpX7t2rbp3767IyEhNmjRJ+fn51jaLxaKpU6eqU6dOuvnmm/XKK6/U4BkBALgTu86M7tix\nQ5MmTdKQIUPqOh4AANxGbeVXi8Wixx57TFdffbXeeOMNnThxQpMnT5YkTZw4UWPHjtVVV12ljRs3\n6pNPPtG4ceP0zjvvqE2bNjp27JjGjBmjsWPHqmfPnlq6dKnGjh2rrVu3qlGjRtq+fbsWL16sxMRE\nBQYGatKkSZo7d66ee+45SVJiYqJSU1OVnJysjIwMxcXFKTg4WHfffXeNnx8AgGuz68xo06ZN1a5d\nu7qOBQAAt1Jb+XXfvn06dOiQ5syZo6uuukqdO3fWU089pa1bt2rXrl365Zdf9Nxzz+nqq6/WqFGj\nFBkZqY0bN0qSNmzYoOuuu04jR47U1VdfrdmzZ+vYsWPatWuXJGnNmjV65JFH1KtXL4WFhWn69Ona\nvHmz8vLylJ+frw0bNmjSpEkKDQ3VbbfdphEjRmjdunU17hMAwPXZVYyOHj1aSUlJysjIqOt4AABw\nG7WVX6+88kqtWLFCvr6+1mUmk0mnT59WWlqarr/+epshv9HR0UpNTZUkpaWlqVOnTta2Jk2aqGPH\njtq7d69KSkq0f/9+m/aIiAiVlJTowIEDSk9Pl8ViUXR0tM2+9+/fr5KSkhr1CQDg+uwapturVy8l\nJyerZ8+eatmypby9vW3aTSaTPvroozoJEAAAV1Vb+bVly5bq0qWL9fG5c+e0bt06denSRVlZWQoM\nDLRZv1WrVtYCuKL2zMxMnT59WoWFhTbtHh4eat68uTIyMuTp6almzZrJy8vL2u7v76+ioiKdOHHi\ngv2er0ULH3l4NL5o3yoSENC0ytscKvK6+EoNjK9P/cRcnefXmY5XX+ina6GfNWdXMRoXF6fjx4+r\nT58+atWqVZ0FAwCAO6mr/DpnzhwdOHBAGzduVHJysjw9PW3azWazioqKJEkFBQUym80XtFssFp09\ne9b6uLz24uLictuk0utYK3PyZH6l7ZUJCGiqrKwzVd4uL7+w2sesD74+XvUWc3We3+qq7uvpbOin\na6GfVd9PeewqRvfs2aNZs2bpnnvuqXEgAACgVG3nV8MwNGvWLK1fv14vvPCCrrnmGnl5eSk3N9dm\nPYvFYj0L6+XldUHhaLFY1Lx5c+sZz/Lavb29ZTKZym2TSof7AgBQGbuuGQ0KCiKpAABQy2ozv547\nd06TJ0/WG2+8oUWLFum2226zHiMrK8tm3ezsbAUEBFy0vawgzc7OtrYVFxcrJydHgYGBCgoK0unT\np20K0qysLJnNZjVr1qxW+gUAcF12FaNjx47V4sWLtX//fhmGUdcxAQDgFmozv86dO1dbt27VkiVL\ndPvtt1uXh4eHKz093ebeoCkpKYqIiLC279mzx9pWUFCg77//XhEREWrUqJHCwsKUkpJibU9NTVXj\nxo3VoUMHdejQQZ6entq7d6/Nvjt27CgPD7sGXwEA3JhdmWLt2rU6evSoBg4cKJPJVO4EC+cnKri+\nRJ//u0YoLr/y64IAAOWrrfyampqqNWvWaPz48QoNDbU509m5c2cFBwcrPj5eTz75pHbs2KG0tDTN\nmjVLktS/f3+tXLlSy5YtU+/evZWUlKTg4GDddNNNkqRBgwbpmWeeUUhIiFq3bq0ZM2aof//+1pl7\n+/XrpxkzZmju3LnKysrSqlWrlJCQUFtPEQDAhdlVjPbs2VM9e/as61gAAHArtZVfP/jgA0nSggUL\ntGDBApu27777TklJSZoyZYpiY2PVtm1bvfTSS7r88sslSZdffrmWLFmiOXPm6OWXX1Z4eLiSkpLU\nqFHp4Km7775bR48e1fTp02WxWNS7d2/Fx8db9z9p0iRNnz5dQ4cOla+vrx5//HH16dOnxn2qLQdO\nJdd3CACACpgMxt3WqerOPmXPzFXnn52sTzU5M8pMZK6Ffroed+lrbfTTXab4ry01eb6r8no5czFa\nn7Ppdmj2V4cdi88Z10I/XUuDmE13y5YtF12nX79+VYsIAAA3R34FALgzu4rR84fjnM9kMslsNsvH\nx4dkCQBAFZFfAQDuzK5i9Ouvv75gWX5+vr7++mstXLhQ8+bNq/XAAABwdeRXAIA7s6sYbdr0wjG+\nTZs21T333KOCggLNmjVLmzZtqvXgcKGGcp0oAKDmyK8AAHdW45uAXXbZZfrpp59qIxYAAPC/yK+o\nT+dP/OTIyYwAuBe7itGcnJwLlp07d07Hjx/XsmXL1LZt21oPDAAAV0d+BQC4M7uK0b/85S8ymUzl\ntpnNZr3wwgu1GhQAAO6A/AoAcGd2FaOzZ8++IFmaTCb5+fkpJiam3GteAABA5civAAB3ZlcxGhsb\nW9dxAADgdsivAAB3VmExWt5085Xp1KlTlda3WCyKjY3V5MmT1aVLF0ml1848++yz2rlzp5o3b64n\nn3xS9913n3Wb9PR0TZs2Tenp6brqqqs0ffp03XDDDdb29957T4sWLdLx48fVpUsXzZw5U61atZIk\nGYahxYsXa8OGDSouLtb999+vCRMmqHHjxrVybAAA7FHX+RUAAGdRYTE6ePBgmUwmGYZR4cbnDy06\ncOCA3QctLCzU+PHjdfDgQZvl8fHxys/P1/r167V//349++yzateunaKiopSfn68RI0aoT58+mj17\ntt544w2NHj1aH374ofz8/LRv3z7Fx8dr+vTpuv766zVr1izFxcVp5cqVkqTVq1dr8+bNevHFF3Xu\n3DlNmDBBLVq00KhRo2p8bAAA7FWX+RUAAGdSYTG6ZcuWSjfcs2ePFi5cqNzcXPXv39/uA/70008a\nP378BUn40KFD2rFjh7Zv36527dopJCREe/fu1T/+8Q9FRUXpvffek6enp+Lj49WoUSNNnjxZn332\nmd5//30NGDBA69at0+23324d8pSYmKhbbrlFv/32m9q1a6c1a9boySeftH7DPGHCBC1cuFCjRo2q\n8bEBALBXXeVXAACcTYXF6HXXXVfu8ry8PC1cuFDr169XmzZttHTpUsXExNh9wN27dysmJkZ///vf\nFRERYV2elpamgIAAtWvXzrosOjpaSUlJ1vaoqCg1atRIUum3xlFRUdq7d68GDBigtLQ0DRs2zLpt\n69atddlll2nv3r3y9vbWsWPHdOONN9rsOyMjQ8eOHavxsQEAsFdd5VcAAJyNXRMYldmxY4eee+45\nZWVlaeTIkXr88cdlNpurdMBBgwaVuzwrK0uBgYE2y1q1aqWMjAxre/v27S9oT09PlyQdP3683O0z\nMzOVlZUlSTbt/v7+kqSMjIwaH7syLVr4yMOj8UXXK09AwIWzKPpWa091a6mvl/X36dXYvrx+uiL6\n6VrcpZ+S+/S1PvtZG/kVAABnY1cxeuLECSUkJOiDDz5QWFiYli9frmuvvbZWAykoKLgg8ZrNZhUV\nFckwjArbLRaLJOns2bMVtp89e9b6+Pw2qXQipZoeuzInT+ZfdJ3yBAQ0VVbWmQuW5/k07D9OsvIv\n/pycr6J+uhr66VrcpZ+S+/S1NvpZnWLWEfkVAICG6qLF6MaNGzVv3jwVFRVp8uTJeuSRRyq8QXdN\neHl5XVDcWSwWeXt7y2QyVdp+se3PLzw9PT2tv0tSkyZNanxsAACqylH5FQCAhqrCYvTQoUOaOnWq\ndu/erVtuuUXTpk3TpZdeWmeBBAUFKTs722ZZdna2AgICrO1lw20raq9o+6CgIOtjX9/Sga5l+ypr\nr8mxAQCwl6PzKwAADVWFxWjfvn1lsVjUtGlTGYah6dOnV7gTk8mkZcuW1SiQiIgIZWZm6siRI7r8\n8sslSSkpKQoPD5ckhYeHa9myZTIMwzol/p49ezRy5Ehre0pKinVCoWPHjun3339XRESEgoKCFBwc\nrJSUFOskRSkpKQoMDFTr1q1rfGwAAOzl6PwKAEBDVWExesMNN1h/z8vLq/NA2rRpo65du2rixIma\nOnWqvvvuO23dulWvvfaaJOnOO+/UggULlJCQoEGDBmnDhg3Ky8tTnz59JEkPPfSQBg8erKioKIWH\nh2vWrFnq3r27rrjiCmv7woUL1bp1azVu3FgLFy7UkCFDauXYAADYy9H5FQCAhqrCYnTt2rWOjENS\n6b1Bp0yZooEDB8rf318zZ85UZGSkJMnPz0/Lly/XtGnT9NZbbykkJEQrVqyQn5+fJCkyMlIJCQl6\n8cUXlZOToy5duighIcG67+HDh+uPP/7QuHHj1KhRI8XGxmr48OG1cmwAAOxVH/kVAICGyGQYhlHf\nQbiy6s7OWNHMjokNfDbdOGbTLRf9dC3u0k/JffpaX7PpurOaPN9Veb0OnEqu9nHqm6+Pl/LyC+s7\nDHVo9tc63T+fM66FfrqW2upnRTmyUY33DAAAAABAFVGMAgAAAAAcjmIUAAAAAOBwFKMAAAAAAIej\nGAUAAAAAOBzFKAAAAADA4ShGAQAAAAAORzEKAAAAAHA4j/oOAAAAAA3XgVPJ1t87NPtrPUYCwNVw\nZhQAAAAA4HCcGUWtSvQxW3+Py7fUYyQAAAAAGjLOjAIAAAAAHI5iFAAAAADgcBSjAAAAAACHoxgF\nAAAAADgcxSgAAAAAwOEoRgEAAAAADkcxCgAAAABwOO4zCgAAXMqBU8n1HQIAwA6cGQUAAAAAOBzF\nKAAAAADA4ShGAQAAAAAOxzWjqDOJPmbr73H5lnqMBAAA1Ibzr8ft0Oyv9RgJAFfAmVEAAAAAgMNR\njAIAAAAAHI5iFAAAAADgcBSjAAAAAACHoxgFAAAAADgcxSgAAAAAwOEoRgEAAAAADkcxCgAAAABw\nOIpRAAAAAIDDUYwCAAAAAByOYhQAAAAA4HAUowAAAAAAh6MYBQAAAAA4nEd9BwD3kOhjtv4el2+p\nx0gAAAAANAScGQUAAAAAOBzFKAAAAKrswKlkHTiVXN9hAHBiFKMAAAAAAIejGAUAAAAAOBzFKAAA\nAADA4ShGAQAAAAAORzEKAAAAAHA47jMKhzv/nqPz6jEOAAAAAPWHM6MAAAAAAIejGAUAwIVYLBbd\nc889+vLLL63LcnJyNG7cOEVFRenWW2/V5s2bbbZJT0/XAw88oPDwcMXGxmrfvn027e+995569+6t\n8PBwjRkzRidOnLC2GYahRYsW6aabblKnTp30/PPPq6SkpG47CQBwCRSjAAC4iMLCQj399NM6ePCg\nzfL4+Hjl5ORo/fr1Gjt2rJ599lnt2bNHkpSfn68RI0YoPDxcmzZtUnR0tEaPHq3c3FxJ0r59+xQf\nH68xY8bozTffVG5uruLi4qz7Xr16tTZv3qwXX3xRL730kt555x2tXLnScZ1GvTtwKtn6AwBVQTEK\nAIAL+OmnnzRw4EAdOnTIZvmhQ4e0Y8cOJSQkKCQkRPfff7/uvfde/eMf/5BUetbT09NT8fHxuuqq\nqzR58mQ1bdpU77//viRp3bp1uv322xUbG6vrrrtOiYmJ2rlzp3777TdJ0po1a/Tkk0+qU6dOiomJ\n0YQJE/T66687tvMAAKdEMQoAgAvYvXu3YmJi9Oabb9osT0tLU0BAgNq1a2ddFh0drdTUVGt7VFSU\nGjUq/ZPAZDIpKipKe/futbZ36tTJum3r1q112WWXae/evcrMzNSxY8d044032uw7IyNDx44dq7O+\nloezcwDgfJhNFwAAFzBo0KByl2dlZSkwMNBmWatWrZSRkWFtb9++/QXt6enpkqTjx4+Xu31mZqay\nsrIkyabd399fkpSRkaHWrVvXoEcAAFdHMQoAgAsrKCiQ2Wy2WWY2m1VUVCTDMCpst1gskqSzZ89W\n2H727Fnr4/PbJFm3r0iLFj7y8GhcvU5JCghoavP4UJFXtffVkPn6OFe//vy61PV2zoZ+uhb6WXMU\nowAAuDAvL68LCkOLxSJvb2+ZTKZK2y+2/fmFp6enp/V3SWrSpEmlcZ08mV/tPgUENFVW1hmbZXn5\nhdXeX0Pl6+PldP368+tij/JeT1dEP10L/az6fspDMYp6NV1Snk/pHzNx+ZV/iw4AqLqgoCBlZ2fb\nLMvOzlZAQIC1vWy4bUXtFW0fFBRkfezr6ytJ1n2VbQ8AQEWYwAgAABcWERGhzMxMHTlyxLosJSVF\n4eHhkqTw8HDt3btXhmFIKr1v6J49exQREWFtT0lJsW577Ngx/f7774qIiFBQUJCCg4Nt2lNSUhQY\nGMj1ogCAi+LMKAAALqxNmzbq2rWrJk6cqKlTp+q7777T1q1b9dprr0mS7rzzTi1YsEAJCQkaNGiQ\nNmzYoLy8PPXp00eS9NBDD2nw4MGKiopSeHi4Zs2ape7du+uKK66wti9cuFCtW7dW48aNtXDhQg0Z\nMsQhfWPmXABwbhSjAAC4uMTERE2ZMkUDBw6Uv7+/Zs6cqcjISEmSn5+fli9frmnTpumtt95SSEiI\nVqxYIT8/P0lSZGSkEhIS9OKLLyonJ0ddunRRQkKCdd/Dhw/XH3/8oXHjxqlRo0aKjY3V8OHD66Wf\nAADnYjLKxuU0EO+8847Gjx9vs6xXr15KSkrS0aNHNXXqVO3Zs0etW7dWfHy8evToYV1v165dmjVr\nlg4dOqQbbrhBM2fOtLmv2tq1a/XKK6/ozJkzuvPOOzV16lT5+PhIKp1wISEhQdu2bZPZbNajjz6q\nkSNHWre92LErUt0Lfiu6WDjRx1zO2s7L19dLeXmlkzO48jWjXOTuWtyln5L79LU2+ukusyrWlpo8\n32Wvl6ufGXXGCYzO16HZX+1aj88Z10I/XUtdT2DU4K4ZPXjwoHr37q2dO3daf+bOnSvDMDR27Fg1\nb95cGzdu1H333adx48bp8OHDkkqvYRkzZozuvfdevf322/L399fYsWN17tw5SdL27du1ePFiTZs2\nTa+99pr279+vuXPnWo+bmJio1NRUJScna8aMGVq2bJneffddSbrosQEAAAAAVdPgitGff/5ZISEh\nCggIsP5ccskl2rVrl3755Rc999xzuvrqqzVq1ChFRkZq48aNkqQNGzbouuuu08iRI3X11Vdr9uzZ\nOnbsmHbt2iVJWrNmjR555BH16tVLYWFhmj59ujZv3qy8vDzl5+drw4YNmjRpkkJDQ3XbbbdpxIgR\nWrdunSRd9NgAAAAAgKppcMXoTz/9pPbt21+wPC0tTddff731GhZJio6OVmpqqrW9U6dO1rYmTZqo\nY8eO2rt3r0pKSrR//36b9oiICJWUlOjAgQNKT0+XxWJRdHS0zb7379+vkpKSix4bAAAAAFA1DaoY\ntVgsOnz4sHbs2KHevXvrtttu0/z582WxWJSVlaXAwECb9Vu1aqWMjAxJqrA9MzNTp0+fVmFhRfaq\nbQAAHtlJREFUoU27h4eHmjdvroyMDGVlZalZs2by8vKytvv7+6uoqEgnTpy46LEBAAAAAFXToGbT\n/e2331RcXCwfHx8tWbJEhw4d0qxZs5SXl6fCwkJ5enrarG82m1VUVCRJKigokNlsvqDdYrHo7Nmz\n1sfltRcXF5fbJpUWyAUFBZUeuzItWvjIw6OxHb2/UHkX+vpWa08Nm69v6ZcAS//33+n1GEtdcpfJ\nTein63GXvrpLPwEAaCgaVDF6zTXXaNeuXWrRooUk6brrrpNhGBo/frwGDBig3Nxcm/UtFou8vb0l\nSV5eXrJYLBe0N2/e3HrGs7x2b29vmUymctuk0uG+Xl5elR67MidP5l90nfJUNHNVngvPplsmywVn\n1WXGNdfiLv2U3KevzKYLAIDjNahhupKshWiZq666SkVFRQoMDFRWVpZNW3Z2tgICAiRJQUFBFbaX\nFaTZ2dnWtuLiYuXk5CgwMFBBQUE6ffq0TUGalZUls9msZs2aVbpvAAAAAEDVNahidPv27erSpYtN\nUfj999/rkksuUUREhNLT05Wf/39nGlNSUhQRESFJCg8P1549e6xtBQUF+v777xUREaFGjRopLCxM\nKSkp1vbU1FQ1btxYHTp0UIcOHeTp6am9e/fa7Ltjx47y8PBQeHh4pccGAAAAAFRNgypGO3XqJMMw\n9Oyzz+qXX37Rp59+qsTERA0fPlydO3dWcHCw4uPjdfDgQa1YsUJpaWkaMGCAJKl///5KS0vTsmXL\n9NNPP2nKlCkKDg7WTTfdJEkaNGiQVq1ape3bt2v//v2aMWOG+vfvL19fXzVp0kT9+vXTjBkztG/f\nPn388cdatWqVhgwZIkkXPTYAAABsHTiVbP0BgPI0qGK0RYsWWrlypY4eParY2FhNnTpVDz74oEaP\nHq3GjRsrKSlJf/zxh2JjY/XPf/5TL730ki6//HJJ0uWXX64lS5bon//8p/r376/s7GwlJSWpUaPS\nLt59990aM2aMpk+frr/+9a8KDQ1VfHy89diTJk1SWFiYhg4dqmnTpunxxx9Xnz59JOmixwYAAAAA\nVI3JMAyjvoNwZdWdEKOiyTQS3WACozgmMHJa9NP1uEtfmcDI8WryfJe9Xq5+xs3Xx0t5+YUXX9EJ\ndGj21wrb+JxxLfTTtdRWPyvKkQ1qNl1Asi24XbEwBQAAANDAhukCAAAAANwDZ0YBAABQp84fUl3Z\nkF0A7oUzowAAAAAAh6MYBQAAAAA4HMUoAAAAAMDhuGYUDRoz6wIAAACuiTOjAAAAAACH48yoEzj/\n7CAAAAAAuAKKUQAAADgMt3kBUIZhugAAAAAAh+PMKAAAAOrFgVPJOlTkpbz8Qs6SAm6IYhROg5l1\nAQAAANfBMF0AAAAAgMNRjAIAAAAAHI5iFAAAAADgcFwzCqfE9aMAALgWbvkCuB/OjAIAAAAAHI5i\nFAAAAADgcBSjAAAAAACHoxgFAAAAADgcExjB6TGZEQAArolJjQDXRjEKAACABuX8IhSA62KYLgAA\nAADA4ShGAQAAAAAOxzBduBSuHwUAAACcA8UoAAAAGjwmMwJcD8UoAAAAnAqFKeAauGYUAAAAAOBw\nnBmFy+L6UQAAAKDhohgFAACA02LILuC8KEYBAADgEihMAefCNaNwC4k+ZpthuwAAAADqF8UoAAAA\nAMDhGKYLAAAAl3P+kN3zMXwXaDgoRuFWmGEXAAAAaBgoRgEAAOA2mOQIaDi4ZhQAAABu6cCp5AqH\n8wKoe5wZhdtiyC4AAJA4WwrUF4pRAAAA4H9RmAKOQzEKAAAAlIPCFKhbFKOAGLILAAAAOBrFKPAn\nFKYAAODP7JnoiLOnQNUwmy4AAAAAwOE4MwoAAADUAq4xBaqGYhSoBEN2AQBAdZQVphSlQMUoRgE7\nUZgCAICqKitKDxV5qa3noHqOBmhYKEYBAAAAB6hoEiTOnsJdUYwC1cBZUgAAAKBmKEYBAACAesRt\nY+CuKEaBGuIsKQAAqGsM8YUrohgFalFZYUpRCgAAHKG8IpUCFc6CYhSoA5wtBQAADQH3PkVDRjEK\n1LFEH7N8JeX5mClMAQBAnatoSC/XpqKhoRgFAAAAIMm+grUy3E8VVUExCjgQw3cBAICrq+oZ2LL1\nOSvrfihGgXpyfmF6PopUAADg6sorWKt6Vpbi1flRjNrJYrEoISFB27Ztk9ls1qOPPqqRI0fWd1hw\nQZw9BeBsyJEA6kN1r4GtyW1yyrZlOHLtoBi1U2JiolJTU5WcnKyMjAzFxcUpODhYd999d32HBhdG\nYQrAGZAjATRUVTnbWtUzszW9vlYqf7jyn5e7MopRO+Tn52vDhg16+eWXFRoaqtDQUI0YMULr1q0j\n0cJhyhvWS4EKoL6RIwGg+moy87EjBASMq9P9U4zaIT09XRaLRdHR0dZl0dHRSkpKUklJiRo3blyP\n0cGdVXTdqT0oZAHUBnIkAKC6KEbtkJWVpWbNmsnLy8u6zN/fX0VFRTpx4oQCAwPrMTqgempSyFak\n7H6qzooCHag6ciQAoLooRu1QUFAgs9n2D+yyxxZL5X+8BgQ0rfZxy7adV+09OAlfr4uv4wroZ8NX\nhdhr8n/b2bhLX92ln7Wtujmyps93QEDTOh8+BgCo2/zYqM727EK8vLwuSKhlj5s0aVIfIQEA0CCQ\nIwEA1UUxaoegoCCdPn3aJtlmZWXJbDarWbNm9RgZAAD1ixwJAKguilE7dOjQQZ6entq7d691WUpK\nijp27CgPD0Y6AwDcFzkSAFBdFKN2aNKkifr166cZM2Zo3759+vjjj7Vq1SoNGTKkvkMDAKBekSMB\nANVlMgzDqO8gnEFBQYGmT5+u7du3y9fXV8OGDdOwYcPqOywAAOodORIAUB0UowAAAAAAh2OYLgAA\nAADA4ShGGxiLxaKpU6eqU6dOuvnmm/XKK6/Ud0i14tChQ3rsscfUqVMnde/eXXPnzlVhYaEk6ejR\noxo2bJgiIiJ011136bPPPqvnaGvHM888o8GDB1sfp6en64EHHlB4eLhiY2O1b9++eoyuZoqKijRn\nzhzFxMQoJiZG06ZNs86k6Uqv56lTpzRhwgR17txZ3bp10/z581VSUiJJysnJ0bhx4xQVFaVbb71V\nmzdvrudoq85iseiee+7Rl19+aV12sX456/u4vL5+9913Gjx4sCIjI3Xrrbdq+fLlOnfunLXdWfvq\nqsiPzv15ej5Xzo8SOVIiRzrTe7m+8yPFaAOTmJio1NRUJScna8aMGVq2bJnefffd+g6rRiwWix57\n7DGZzWa98cYbmj9/vj766CMtWrRIhmFo7Nixat68uTZu3Kj77rtP48aN0+HDh+s77Br5z3/+o7fe\nesv6OD8/XyNGjFB4eLg2bdqk6OhojR49Wrm5ufUYZfUlJibqww8/VFJSkpYtW6bPP/9cS5cudbnX\nc8aMGcrMzNS6des0b948bdmyRcnJyZKk+Ph45eTkaP369Ro7dqyeffZZ7dmzp54jtl9hYaGefvpp\nHTx40GZ5Zf1y1vdxeX3NycnRyJEjde2112rTpk2aOnWqVq1apddff12S8/bVlZEfnfvztIyr50eJ\nHCmRI53lvdwg8qOBBiMvL88ICwszvvjiC+uypUuXGg8++GA9RlVzX3/9tdGxY0cjNzfXuuxf//qX\n0aVLF+PLL780wsLCjDNnzljbhg4daixcuLA+Qq0VeXl5Rq9evYwHH3zQeOSRRwzDMIy33nrLuOWW\nW4ySkhLDMAzj3LlzRu/evY0NGzbUZ6jVcurUKaNjx47Gzp07rcvefvttY/jw4S73ekZFRRkffvih\n9fGcOXOM4cOHG7/99ptx7bXXGr/++qu1bfLkycb48ePrI8wqO3jwoHHvvfcaffv2Na699lrrZ87F\n+uWM7+OK+rplyxajW7du1r4YhmEsW7bMGDBggGEYztlXV0Z+LOXMn6eG4fr50TDIkeRI53kvN5T8\nyJnRBiQ9PV0Wi0XR0dHWZdHR0dq/f7912IMzuvLKK7VixQr5+vpal5lMJp0+fVppaWm6/vrr5efn\nZ22Ljo5WampqfYRaKxYtWqTOnTurc+fO1mVpaWmKiopSo0al/+VMJpOioqJs7svnLFJSUtSkSRN1\n6dLFuiw2Nlavvvqqy72ezZs317/+9S8VFBQoMzNTn3/+uTp27Ki0tDQFBASoXbt21nWdqZ+7d+9W\nTEyM3nzzTZvlF+uXM76PK+pr586dtXDhQmtfpP/7XJKcs6+ujPxYypk+Z8rj6vlRIkeSI53nvdxQ\n8iPFaAOSlZWlZs2aycvLy7rM399fRUVFOnHiRD1GVjMtW7a0+VA+d+6c1q1bpy5duigrK0uBgYE2\n67dq1UoZGRmODrNW7N27V9u2bdPEiRNtllfUz8zMTEeGVysOHTqk4OBgvfPOO7r77rvVs2dPPf/8\n87JYLC73ek6bNk27d+9WVFSUunfvLn9/fz355JNO389BgwZp8uTJatKkic3yi/XLGd/HFfW1devW\nuvHGG62Pz549qw0bNlg/q5yxr66M/FjKmT5n/swd8qNEjiRHOs97uaHkR49qbYU6UVBQILPZbLOs\n7HHZhe+uYM6cOTpw4IA2btyo5ORkeXp62rSbzWYVFRXVU3TVZ7FYNGXKFE2ePFnNmjWzaavotXXG\n1zUvL09HjhzRunXrNGPGDOXl5WnGjBkqLi5WQUGBy7yeUukfFddff70ef/xx5ebmKiEhQc8//7ya\nN29e7utZVFQkwzBkMpnqKeKaqeh9WtYvV3ofn6+kpET/8z//o4KCAo0ZM0aSa/2fdQXkx1LO+nnq\nLvlRIkeSI13nvSw5Jj9SjDYgXl5eF7yQZY///K2FMzIMQ7NmzdL69ev1wgsv6JprrpGXl9cFFzxb\nLBZ5e3vXU5TVt3TpUrVr10533XXXBW0VvbbO2E8PDw/l5uZq3rx5atu2rSQpLi5OcXFxuu+++1zm\n9Tx06JBmz56tTz75RJdeeqmk0tdx2LBh+tvf/lbh6+msSVaq/H1qMplc6n1cxmKxaMKECdq5c6dW\nr16tgIAASa71f9YVkB9LOet70F3yo0SOJEe6znvZUfmRYrQBCQoK0unTp2WxWKzfOGRlZclsNl/w\nTaKzOXfunKZMmaKtW7dq0aJFuu222ySV9jk9Pd1m3ezsbOsb3pls3bpVWVlZioyMlFQ6tXtJSYki\nIyN1zz33KCsry2Z9Z+1nYGCgPDw8rElWktq3b6/CwkIFBAToxx9/tFnfWfv57bffqmnTptYkK0mh\noaEqKSmRxWJRdna2zfrO2s/zBQUFVdqvoKAgl3kfS6VDjx5//HGlpqbq1VdfVXh4uLXN1frq7MiP\npZz1Pegu+VEiR5IjXeO97Mj8yDWjDUiHDh3k6elpcwFwSkqKOnbsKA8P5/7eYO7cudq6dauWLFmi\n22+/3bo8PDxc6enpys/Pty5LSUlRREREfYRZI2vXrtU777yjLVu2aMuWLRowYIBCQ0O1ZcsWhYeH\na+/evTIMQ1Lpt+B79uxxyn5GRESouLhYP/zwg3XZzz//LF9fX0VERLjM6xkYGKjTp0/r+PHj1mU/\n//yzJKl79+7KzMzUkSNHrG0pKSk2H9bOKCIiotJ+udL7WJImTJigffv2KTk52WZiHMn1+ursyI+l\nnPXz1F3yo0SOlMiRrvBedmR+pBhtQJo0aaJ+/fppxowZ2rdvnz7++GOtWrVKQ4YMqe/QaiQ1NVVr\n1qzRuHHjFBoaqqysLOtP586dFRwcrPj4eB08eFArVqxQWlqaBgwYUN9hV9lll12mdu3aWX8uueQS\neXt7q127drrzzjuVn5+vhIQE/fTTT5ozZ47y8vLUp0+f+g67yq644gr16tVLkyZN0rfffqtvvvlG\n8+fP18CBA3XTTTe5zOsZERGha6+9VnFxcUpPT1dqaqqmTp2q//f//p9uuOEGde3aVRMnTlR6erre\nfvttbd26VY888kh9h10jbdq0qbRfrvQ+fu+99/Thhx9q6tSpat26tfUz6Y8//pDkWn11BeRH5/48\ndZf8KJEjyZHO/152eH6s1g1hUGfy8/ONuLg4IyIiwrj55puNlStX1ndINTZ37lzj2muvLfenqKjI\n+PXXX42HH37YCA0NNfr06WN8/vnn9R1yrVi4cKH1PmqGYRhpaWlGv379jNDQUKN///7G/v376zG6\nmjlz5owRHx9vREVFGZ07dzZmz55tFBYWGoZhuNTrmZGRYYwbN87o3LmzcfPNNxsJCQlGQUGBYRiG\nkZ2dbYwePdoICwszevbsaWzZsqWeo62e8+8tZhgX75czv4/P7+uTTz5Z7mdSt27drOs7c19dEfnR\nuT9Pz+fK+dEwyJGGQY50tvdyfeZHk2H87zlWAAAAAAAchGG6AAAAAACHoxgFAAAAADgcxSgAAAAA\nwOEoRgEAAAAADkcxCgAAAABwOIpRAJViwu3y8bwAgHsjD5SP5wVVQTEKnCc+Pl4hISGV/ixZsqTC\n7QcPHqzRo0fXagwdOnTQjTfeqEceeUTvv/9+jfZdVR999JGmTZtmfbxkyRJFRkY67PiffvqpBg8e\nXO3tjxw5opCQEG3btq0Wo5K++eYbjRs3rlb3uWPHDg0dOrRW9wkAtYX8aIv8WD7yI6rKo74DABqS\nsWPH6sEHH7Q+njhxotq1a6exY8dal1166aUVbj9t2jQ1alTz73jatGmj+fPnS5KKi4t18uRJbd26\nVX/72990+PBhjRo1qsbHsMeaNWvk4+NjfTxgwAD16NHDIcfOzc3V9OnTK/3j5mICAwP15ptv6oor\nrqi9wCRt3LhRv/zyS63us2fPnlq1apU2bNiggQMH1uq+AaCmyI+2yI/lIz+iqihGgfO0bdtWbdu2\ntT729vZWy5YtFRERYdf2V199da3E4e3tfcExe/furbi4OC1evFi33357rScQe1x66aWV/rFRm1av\nXq327dsrLCys2vswm812v3YNwYgRIzR58mT169dPZrO5vsMBACvyY+XIj3WL/Oi6GKYLVEPZ8JY1\na9bo1ltvVXR0tL755hubYUhfffWVQkJCtHPnTt1333264YYbFBsbq//85z/VPu6TTz6pkpISbdq0\nyeYY+/fvt1nvxhtvtH5jumnTJsXExOjVV19VTEyMevToofz8fOXm5mrmzJnq2bOnQkND9Ze//EUT\nJ07U6dOnJZUOqdq9e7c+/fRThYSE6MiRIxcMQyoqKtKKFSt0xx13KCwsTH379tXWrVsveJ4++eQT\nDR8+XOHh4erWrZuWLVtWaT8LCwv1j3/8Q3369LEuK+vHv//9b/Xt21dhYWG6//779fPPP+vDDz/U\nHXfcocjISI0ePVonTpywOX7ZMKT4+HiNGzdOa9asUc+ePXXDDTdo8ODB+vnnn63HKW8o2erVqxUS\nEmLdx+bNm3Xw4EGFhIToq6++kiSdOHFCcXFx6ty5syIjI/XYY4/p8OHD1n2UlJQoMTFRt9xyi0JD\nQ9WnTx+tX7/e5jg333yziouLtWXLlkqfHwBoqMiPpciP5EfYh2IUqIGkpCRNmDBBU6dO1Q033FDu\nOuPHj1evXr20ZMkStWzZUiNHjtSPP/5YreO1adNGl112mfbu3Vul7c6cOaOtW7dq/vz5mjRpknx8\nfDR+/Hh98sknGj9+vFauXKlhw4bpnXfeUVJSkqTSIVXXX3+9oqKi9OabbyowMPCC/U6cOFFJSUka\nOHCgli1bpsjISE2YMEFvvfWWzXqTJk1SeHi4Xn75ZfXs2VOLFy/WZ599VmG8u3bt0h9//KHevXvb\nLM/Ly9P06dM1atQoLVq0SBkZGdbfn376aU2bNk1ffvmlXnjhhQr3/eWXX2rLli2aMmWK5s2bp99+\n+03x8fF2P5djx45Vjx491KZNG7355pvq2LGjzp49qyFDhiglJUXPPPOMEhMTlZ2drUceeUSnTp2S\nJC1fvlxvv/22/va3v2nlypXq1q2bpk+frs8//9y6bw8PD916661699137Y4HABoi8iP5kfwIezBM\nF6iBvn372nw7WZ4BAwboiSeekCTddNNNuu2225ScnKw5c+ZU65itWrVSdnZ2lbYpKSnRE088oW7d\nukkq/Wa1qKhI06dPV/fu3SVJMTEx2rt3r3bv3i2pdEiVn5+ffHx8yh3K88MPP+jdd9/VjBkzrNcR\nde3aVbm5uVq4cKFiY2Ot6951113WCQ1iYmL0wQcf6N///neF19fs2rVLwcHBat68uc3yoqIiTZgw\nwfqcp6WlacWKFVq3bp06deokSfriiy+UlpZW4XORl5en5cuXW/94yMzM1KxZs3Ty5Em1aNHiIs9k\n6VC1li1b6vfff7c+L2+88YZ++eUXbd26VVdddZWk0te6Z8+eWrt2rZ544gmlpKQoNDRU/fr1sz4P\n3t7eatKkic3+r7/+er3zzjuyWCwMRQLgtMiP5EeJ/IiL48woUAPt27e/6Dp333239Xez2axu3brp\nm2++qcuwynV+rF5eXlq1apW6d++uI0eOaOfOnUpOTtbPP/+soqIiu/ZX1oc777zTZnmfPn30xx9/\n2AztOT9ZN2rUSIGBgcrPz69w30ePHlXr1q3LbTv/GplWrVpJkkJDQ63LmjdvrjNnzlS47+DgYJtv\nscuu8SkoKKhwm4v56quv1K5dO7Vr107FxcUqLi6Wt7e3oqOjtWvXLkmlQ8N27typwYMHa82aNTp8\n+LD+/ve/68Ybb7wgPovFUuU/qACgISE/kh8l8iMujjOjQA2UfdhXJiAgwOZxy