eda_ford_gobike_system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I - Ford GoBike System Data <a id=topic0></a>\n",
    "## by Chisom Urom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content Outline \n",
    "\n",
    "- [Preliminary Wrangling](#topic1)\n",
    "- [Assessing and Cleaning Issues](#topic2)\n",
    "- [Univariate Exploration](#topic3)\n",
    "- [Bivariate Exploration](#topic4)\n",
    "- [Multivariate Exploration](#topic5)\n",
    "- [Conclusions](#topic6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "> This document explores the Ford GoBike System Data which contains information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area.\n",
    "\n",
    "> The dataset contains 183, 412 rows for each indivisual ride, and 16 column variable characterising them.\n",
    "\n",
    "> It contains information on the trips taken like the station name and geographical positioning in latitudes and longitudes. Also, it captures genric information on the user of the bike for the respective trips like the age, gender and the type of user: Subscriber or Customer.\n",
    "\n",
    "> The following variables are contained in the dataset:\n",
    "\n",
    "- Trip Duration (seconds)\n",
    "- Start Time and Date\n",
    "- End Time and Date\n",
    "- Start Station ID\n",
    "- Start Station Name\n",
    "- Start Station Latitude\n",
    "- Start Station Longitude\n",
    "- End Station ID\n",
    "- End Station Name\n",
    "- End Station Latitude\n",
    "- End Station Longitude\n",
    "- Bike ID\n",
    "- User Type (Subscriber or Customer)\n",
    "\n",
    "\n",
    "## Preliminary Wrangling <a id=topic1></a>\n",
    "\n",
    "> I will perform some preliminary wrangling to clean and ready the data for use in the exploratory analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183412, 16)\n",
      "duration_sec                 int64\n",
      "start_time                  object\n",
      "end_time                    object\n",
      "start_station_id           float64\n",
      "start_station_name          object\n",
      "start_station_latitude     float64\n",
      "start_station_longitude    float64\n",
      "end_station_id             float64\n",
      "end_station_name            object\n",
      "end_station_latitude       float64\n",
      "end_station_longitude      float64\n",
      "bike_id                      int64\n",
      "user_type                   object\n",
      "member_birth_year          float64\n",
      "member_gender               object\n",
      "bike_share_for_all_trip     object\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>2019-03-01 08:01:55.9750</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>2019-03-01 06:42:03.0560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>2019-03-01 05:24:08.1460</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.0100</td>\n",
       "      <td>2019-03-01 04:02:36.8420</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.5490</td>\n",
       "      <td>2019-03-01 00:20:44.0740</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</td>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "3         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "4          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.393170     2535    Customer                NaN   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3         Other                      No  \n",
       "4          Male                     Yes  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv file into dataframe\n",
    "bike_df = pd.read_csv('201902-fordgobike-tripdata.csv')\n",
    "\n",
    "# get high level overiew of dataset\n",
    "print(bike_df.shape)\n",
    "print(bike_df.dtypes)\n",
    "bike_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24231</th>\n",
       "      <td>134</td>\n",
       "      <td>2019-02-25 18:40:45.6810</td>\n",
       "      <td>2019-02-25 18:43:00.0390</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>258.0</td>\n",
       "      <td>University Ave at Oxford St</td>\n",
       "      <td>37.872355</td>\n",
       "      <td>-122.266447</td>\n",
       "      <td>4789</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42662</th>\n",
       "      <td>1165</td>\n",
       "      <td>2019-02-22 17:09:55.9830</td>\n",
       "      <td>2019-02-22 17:29:21.6200</td>\n",
       "      <td>370.0</td>\n",
       "      <td>Jones St at Post St</td>\n",
       "      <td>37.787327</td>\n",
       "      <td>-122.413278</td>\n",
       "      <td>124.0</td>\n",
       "      <td>19th St at Florida St</td>\n",
       "      <td>37.760447</td>\n",
       "      <td>-122.410807</td>\n",
       "      <td>5570</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110121</th>\n",
       "      <td>999</td>\n",
       "      <td>2019-02-12 18:06:42.7040</td>\n",
       "      <td>2019-02-12 18:23:22.1380</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Washington St at Kearny St</td>\n",
       "      <td>37.795393</td>\n",
       "      <td>-122.404770</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Koshland Park</td>\n",
       "      <td>37.773414</td>\n",
       "      <td>-122.427317</td>\n",
       "      <td>4942</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157847</th>\n",
       "      <td>75</td>\n",
       "      <td>2019-02-05 20:32:18.0690</td>\n",
       "      <td>2019-02-05 20:33:33.9340</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Webster St at 19th St</td>\n",
       "      <td>37.806970</td>\n",
       "      <td>-122.266588</td>\n",
       "      <td>3049</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec                start_time                  end_time  \\\n",
       "24231            134  2019-02-25 18:40:45.6810  2019-02-25 18:43:00.0390   \n",
       "42662           1165  2019-02-22 17:09:55.9830  2019-02-22 17:29:21.6200   \n",
       "110121           999  2019-02-12 18:06:42.7040  2019-02-12 18:23:22.1380   \n",
       "157847            75  2019-02-05 20:32:18.0690  2019-02-05 20:33:33.9340   \n",
       "\n",
       "        start_station_id          start_station_name  start_station_latitude  \\\n",
       "24231              244.0  Shattuck Ave at Hearst Ave               37.873676   \n",
       "42662              370.0         Jones St at Post St               37.787327   \n",
       "110121              10.0  Washington St at Kearny St               37.795393   \n",
       "157847             182.0    19th Street BART Station               37.809013   \n",
       "\n",
       "        start_station_longitude  end_station_id             end_station_name  \\\n",
       "24231               -122.268487           258.0  University Ave at Oxford St   \n",
       "42662               -122.413278           124.0        19th St at Florida St   \n",
       "110121              -122.404770            56.0                Koshland Park   \n",
       "157847              -122.268247           337.0        Webster St at 19th St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "24231              37.872355            -122.266447     4789  Subscriber   \n",
       "42662              37.760447            -122.410807     5570  Subscriber   \n",
       "110121             37.773414            -122.427317     4942  Subscriber   \n",
       "157847             37.806970            -122.266588     3049  Subscriber   \n",
       "\n",
       "        member_birth_year member_gender bike_share_for_all_trip  \n",
       "24231              1998.0          Male                      No  \n",
       "42662              1997.0        Female                      No  \n",
       "110121             1997.0          Male                      No  \n",
       "157847             1987.0          Male                      No  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_df.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>member_birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183215.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183215.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>175147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726.078435</td>\n",
       "      <td>138.590427</td>\n",
       "      <td>37.771223</td>\n",
       "      <td>-122.352664</td>\n",
       "      <td>136.249123</td>\n",
       "      <td>37.771427</td>\n",
       "      <td>-122.352250</td>\n",
       "      <td>4472.906375</td>\n",
       "      <td>1984.806437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1794.389780</td>\n",
       "      <td>111.778864</td>\n",
       "      <td>0.099581</td>\n",
       "      <td>0.117097</td>\n",
       "      <td>111.515131</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.116673</td>\n",
       "      <td>1664.383394</td>\n",
       "      <td>10.116689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>-122.412408</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398285</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>4958.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>796.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>37.797280</td>\n",
       "      <td>-122.286533</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.288045</td>\n",
       "      <td>5502.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85444.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>6645.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec  start_station_id  start_station_latitude  \\\n",
       "count  183412.000000     183215.000000           183412.000000   \n",
       "mean      726.078435        138.590427               37.771223   \n",
       "std      1794.389780        111.778864                0.099581   \n",
       "min        61.000000          3.000000               37.317298   \n",
       "25%       325.000000         47.000000               37.770083   \n",
       "50%       514.000000        104.000000               37.780760   \n",
       "75%       796.000000        239.000000               37.797280   \n",
       "max     85444.000000        398.000000               37.880222   \n",
       "\n",
       "       start_station_longitude  end_station_id  end_station_latitude  \\\n",
       "count            183412.000000   183215.000000         183412.000000   \n",