5YtrUNTqiMzM1NX\nXnlllbdr2bKlzeOPP/5Yc+bM0eHDh9WiRQuFhobK29tb586ds2t/p06dkoeHxwXfzvr7+0sqne2v\nbKZBb29vm3UaNWpU6X3IcnNzL9imjK+v7wXL/vztaWX+vG7Z7I729rs8OTk5+u9//6uOHTte0FY2\nkcaoUaPUpEkTbdy4UbNnz9bs2bMVHR2tuXPn2kwKUhZfZX8wAEBDR34kP0rkR1wcxShQx3JycqwJ\nSCq9kP/Pic9ev/76qzIzMzVgwABJkslkkmR7g2nDMC76Leavv/6qp556Svfdd5/WrVtn/fbzqaee\nsvnGtjLNmjVTcXGxcnJybBJu2TeWf07CVdG8eXMdPXq02tvX1J8Tb2XfUktS06ZNdd1112nmzJkX\ntJUNJWrcuLEeffRRPfroo/r999/10UcfacmSJXruuef06quvWtcv+0OsJs8fADgD8mPVkR/Jj66G\nYbpAHduxY4f1d4vFon//+9+KiYmp1r6WL18uT09P63UVfn5+kqTjx49b10lNTVVxcXGl+/n+++9V\nVFSkUaNGWRNtfn6+UlJSbBJ3ZfeEi46OlqQLbpj93nvvqVWrVjWaWv/SSy9VRkZGtbevCT8/P5vn\nU5JSUlJsHv/5eYmKitKRI0d02WWXKSwsTGFhYQoNDdXq1av16aefSpKGDRtmvQ4qODhYQ4YM0W23\n3aZjx47Z7CszM1Nms9nmDzQAcEXkx6ojP5IfXQ1nRoE6lpSUJE9PT7Vv316vvfaa8vPzNWLEiEq3\nOXv2rFJTUyWVTq5w4sQJvfvuu9q2bZsmTpyoNm3aSJJCQkIUFBSkF154QR4eHsrNzdWLL76opk2b\nVrr/Dh06qHHjxpo3b54eeughnTx5UqtWrVJ2drbNpACXXHKJDhw4oK+++krh4eE2+7juuut0xx13\naO7cucrLy1NISIg+/vhjvfvuu3r22WdrdHPzm266SStXrlRmZqaCgoKqvZ/q6N69u/Vm4p06ddIH\nH3ygb7/91madSy65RBkZGfriiy8UGhqq+++/X2vXrtWwYcM0atQoNW/eXG+++aa2b9+ue++9V1Lp\nHyfLli1TQECAwsLC9PPPP2vbtm0aOnSozb5TU1MVExOjxo0bO6zPAFAfyI9VR34kP7oazowCdSwu\nLk5vvfWWxo0bJ4vFotdff92aLCty+PBhPfDAA3rggQf08MMPa8qUKTp16pSSkpI0bNgw63qNGzfW\n4sWLZTab9cQTT+ill17S//zP/9hcY1Ge9u3b6/nnn9cPP/ygUaNGaf78+QoLC9O0adN07NgxZWZm\nSpIeffRRWSwWjRgxQt9///0F+5k/f74efvhhrV69WmPGjNGePXs0b948Pfzww9V4pv5PTEyMmjVr\nZjOtu6MMGDBAQ4cO1bp16zRmzBjl5uZq8uTJNus88MADatWqlUaPHq0vvvhCfn5+ev3113XllVdq\n+vTpGjt2rH7//XclJSVZZ0R87LHHNHr0aK1fv17Dhw/XK6+8oqFDh1pnkpRKZ0P86quvdPvttzu0\nzwBQH8iPVUd+JD+6GpNR2VXSAKrtq6++0pAhQ7Rx40abGe5gnyVLlujLL7+84MbXrmz79u167rnn\n9PHHH8vLy6u+wwGAOkF+rBnyI/nRlXBmFECDNHToUB06dKjSe6K5muTkZI0ZM4ZECwCoEPkRroRi\nFECDdMkll2jGjBmaN29efYfiEB999JE8PDw0aNCg+g4FANCAkR/hShimCwAAAABwOM6MAgAAAAAc\njmIUAAAAAOBwFKMAAAAAAIejGAUAAAAAOBzFKAAAAADA4f4/xd7ATJUVqGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1251bccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms for Tripdurations\n",
    "fig, (ax7, ax8) = plt.subplots(1, 2, figsize=(15, 7), sharey=False, sharex=True)\n",
    "ax7.hist(daily_subs_dur, bins=120, color='aqua', alpha=0.5)\n",
    "ax7.set_title('Distribution of Trip Duration: Subscribers', fontsize=18)\n",
    "ax7.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax7.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax7.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax7.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "    \n",
    "ax8.hist(daily_nonsubs_dur, bins=120, color='yellowgreen', alpha=0.5)\n",
    "ax8.set_title('Distribution of Trip Duration: Non-Subscribers', fontsize=18)\n",
    "ax8.set_xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "ax8.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax8.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax8.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12f4f4c88>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJ49GX6OhoSHnz59n7NixdO7cmbp169KgQQOuXLmSbp/kmzx8+BBvb28KFizI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\nkRYYpERERFpgkBIREWmBQUpERKSF/weL3XUnHdlq2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120a79e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This time normalized\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.hist(daily_subs_dur, bins=120, normed=True, color='aqua', alpha=0.5, label='Subscribers')\n",
    "plt.hist(daily_nonsubs_dur, bins=120, normed=True, color='yellowgreen', alpha=0.5, label='Non-subscribers')\n",
    "plt.title('Distribution of Trip Duration:\\n Subscribers vs Non-subscribers (Normalized)', fontsize=18)\n",
    "plt.xlabel('Trip Duration (minutes)', fontsize=16)\n",
    "plt.ylabel('Fraction of total rides', fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "plt.xlim([-2, 80])\n",
    "plt.ylim([0, 0.1])\n",
    "plt.legend(loc='upper left', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Test for normality and test for differences between subs and non-subs -----\n",
      "Is subscriber trip duration normally distributed?: No (p-value=0.000000)\n",
      "Is non-subscriber trip duration normally distributed?: No (p-value=0.000000)\n",
      "----- Are subscriber and non-subscriber trip duration patterns different? -----\n",
      "Mann-Whitney U test: Yes (p-value=0.000000)\n",
      "T test: Yes (p-value=0.000000)\n"
     ]
    }
   ],
   "source": [
    "# Normality Test & Test for significant difference\n",
    "subs_normaltest_ressult = scs.normaltest(daily_subs_dur)\n",
    "nonsubs_normaltest_ressult = scs.normaltest(daily_nonsubs_dur)\n",
    "\n",
    "type_MWtest_result = scs.mannwhitneyu(daily_subs_dur, daily_nonsubs_dur)\n",
    "type_Ttest_result = scs.ttest_ind(daily_subs_dur, daily_nonsubs_dur)\n",
    "\n",
    "print('----- Test for normality and test for differences between subs and non-subs -----')\n",
    "print('Is subscriber trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(subs_normaltest_ressult), subs_normaltest_ressult.pvalue))\n",
    "print('Is non-subscriber trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(nonsubs_normaltest_ressult), nonsubs_normaltest_ressult.pvalue))\n",
    "\n",
    "print('----- Are subscriber and non-subscriber trip duration patterns different? -----')\n",
    "print('Mann-Whitney U test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(type_MWtest_result), type_MWtest_result.pvalue))\n",