       "mean               -122.352664      136.249123             37.771427   \n",
       "std                   0.117097      111.515131              0.099490   \n",
       "min                -122.453704        3.000000             37.317298   \n",
       "25%                -122.412408       44.000000             37.770407   \n",
       "50%                -122.398285      100.000000             37.781010   \n",
       "75%                -122.286533      235.000000             37.797320   \n",
       "max                -121.874119      398.000000             37.880222   \n",
       "\n",
       "       end_station_longitude        bike_id  member_birth_year  \n",
       "count          183412.000000  183412.000000      175147.000000  \n",
       "mean             -122.352250    4472.906375        1984.806437  \n",
       "std                 0.116673    1664.383394          10.116689  \n",
       "min              -122.453704      11.000000        1878.000000  \n",
       "25%              -122.411726    3777.000000        1980.000000  \n",
       "50%              -122.398279    4958.000000        1987.000000  \n",
       "75%              -122.288045    5502.000000        1992.000000  \n",
       "max              -121.874119    6645.000000        2001.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect for duplicate entries\n",
    "bike_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 183412 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration_sec             183412 non-null  int64  \n",
      " 1   start_time               183412 non-null  object \n",
      " 2   end_time                 183412 non-null  object \n",
      " 3   start_station_id         183215 non-null  float64\n",
      " 4   start_station_name       183215 non-null  object \n",
      " 5   start_station_latitude   183412 non-null  float64\n",
      " 6   start_station_longitude  183412 non-null  float64\n",
      " 7   end_station_id           183215 non-null  float64\n",
      " 8   end_station_name         183215 non-null  object \n",
      " 9   end_station_latitude     183412 non-null  float64\n",
      " 10  end_station_longitude    183412 non-null  float64\n",
      " 11  bike_id                  183412 non-null  int64  \n",
      " 12  user_type                183412 non-null  object \n",
      " 13  member_birth_year        175147 non-null  float64\n",
      " 14  member_gender            175147 non-null  object \n",
      " 15  bike_share_for_all_trip  183412 non-null  object \n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "bike_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing and Cleaning Issues <a id=topic2></a>\n",
    "- Max value for `duration_sec` appears outrageous (about 23 hours)\n",
    "- Incorrect Datatypes for `start_time` and `end_time` columns\n",
    "- Missing values in `member_birth_year`, `member_gender`, `start_station_name`, and `end_station_name` columns\n",
    "- Some columns in the dataset are extraneous\n",
    "- Columns of different observations unit types in one table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the dataframe before cleaning\n",
    "bike = bike_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "Correct the datatypes for the start_time and end_time columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the datatypes to datetime\n",
    "bike['start_time'] = pd.to_datetime(bike['start_time'])\n",
    "bike['end_time'] = pd.to_datetime(bike['end_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                   datetime64[ns]\n",
       "start_station_id                  float64\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                    float64\n",
       "end_station_name                   object\n",
       "end_station_latitude              float64\n",
       "end_station_longitude             float64\n",
       "bike_id                             int64\n",
       "user_type                          object\n",
       "member_birth_year                 float64\n",
       "member_gender                      object\n",
       "bike_share_for_all_trip            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "\n",
    "Drop rows with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike.dropna(inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 174952 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             174952 non-null  int64         \n",
      " 1   start_time               174952 non-null  datetime64[ns]\n",
      " 2   end_time                 174952 non-null  datetime64[ns]\n",
      " 3   start_station_id         174952 non-null  float64       \n",
      " 4   start_station_name       174952 non-null  object        \n",
      " 5   start_station_latitude   174952 non-null  float64       \n",
      " 6   start_station_longitude  174952 non-null  float64       \n",
      " 7   end_station_id           174952 non-null  float64       \n",
      " 8   end_station_name         174952 non-null  object        \n",
      " 9   end_station_latitude     174952 non-null  float64       \n",
      " 10  end_station_longitude    174952 non-null  float64       \n",
      " 11  bike_id                  174952 non-null  int64         \n",
      " 12  user_type                174952 non-null  object        \n",
      " 13  member_birth_year        174952 non-null  float64       \n",
      " 14  member_gender            174952 non-null  object        \n",
      " 15  bike_share_for_all_trip  174952 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(7), int64(2), object(5)\n",
      "memory usage: 22.7+ MB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "Confirm that the max vaue for duration_sec column is indeed correctly calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum number of seconds is: 84548  = 84548\n"
     ]
    }
   ],
   "source": [
    "max_value = bike.duration_sec.max()\n",
    "\n",
    "# isolate the row for which the duration is equal to the max_value\n",
    "max_row = bike[bike.duration_sec == max_value]\n",
    "\n",
    "# confirm that the original  entry is correct\n",
    "print(f\"The maximum number of seconds is: {int((max_row.end_time - max_row.start_time).dt.total_seconds().values[0])}  = {max_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "Remove columns from dataset that wouldn't be necessary for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',\n",
       "       'start_station_name', 'start_station_latitude',\n",
       "       'start_station_longitude', 'end_station_id', 'end_station_name',\n",
       "       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',\n",
       "       'member_birth_year', 'member_gender', 'bike_share_for_all_trip'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the unnecessary columns\n",
    "ext_columns = ['start_station_id', 'start_station_latitude', 'start_station_longitude', 'end_station_id', 'end_station_latitude', 'end_station_longitude', 'bike_share_for_all_trip']\n",
    "bike.drop(columns=ext_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'end_time', 'start_station_name',\n",
       "       'end_station_name', 'bike_id', 'user_type', 'member_birth_year',\n",
       "       'member_gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "Create a table for the user's info and one for the trip information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe for the user's information\n",
    "user_list = ['bike_id', 'user_type', 'member_birth_year', 'member_gender']\n",
    "user = bike[user_list]\n",
    "\n",
    "# create dataframe for the bike trip info\n",
    "trip_list = ['bike_id', 'duration_sec', 'start_time', 'end_time', 'start_station_name', 'end_station_name']\n",
    "trip = bike[trip_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5200</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bike_id   user_type  member_birth_year member_gender\n",
       "0     4902    Customer             1984.0          Male\n",
       "2     5905    Customer             1972.0          Male\n",
       "3     6638  Subscriber             1989.0         Other\n",
       "4     4898  Subscriber             1974.0          Male\n",
       "5     5200  Subscriber             1959.0          Male"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4902</td>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5905</td>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6638</td>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4898</td>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5200</td>\n",
       "      <td>1793</td>\n",
       "      <td>2019-02-28 23:49:58.632</td>\n",
       "      <td>2019-03-01 00:19:51.760</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bike_id  duration_sec              start_time                end_time  \\\n",
       "0     4902         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "2     5905         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3     6638         36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4     4898          1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "5     5200          1793 2019-02-28 23:49:58.632 2019-03-01 00:19:51.760   \n",
       "\n",
       "                                 start_station_name  \\\n",
       "0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "2                           Market St at Dolores St   \n",
       "3                           Grove St at Masonic Ave   \n",
       "4                               Frank H Ogawa Plaza   \n",
       "5                      4th St at Mission Bay Blvd S   \n",
       "\n",
       "                               end_station_name  \n",
       "0                Commercial St at Montgomery St  \n",
       "2  Powell St BART Station (Market St at 4th St)  \n",
       "3                        Central Ave at Fell St  \n",
       "4                         10th Ave at E 15th St  \n",
       "5                            Broadway at Kearny  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(174952, 9)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> Following the wrangling process, there are 174, 952 individual rides to and from different stations with 9 variables describing the features of the each ride. Most variables are numeric occuring in both float and integer types, but strings and date type variables are present as well. \n",
    "I have divided the dataset into two tables to conform with tidiness standards. The `trip` table contains information on the each trip taken with a bike, while the `user` table carries information on the  user for each trip. I also dropped 8, 460 rows with missing values.\n",
    "\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> I'm most interested in finding out the features that best predict the pattern and rate of use of bikes in the bike share system.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> Chiefly, I expect that the `duration_sec` measuring bike-journey time from station to station to influence the bike use. Hence, for shorter distances (shorter durations) there should be more bike use.\n",
    "\n",
    "> Similarly, I expect the user type to influence user behavior and rate of bike use. The age and gender could also be indicative of a pattern of usage to some degree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration <a id=topic3></a>\n",
    "\n",
    "To begin with, I would like to see the distribution of the duration of each use for rides taken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (7,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(61, 84548+10, 10)\n",
    "# make a frequency distribution for the durations\n",
    "plt.hist(data = trip, x= 'duration_sec', bins=bins)\n",
    "plt.xlabel('Duration (seconds)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, it is difficult to observe changes in both shorter durations and longer ones alike. Hence, I'll perform an axis transformation to capture these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(61, 84548+20, 20)\n",
    "ticks = [30, 100, 300, 1e3, 3e3, 1e4, 3e4, 1e5]\n",
    "tick_text = ['30', '100', '300', '1K', '3K', '10K', '30K', '100K']\n",
    "plt.hist(data = trip, x= 'duration_sec', bins=bins)\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Duration (seconds)')\n",
    "plt.xticks(ticks, tick_text);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the log scale on the x axis, the distribution is more clearly shown. The graph looks unimodal and has most of the duration in lower ranges, mostly below 1000 seconds. It presents a skew to the right, however, there is still a tail that trails higher values further. As a result, outliers are expected to the far right of the graph including the maximum value of 84548 seconds seen earlier. I strongly expect this to be the major outlier since the duration at the 75th percentile was only 789 seconds.\n",
    "\n",
    "\n",
    "Next, I'm interested in how much difference there is usage of bikes based on gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colour = sns.color_palette()[0]\n",
    "sns.countplot(data = user, x= 'member_gender', order = user.member_gender.value_counts().index, color = colour);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is apparent that males had a lot more use of the bikes than females.\n",
    "\n",
    "However, some users were subscribers while others were customers. I'd like to know who had more use of the bikes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# values = \n",
    "sns.countplot(data = user, x='user_type', color = colour, order = user.user_type.value_counts().index);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subscribers expectedly had a much greater use for the bikes than the Customers.\n",
    "\n",
    "Now, I'll turn to compare how the rides were distibuted across the days and months in that year. Were there any notable variations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, create columns to record the day and month names for each ride from start_time column\n",
    "trip['day_name'] = trip.start_time.dt.day_name()\n",
    "trip['month_name'] = trip.start_time.dt.month_name()\n",
    "\n",
    "# next, order the day_name and month_name columns in categorical format\n",
    "day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "def make_category(df, ordering):\n",
    "    '''quick function to make order for ordinal categorical variables'''\n",
    "    set_order = pd.api.types.CategoricalDtype(ordered=True, categories=ordering)\n",
    "    return df.astype(set_order)\n",
    "trip.day_name = make_category(trip.day_name, day_order)\n",
    "trip.month_name = make_category(trip.month_name, month_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Now, visualise the distribution across the days of the week\n",
    "sns.countplot(data=trip, x='day_name', color=colour)\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Amount of rides per Day');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the use of the bikes per day seemed fairly even across the week days, however, it fell drastically on the weekends. This is indicative that there are less people moving between train stations making use of bike share systemon weekends.\n",
    "\n",
    "#### For distribution by month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only recordings for the month of February were contained in the dataset.\n",
    "trip.month_name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, only records for the month of February were contained in the dataset.\n",
    "\n",
    "Now I'd like to know the most frequent start stations, end stations and start to end trip combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the most frequent start stations\n",
    "start_stations = trip.start_station_name.value_counts(ascending=True).tail()\n",
    "\n",
    "# make a plot depicting the information\n",
    "start_stations.plot(kind='barh')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Station')\n",
    "plt.title('Most Frequent Start Stations')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the most frequent end stations\n",
    "end_stations = trip.end_station_name.value_counts(ascending=True).tail()\n",
    "\n",
    "# make a plot depicting the information\n",
    "end_stations.plot(kind='barh')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Station')\n",
    "plt.title('Most Frequent End Stations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've created this plot since its often beneficial to know where most of the user are headed off to and from where. It's also interesting to see that almost all of the 5 most frequent start stations are also some of the most frequent end stations.\n",
    "\n",
    "What's more interesting however, will be to find out the most common route taken between stations. I'll like to see what the most frequent combination of start and end destination was."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fins the most frequent combination of start and end destinations\n",
    "combo = trip.groupby(['start_station_name', 'end_station_name']).size().nlargest(1)\n",
    "combo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent start and end destinations were from *`Berry St at 4th St`* to *`San Francisco Ferry Building (Harry Bridges Plaza)`* with 327 trips.\n",
    "\n",
    "Since we've considered the peak day, I'll look at the distribution of usage by hour across all days. What was the peak hour and were there other close peaks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trip['hour'] = trip.start_time.dt.hour\n",
    "x_tick = np.arange(0, 25, 3)\n",
    "plt.hist(trip.hour, bins=26)\n",
    "plt.xticks(x_tick, x_tick)\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the hours of use takes a bimodal form. The first peak is around the 8th, while the next peak a little at the 17th hour. My first indication would be pointing to office workers commuting to work, but I'll perfomr further investigations to solidify my inference.\n",
    "\n",
    "Finally, age could be a factor influencing the use of bikes and I'd like to know exactly how."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'member_birth_year'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10068/2454267954.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# calculate age of each user at time of usage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0muser_age\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrip\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_time\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m  \u001b[0muser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmember_birth_year\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# add the user_age series as a column in the user dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0muser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muser_age\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5573\u001b[0m         ):\n\u001b[0;32m   5574\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5575\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5577\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'member_birth_year'"
     ]
    }
   ],
   "source": [
    "# calculate age of each user at time of usage\n",
    "user_age = trip.start_time.dt.year.unique()[0] -  user.member_birth_year \n",
    "\n",
    "# add the user_age series as a column in the user dataframe\n",
    "user.loc['age'] = user_age\n",
    "\n",
    "# drop the birth_year column for tidiness\n",
    "user.drop(columns=['member_birth_year'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract customers who specified their birth years\n",
    "df = user[user.age.notnull()]\n",
    "\n",
    "# visualise the age distributions \n",
    "bins = np.arange(df.age.min(), df.age.max()+2, 2)\n",
    "plt.hist(data = df, x='age', bins =50)\n",
    "plt.xlabel('age')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age distibution looks unimodal and skewed to the right. This shows us that most users were younger and between ages 20 and 40. Use of bikes in the program steadily declined with users above 40. There is also the presence of outliers in the far right, suggesting very old users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "> The range of values for the duration of use were largely spread out. I performed a log transformation on the data to capture the frequency of occurrence at both small durations and larger ones. Under the transformation, the distriution appeared to be unimodal with a peak around 300 seconds. \n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "> During investigation of both the duration and age variables, a few outliers were noted. For the age however, I derived the age column using the difference between the pre-specified users' birth year and when they used the bike (2019). I made my visualisation using this age variable to get a more comprehendable age distribution and not their birth years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration <a id=topic4></a>\n",