    "print('T test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(type_Ttest_result), type_Ttest_result.pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Usage by the day of the week\n",
    "subs_dayofweek = daily_subs.groupby(['start_year', 'start_month', 'start_day', 'start_dayofweek', 'usertype'])\\\n",
    "                                                    .size().unstack('start_dayofweek')\n",
    "nonsubs_dayofweek = daily_nonsubs.groupby(['start_year', 'start_month', 'start_day', 'start_dayofweek', 'usertype'])\\\n",
    "                                                                 .size().unstack('start_dayofweek')\n",
    "    \n",
    "subs_dayofweek.columns = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "nonsubs_dayofweek.columns = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195</td>\n",
       "      <td>194</td>\n",
       "      <td>195</td>\n",
       "      <td>194</td>\n",
       "      <td>196</td>\n",
       "      <td>194</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26501</td>\n",
       "      <td>28438</td>\n",
       "      <td>29731</td>\n",
       "      <td>28911</td>\n",
       "      <td>26914</td>\n",
       "      <td>18914</td>\n",
       "      <td>18325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11563</td>\n",
       "      <td>12942</td>\n",
       "      <td>13292</td>\n",
       "      <td>13019</td>\n",
       "      <td>12189</td>\n",
       "      <td>8554</td>\n",
       "      <td>7791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1449</td>\n",
       "      <td>1185</td>\n",
       "      <td>2423</td>\n",
       "      <td>873</td>\n",
       "      <td>1123</td>\n",
       "      <td>1096</td>\n",
       "      <td>1768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17644</td>\n",
       "      <td>18390</td>\n",
       "      <td>20918</td>\n",
       "      <td>19961</td>\n",
       "      <td>17471</td>\n",
       "      <td>12371</td>\n",
       "      <td>12855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27696</td>\n",
       "      <td>29608</td>\n",
       "      <td>31463</td>\n",
       "      <td>29525</td>\n",
       "      <td>28864</td>\n",
       "      <td>20229</td>\n",
       "      <td>19142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32309</td>\n",
       "      <td>35137</td>\n",
       "      <td>36726</td>\n",
       "      <td>36654</td>\n",
       "      <td>33759</td>\n",
       "      <td>24139</td>\n",
       "      <td>22935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>46103</td>\n",
       "      <td>51483</td>\n",
       "      <td>51879</td>\n",
       "      <td>50009</td>\n",
       "      <td>46543</td>\n",
       "      <td>32129</td>\n",
       "      <td>30969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>54426</td>\n",
       "      <td>59174</td>\n",
       "      <td>59729</td>\n",
       "      <td>61139</td>\n",
       "      <td>57699</td>\n",
       "      <td>39489</td>\n",
       "      <td>39699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>56001</td>\n",
       "      <td>61217</td>\n",
       "      <td>62131</td>\n",
       "      <td>61518</td>\n",
       "      <td>58278</td>\n",
       "      <td>44147</td>\n",
       "      <td>40586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mon    Tue    Wed    Thu    Fri    Sat    Sun\n",
       "count    195    194    195    194    196    194    194\n",
       "mean   26501  28438  29731  28911  26914  18914  18325\n",
       "std    11563  12942  13292  13019  12189   8554   7791\n",
       "min     1449   1185   2423    873   1123   1096   1768\n",
       "25%    17644  18390  20918  19961  17471  12371  12855\n",
       "50%    27696  29608  31463  29525  28864  20229  19142\n",
       "75%    32309  35137  36726  36654  33759  24139  22935\n",
       "95%    46103  51483  51879  50009  46543  32129  30969\n",
       "99%    54426  59174  59729  61139  57699  39489  39699\n",
       "max    56001  61217  62131  61518  58278  44147  40586"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs_dayofweek.describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1617</td>\n",
       "      <td>1447</td>\n",
       "      <td>1325</td>\n",
       "      <td>1359</td>\n",
       "      <td>1664</td>\n",
       "      <td>3552</td>\n",
       "      <td>3283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1792</td>\n",
       "      <td>1507</td>\n",
       "      <td>1500</td>\n",
       "      <td>1677</td>\n",
       "      <td>1855</td>\n",
       "      <td>3674</td>\n",
       "      <td>3426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>105</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>107</td>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>600</td>\n",
       "      <td>592</td>\n",
       "      <td>475</td>\n",
       "      <td>557</td>\n",
       "      <td>782</td>\n",
       "      <td>2146</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2983</td>\n",
       "      <td>2780</td>\n",
       "      <td>2593</td>\n",
       "      <td>2422</td>\n",
       "      <td>3390</td>\n",
       "      <td>6601</td>\n",
       "      <td>6354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>4509</td>\n",
       "      <td>4050</td>\n",
       "      <td>4138</td>\n",
       "      <td>3672</td>\n",
       "      <td>4892</td>\n",
       "      <td>9736</td>\n",
       "      <td>9436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>6346</td>\n",