    "\n",
    "> First, it's interesting to figure out how the duration relates with the age of the user. Typically, I would expect younger users to ride longer periods. Do they?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b5x33D/G2ewaDbSEVn329Mfb0xImGbA73JxnqjTUtEr4RVW5aKWS+lQu1K6VuDW2bbhWRTuAx4KcAjDFloCwibwfeWH3YR4EvA78EvB34E2NMCbgoIueAh0TkEtBpjHm2et4/BB4H/rL6nH9bPdefAR8UETHGmHbd10aqbeXIFj1GZkp0xhw6ow7nJ7Lc4XQsOgJb7yo3jcURauaOEps1o9b1SKXUZtHONclDwDjwByJyL3ACeB+w0xgzAmCMGRGRWmXrPcDXG54/XD3mVr+ee7z2nKvVc3kikgL6gIm23NEGq23l6I6FyJcqFMoVckWXTNFjd1dsUxUJb7WQuZaoU0ptZu2cbnWA+4HfNsa8FshRnVptQhY4ZhY5vthzZp9Y5D0iclxEjo+Pjy9+1ZtYbStHf0eUQwMJYmGbYsXH882mKxKuhcyVUttBO0eSw8CwMea56vd/RhAkb4jIYHUUOQiMNTx+b8Pzh4Dr1eNDCxxvfM6wiDhAFzCvjIwx5sPAhwGOHj26ZadiG7dyJCIOu22L7liI/X2JtgWf1Wz211GiUmqra9tI0hgzClwVkTuqh74DeBn4FPCu6rF3AX9e/fpTwDurGasHCRJ0nq9OzWZE5OFqVutPznlO7Vw/DPz1dl2PhGArx527Ook4FqmiR8SxuHNXJ0d2JNvyerrZXyl1q2v3Psl/BPyRiISBC8BPEwTmT4jIu4ErwI8AGGNeEpFPEARSD/h5Y0ylep5/CHwEiBEk7Pxl9fjvAR+rJvlMEWTHblsPHuhlNHWdO3Z1zFrna1c2qG72V0rd6mQbD7wWdPToUXP8+PGNvowVW2j6E2hL/dMPPXOeHR0RLLm59Osbw1imxM89dnjV51dKqc1CRE4YY47OPa4Vd7aYuet8rVS2WYmrU3kuT+Q4cXmaHR0RDvcn6U2GdbO/UuqW0lKQrG7TeBTYDRSAU8BxY8zGt5G4xc2dEnU9n/PjGX71c2d40507VjSqrAXeXV0RUgWXVMHl+OUp7tzViW3JptpqopRS7bRo4o6IvElEPg/8BfA9wCDwGuBfA98SkX9XLRqgNkhjZZvJbImTV2YQBIQVJ9rUAu/+viQP7O+hKxbC9X1GUgXdxqGUuqUsNZJ8G/Czxpgrc39Q3XLxfcB3AZ9sw7VtehvZC7H22qeGU4Qd4e7dXVyYyBELBT0nu8KhFSfaNBYe70tG6EtG6muRGiCVUreSRYOkMeafL/IzD3hqrS9oq2jXWuByX/u1e7t59sIkz56fxDeGnkSYYtnnNbuCAf5KumoMJCNcncoxlimTLrh0xkLs6Agz1JNox+3Mo42YlVKbRUv7JEXkfSLSKYHfE5GTIvLd7b64zayVLhfr8dp9HRFef7ifzliIyVwJY+CBfT30JoNekStJtBnqjnH80gypgktn1AnWJC/NMNTdvtZaNbo3Uym1mbRaTOAfGGPSwHcDAwT7HX+5bVe1BbTS5WK9Xrs3GeYNt/XzyOF+Dg8kcRxZVVeN4ZkCDx7opSsWIl2qLNmKay1t5B8fSik1V6tbQGob5d4G/IEx5sVaT8dbVStdLtb7tW/b0cGDB3pX3VVjPFtiqDfGvr6bz6utSbabNmJWSm0mrQbJEyLyBeAg8AER6QBu6e0frXa5WO/XXot6qZvxDwDdm6mU2gitTre+m6A4+YPGmDwQJphyvWVtZJeLvb1xju7v4fT1NJ964Tqnr6c5ur9nzV57I5shayNmpdRmsmhZOhG5f7EnG2NOrvkVtdl2KEv3+VOjfOXcOH3JMHcNdjIyk+fYpRkSkRAH++M8ft8eHjrUt+rX2ejtLZrdqpRaLystS/dfqp+jwAPANwnWJ78deA54w1pepFpcLfPz/HiWWMjmylSB5y9OUnQNvYkQyYhNpuTxwafP8V5YVaDcyDZX2mJLKbVZLDrdaox5kzHmTcBl4AFjzFFjzAPAa4Fz63GB6qZa5me64DKSLmKJUHSDpstF1yddrNATD9MVD/HUC9c2+nKVUmrLazVx505jzLdq3xhjTonIfe25pFvLcqYWa5mfubKHW/HJlytkih62BRXfpuQFncW6og7XZorreRtKKbUttRokT4vI7wIfBwzwE8Dptl3VFracoLfcqj21zE8BprJlIiEbxxZ835AtecTDNgCposdgV7Sdt7lp6XqmUmottZrd+tPAS8D7gF8kaIx8S2e3LmS51WKWu3G+lvlZ8ny6EyEsgZBlYVkWIdvCN4bpfJlU3uXx+/Ysep1PnhzmQ8+c58mTw9ummo1W61FKrbWWRpLGmCLwG9UP1cTctlVLFRhf7sb52raTE5enyZY8+pIRvn2oi+lcmQsTOcoVQ0fE4YnX7W+atLORNWcbr6Edo73l/v6VUmoprfaTfBT4t8D+xucYYw6157K2prlBbzJb4tx4lpHq+uDcYLCSjfN7e+N877cPcm26wI1MkXTRY29fgqMH+tjTE+Md9w8teo0bHUiaBemj+3sYnimsKnBqtR6l1Fprdbr194BfJ9jy8WDDh2pQC3pws7djpuixuzu64NTfSjfOP3igF9sS7tjVwZvuGOCOXR3YlrS04X4ja87CwlPMnl/hI89eWvU0aePvv0ar9SilVqPVIJkyxvylMWbMGDNZ+2jrlW1BjUHv3HgWETAGjgx0LLjeOLdqT7FcIWxbfPbUyKJrhaup9rPRgWShID2aLuH5ZtVFzbVaj1JqrbWa3fq0iPwa8CRQH3JsxYo77VQLXscuTTEyU2R3d5QjAx31tlULTf3VNs7fnIa06/VYF1srXOmG+42sOQsLTzFPVKdYG61kmrTx97+aAu9KKVXTapB8XfVzY8keA7x5bS9n62sMXrly6+uN67VWuNGBZKEg7YjFrs7ZvSpXOrrVaj1KqbXUanbrm9p9IdvNckds65l0stJAshZZqQsF6Z96/QGOX54mXXQ3ZHSrlFLNtJrd2gX8G+Cx6qG/Af69MSbVrgvbqhoDScgWSmWffHnpEdtyM13Xe9P8Wm4dWShID3bHdJpUKbXptDrd+vvAKeBHq98/AfwB8I52XNRWtVAgyRS8lgLJckaeG7HXsd3TwTpNqpTajFrNbj1sjPk3xpgL1Y9/B+geyTmWW0Gn0XIyVlfzOiu10VtHlFJqI7Q6kiyIyBuMMV+BenGBQvsua2ta7bpiq6Opjdg0v5LCB0optdW1GiT/IfDR6tokwDTwU225oi1svQLJQDLC1akcY5ky6YJLZyzEjo4wQz2JNX2dRhu9dUQppTZCq9mtLwD3ikhn9ft0Oy9qq2o1kKw26WaoO8ZT37hGVzxEV9QhVXC5Mpnn4YP9a/o6jTZ664hSSm2EVrNb/z/gV40xM9Xve4B/aoz51228ti2nlUAyN+lmeKrAF0+fZn9fnNt2dLQUyIZnCjx4oLdeu7UrFuL2HR0MzxR4qMnrNEvuef7CJE+9cI2RVJHBriiP37enaXF0Ta5RSt1qWp1u/R5jzL+sfWOMmRaRtwEaJOdYKpA0Jt1MZct84+oM07kS12cKjKVLnBnN8MTD+xc9x3i2xFBvjH19Nx/jGzNrTbKVbNTnL0zywafP0RUPsac7Sqro8cGnz/FeaBoolVLqVtJqdqstIvWFNRGJAZqxsQy1Ho6ffvE6Z0bSTGXLvDg8w1i6SMi2sAQsS7g4nuXzp0YXPVcr9VdbyUZ96oVgyrYnHsayLHriYbriIZ564doa3LFSSm19rY4kPw58SUT+gKAc3T8APtq2q9pmGqc+B7ujpIseJ65Mc2E8SzLqIAixsEMsZGPiIV4cnpn3/Ma1xaHuGMcvTwPN1z5bSSIaSRXZ0x2d9VpdUYdr1dZeC712u4sWKKXUZtLSSNIY86vAfwDuAu4G/t/qMdWCxqnPIwNJ8kWPa9N5rk3nGU0VyRbdm1s6DBhM/bm1ANvYRur45WmO7u9ZdE9lKx0xBruCKdZGqaLHYFe06WuvpIWVUkptVa2OJAFOA54x5osiEheRDmNMpl0Xtp007msUBB9wLIuIY1H2fMq2D8ZQKFdIFVweOngzkDVbWxyeKSzaYLmVJKLH79vDB58+BwQjyFTRI5V3eeJ1+xd97fVq0KyUUhut1ezWnwXeA/QCh4E9wP8EvqN9l7Z9NE59np/I0t8RoT8ZYU9PjFTBZTpf5uJknsMDCQ72J3jrPYP1566mcMBSSUQPHerjvQRrk9dmguzWJ163v560sxFFC5RSajNpdST588BDwHMAxpizIqK7yFvUuH8yVXCJ2BZFz+f+fd0IwrnxDNdnijx6ZKC+5ldbC/zW8AwRx+ae3V31vpRrWaDgoUN9TTNZtcqOUupW12qQLBljyiICgIg40LBwphbVOPVZW3O8f183fdVgc6fTyQP7e+vTp42JPvft7ea5C1N87fwEjxzqIxSy1q3SzWqr7GjSj1JqqxNjlo51IvKrwAzwk8A/Av4v4GVjzL9q69W1wdGjR83x48c37PWvTuX52LOXmMyVKFcMYVvoS0R44pED9QDy5MnhWQ2bJ7MlXrqeouwZ3nTnjnUNNisNdI2BvjHAzk0w0kCqlNoMROSEMebo3OOtjiTfD7wb+Bbwc8Bngd9du8u7xYgAQjAYl+r3N81dC+xLRnjDbQOMZUqLJuu0w0qr7LSS9LMRLb+UUmo5Wq3d6gO/A/yOiPQCQ6aVIaiaJwgSMe7e3Vk/li66s4LHdlgLbCXpR7NnlVKbXavZrV8GfqD6+BeAcRH5G2PMP2nfpW1PrQSPVtYCl1NzdSO0Eug1e1Yptdm1Wpauq9r54x3AHxhjHgC+s32XtX21UlJuqQbMtZqrmZLHnu4omVJQc/X5C5Nrfr21cnofeuY8T54cbrmQQCvFDFr5XSil1EZqNUg6IjII/CjwmTZez7bXSvCAIFC+4/4hfu6xw7zj/qFZ04/rVXN1NRV3lgr00PrvQimlNkqriTv/Hvg88BVjzDEROQScbd9lbV9r0ZexlZqra2G1a4ZLJf1oj0ql1GbXauLOnwJ/2vD9BeCH2nVR291q+zLWaq72xMP1Y401V9fKeqwZao9KpdRmtuh0q4j862o2a7Ofv1lEvm/tL0st5vH79pDKB+XsfN9nOl8mlXd5/L49a/o6umaolLrVLTWS/BbwaREpAieBcSAK3AbcB3wR+P/aeYFqvqVqrq6VuVm2V6dynBnJcqA/wZMnh3Xjv1Jq22u14s5twKPAIFAg6AjyjDGm0N7LW3sbXXFnq6lVxDk7luHyZJ67dnUy1BtrWkFHKaW2olVV3DHGnEUTdW5JtTXDJ08Os7s7phv/lVK3lOX0k1wREbGB48A1Y8z3Vdc4/zdwALgE/KgxZrr62A8QlL+rAL9gjPl89fgDwEeAGEFJvPcZY4yIRIA/BB4AJoEfM8Zcavc9bTZrVf90sfPoxn+l1K2o1X2Sq/E+gunZmvcDXzLG3AZ8qfo9IvIa4J3A3cBbgd+qBliA3yboZ3lb9eOt1ePvBqaNMUeA3wB+pb23sjZa2aDf6ib+1exlXM55NIlHKXUramuQFJEh4HuZXQz97cBHq19/FHi84fifGGNKxpiLwDngoWoRg05jzLPVerF/OOc5tXP9GfAdInOqhW8yrQS15QS+xr2Mlgid0RAdMSdoy7UMS51nJRv/V1qtRymlNouWgqSIDIjIvxSRD4vI79c+WnjqfwX+BeA3HNtpjBkBqH6uFSTdA1xteNxw9die6tdzj896jjHGA1LA5ilguoBWgtpyAt94tkQyMnvWPBlxGM8ubxp0qfO0UkGn0VqNcJVSaiO1uib558DfEmz5qLTyhOr+yTFjzAkReWMrT1ngmFnk+GLPmXst7yGYrmXfvn0tXEr7tLK218pjauuHp4ZThB3h7t1d9SbOK5kGbaUg+XI2/muHD6XUdtBqkIwbY35pmed+FPgBEXkbwd7KThH5OHBDRAaNMSPVqdSx6uOHgb0Nzx8CrlePDy1wvPE5wyLiAF3AvOGWMebDwIch2AKyzPtYU60Eo6Ue09iH8bV7u3n2wiTPnp/kdYd6iYTseR1DWtFK55FGSyULaaKPUmo7aHVN8jPVYNcyY8wHjDFDxpgDBAk5f22M+QngU8C7qg97F8Eolerxd4pIREQOEiToPF+dks2IyMPV9cafnPOc2rl+uPoam7rPZStre0s95tilKTy/wiujGV4YniERcbAteOHqzJLToM0sZzq1lalUTfRRSm0HrY4k3wf8SxEpA271mDHGdC7ynGZ+GfiEiLwbuAL8SPVkL4nIJ4CXAQ/4eWNMbWr3H3JzC8hfVj8Afg/4mIicIxhBvnMF17OuWinqvdRjzo5lOD2SJleq4FV8HNsiEbG5ayDJO+4favbSLV1bK8G1lanU5Y5MlVJqM2qp4s52sh0q7vzC/zrBqzeydMXDhGwhW/S4kS7SHQ/xs48dbnu5uA89c54dHRGshkRi3xjGMiV+7rHD9WNrtX9TKaXabVUVd6on+AHgseq3XzbGaF/JDZIr+dhWEKBKboWJbBmMAaE+9blU5ulqglcr66qgHT6UUltfq1tAfplgyvXl6sf7qsfUBohFLPb3xnFsYTxTJuIIO7qiRB17yX2Sa7E1Q5slK6VuFa2OJN8G3GeM8QFE5KPAN6hWy1Hr696hbv7m1XHKnk+5UiFq2RRdnzsHO5jKljk3nuF6tQHz3FHiWmzN0GbJSqlbxXJqt3Zzc3tF19pfimpm7vToYGeUklvB9Q2ObVGu+Bhj6I6FOHFlGhEY7I4uOPW6VlszdCpVKXUraDVI/ifgGyLyNMEG/seAD7TtqlRd457IHR3BWuAXz0/y7UOdlCowmioykS3Rmwjz6o0M/R1RjIEjA8lZo8Ta57UqPqCUUreCVltl/bGIfBl4kCBI/pIxZrSdF6YCC02Per6h4BledzCowDeZLXFuPMtz56c4OJDkyECyHgCTEYdXb2QYTRXXtPiAUkrdChYNkiJypzHmjIjcXz1Uq6G6W0R2G2NOtvfytp613vaw0PToQDI8qzZrXzJCyLHAwF2DnfOyTlN592YvyCi8/nA/p66neOHqDG++c6euJyqlVBNLjST/CUHN0/+ywM8M8OY1v6ItbKGp0aW2Yyxloe0WuzpjTOdc0kV31kb9x+/bw/HL08DsDfzd8fCs4uW9yTBvuK2fsUxpVcUHlFJqu1s0SBpj3lP98nuMMcXGn4lItG1XtUW1o6j3QpVrbEv4qdcfYHimMC+7dLA7Ni/r9NilqZb2NTZ6/sIkT71wjZFUkcGuKI/ft4eHDm3qBitKKbXmWk3c+RpwfwvHbmntKOq9tzfO0f098wLWYHeM4ZnCgo9fKCB/7OuXmczOUK74hG2LvmSEJx7ev+D08MhMgQ8+fY6ueIg93VFSRY8PPn2OH00XKftmRVPJWn1HKbUVLVpMQER2icgDQExEXisi91c/3gjoO9wc7SjqfXUqz/HL09y1u5MfuG83d+3u5Iunb/Cxr19eXkEAY7jZRcyAMYzMFBYsLPDxr1+mKx6iJx7Gsix64mEcW/idv72woiIE2ltSKbVVLTWSfAvwUwTtqX694XgG+JdtuqYtq9Wi3ssZVS00hTuZKwHC3bs768dqj13oPMcuTbG3L87de25ub00XXZ564Rp37e6cNz18dizLA/u7Z52jWK5QcCsrmkpuZRpaR5pKqc1oqTXJjwIfFZEfMsZ8cp2uactqpRLN1al8deqzVJ/6PDOa4YmH9y8YFBaawi1XGkeFgcWmdZtNA4+kijx4sHfe8UhISBU9euLh+vHpgktfMjzvsa1MJS81Dd2OhCellFoLre6T/KSIfC9wN0ED5drxf9+uC9uqGtcEa6Ojz54aqY+OPn9qlIvjWboTYbrDIYqez8XxLJ8/NcrPPHZo3vkWym4N20KwXfWmxaZ1B5IRhqcK3MgUSRc9OqMOOzuiDHZFZ517MlvipespYiGHV0cz7OmJ0RFxuDKVYzxTZEeyi6lsmd5qsGx1KnmpgujtSHhSSqm10GqB8/8J/BjwjwjenX8E2N/G69rymq3DPXt+kq54iFjIRkSIhWy64iFeHJ5Z8DwLFRPvS0ToS0ZaLjA+VM14TRVcOiM2qYLLsUtTvP5QX/3c45kiz56fJF3w+K67dvJ3butnZLrAictT2JbN2+7ZRdix+Nr5CSYzpWUVNV+qIPp4tjRriwoEI83GvaBKKbURWs1ufb0x5ttF5JvGmH8nIv8FeLKdF7bVNRsdTeZL9CRCsx9swLBwX8+FpnCfeORA/TVaKTA+PFPg6IFuxjJl0gUXSyAetvj8yzfY1xvnzEiKs2NZEhGH1x/up68jQl9HhOm8Cxgeq66p1kaa37g6w5vu3NFyEYKlpqFbbb2llFLrrdUgWdsjmReR3cAkcLA9l7Q9NFuH64mHSBVcRIRoyKLo+qQKLg8dbD4ia7ato9WpyPFsib29Cfb3JZnMlvjq2QnSRZepXJkLE1l64mF2d8foSYS5MJGjJx6mNxmmXPFnnacvGeENtw2sqAjBYgXRW014Ukqp9dZqkPy0iHQDvwacJMga+Z12XdR20Gx09PrD/YxnSkzmSswUKoRt4WB/grfeM7is8y8nG7TxWr45nOJGpkTYsRBLiDg2M3mXsGPRm4gQC9ucn8jSm+wlbFvMTRBqxwhPW28ppTarJYOkiFjAl4wxM8AnReQzQNQYk2r3xW1lzUZH33/vboBVbXdYbjZo47VcnswRsgVjIOrYxMM2jiVkSy4Ft0LUsUgVgpJ3fckIVNcQ2z3C09ZbSqnNaMkgaYzxq2uQj1S/LwGaUbGEpUZHqwkIc9c7x9MlvnZ+gk+/eI3X7uuZV0Ku8VqKboVExGFfb5wbmRJuJRgpRkIO9+/r5qXrKTCQCDs88fD++uvpCE8pdStqdbr1CyLyQ8CTxpiFM0zUPO0aHTWud54fy/KZb17Hq/h4vuH0SIqzYxn++XffOS9Q7u2NM5kt8fzFKWzLYkcyzNmxHJ7vc8fOJCHH4vBAx7wR6UrvodmUsBYOUEptFa0GyX8CJABPRIoE20CMMaazbVemmmpcY/zK2XGK5QqhkE0iZBF2HFL5Mh//+qUFC5K/9Z7B+pqoZ6A37nAjXWIs43L6eprH79uz7IC1UNADFpwSPrq/h+OXp7VwgFJqS2i1mEBHuy9Eta5xjXEkVSDk2AjQFXMIOxYdUYezY9kFn7u3N84Tjxzg2KUpzo5lqPiGBw/2M9QbI1vyOH55msHu2LIKly8UDMO2teAWmGal8LRwgFJqM2opSIrIYwsdN8Y8s7aXo1rRuMaICAL0J8PEwsE/p2cMkZC96PP39sZ58uQwsZDFjUyRs2MZOmMhdnSElxWwmu0Hff7CFG++a3aCz2Kl8FbTKUUppdql1enWf97wdRR4CDiBNl3eMLVAd2Esy+deGsU34Ps+BdcnU/B46927ljzH2bEMVybzxMMOXbEQRdfnldEsBddf8rk1zfaDGsyCW2DmlsKrHdfCAUqpzajV6dbvb/xeRPYCv9qWK1LL8mMP7WMiV+LcWI6pvEsi7HD0QA8/9tC+JZ+byrtYlhALB6POWNim6FVI5d2WX7/ZftB7h7rJFIK2YY3bRx6/bw/HL0/PO66FA5RSm1GrI8m5hoF71vJC1Mrs7Y3z82+6bVnZorVEm4sTOfJlj0rF0JcMU/R8fN/QHQ83fe5crewHnbt9ZLBaS1a3lSilNrtW1yT/BzdLr1jAfcCLbbqmbWO9tjosZ6tJY6LNwYEEN1IFrs8UcCs+O7ui7OvpZE9PbFmv3cp+0IU6omhgVEptdq2OJI83fO0Bf2yM+Wobrmfb2Kw9EhsTbY4MJEkXPPb2heiMOtyxq6Plzh6NlgrSm/V3oZRSS2l1TfKjIjJQ/Xq8vZe0PWzWHomNiTZ9yQj37+vm3HiW6zNFHtjf25apz836u1BKqaUsGiRFRIB/A7yXoICAJSIe8D+04fLimmV9rnarw3KmcBd67NxEm75khJBjcXR/77I7e7SqXb8LpZRqt6WaLv8i8CjwoDGmzxjTA7wOeFRE/nG7L24rqwWjRqvd6tDYyDkkwlfPTfCv//xb/M4z57k6lW/62Mamz0PdsUUbILdDO34XSim1HpYKkj8J/Lgx5mLtgDHmAvAT1Z+pJh480Lvmwag2bel5hpNXZ7AsYSAR4cxomk+/eH1WoGyc4rRE6IyG6Ig5DM8U+P57d5MIByO5RNhZcG3w6lSeJ08O86FnzvPkyeF5QXijfxdKKbUellqTDBljJuYeNMaMi0hooSeoQDt6JNamLU+MThML28RCNsYYUgWXjpgza41vsSnOxkSbxqxTG8FgmMqXuTyZ565dnfVydatJtNF+kUqprWqpIFle4c8Ua98FpDZtmS56dEWDf7qi69MZC81b42u2yb9xirMx6zQkwrMXJgGIhS1sS3jlRoZkxKE3GeybXG6iTeOaqAUEBfSUUmrrWCpI3isi6QWOC0F5OtUmCyXd1Dbuhyyh4FYQgs93DXbMC4DNNvk3VrY5dmkKz6/wymiBl66ncCyL/kSYq1MF7tnTRdHzOT+RpTfZu+xEm6tTeT729ctMZktM58vcSBXpSUR48x0D9fVR3QKilNrsFl2TNMbYxpjOBT46jDE63domzZJuAL7/3t3ctauTiUwJ3/jct7eLkGPNW+OrTXEutvZ4dizDK6NZStVarQKMpIuUvApF1yfqWNxIFTl2aYq/PDXK5Ylcy2uTnz81ysXxLJYlFN0KkZDNTL7MN6+l6uujxy5Nrd0vTSml2mClZelUGy22r/Ad9w/xM48d4tuHunjqhWt89dwkg13RBftALjXdm8q7FN0KMwWXqWwZx7KIhiyiIZuCW2EqV2IiUyIatglZwq6uSMsjwBeHZ+iKh4iFbEquTzxs49jClckgyOoWEKXUVqBBchNaal/h1ak8xy9Pc9fuTh482LuiPpAAlgjD0wUSEYf+ZISRmQKZouHIzg5u35ngi6fH6IqH6IqFONSfoC8ZIV10W1qbNJh6IcNo2MatBN+b6sGF1kfXo4SfUkothwbJTWippJvVVrCpBaRT11NYlsES8A3s7omRK7vkyx5DPQkePdLP7Ts7sORmws1SI8DaufOlChfHc+zvizOQDHNuLIdX8bljV0d9C0htfVTL1imlNqul9kmqDbDUvsLxbIlkZPbfN8mIw3h26enLxvXO3kQYC4ui67O/N8bB/iSH+jt45HAf77h/iNt2dCyrCEDjud9wpJ/ueIjLk3kyRY+hngi7uqLs6YnPWx9ttqdT1yyVUhtNR5Kb0FL7ClvZ3tFMY0Da3R0j4lhMZMpcmspz9+4u9vYmGepJAK1lyDY7N1H4zrt2cep6ipJX4c137mw6hapl65RSm5UGyU1qsaSbucHr6lSOMyNZDvQnePLk8KLreePZErbAsdEMo6kiE9kSuzojhBx7XheQ5RYBmBvsepNh3nBbP2OZ0qJ1YVcT9JVSqp00SG5Be3vjHN3fw1MvXOPCeJZCucKDB3u5bWdyyfU8C3juwhTd8TCDXVHCtsXlyRw7OiMkws68INhKQYTaOuS3hmeIODb37O6qFyBoJdgtd8SqlFLrRYPkFtSY3YpApugxmi6xp9tdsjpOvepN9VMi6rC7O8brDvatqAtIY9LNfXu7ee7CFF87P8Ejh/q4NpPj2KUZEpEQxy9N8fh9e3joUN+8czQG/ZFUsemWFqWUWm8aJDdYK1sf5j5mMluqr/1lix7dsVDL1XEqGB451MeFyRypokdn1OGRQ324xqzo+mavQ4Z45HAfL11P8VcvjzKVdxnqiTHYFSVV9Pjg0+d4L8wLlGu1pUUppdaaBsl1slCwAZbc+rDQ9oivnJ/g7xzph2iIzlgoqI4TskgVPaay5XqyzELrkwPJoIJPY3WedNGlOzz/P4VWtmbMXYfsS0Z4w20DfPRrF7l9Vwc98WBkW/v81AvX5gXJtdrSstIemxqIlVLNaJBcB1en8vzKZ1/m1EiafLlCPGxzz2An9+7rWTI4LBRA+hMRTo+kGeiIcqg/wckrMxS9ChbwpdM3mM6X2dkV5avnxjkzkuaJRw7Uz7ec9b/G2q7pgktnLMSOjvCs62uWdFNyTb0Ie01X1OHaTHHe66wmu3U5eyx1P6ZSarl0n+Q6+PDfnOVrF6ao+EGgqPjwtQtT/MU3ry2533GhPZF3D3YyPFXgmVfH+MbVGbyKR6bgMpYJionv642zpzuGJRYXJ3J87tRI/bm19b/T19N86oXrnL6e5uj+ngWDRGNt165YiJLr88polrNjmfpjmu3pvG1HklRx9h7LVNFjsGt+XfyVNGWu9bv8lc+d5vx4Fs8zS+6x1P2YSqnlaluQFJG9IvK0iJwWkZdE5H3V470i8lcicrb6uafhOR8QkXMi8oqIvKXh+AMi8q3qz/67SFACRkQiIvK/q8efE5ED7bqf1fibsxPEwzbxiINlB5/jYZsr08Ulg8NCASRXrhCLONSybxKRMLfv6iQWtrlrsJOBzigiQixs0xUL8eLwTP25jet/P3Dfbu7a3cnxy9MLFi5P5V0sKzhP7XyWJaTybv0xzQqp/8TD+0nlXabzZXzfZzpfJpV3efy+PfNeZ7lNmRuLFogIInDiyjRT2aB7W7PCCqspwqCUujW1c7rVA/6pMeakiHQAJ0Tkr4CfAr5kjPllEXk/8H7gl0TkNcA7gbuB3cAXReR2Y0wF+G3gPcDXgc8CbwX+Eng3MG2MOSIi7wR+BfixNt7TihTLPomIPetYyBakDJlCEADn7nf83WcuNG2AfHo0zf37utjfl6yfL110ef7CJL2J8OwXlyCj9fkLkzz1wjW+cWWGRMTmkUN9dO7sWHT9rzseZmSmwI1UAc8Hx4JE2GFvz9KF1Pf2xnkvwRrktZkgY/WJ1+1vmt3abD/mQmuIjSPC2gg3FrbriUvNRqG6H1MptVxtC5LGmBFgpPp1RkROA3uAtwNvrD7so8CXgV+qHv8TY0wJuCgi54CHROQS0GmMeRZARP4QeJwgSL4d+LfVc/0Z8EEREWOapGpukD09Ma5PF7AsC8cSPN+QL1XY13czOLx6IxMEw8FOEmG73gD5dYd6iYUsTo+kKbgVjuxIsr8vzt7exKzXSEYc+pIRUnkXESHqWBQ9n1TeZVdXlF/7/Blc35AqlMmXLT7zzet837fv5sjOjqbrf73xMEEXLSGoVi64fnC8FYPdMY4e6K03Xf7mcIpvDM8smDCzUKBttoaYKbncvrMDoL4mG3UsUgV3Xl3YRrofUym1XOuyJlmdBn0t8BywsxpAa4G09g61B7ja8LTh6rE91a/nHp/1HGOMB6SA+UOVdVRbK/vQM+d58uQwV6fyvPvRgziORcmtUCh7lNwKjmPx7kcPsrc3Xq+T+sjhPvb1xbkwmaM7EaY7HubSZJ5kJEQsbNWnRHvj4QWnaR8+1MvBgSS+b5gpuPi+4eBAkuvTeTIlj4hjk4g4IELRM3z13Hj9uXNHU1en8nxzeJrRdAED7O+Nsb8vQSxk1Tt5LPV7qE2J2gLPX5ziuYuThETq/TGX6k3ZbA0xlXfr99+XjHD/vu5615GF+mbWtNJjUymlGrU9u1VEksAngV80xqSloaPE3IcucMwscnyx58y9hvcQTNeyb9++pS55xRbLnvzAW+/k489fYTxdYqAzwk88tI/va1ifa8zwTBe9embo9ZkCM3mXqGOBGHJlj/FsmXShjFsxlCs+rudT9HzuGuxgIBlmRzJCBVMfsb3nD4/REXEIOxbdsTBjmRIhWxhJlxYcedXuo1gx3D3YwfWZEmdGs9yxK8nrDvVSaWGc3hjgjo1m6I6HQeDCZK6+1rjUFo9mWa/d8fCsaeqQY3F4oKOlgNdKBSGllKppa5AUkRBBgPwjY8yT1cM3RGTQGDMiIoPAWPX4MLC34elDwPXq8aEFjjc+Z1hEHKALmJeqaIz5MPBhgKNHj7ZtKnapZsm1oFhbZ/vQM+frgaxxvawz6lD0fDBQcCv0JiIg0BUOBaOpiMs3r0zjGcN03qVQ9hjsjDKQiBAKWWQK3qyAEXFsPD+47WjYZkdHhBvpAgDFcoWwbfHZUyPYCAbDi9XycvGQTcixuGt3hEK5QiRkEQnZJBbYUznXrKBfcOmKBb+LWsbrYls8lipzd2RHsr422UpN2c1K92wqtfm1M7tVgN8DThtjfr3hR58C3lX9+l3Anzccf2c1Y/UgcBvwfHVKNiMiD1fP+ZNznlM71w8Df72R65GtZE82TkPu6IjUpx6HumP1DM9DfQlmcmVm8mViIRuDoVCucLg/SNQ5N5ZhNFNERPAqPo5tky17fPNaasFtDY8e6WcyV+bKZI4rkznGMkUQ4Tvv2olbMUTCFiERnrs4yfMXpyiUK+RKLq+MZjh5aZrTI2nKnsdYprho1mmjxqzcWsGDoufTWR0hN0uYafz93Le3m3TB5WvnJ5jMlGZlvdamqX/uscO84/6hLRdcmv13sNQUtFJqfbVzTfJR4AngzSLyQvXjbcAvA98lImeB76p+jzHmJeATwMvA54Cfr2a2AvxD4HeBc8B5gqQdCIJwXzXJ558QZMpumFb2+zVbZxueKdTXy1xjeN3BPh462EssbGMMPLCvpz6aOj2aplzxsarbHxwLcqUKr1b3L84NzH/39gG6YyEMUPYqGKA7FqKr+tqd0dCsddDpXInh6SKxsE13PAhqL41kiNpWy2t4jds6DvTFmcmXmcmVOdSXWHSLR+PvZ6AjyiOH++iMOXzj6sy2WkPUPZtKbQ3tzG79CguvGQJ8R5Pn/EfgPy5w/DhwzwLHi8CPrOIy18zVqTyT2RJfOT9BfyLC3YOd9anPxvW+xarLLJbh6TiCb0w9I7MnHiLsWIQdm0LZo+BWyE57HLs0xY6OcL0nJMDwTIHvfM0OxjLlWZVzzt7IcXhnMDptXActun5DJi4c6E/QnQvx7UM3iw4sNVXYuK0jX67w0MFeBME1hu4Fuo00+/3UytzNbbe10qnKzTLFqT00ldoatCzdGmhM2Pk7R/o5PZLmmbPjvOHIwLyRz2J79Zq9gc/dQ3igPxiNlT2fkAXXci4V3ydkCy9dm+EV2+Kff3d//fzj2RJ7exOz9lX6xvDqjeyC66CRsM1AIsLZ8Rwl1yMasnhgXw+Vak5Uq+XdVpIk08pexqtTeT727CUmcyXKFUPYlnnl95b6d9rosnS6Z1OprUGD5BqY2wljoCNKuugGG+/nvPk226t3x46ORd/AG88zmS3xN6+OU/J8pvMuIoaQbdERdQjZNmHH5pvDqfrG/SAA5+aNJO8d6q5niR7qS9T3ZvYlIlyZypMI29w71EXIsTgzmuF1B/vm3y+zE5Rqn+cG+qVGcLWfnxvLcmkix52DSfb2Jhbcy/i5UyNcnMjRHQ/THbMoun69/N7PPnZ4wX+jq1N5fvvL55jIldhRrXnbVw1IrRZSX0u6Z1OprUGD5BpYztRZs+oycwOP6/mcH8/wq587w5vu3DErqLz1nkHGMyUmcyWGp/P0xsMkoyHu3NVJMuqQL3u8ODxTDzwnL09x7NI0Qz1RBrtipAouVybzvPdNRxjsjgXBq1zidQf7MBiePT+JbQn7emN0RB2KQUWB+v7Iufd77kaGY5enuT5ToDsW4sED3dwz1FMP9Ef39/DF0zeajvwaR3i37UwSC9mcHk1TcH1u29Exb2r2xeEZumIhYuGgilGwbju7/F6j2vkncmV2JCOUXJ+TV2a4f183PYnwmk5xtjqdu1iVIaXU5qFBcg0sd+psoWnIz54aqQeeyWypXkWmtjdy7qjyiUcOcOzSFC9fTxOyLcKOxaWpHLGQTVfEoVQx9cDjA0M9MSZzZUQsdnVFuX1HB8MzBR461DfvWnzAFrg0mSdVHXk27o9svN9zNzJ84eUbwfqoLRjgK+emiIdDHKlWxfn41y9RcP2mI7+5fyDs64vTnQiRCDsLNoKWoNbe3IM3G0rPUTv/jo4IJc+vB9cLEznucKw1m+Jc7nSu7tlUavPTILkG1mLqrDHwXJjIMZEpcW4iS7lc4cWr0wx2xDg9kp41qtzbG+fCWJZPf/P6rHMNA0cGkrMaM+/ujtGbjBBxLB480ItvTNMRVLOek7X9kY33e+zyNGHHwrEsoiGHrliIfLnCiSvT9ZJ3Z8ey3L27q+nIb7lJLPcOdfPcxcl55fdq08Fz1c5/uD/JiSvTAEQcYSxTZHdXbM2mOFfbF1MptflokFwDazF11hh4Tl9PcXokg2MLHRGHmbzHRDbFXb7PtekCXzx9mv19cW7b0UGqUMIYU++GUXZ9il6Fs2NZdnZHOTKQnNeYeTJb4qXrKcqeWbAx81JBv/F+x9MldnaG2dUV50amiFsxxEIWU3m3/jozhWDPZThkgQkKGnREbEJ2EDSbrZk2Zug2ess9uxjLlpjMlpgpuIRti4MDSd5yz64FH1/7A6Q3GeaBfT2cn8gylinRn4isadKOZqwqtf1okFyFWmeNkVTQ5eLx+/YsOD3YisbAc3EyTzhk0RMPUyhXiIZsyp5wcSpP+NwE11IFvnFlmj3dMUZnChwaSFBBmMmV8Y3HzkSIXNEjU/Q4eWWGg31xLubz1cbMwZojwCOH+uZN5c69lrM3sszky3TFQ/XEnNoodm9vnOOXpsiUPJJRB4hycSJH3veJOlJ/nUN9cV65kSPkWOzuCqr33EgVeevdQVAb6o7x1Deu0RUP0RV16mumDx/sb7rG98TD+1veytEY9LsTIe5wOtjdFVvzrFbNWFVq+5FN1jCj7Y4ePWqOHz++6vM8f2GSDz597uYbe9EjlXd575uOLNgOajn+zi9/CREIOTapQhmhukey6NIdjxANWYhAbyLCK6Np9nTFePhIf9B8uGIwGLJFj0MDSUSgI+qwqzPCmZEsJc+nOx6aVeqtNpVaK/VWCzxD3TGOX56mI+bMGlE2BpfnL0zya184g1fxAaHk+RTKFXZ2hqvXanP2RoaJXBnbgpBlsbMzStixeePtA/zMY4d48uQw16YL3MgUSRc9OqMOOzuixEI25Yq/6OtDa8ky67E/snFNcrHrVUptPiJywhhzdO5xHUmu0FMvBCOfnmrbqNrnp164tmSQXOoNe09vnKlsCUTqFd5DtgUI0ZCFY1v4flBYwLKEy9N5jmSKFMoejmWRL1e4fWcH9w51c248w/WZIkf39/KDr91bTxCyqoXmp7Jlzo1nuDCe44unb8zqW/mRZy9x564kndEYsPAa22B3jMMDSc6N5ciVPRJhh28f6sKxhOm8S8ixiIQsdnVEmC64hB2L23d1cKgvgWtuZssO9cbY13fzd+Abw1+fHuOhQ72LrvG1c8/mcmnGqlLbjwbJFRpJFdnTHZ11rCvqcG2muOjzWnlT/4mH9vGf/+pVOqI2+7tjXEsVKXvBmqJb8UkVXVzPxxbBNz6+D2dvZAnZQiQEOzqj3DvUTW8yzJ1OJw/s762PEhuLhgOcuDKNSNA6pehW+NKZG/QnI+zqipIuuIymS7OKEMxdYzt2aYrd3VEc25q1nvi181MMdESIhWxiYYds0UWAciXYTpItu/U1x2bTlAazYC3cua+/mZJlNGNVqe1Fg+QKDXZFSRW9+ggSgg4XHRGbJ08ONx0lHrs0RcU3vDKaIV30EAxl18zKXK11C6m11uqNh+hJRnhlNBM0MDbBNg1LBGMM3bGgRF1n1KEnGeGuXZ10J0L1GqmNhQru29vNl18Z58xoGt83REM2vfEwgmEqV8YSIV/2mMoGBdEvjgeb++Nhh11d0XpCTW00/IljVyi4FfZ0x+lLhim6Pq+MZimVPXw/WFONhyzO3ShiidCTCM1ac4TmiUL3DnUvucanyTJKqXbSILlMteBgifDqaIY9PTF2VwPm6EyBIzs66p0dFholnhvLcvLSJGOZEtlyhaJbISRCTzJCuFrZ5omH93Pvvh7KvuHsWIbLk3nu2tWJGMOJKylKFR8BfDEYAyLC3t4Yjm3xkw8f4KkXrnHyynQ9mWh4plAfbU15hoofrFlO5cr0J8NEQzYlz+B6PuWK4dq0SzRUwDeGTKlCpuiRK3kY43NlMs+hvmQ96CKCb4KRdTRkk4w6FL0KkbDDHbuSjGXKTGTL7OwKRt2OWHTFQvV9moPV32em5DI8nac7HubIjmQ9k3aprTWaLKOUaicNksvQOFX6wIEeoiGLYxenyJeCJJl9B/vY1R1ddOrvlZEU58bzJKIOFb/aMNlApLq+eHE8y588f4VExKEjFmR62pbwyo0MMwWP/b0xzo/n8E2wVmlbkC17nB/P0Z8I88UzY5Q8n2TUoeT5fPHMGI4l3FYtZP7i8AyZosfOzigIOJZweiRDoexiiUUy6mAJ5MsVpvNlbGAsXSQetklEHB451M/XLkwSCwlfOZfl7I0Mxhj6kmFupC1sO47vG44MJHEsmzt2dZAquEScoIhArZtJUDs2w2iqSEfM4fadHfUg2Dj6brbGV/tjpfGPiNpaqpZ3U0qtFQ2SyzB3/evbhrrZ35+oV4b50DPnm66h1RsJX0vhVgy+71PyDLZtIb6h4FaC3pHxEF89N8EPHR2qFwLojoUoej7ZUhDcOqIlZqo1Vys+VFyfG+kCxsDL11KUKxU8P2ihdSNVpDcRJhayuZEp8vzFSaIhG8cSIrbFtZkCxje4FXBsn3TBJewI5YrB94Mg7Jlg5Akw1Bvjr0+Pknd9EhGHnkSYbNFlLFOi7BkODiTZ19PJnp5YfR3UGDOv3Ve2FGQD7+6OLfpHxWKdUWrBNRayOD2SpuBW6qPQ7bIuuFm6lih1q9IguQyLrX9dncpzeSLHicvT9eouvclwkIEK9Td127JIRCBf9vG8CrYdtPQsuD7nx7NBSTmvUg+2nbEQE5kSqaJLoVzhWqpAoexhoF4mTkzQ3upGukjEsUhGQ8TDglsxTOfL5EouMwWXrngIxxLKFZ8b6VKQ9GNMELQBrwKRUHAu27KwLYNlC7ZYFL0Kl8ZzfOXsBJenCsTDNh1Rh+5YCK9isMTCsYU7dnXMGg3u7Y3X1xzntvvqjoeXTMxZyNw/Vvb3JelJRJqWsduqNlPXEqVuVRokl6HZ+peN8OkXr7OrK0Kq4JIquBy/PMVgV5TrqSJFt0JPPMw9u7sY7IoxmiqQjDr4vk/e9RF8EpFQfZP9/t4Yw1PBvsHz4xkujefpjDkM9cSYKbgUq62ohSArNeQIgqFQrlD0PCYny5Rcn0jIIhGxyJQM9+/v5vRolqJXwfUMfckQVybLVHywbCEsBhvB9QyeAcc22JbgVQz5crAeOZWvcGYkTcX4zBS8+p5GAN/4uBUhsUCvyMWKuq9kPfFWSdbZbJm7St2KNEguQ7MszJAt1TezGMlIUHv18mSOq9N5vvOuHZwdy5Evefz5i9ewBTIlj2KmhFsdCdoCxhimc2V6EyF2VztzdMVDiBGiYZvRdInumE9fIoKQxxBkuAKUPINV/f7qdJFYyCbqWGRLLuNZH3z4wstjHO5P8ujhfk5dS5Mre7jVKdmQBZGQg1sBqOB5BjGGirGwxOBWfDzPYNuwrzdOpuQykS1Tcj1KrkdnLEwsbHPfUFdLI7nJbInPnxplKl9esC3WHTs6Fs0QvlWSdW6VPwaU2sysjb6AraQ2IkqEgzeqRNjh++/djQ/1acO+hjf1XV0x9vclsUW4Ol3AEiFddHErPobgl28RTJt6FR/BELKEazNFjh7opisWYixTolLxGeqJMdQT4/DOZP0fTbjZDKMWMB3A8wy5kkehbDBBH2Vm8mVOXJnm1LV0UCUo7mABtmURCdmEbQtHwK/20nB9iDpCdzxMIuxggM6wzUBnlKjjUKkEPSxDtoVjCZPZElen8zx5cpirU/lZv7fatGGu7GELPH9xiucuTtKfCHPXYCdnRrO8eiNDIuxwdH8Pxy9P1zOEa2XzGs/54IFeMgWPdNHFN6a+1eXBA71cnQqu4UPPnF/wWppZ6fPaqfbHQKPt+MeAUpuZBsk1sNCb2US2xEA1ScVg6pmk12eKWICRILAZwBEAi4HOGDMFj5FUgXi144Zb8Sm5PjdSRb51Lc3zFyaozrbWp1sbg2Z3IhjVlTyDJYawA5YFbsXgehWm8mV8A74v7OmJ0p8M4VgWZc8n5FgMJEMMJEMM9caJhG1Kno9tC9GQ4DhBQfJypUJvPIRtBeue5YrPYFcUEIanc/ynz57mVz93ph5sGqcNL00G2zy6E2EuTObY1xfnkcN93Lajg3fcPzRru4olQmc0REfMqdeNheZ/rAD1YNwswC6kMYgv53ntttgfA0qp9aHTrcvQLJGiNvqBm9Owjljs6gzKuRmEIwMJrk0XKbo+FXMzsBnAM5CrBlnbEoqux9Nnxih7hpl8iWzZxxGIOhZj6ZtTbX7D59p0a6ViAIPnG0LWzZGmZQkYIVfyiIVteuIhBCFVKOP6BiGouFN0K9iWRTJi05cI45ugtdVEush0waXgVjAGwiGHLtvGtmFXZwxjDNmSy6s3cth2cN5ax5KpbInDO5IcGeggXXDpigXTpKlqxmzjFGKrU4wLZb0+eXJ4RWt4m3XtT8vcKbXxNEguQ7M30+GZwrw3s596/QGOX54mXXTpiNikix47u6JYUs1KrQ0DqyrAZK7Mns4w11JB9mlnLIRvJJgGNZB3/canzOIDETvYMylUg6YB34dQtUexb4IKO7u7YuTLHvv64ty9q5OPP3+F69MFDIZHDvVR9Cq8OprFGJ/bdySxHQtB6EmE8X1DImQzXSjTEQ3h2EHVn7xbIepYwXpoyGIkVWQkVWI6V2IyV6bk+YykSnRGHYquD0I96adxCnE1643j2RK2wLHRTL1E3oG+OPlyZcnnbda1Py1zp9TG0iC5DIu9mS70ZjZYS8CJhbk2VSAatvCqwz9/gWjnehUuTuYJ2cIdOztJlTwqxsexLSKOxUzeI2SBN/+pwTl9iMccSm4Fywr2UIqBkoBtfIyBZNSiXKlQ8X1eHclyeSLP6w/3ciNdIl30yLs+B/sSjGfKXJsp8OyFSfb0xLhtRwc/+Nqges/OzigvX0sRDVuMpkuUKz4DyQi2FRRgL3o+U9ky5YofbBWJOLi+4fp0HqsnhlSLqz9yqK8+heh7Pu/88LOzgvUjR/pnFQdYas+gBXz5lXFKno/n+zgpi0sTOf7u7QOLPrfdiUALtVRbbacYpdT60CC5DIu9mS70JlxjMBQ9n8lcae4AchZD0DgZoDZmjIUcXK9CyfXxgZLf5MlQTyDq74gwmS6SKQXB0PODNVDHFnYkQkxmS0zn3OrI0qLoVhALdnfFKLo+V6bzREM2OzuiTOXLAFwYz/DN4RRvuWcX77h/aMGKNyPpAjMFF2Og6FVIRh0EoTsRZkdHhOGpAtdmirzj/j0IgmsM3WEH3/P58FcuBiNggULZ4zPfGuHUtRT37evh8Wot26X2DM7kXUZTRTpjIeIhm4LrM5oqcmUyt+hzl2oyvRqNLdX2dAflCz/49DneCxooldoCNEguQ7M308YC4rU34Y99/TIYw96+OCPTBWbyZRzbIh4WsuX5YVKAiCPs6Y4zPJXn5etpRIJM01zJr68tNmMTBJgdnVHcik/BM3THQ1SMwTdBN41iucJwqkSqVCFfrpAreYQdi65YmYHOCBHbZqZQ5uJEjqGeOImwjes7HOxPYjCcHk1Trvj14NLYrurYpSnyrsdM3uWuXZ0MT+VwPR9E2N0dpyMaYn+fUPQq/Oxjh2dd+w/+5lfwPJ9kLIRvDJ4Pvu9zfabIXbuD0noDyfCS64ZXpvLs6YpwLV1mNOURjzjs6Yrwretpbh/sbPrcdq79raalmlJq42mQXIbFNsXPfQOfzM6QL1XIuxWevzRF1LGJhmxAcDBUmD2iTIQtEuGgQXG25AVTrCEbYwyxiEW5YoIMnyYqBNONubKHMQbLMlgCtm2zqzNCruSRKrhkih6ZokciYhMJWXiVoDarV6lQKPuEbCFsW3gVnzNTee7c1UEsHFzH9VSB8+MZfvVzZ7h3qBtDUKmnNnJuHGH2JiJcmc4Tc2wuT+awxSLsCG+8Y/bo7OpUnnNjWWwLpCSUPR+3YrAlCKiFssdXz6YpeT4PHerlyECSvuo06Nx1w2zJZSLn0pcIs6szglsxTORciq5X36IzlS1zfiJLquCCYVZloHas/a20pZpSanPQILlMC72Z1hoZN0rlywzPFEjGugnZFhVgOl+mYgzJmBOM9so+lgUhy8JgmMi6eL5BRNjZGSFXquA7QWcN3wSFAhabrq34ELagIxImU/AQEbIll5euFYM6sdbNjNh8uUIiYuNWKrg+jGZcUkWPrphDbzyCYwfdOmr9HyeyJSazZXpiYQpll+cuTgLwukO9DE/n+OLLNzjQn+DIjiQPHuhlqDvGf/iLl5kpBPtCQ7ZFdyzEvUPd9eutZQs7llAx1Ee3tb2jBnju4hQ98RDliiFT9Dh5ZYb793UznSvz7IVJcqUKLw2n2NsbYzRVZCbvVvdvOhTdoNMJwFfOjrOnK86FyRyxsE3EthjPFHnvH50gFrY5NJBccq1wJWuLzVqqDXZFF3mWUmqz0CC5BhZaq5wpuERDNrGQTV8yzFi6jFT3SkZDdrBlPwQhx8L3fSpGqPg+liW4FZ+pnEvJq5AveVhiEar+SzUfSwY/M0Deq/Add/TzJ8eH8XyDW0sWaljPrBhIF2dnfZZdw0TFpTMawq34hG2LTNGj4FYYSRXZ3RVDLCHvVtjTEwcDp66lSBc8pvMlRmYK3EgXOTOaIWxJUGhABA/BkeD7bw6n6glNf33mBhHHZndXhNOjOYRgRFy7Kqd6P2OZEj3xcLUtGHz13DiXJws4lrCvN8ap6ym+fmESxw5+hyPpIp1lh0K5QkfEob8zSrrgcWbkBn2JEDdShhuZIoVyELwSEYdMafG1wpWuLT5+3x4++PQ5IBhBjqSKDE8XOHqgmydPDmvBcqU2OQ2Sa2ChtUrbCrZunL6epuT6pAslShUf30C5UqYn5nCwv4OpXImpvIfgU6r49MVDVHyfiXQJqWao+vgUmqW0NjBAKu/RkwjzpTNjlDyzaFCd93wJXu/sWJ6h7ii+CfZPXhzPkS16jFsFim6YeDhIJhrLlDg3liVkW/Qlwji21Nt9XRzPVksCCWFHQIK9k186PUq54tMRc4I1V4F0qYLI/IxfS8CuBlmDoT8R5m/PjXN1Mk8yGuLu3Z1M5l3KFUMkZFPyKgx0REgVgv6XQz1x+pMRepNhDvcn+ePnL/PqWI69vXHEGKKhoJ1YquByZGcH0HytcKVriw8d6uO91cedH8+TK7m84Ugv9wz1tFSwXLuAKLWxNEiugYXWKo/u7+HF4RkqnqHkevgGHMuiM+oQsi2mci4XJ7JUjHBkIE666DGddxmeKdYzWVkkk7WZgc4o2ZLLlZkSjhWMXIuLrGU2qgUpA0zkSnieoScRAgwRW7iRLnH2Ro6i52FZFgPJCJWKj2NZXJ0uEA9ZxMI2HZGgLVc0VCt+brCqabu5ksfbquu3XbEQJdcnlXexBUL27GstG0gXy8Qdm4pn+PrFKeJhh3jEIRKSoO2YVyEeDZEI2/UiBwOOzWiqwL7eOEXP51B/gt5kmL5kGNu2uGuwk+szBRJhi1LFUKq2U1lsrXA1a4sPHerjoUN9PHlymFzZa7logXYBUWrjaZBchcX+yv/dZy4QCzkMdoeZzpdJRh3KnsEg5MoVPN+n6BmSYYfxbImyF/RytC1ZSWysu5EuUK5WTg/2ZC5nLHlTxTfBOmGpwsGBJC9dnWYsUybsWDgSlLG7PlMkGhLy5SIlD9JCdU1QyJd8Sm6ZkGPf3Nbi+8Hoza1wbDTDyEyByVyZglvBNxCxLUpeZdYV2yKUKoZKyaW3I1grTURscqUKliV4BoxvmMkHrcAO9MUZniqABKPP+/d11xN9QrYNxuXMSJqiWyFTcrEQwo7F+fEsUcdqula4FmuLyy1asFkrASl1K9EguUKLlagbninw1fMTdEUdyl6F6VwZYwwd0WCPYt4NNvYboOh55FJBf8igKs7qrmsqW6bo3VxrrKww4vomGMjmXJ/rMwVG0yUs8Sl7hpJnCNkQsiFb8uvXjgn6Yhbc4ByeDxW3Uh1Fmuo9V/g/J6+xrzfO7u4YYdvi1HAK3wT1ZRsTk6zqeXPVRdXT19Mc7o/TEw+TKRZwDIgxlCsG4xuSYZuSWyFf9jjQl6Dg+uRKFXoSQQ9LyxLiIRswdEYdrs8UsQW6YiHSBZdrRY8frO7JnGvu2mKqGDSNfuJ1+2c9brHknuUWLdjMlYCUulVokFyhY5emuDSe5uRwmnQ1KpTLHr9R9omEg84YsZBDPGxT8YPpRt8YCtU3/FogKM5ba1xdlEyXZifjrPRstcpABjh+aYpSdRrUFkM0qMiO27DmaQkYQ73sniFoweX74FbXNgXwPJ/xTKk++k5EHMIOFF0oerMjehCkb7YBAzg7kWdHMsL+3hjXUkVCjk2p7FHy4aWRLC+NZBlI2Pzg/bfxlVdu8D+/fA7PGLqiIW7bEac3GaE7HsaxLXxjGM+WGc8UCTkWD+3votzkr5TGtcVrM0EAfOJ1+2etRy6V3DN37frqVI4zI1kO9CcWTOK5VVqCKbWZiTGrHLpsMUePHjXHjx9f8fNrU6y/+7fnuTIZNE82vs941q2/kYesoNVUYxFzIejG4a1mLnWTCElwT7XRZqNaMGx2m44FybBNrhwk63TFQmQaGknPey0rKM5e+8/U94MIfNeeTsqejyM+p0fz9cze2usPdYeYyFVwbIuoI5R9QyrvYQOxSLDvMxyyGUiESUQdDg901OvZ/tJb71rR7+VfPvlNMqVgSjZb9LiRKTKZK9EdC/NrP3wve3vjC1YqGuqN1QtTNK43Ns5WNBav0DVJpdaeiJwwxhyde1xHksvQ+KY1nikHa2K+IVNwcWyo1dGub7moPq+a5LktAiSAa6qNohf4WS1YLUQIfgdFzxB2gj8hIo7NeMVt+lrGh7IfFEaQhlHqTL7MIwf7+PNvXseqltwzJuiiUq74XJlxiTtQcA2Z4s0RrwdUjKFY9gk7NrmyTyIadDopehVSebeljNKFHlNL7skWPS5O5Ag7Fr2xEBPZ0qyEm729cZ48Oczu7tii643aBUSpjadBchkaEyl8YzDGkCmWKS3eZAJDtfPHNlIxQR/MFhNngx6a1a9930dEsJD6VHUzHjcbpvjVBtJRB37k6BBnRrKUPUPYgrBj15tZ29Xn5T1YKGTbErRFyRRdIqGgR2bBreBXg3GzjFII/hs4N5bl0kSOOweT7O1N1B/TEQnWKqdyQYJT2LHIlTz6OyL1npi1ALealmDLpdtIlFo5DZLL0PjGFnNsZvLuqhNttrJWAyTc7HlZX4t1DYagClCjhSoKGaBxVaArFqIjEjSXtiQYuVfKXpAYZJZeh/Wq/TM9H3rjDgXXJ+JY7OvpZCRVWDCj9POnRhmrTp9eGM9hCAJrMhKqZ8/u7Y3xrWtpJnMlemMhcqVgv+ajh3fOC4Drtd6o20iUWh1r6YeomtobG4AxQWGApQqPq5tqs81l/2YgM3OmoJcKcBaQKrh84vgVXh1NBb02CQKeqa6RLhkkq0UdgGqB+iQ7O6KcHk1zcSLHmZE0U9ly/fHJiMPXzk9w4vIUJy/PcG4sy/B0nksTGV4cnqk/pjMe5r1vOkJ3LMxEtkw0ZPHdr9lJTyLMV86Oc2o4xZMnh7k6lefBA71kCh7pYtCNpdYyrLF7zFponP2wROiMhuqjWqXU0jRILkPjG1u2XCFib/QVbX2tLNNaBGugtYBY9ODyRI5rM0USUYeINftcS/2zuNUgbQMj6SJfPTfBX50eZXdnlIMDCdJFjxNXpuuBMlvyuDKZ49JknkwxKCBfKFcYz5R56Vqq/piBZISHDvXxaz98L4+/dojv+bZBuuIhnj0/Sbrg8dq93eTKXj3D9fvv3U0iHIwwE2GnLaO78WypXty9JhkJ9uYqpZam063LsLc3zr6eGB9//gqZYmVVm/5vZZFqklOrs7UL1UQoeIaC5xF1hETUoUOEQtnD9W9mwC5Gqv9jfEiXXPZInPMTOb5tdxfpgocInBvPEJuxOXZxistTeYyBjmiIWMQhX6rgehUmsuX6KLDWf7Ix4ea5C5N0xkLcs7uL3uTNQgTHLk3xjvuH2j7lqdtIlFodDZLL8PyFST5xYpiBjogGyFUoVWZvj1mJ2vMKngHxscSQq1YaWmp6JOIIlYpBqhm6nhcE1lfHMtxIBxmqE9kSN9IlfAMH+2NYIlSMIVt26ZAQIVsouUGZvdPX0zx+355ZAa+WcFNbx66V5YP1LQjQzobSSt0KNEguw9wi12rl1vKPjJI7+2xLndutmGBNsiFCv3ojS9iGdL5MuuAG5fcsIRa2uT5TwjfV5/hB6T3HDgq3d8XC7OqK8JGvXeLLr47XW4XVAuZGj+R0G4lSq6NBchlGUkVKrstzFzTpYTNZbsBdKCO56Pn16keFUhHbBhEh4lj1lNvGad9yxVDxYbArwotXUkzly1yfyddbhX3nnTsYnilwbizLyyNpoo4QcmzCtkVfMsITD++ffxEt+swL1/j481cYT5cY6IzwEw/t4/ualNNTSq2OBsllKJZdTl6dwRHNd9rOPCAof2tm1cGdq2LgzGiWjliJsB00jvbJcPzSJF98+Qav3dfN7q4onudzNV1mZ1eUcDw0ez/LEubucczky/zml8/h+kEt3Kl8iX//mZcAFgyUugVEqdXRILkM45ly0LhYf2uqKu/6VEwZjCHi2GQLLqWKj20JJ68Ynim5DHbF2N8X1I3tiYV49sIkf3t2gp54iJl8mZJn6IzZ3LO7i6G+RH3DP8DHnr3EZK5EuWII28KXXr5BqWJIhB1sOyhgnyt7/ObTZxcMktpJRKnV0bf7ZUgXPfqTYfKuz9quqqmtrOQFRdgxFWzHwq0YPC8Y6ZVcn+vTeYan8rgVn0LJZ25N+964zXgWLk7k+YF7B4mHbT794nVyJZcvnxnjerpI2fMJOxbZUoWoA6FqWT/LAoPNcJO+luPZErbAsdEM6YJLZyxoJ5YvV7QSj1It0CC5DNGwzVS2VO/koVSND5R8CHk3CxXcSBcpuv6SGbxT+UrQIFvgb16d4GB/B+fHs3zhpREyJb+eretVgqnfogc3UsV6QXdHIBZd+P/KFvDchSm642G6YiGKrs9zF6a4Y2fHvFHqmZE0TzxyQAPlFqF/5KwPDZLL0BWxuTKlAVI11/j303L+mPL8YC308lSB//H0WW7rj5MpBc9f6Cy1YwYoG4j6FX7nmfMIQgVTf9OUWk2o2g6U6uczo2lEhO54mO6YRdH1uTiR43OnRvjZxw4v447VRrg6lecX/tcJXh5N43oQcuA1uzr573/vgVsmUK7XHwkaJJfh9Eh2oy9B3QKmci7P5VLLek7FN3z+1CjX00XiIYvueJhjF6foSYR55FAfFyZzpIoenVGHRw718acnrrK/L871VIFiuUI0bNMRsetl9lZLRznt9f5PvsA3htP170sefGM4zfs/+QJ/9LOv38ArWx/rmZCmQXIZ5vVHVmqTyLlwfjxLueIzbQw30iVevpYiFnbY3R1lV1eMWLU8Xa7a0+3CeA7PN1R8g28MZc8nGXEWbADdzELBEGYnHLmex6deuMZrdnfN20e6naznHwZfPT+9rOPbzXompGmQVGqbmC40/hkXBMK85zKZd/nW9UywfmnDzmSEHV1RzlxPUfaDqV6R4M2gPxGu15dd6q/yq1N5fvS3/paR7M3XHUw6/NDR/Tx18goTuZulG5PhoHvLYHd0Xuuxs2MZUnmX7nh4zYLo8xcmeeqFa4ykigx2RXn8vj08dKhvVedcTLtHNj/5O1/lK+dnNF2wqtVWc2thywdJEXkr8N8I6lX/rjHmlzf4kpTalGotx0ZSJSazJQoNW0CNARd4dSzLX35rhLt2JZv+VV4LQJ94/uq82ZWRrMcHv3x+3nOyZfibV8Z48107gaD1WLni4/kVrkzmsSwhVXCJhWxGU6sLLs9fmOTXvvAKXiUIKTP5MmfHsvzz776jbYGynSObn/ydr/LM+ZnVXuK2sp6VrLb0rngRsYHfBL4HeA3w4yLymo29KqU2r2QkRNiReoCc2+rN9aHo+nzl3BQnLk/Oe/7zFyb54NPnyJS8ZS8/zBQr1WtweHF4ho6Yw1imTDzs0BMPE4843MgUV93K6+Nfv0ym4BJ2bBIRh7Bjkym4fPzrl1d8zqW0s9uKBsj51qvVHGzxIAk8BJwzxlwwxpSBPwHevsHXpNSm1tjoeqF+qEFgsTg/np/3s7WoX5wteRgMyYhDuuASDQUXFHUs0kVv1cHl7FiWjlhwDyJC2LHoiDmcHWtf4l1jr9ka7bbSPrWaxO1uNQdbf7p1D3C14fth4HXterGwFTQMVmqr8o2hXGnYuVmtS9uo7PlYws3tIw1GUkGXlJWq/cV/71B3MF1W3bsZC9sUPZ/OqLPq4BIJCV7FwM2ZOLyKIRJqX4t07bay/mqddtptq48kF/qvft7ebRF5j4gcF5Hj4+PjK36xt33bri3/C1O3NtergFgkQsH/+WXO/1tCNji20J+McLB//hvQYFeUVHHled61v/jfes8gmYLHjo4w+bLHdL5MvuSxsyO66mmzRw/3kyl65Eoevu+TK3lkih6PHu5f8TmX0s6RzUBiq49ltrat/tsfBvY2fD8EXJ/7IGPMh4EPAxw9enSlLQz5p2+5i3TB48XrKSaz7kpPo9SG6YiG+HsP7uXIzg7+1VPfDGrPVtcnHYHXHeilJxEmlXd5fIFasI/ft4cPPn0OgIGYxXhheVMr77h/qP51rYVXwfXr2a17emKrzm5950P7mcyWOTeeZSrvkgjbHN3fwzsfWnnnlVa0a2Tzm3//KD/zkedIl1f81qVWQcwyOhJsNiLiAK8C3wFcA44Bf88Y81Kz5xw9etQcP358xa/ZuBfqP332zIrPo9RauvTL38sbf/kLXJqZ/8ebBbzhcDd/+LOPzjre2HIrbENPMkLEsZfcMtG4veLUlcl6oGx8nQPv/4sFr3G9bLdiBgttafnRD3993uPW83e83YjICWPM0XnHt3KQBBCRtwH/lWALyO8bY/7jYo9fbZBUSim1/TQLklt9uhVjzGeBz270dSillNp+NA9FKaWUakKDpFJKKdWEBkmllFKqCQ2SSimlVBMaJJVSSqkmNEgqpZRSTWiQVEoppZrY8sUElktExoH29cyBfmCijeffDG6FewS9z+3mVrjPW+EeoT33ud8YMzD34C0XJNtNRI4vVLVhO7kV7hH0PrebW+E+b4V7hPW9T51uVUoppZrQIKmUUko1oUFy7X14oy9gHdwK9wh6n9vNrXCft8I9wjrep65JKqWUUk3oSFIppZRqQoPkConIXhF5WkROi8hLIvK+6vFeEfkrETlb/dyz0de6WiJii8g3ROQz1e+34z12i8ificiZ6r/pI9v0Pv9x9b/XUyLyxyIS3Q73KSK/LyJjInKq4VjT+xKRD4jIORF5RUTesjFXvXxN7vPXqv/dflNE/o+IdDf8bNvcZ8PP/pmIGBHpbzjWtvvUILlyHvBPjTF3AQ8DPy8irwHeD3zJGHMb8KXq91vd+4DTDd9vx3v8b8DnjDF3AvcS3O+2uk8R2QP8AnDUGHMPQaPyd7I97vMjwFvnHFvwvqr/P30ncHf1Ob8lIvb6XeqqfIT59/lXwD3GmG8HXgU+ANvyPhGRvcB3AVcajrX1PjVIrpAxZsQYc7L6dYbgTXUP8Hbgo9WHfRR4fEMucI2IyBDwvcDvNhzebvfYCTwG/B6AMaZsjJlhm91nlQPERMQB4sB1tsF9GmOeAabmHG52X28H/sQYUzLGXATOAQ+tx3Wu1kL3aYz5gjHGq377dWCo+vW2us+q3wD+BdCYTNPW+9QguQZE5ADwWuA5YKcxZgSCQArs2MBLWwv/leA/Sr/h2Ha7x0PAOPAH1Wnl3xWRBNvsPo0x14D/TPBX+AiQMsZ8gW12nw2a3dce4GrD44arx7aDfwD8ZfXrbXWfIvIDwDVjzItzftTW+9QguUoikgQ+CfyiMSa90dezlkTk+4AxY8yJjb6WNnOA+4HfNsa8FsixNaccF1Vdk3s7cBDYDSRE5Cc29qo2hCxwbMun+YvIvyJYBvqj2qEFHrYl71NE4sC/Av6fhX