       "      <td>5135</td>\n",
       "      <td>4951</td>\n",
       "      <td>5499</td>\n",
       "      <td>6008</td>\n",
       "      <td>13557</td>\n",
       "      <td>11361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8287</td>\n",
       "      <td>5367</td>\n",
       "      <td>4964</td>\n",
       "      <td>10773</td>\n",
       "      <td>8175</td>\n",
       "      <td>13648</td>\n",
       "      <td>12243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mon   Tue   Wed    Thu   Fri    Sat    Sun\n",
       "count    90    86    92     91    91     91     89\n",
       "mean   1617  1447  1325   1359  1664   3552   3283\n",
       "std    1792  1507  1500   1677  1855   3674   3426\n",
       "min       1     1     1      1     1      1      1\n",
       "25%     105    95    49    107    97     89    174\n",
       "50%     600   592   475    557   782   2146   1441\n",
       "75%    2983  2780  2593   2422  3390   6601   6354\n",
       "95%    4509  4050  4138   3672  4892   9736   9436\n",
       "99%    6346  5135  4951   5499  6008  13557  11361\n",
       "max    8287  5367  4964  10773  8175  13648  12243"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonsubs_dayofweek.describe(percentiles=[0.25, 0.5, 0.75, 0.95, 0.99]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YWnMr58+n3rLd1nl7Fycp6VJhl/HAUv/nHz24596jz37h0b89hUd9X7ju9f7P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12052dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the weekly usage distribution for subscribers\n",
    "ax9 = subs_dayofweek.plot(kind='box',  figsize=(12, 6))\n",
    "ax9.set_title('Usage Distribution By Day : Subscribers', fontsize=18)\n",
    "ax9.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax9.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax9.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E7Bft/T6X/P1M/W8/6nv7Uv/bl/rfftT39nW/939+HzYcJrBv2LCBokWL8tln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\n1cVe56VdVQP35l6O4jL97sGoXr26goODS2zjBNMH6+DBg1q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f42aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the weekly usage distribution for non-subscribers\n",
    "ax10 = nonsubs_dayofweek.plot(kind='box',  figsize=(12, 6))\n",
    "ax10.set_title('Usage Distribution By Day : Non-Subscribers', fontsize=18)\n",
    "ax10.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax10.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax10.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test for different between days for each user type\n",
    "subs_dayofweek_usage = subs_dayofweek.reset_index()[['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']]\n",
    "nonsubs_dayofweek_usage = nonsubs_dayofweek.reset_index()[['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare data for pair-wise statistical test\n",
    "sdayofweek_usage = []\n",
    "nsdayofweek_usage = []\n",
    "\n",
    "# Create lists containing weekly usage data by iterating over days of the week\n",
    "# Each element in a row is a vector containing usage data for each day of the week\n",
    "for i in range(7):\n",
    "    s_usage = np.array(subs_dayofweek_usage.iloc[:, i].dropna()).reshape(-1,1)\n",
    "    ns_usage = np.array(nonsubs_dayofweek_usage.iloc[:, i].dropna()).reshape(-1,1)\n",
    "    \n",
    "    sdayofweek_usage.append(s_usage)\n",
    "    nsdayofweek_usage.append(ns_usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create tables to store pair-wise statistical test results\n",
    "daysofweek = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday','Saturday', 'Sunday']\n",
    "\n",
    "subs_MWtest_table_pvalues = pd.DataFrame(columns=daysofweek)\n",
    "subs_MWtest_table_pvalues['days'] = daysofweek\n",
    "subs_MWtest_table_pvalues = subs_MWtest_table_pvalues[['days', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday','Saturday', 'Sunday']]\n",
    "subs_MWtest_table_significance =subs_MWtest_table_pvalues.copy()\n",
    "\n",
    "nonsubs_MWtest_table_pvalues = subs_MWtest_table_pvalues.copy()\n",
    "nonsubs_MWtest_table_significance = subs_MWtest_table_pvalues.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Conduct pair-wise Mann-Whitney U test to assesse ridership difference between days of the week\n",
    "for i in range(7):\n",
    "    for j in range(7):\n",
    "        subs_result = scs.mannwhitneyu(sdayofweek_usage[i], sdayofweek_usage[j])\n",
    "        subs_MWtest_table_pvalues.iloc[i, j+1] = subs_result.pvalue.round(4)\n",
    "        if subs_result.pvalue < 0.01:\n",
    "            subs_MWtest_table_significance.iloc[i, j+1] = '*'\n",
    "        else:\n",
    "            subs_MWtest_table_significance.iloc[i, j+1] = ''\n",
    "        \n",
    "        nonsubs_result = scs.mannwhitneyu(nsdayofweek_usage[i], nsdayofweek_usage[j])\n",
    "        nonsubs_MWtest_table_pvalues.iloc[i, j+1] = nonsubs_result.pvalue.round(4)\n",
    "        if nonsubs_result.pvalue < 0.01:\n",
    "            nonsubs_MWtest_table_significance.iloc[i, j+1] = '*'\n",
    "        else:\n",
    "            nonsubs_MWtest_table_significance.iloc[i, j+1] = ''\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        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>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</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>Monday</th>\n",
       "      <td>0.4998</td>\n",