68wLE1u08NkqsgIiGCAPlHxpgnq4dviMhg9eeDwNhGXd8aeBT4ARG5BPwJ8GYR+Tjb6x4h+Mtz2BjzXPX7PyMImtvtPr8TuGiMGTfGuMCTwOvZfvdZ0+y+hoG9DY8bIph23rJE5F3A9wF/39zc17ed7vMwwR93L1bfj4aAkyKyizbfpwbJFRIRIVjDOm2M+fWGH30KeFf163cBf77e17ZWjDEfMMYMGWMOECyM/7Ux5ifYRvcIYIwZBa6KyB3VQ98BvMw2u0+CadaHRSRe/e/3OwjW0rfbfdY0u69PAe8UkYiIHARuA57fgOtbEyLyVuCXgB8wxuQbfrRt7tMY8y1jzA5jzIHq+9EwcH/1/7vtvU9jjH6s4AN4A8GQ/pvAC9WPtwF9BJl0Z6ufezf6Wtfoft8IfKb69ba7R+A+4Hj13/MpoGeb3ue/A84Ap4CPAZHtcJ/AHxOss7oEb6DvXuy+CKbuzgOvAN+z0de/yvs8R7AmV3sf+p/b8T7n/PwS0L8e96kVd5RSSqkmdLpVKaWUakKDpFJKKdWEBkmllFKqCQ2SSimlVBMaJJVSSqkmNEgqtYmIyA9WOxzcucbn/UUR+cm1PGeLrzsgIp9b79dVaq1okFRqc/lx4CsExRvWRLWY+T8A/tdanbPJa8xjjBkHRkTk0Xa9tlLtpEFSqU2iWgf4UYIN4u9sOG6JyG9V+0B+RkQ+KyI/XP3ZAyLyNyJyQkQ+XyvDNsebgZPGGE9EDovIyYZz3yYiJxY7l4j8rIgcE5EXReST1TqaiMhHROTXReRp4FdE5O+KyAvVj2+ISEf1ZZ4C/v6a/8KUWgcaJJXaPB4n6Gn5KjAlIvdXj78DOAB8G/AzwCNQrx38P4AfNsY8APw+8B8XOO+jwAkAY8x5ICUi91V/9tPAR5Y415PGmAeNMbU+m+9uOPftwHcaY/4p8M+AnzfG3Af8HaBQfczx6vdKbTkLTpEopTbEjxO0JoOgoPyPAycJSiD+qTHGB0arIzeAO4B7gL8KSrFiE5TymmuQ2U2zfxf4aRH5J8CPEfTeW+xc94jIfwC6gSTw+YZz/akxplL9+qvAr4vIHxEE1uHq8TGCriNKbTkaJJXaBESkj2Ba9B4RMQRByojIv2DhVkBUj79kjHlkidMXgGjD958E/g3w18AJY8ykiOxe5FwfAR43xrwoIj9FUMe3Jlf7whjzyyLyFwQ1jL8uIt9pjDlTfe0CSm1BOt2q1Obww8AfGmP2m6DTwV7gIsEo8ivAD1XXJndyM0i9AgyISH36VUTuXuDcp4EjtW+MMUWC0eBvA3/Qwrk6CJJvQiyytigih03QreFXCKZYaxm6txMUVFdqy9EgqdTm8OPA/5lz7JPA36t+HiYINB8CngNSxpgyQXD9FRF5kaADxOsXOPdfAo/NOfZHBF1svgCwxLn+7+pr/hVBB5FmflFETlWfX6i+LsCbgL9Y5HlKbVraBUSpLUBEksaYbHVa9nngURP00mv1+f8H+BfGmLPV7/8Z0GWM+b/bc8WzXvsZ4O3GmOl2v5ZSa03XJJXaGj4jIt1AGPh/lxMgq95PkMBzthowDxOsgbaViAwAv64BUm1VOpJUSimlmtA1SaWUUqoJDZJKKaVUExoklVJKqSY0SCqllFJNaJBUSimlmtAgqZRSSjXx/wOjxf/VCK68HAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=user.age, y=trip.duration_sec, alpha=0.35)\n",
    "plt.xlabel('Age (years)')\n",
    "plt.ylabel('Duration (seconds)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the plot above shows that the riding duration was highest for younger users. This was particularly the case for users less than 40 years. We can notice a build up of over plotted points between ages 20 to 60, and then many other outliers representing higher durations. However, there is a very weeak correlation between the age and duration. \n",
    "\n",
    "Onward, I'd also like to see how long *Subscribers* rode the bikes compared to *Customers* overall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain the total numner of users in each type\n",
    "user_num = user.user_type.value_counts()\n",
    "\n",
    "# create a new table joining the users and the duration from previous tables \n",
    "user_duration = user[['user_type']]\n",
    "user_duration = user_duration.join(trip.duration_sec)\n",
    "duration_num = user_duration.groupby('user_type').sum()\n",
    "duration_num = duration_num.duration_sec.sort_values(ascending = False)\n",
    "\n",
    "# make a plot of the total duration for each user type\n",
    "plt.bar(x = duration_num.index, height= duration_num.values)\n",
    "plt.ylabel('Duration (seconds x 1e8)'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above comparison, Subscribers clearly have a longer total duration than Customers. However, this could be attributed to the higher population of subscribers in the dataset. To validate, I'll calculate the average duration for each user type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use a barplot to show mean duration for each user type\n",
    "sns.barplot(x=user.user_type, y=trip.duration_sec, color=colour);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple bar plot shows the mean duration (seconds) covered by each user type. It's pronounced that Customers had more ride-time on average than Subcribers. This is a bit unexpected since Subcribers from our earlier plot were more populated than Customers by headcount. However, the error bar on the Customer barplot suggests the presence of large values in duration that may influence the mean. This suggest that more Subscribers weren't intensively taking long durations trips compared to the Customers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier, the histogram distribution of usage across the hours of the day showed two peaks around the 8th and 17th hours, and I speculated they could be due to workers commuting. To increase confidence, I would like to see if Subscriber used the bikes more around this period. (Routine need for the service could be a factor responsible for their subscription)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(user['user_type']).join(pd.DataFrame(trip.hour))\n",
    "sns.violinplot(data = df, x='user_type', y='hour', color=colour);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The violinplot for Subscribers show the peaks earlier observed around the 8th and 17th hours repectively. The rate of use is fairly low for most other periods but these two. For Customers however, there is also increased activity around the peak hours as well but not comparable to the Subscriber group. I'll eneunciate this difference using a Facted Grid plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data=df, col='user_type', height=4)\n",
    "grid.map(plt.hist, 'hour', bins=26)\n",
    "grid.set_titles('{col_name}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram for Subscribers is explicitly bimodal showing the two peak periods. On the other hand, Customers have a fairly evenly distributed plot.\n",
    "It becomes clearer that Subcribers have a greater amount of recorded use than Customers by the peak hours.\n",
    "\n",
    "Moving on, I suspect gender may have a benign effect on users' choice to use the bike service. How do the different gender groups compare?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot to visualise number of user types for each gender\n",
    "sns.countplot(data = user, x = 'member_gender', hue='user_type', order = user.member_gender.value_counts().index)\n",
    "plt.title('Gender Distribution Among Users');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that more males chose to subscribe than any other gender. But I would also like to do a comparative anaysis of what proportion of each gender subscribed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member_gender  user_type \n",
       "Female         Customer        4623\n",
       "               Subscriber     36182\n",
       "Male           Customer       11493\n",
       "               Subscriber    119007\n",
       "Other          Customer         450\n",
       "               Subscriber      3197\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdata = user.groupby(['member_gender','user_type']).count()['age']\n",
    "subdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = user.member_gender.value_counts()\n",
    "# obtain the number of Subcribers for each gender\n",
    "subdata = user.groupby(['member_gender','user_type']).size()\n",
    "\n",
    "# loop through the different genders and create list for proportion of subscribers for each\n",
    "gender = ['Male', 'Female', 'Other']\n",
    "sub_prop = []\n",
    "for name in gender:\n",
    "    sub_prop.append(subdata[name]['Subscriber']/total[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot to show the proportions of sucscriptions\n",
    "plt.bar(x=gender, height=sub_prop)\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Proportion')\n",
    "plt.title('Proportion of Subscribers by Gender');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Males had a slightly higher subscription rate than the other genders. However, it is evident that although Males had a higher population, their rate of subscription compared similarly to other two genders.\n",
    "\n",
    "Now, I would like to know if age was a factor affecting subscription."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(14, 141+5, 5)\n",
    "grid = sns.FacetGrid(data=user, col='user_type', height=4)\n",
    "grid.map(plt.hist, 'age', bins=bins)\n",
    "grid.set_titles('{col_name}')\n",
    "grid.set(xlim=(10, 100))\n",
    "plt.suptitle('Age Distribution among User Types', x=0.5, y=1.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age as a factor seemed to  affect the general use of the bike share system. I have set the X axis to a smaller limit to zoom in on the main detail. As shown in the chart, more *Subscribers* as well as *Customers* were younger by age. There were few people older than 60 who were Subscribers and Customers alike. Both charts are right skewed which shows a similar decrease in frequency as age increased for both categories. Thus, neither is more favored by the age of users.\n",
    "\n",
    "\n",
    "Wraping up, I would like to investigate if there is an unsual pattern in days the bikes are used by users based on their type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_day = trip[['day_name']]\n",
    "user_day = user_day.join(user.user_type)\n",
    "sns.countplot(data=user_day, x='day_name', hue='user_type')\n",
    "plt.title('Daily use for User types');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There was a slight rise in the use on thursdays for both Subscribers and Customers. Beside that, the pattern remained generally similar for user types. However, Subscribers were clearly more active during week days with a peak on Thursdays, followed by Tuesday. However, bike use on the weekends was particularly low among subscribers which could indicate that most Subscribers were more frequent user like Office workers commuting.\n",
    "\n",
    "To look a little into this, I'll inspect how age played on the different days of the week (juxtaposing working ages)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_day = trip[['day_name']]\n",
    "user_day = user_day.join(user.age)\n",
    "grid = sns.FacetGrid(data =user_day, col='day_name', col_wrap=4)\n",
    "grid.map(plt.hist, 'age', bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look at the working age range reveals that the most use was within this range (between 25 and 50). It backs my expectation that most users could possibly fit into job roles. There is also a noticeable decline in bike use for users within this age range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> There was a very weak correlation between the age of the users and the duration of the rides they took. However, I intuitively expected the younger riders to take loger rides than older people which was the case. Something unexpected though was that **Customers** who had a lesser population than **Subscribers** had longer durations of usage. Similarly, among the **Genders**, *Males* dominated the user population by head count, yet considering the proportion of subscribed Males, they were only marginally above the other two gender groups (Female and Others).\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> An expected relationship was found between the **age** variable and the **duration** of bike use per ride, however, the impact of age on subscription rates was that young people were majorly the users both Subscribed and Customers. Also, the **day** of the week with the most active use was *Thursday* among both subcribed users and other customers. While the weekends saw a rather relaxed engagement from Subscribers, Customers' use remained fairly stable each day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration <a id=topic5></a>\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My exploration focus for this part covers how bike use is affected by the time of day, day of week, the user type and the duration of ride.\n",
    "\n",
    "First, I'll consider the effect of the peak hours on different user groups on the days of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "df = trip[['day_name', 'hour']].join(user.user_type)\n",
    "\n",
    "sns.violinplot(data = df, x='day_name', y='hour', hue='user_type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we still notice that Subscribers have more utility for the bike during the peak hours particularly on week days. Although Customers share a similar pattern of use, it is more pronounced among subscribers. During the weekends, the rate declines and the pattern changes, the most use comes just once toward the evening at about the 15th hour.\n",
    "\n",
    "Sicnce users are affected by hour of the day, I'd like to see how the duration of rides relate to user types and different week days. Are there days where people go farther or is the pattern uniform for both user types?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create categories for the duration variable\n",
    "# bin size between ranges from the minimun duration, through the first, third and fourth quartile\n",
    "minimum = trip.duration_sec.min()\n",
    "median = trip.duration_sec.quantile(0.25)\n",
    "quartile_3 = trip.duration_sec.quantile(0.75)\n",
    "maximum = trip.duration_sec.max()\n",
    "bin_size = [minimum, median, quartile_3, maximum]\n",
    "label = ['Short', 'Medium', 'Long']\n",
    "aband = trip.duration_sec.dropna()\n",
    "trip['timing'] = pd.cut(aband, bins=bin_size, labels=label)\n",
    "\n",
    "# create a sub-dataframe for the variabels to plot\n",
    "df = trip[['day_name', 'timing']].join(user.user_type)\n",
    "\n",
    "# create the plots for each user type\n",
    "plt.figure(figsize=(14,6))\n",
    "users = ['Subscriber', 'Customer']\n",
    "for num in range(len(users)):\n",
    "    subdata = df[df.user_type == users[num]]\n",
    "    plt.subplot(1,2,num+1)\n",
    "    sns.countplot(data = subdata, x='day_name', hue='timing', palette='Blues')\n",
    "    plt.xticks(rotation=30)\n",
    "    plt.title(users[num])\n",
    "    plt.legend(title='Duration')\n",
    "    plt.ylim(0,16500);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The duration of trips among the Subscriber and Customers on different days are clearly distinctive but siilar within each user-type group. I have grouped the trip durations into Short, Medium and Long categories in the following order\n",
    "- Miniumum duration to first quartile = `Short`\n",
    "- First Quartile to third quartile = `Medium`\n",
    "- Third quartile to maximum duration = `Long`\n",
    "\n",
    "While more Subscribers used the service for short and medium durations, Customers where more invested in longer duration trips. \n",
    "\n",
    "Also, Subscribers had lower use over the wekends than during weekdays but this was slightly different for Customers with higher use toward the weekend. For Customers also, weekends had the most long-duration trips with Sunday topping the chart. Similarly, short duration trips were also lowest on Sunday and Saturday for Customers. \n",
    "\n",
    "Since it is a little difficult to make out the exact proportions of bars on the scale above for customers, I plotted the same graph for only Customers this time on its own scale below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "subdata = df[df.user_type == 'Customer']\n",
    "sns.countplot(data = subdata, x='day_name', hue='timing', palette='Blues')\n",
    "plt.xticks(rotation=30)\n",
    "plt.title(users[num])\n",
    "plt.legend(title='Duration');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is clearer the number of rides and their durations on diferent days of the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> This part of my exploration focused on how bike use was affected by the time of day, day of week, the user type and the duration of ride. It was made clear that both Subscribers and Customers had similar use patterns for the hours of the day though the week. However, While the usage pattern was similar by the hour, the duration of trips were different among the user types. Customers were shown to take longer trips than Subscribers through out the week, and though the Subscriber had more trips in general, Customer trips were mostly longer in nature by duration.\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> While Customers (user type) were mostly interested in medium and long duration trips (particularly long), Subscribers were more engaged with short and medium trip durations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions <a id=topic6></a>\n",
    ">You can write a summary of the main findings and reflect on the steps taken during the data exploration.\n",
    "\n",
    "In my exploration, I found that the general trend in usage of the bike share system was largely predicated on the user-type. As such, Customers generally took longer duration trips than Subscribers. This was initially surprising for Customers to have longer average trip durations than Subscribers, but further analysis revealed that Subscribers were mostly using the service particularly for shorter durations than Customers. \n",
    "To obtain this relationship, I grouped the durations into Short, Medium, and Long trips in the following order:\n",
    "\n",
    "- Miniumum duration to first quartile = `Short`\n",
    "- First Quartile to third quartile = `Medium`\n",
    "- Third quartile to maximum duration = `Long`\n",
    "\n",
    "Furthermore, I found that the peak periods of use varied by day of the week and time of the day, with more Subscribers using the service around the 8th and 17th hour on weekdays but not weekends. Customers however, had a smilar pattern by the hours of each day, but the distribution was more uniform over the hours of the day. This was because Customers had slightly more use during off-peak hours on those weekdays. In my analysis of bike use by the genders, I discovered the Males had a predominant population, but had a similar proportion of subscribed users only marginally higher than Females and the Other genders. \n",
    "\n",
    "Outside the main variables of interest, I also discovered that Thursday had the most trips taken, also, the most visted start and end stations were `Market St at 10th St` and `San Francisco Caltrain Station 2  (Townsend St at 4th St)` respectively.\n",
    "Similarly, I found that most users folowed the route from `Berry St at 4th St` to `San Francisco Ferry Building (Harry Bridges Plaza)` stations, as it was the most frequent trip taken (Start to end).\n",
    "\n",
    "In summary, here's a walkthrough to highlight analysis and exploration steps and outcome on the bike share system in the Bay area of San Fransisco:\n",
    "\n",
    "> I found the population of Gender groups using the bikes. A greater population of users were `Males` among the gender groups and `Subscribers` among the user types. Despite the population by head count, the proportion of Subscribed users in each gender was very comparatively similar. \n",
    "\n",
    "> I investigated the peak periods over the week in days and hour of day as well. `Thursday` was the most active day in the week for use of the service, and `Customers` had longer trip durations on average than the Subscibers. Also, peak hours across the week was usually around the 8th hour and the 17th on week days, with the 17th being the busiest hour. Weekends on the other hand had lower activity at those times. \n",
    "\n",
    "\n",
    "> While `Market St at 10th St` was the most frequent Start Station for users, `San Francisco Caltrain Station 2  (Townsend St at 4th St)` was the most frequent end station. Similarly, moving from `Berry St at 4th St` to `San Francisco Ferry Building (Harry Bridges Plaza)` was the most frequent route taken by users.\n",
    "\n",
    "> I evealuated the imact of age of choice of Subscription and trip durations. Younger users were revealed to have more trip durations than older ones, however, `age` had no direct effect on whether users subscribed or not. \n",
    "\n",
    "> Eventually, the analysis uncovered the surprising relationship between the user type, trip duration and day of week. While Subscribers had lower weekend utility, Customers took the most long-duration trips over the weekends. Explicitly, `Customer` type users were more inclined to take longer trip durations than Subscribed users. Hence, Customers had a higher average duration among the user tyes inspite of `Subscribers` being more populated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dataset as csv files\n",
    "bike.to_csv('clean_bike_data.csv', index=False)\n",
    "user.to_csv('clean_user_data.csv', index=False)\n",
    "trip.to_csv('clean_trip_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Back to Top](#topic0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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