       "      <td>0.0594</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0302</td>\n",
       "      <td>0.2687</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>0.0594</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.1026</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>0.2048</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>0.0302</td>\n",
       "      <td>0.3382</td>\n",
       "      <td>0.2048</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>0.2687</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>0.1737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1737</td>\n",
       "      <td>0.4998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Monday Tuesday Wednesday Thursday  Friday Saturday  Sunday\n",
       "days                                                                 \n",
       "Monday     0.4998  0.0594    0.0022   0.0302  0.2687        0       0\n",
       "Tuesday    0.0594  0.4998    0.1026   0.3382  0.1556        0       0\n",
       "Wednesday  0.0022  0.1026    0.4998   0.2048  0.0106        0       0\n",
       "Thursday   0.0302  0.3382    0.2048   0.4998   0.074        0       0\n",
       "Friday     0.2687  0.1556    0.0106    0.074  0.4998        0       0\n",
       "Saturday        0       0         0        0       0   0.4998  0.1737\n",
       "Sunday          0       0         0        0       0   0.1737  0.4998"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P-values for subscribers\n",
    "subs_MWtest_table_pvalues.set_index('days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</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>Monday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Monday Tuesday Wednesday Thursday Friday Saturday Sunday\n",
       "days                                                              \n",
       "Monday                           *                        *      *\n",
       "Tuesday                                                   *      *\n",
       "Wednesday      *                                          *      *\n",
       "Thursday                                                  *      *\n",
       "Friday                                                    *      *\n",
       "Saturday       *       *         *        *      *                \n",
       "Sunday         *       *         *        *      *                "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subscribers: Significance indicated as *\n",
    "subs_MWtest_table_significance.set_index('days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</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>Monday</th>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.3724</td>\n",
       "      <td>0.1575</td>\n",
       "      <td>0.1872</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>0.3724</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.3073</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>0.1575</td>\n",
       "      <td>0.2272</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.4716</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>0.1872</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.4716</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.1499</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.3073</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.1499</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.4994</td>\n",
       "      <td>0.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.3781</td>\n",
       "      <td>0.4994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Monday Tuesday Wednesday Thursday  Friday Saturday  Sunday\n",
       "days                                                                 \n",
       "Monday     0.4994  0.3724    0.1575   0.1872    0.37   0.0016  0.0024\n",
       "Tuesday    0.3724  0.4994    0.2272   0.2597  0.3073   0.0009  0.0017\n",
       "Wednesday  0.1575  0.2272    0.4994   0.4716   0.133   0.0001  0.0002\n",
       "Thursday   0.1872  0.2597    0.4716   0.4994  0.1499   0.0003  0.0005\n",
       "Friday       0.37  0.3073     0.133   0.1499  0.4994   0.0019  0.0026\n",
       "Saturday   0.0016  0.0009    0.0001   0.0003  0.0019   0.4994  0.3781\n",
       "Sunday     0.0024  0.0017    0.0002   0.0005  0.0026   0.3781  0.4994"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P-values for non-subscribers\n",
    "nonsubs_MWtest_table_pvalues.set_index('days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</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>Monday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td>*</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Monday Tuesday Wednesday Thursday Friday Saturday Sunday\n",
       "days                                                              \n",
       "Monday                                                    *      *\n",
       "Tuesday                                                   *      *\n",
       "Wednesday                                                 *      *\n",
       "Thursday                                                  *      *\n",
       "Friday                                                    *      *\n",
       "Saturday       *       *         *        *      *                \n",
       "Sunday         *       *         *        *      *                "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# non-subscribers: Significance indicated as *\n",
    "nonsubs_MWtest_table_significance.set_index('days')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Hourly Usage pattern: subscribers vs non-subscribers</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E9u3b4+7ujq+vLxcuXDDZ9urVq2nUqBGenp4EBASQmPj3c31SUlIYN24c3t7e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\nrGqW6KOTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1250e2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the subscriber and non-subscriber date by the hour of the day of the ride\n",
    "# Plot frequency distribution for each set\n",
    "usage_by_hour_usertype = data.groupby(['start_hour','usertype']).size().unstack('usertype')\n",
    "fig, (ax11, ax12) = plt.subplots(1, 2, figsize=(15, 7), sharey=False, sharex=True)\n",
    "ax11.bar(usage_by_hour_usertype.index, usage_by_hour_usertype['Subscriber'], color='aqua', alpha=0.4)\n",
    "ax11.set_title('Hourly Rides by Subscribers', fontsize=18)\n",
    "ax11.set_xlabel('Hour of the day', fontsize=16)\n",
    "ax11.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax11.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax11.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "    \n",
    "ax12.bar(usage_by_hour_usertype.index, usage_by_hour_usertype['Customer'], color='yellowgreen', alpha=0.5)\n",
    "ax12.set_title('Hourly Rides by Non-subscribers', fontsize=18)\n",
    "ax12.set_xlabel('Hour of the day', fontsize=16)\n",
    "ax12.set_ylabel('Number of rides', fontsize=16)\n",
    "for tick in ax12.xaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)\n",
    "for tick in ax12.yaxis.get_major_ticks():\n",
    "    tick.label.set_fontsize(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare datasets for plotting differently\n",
    "hourly_subs = data[data.usertype == 'Subscriber']['start_hour']\n",
    "hourly_nonsubs = data[data.usertype == 'Customer']['start_hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120792b00>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1aqX1GS0hhBAiIz5qj1KlUpE3b16Nt3oULFiQV69eqV/t9qaAgADc3NxSrcvB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\nJRERkQgGJRERkYj/B8lvi1m3tsDEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126959e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This time normalized frequency distribution plotted\n",
    "plt.figure(figsize=(7, 7))\n",
    "plt.hist(hourly_subs, bins=24, normed=True, color='aqua', alpha=0.5, label='Subscribers')\n",
    "plt.hist(hourly_nonsubs, bins=24, normed=True, color='yellowgreen', alpha=0.5, label='Non-subscribers')\n",
    "plt.title('Usage by hour: Subscribers vs Non-subscribers', fontsize=18)\n",
    "plt.xlabel('Hour of the day', fontsize=16)\n",
    "plt.ylabel('Fraction of total rides', fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "plt.xlim([-0.5, 23.5])\n",
    "plt.ylim([0, 0.12])\n",
    "plt.legend(loc='upper left', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Test for normality and test for differences between subs and non-subs -----\n",
      "Is subscriber trip duration normally distributed?: No (p-value=0.000000)\n",
      "Is non-subscriber trip duration normally distributed?: No (p-value=0.000000)\n",
      "----- Are subscriber and non-subscriber trip duration patterns different? -----\n",
      "Mann-Whitney U test: Yes (p-value=0.000000)\n",
      "T test: Yes (p-value=0.000000)\n"
     ]
    }
   ],
   "source": [
    "# Normality Test & Test for significant difference\n",
    "hourly_subs_normaltest_ressult = scs.normaltest(hourly_subs)\n",
    "hourly_nonsubs_normaltest_ressult = scs.normaltest(hourly_nonsubs)\n",
    "\n",
    "hourly_type_MWtest_result = scs.mannwhitneyu(hourly_subs, hourly_nonsubs)\n",
    "hourly_type_Ttest_result = scs.ttest_ind(hourly_subs, hourly_nonsubs)\n",
    "\n",
    "print('----- Test for normality and test for differences between subs and non-subs -----')\n",
    "print('Is subscriber trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(hourly_subs_normaltest_ressult), hourly_subs_normaltest_ressult.pvalue))\n",
    "print('Is non-subscriber trip duration normally distributed?: %s (p-value=%f)'\\\n",
    "      % (normal_or_not(hourly_nonsubs_normaltest_ressult), hourly_nonsubs_normaltest_ressult.pvalue))\n",
    "\n",
    "print('----- Are subscriber and non-subscriber trip duration patterns different? -----')\n",
    "print('Mann-Whitney U test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(hourly_type_MWtest_result), hourly_type_MWtest_result.pvalue))\n",
    "print('T test: %s (p-value=%f)'\\\n",
    "                  % (significantly_different_or_not(hourly_type_Ttest_result), hourly_type_Ttest_result.pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    34591106\n",
       "mean           13\n",
       "std             4\n",
       "min             0\n",
       "25%             9\n",
       "50%            15\n",
       "75%            18\n",
       "max            23\n",
       "Name: start_hour, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_subs.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1282787\n",
       "mean          14\n",
       "std            4\n",
       "min            0\n",
       "25%           12\n",
       "50%           15\n",
       "75%           18\n",
       "max           23\n",
       "Name: start_hour, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_nonsubs.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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