Part_I_Ford_GoBike_System_exploration_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I - Ford GoBike System Data Exploration\n",
    "## by Richmond Tetteh\n",
    "\n",
    "## Introduction\n",
    "> This document explores a Ford GoBike System dataset containing bike sharing data for approximately 183,412 customers.\n",
    "\n",
    "\n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe, print statistics\n",
    "bike_data = pd.read_csv('201902-fordgobike-tripdata.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "   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",
      "5          1793  2019-02-28 23:49:58.6320  2019-03-01 00:19:51.7600   \n",
      "6          1147  2019-02-28 23:55:35.1040  2019-03-01 00:14:42.5880   \n",
      "7          1615  2019-02-28 23:41:06.7660  2019-03-01 00:08:02.7560   \n",
      "8          1570  2019-02-28 23:41:48.7900  2019-03-01 00:07:59.7150   \n",
      "9          1049  2019-02-28 23:49:47.6990  2019-03-01 00:07:17.0250   \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",
      "5              93.0                      4th St at Mission Bay Blvd S   \n",
      "6             300.0                              Palm St at Willow St   \n",
      "7              10.0                        Washington St at Kearny St   \n",
      "8              10.0                        Washington St at Kearny St   \n",
      "9              19.0                              Post St at Kearny St   \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",
      "5               37.770407              -122.391198           323.0   \n",
      "6               37.317298              -121.884995           312.0   \n",
      "7               37.795393              -122.404770           127.0   \n",
      "8               37.795393              -122.404770           127.0   \n",
      "9               37.788975              -122.403452           121.0   \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",
      "5                            Broadway at Kearny             37.798014   \n",
      "6                      San Jose Diridon Station             37.329732   \n",
      "7                        Valencia St at 21st St             37.756708   \n",
      "8                        Valencia St at 21st St             37.756708   \n",
      "9                            Mission Playground             37.759210   \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",
      "5            -122.405950     5200  Subscriber             1959.0   \n",
      "6            -121.901782     3803  Subscriber             1983.0   \n",
      "7            -122.421025     6329  Subscriber             1989.0   \n",
      "8            -122.421025     6548  Subscriber             1988.0   \n",
      "9            -122.421339     6488  Subscriber             1992.0   \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  \n",
      "5          Male                      No  \n",
      "6        Female                      No  \n",
      "7          Male                      No  \n",
      "8         Other                      No  \n",
      "9          Male                      No  \n"
     ]
    }
   ],
   "source": [
    "# high-level overview of data shape and composition\n",
    "print(bike_data.shape)\n",
    "print(bike_data.dtypes)\n",
    "print(bike_data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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>183407</th>\n",
       "      <td>480</td>\n",
       "      <td>2019-02-01 00:04:49.7240</td>\n",
       "      <td>2019-02-01 00:12:50.0340</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>4832</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183408</th>\n",
       "      <td>313</td>\n",
       "      <td>2019-02-01 00:05:34.7440</td>\n",
       "      <td>2019-02-01 00:10:48.5020</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>66.0</td>\n",
       "      <td>3rd St at Townsend St</td>\n",
       "      <td>37.778742</td>\n",
       "      <td>-122.392741</td>\n",
       "      <td>4960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183409</th>\n",
       "      <td>141</td>\n",
       "      <td>2019-02-01 00:06:05.5490</td>\n",
       "      <td>2019-02-01 00:08:27.2200</td>\n",
       "      <td>278.0</td>\n",
       "      <td>The Alameda at Bush St</td>\n",
       "      <td>37.331932</td>\n",
       "      <td>-121.904888</td>\n",
       "      <td>277.0</td>\n",
       "      <td>Morrison Ave at Julian St</td>\n",
       "      <td>37.333658</td>\n",
       "      <td>-121.908586</td>\n",
       "      <td>3824</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183410</th>\n",
       "      <td>139</td>\n",
       "      <td>2019-02-01 00:05:34.3600</td>\n",
       "      <td>2019-02-01 00:07:54.2870</td>\n",
       "      <td>220.0</td>\n",
       "      <td>San Pablo Ave at MLK Jr Way</td>\n",
       "      <td>37.811351</td>\n",
       "      <td>-122.273422</td>\n",
       "      <td>216.0</td>\n",
       "      <td>San Pablo Ave at 27th St</td>\n",
       "      <td>37.817827</td>\n",
       "      <td>-122.275698</td>\n",
       "      <td>5095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183411</th>\n",
       "      <td>271</td>\n",
       "      <td>2019-02-01 00:00:20.6360</td>\n",
       "      <td>2019-02-01 00:04:52.0580</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>1057</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</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",
       "183407           480  2019-02-01 00:04:49.7240  2019-02-01 00:12:50.0340   \n",
       "183408           313  2019-02-01 00:05:34.7440  2019-02-01 00:10:48.5020   \n",
       "183409           141  2019-02-01 00:06:05.5490  2019-02-01 00:08:27.2200   \n",
       "183410           139  2019-02-01 00:05:34.3600  2019-02-01 00:07:54.2870   \n",
       "183411           271  2019-02-01 00:00:20.6360  2019-02-01 00:04:52.0580   \n",
       "\n",
       "        start_station_id                                start_station_name  \\\n",
       "183407              27.0                           Beale St at Harrison St   \n",
       "183408              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "183409             278.0                            The Alameda at Bush St   \n",
       "183410             220.0                       San Pablo Ave at MLK Jr Way   \n",
       "183411              24.0                             Spear St at Folsom St   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "183407               37.788059              -122.391865           324.0   \n",
       "183408               37.789625              -122.400811            66.0   \n",
       "183409               37.331932              -121.904888           277.0   \n",
       "183410               37.811351              -122.273422           216.0   \n",
       "183411               37.789677              -122.390428            37.0   \n",
       "\n",
       "                           end_station_name  end_station_latitude  \\\n",
       "183407  Union Square (Powell St at Post St)             37.788300   \n",
       "183408                3rd St at Townsend St             37.778742   \n",
       "183409            Morrison Ave at Julian St             37.333658   \n",
       "183410             San Pablo Ave at 27th St             37.817827   \n",
       "183411                  2nd St at Folsom St             37.785000   \n",
       "\n",
       "        end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "183407            -122.408531     4832  Subscriber             1996.0   \n",
       "183408            -122.392741     4960  Subscriber             1984.0   \n",
       "183409            -121.908586     3824  Subscriber             1990.0   \n",
       "183410            -122.275698     5095  Subscriber             1988.0   \n",
       "183411            -122.395936     1057  Subscriber             1989.0   \n",
       "\n",
       "       member_gender bike_share_for_all_trip  \n",
       "183407          Male                      No  \n",
       "183408          Male                      No  \n",
       "183409          Male                     Yes  \n",
       "183410          Male                      No  \n",
       "183411          Male                      No  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>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>103696</th>\n",
       "      <td>447</td>\n",
       "      <td>2019-02-14 11:31:51.0640</td>\n",
       "      <td>2019-02-14 11:39:18.7000</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Berkeley Civic Center</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.270556</td>\n",
       "      <td>254.0</td>\n",
       "      <td>Vine St at Shattuck Ave</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-122.269592</td>\n",
       "      <td>5455</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94477</th>\n",
       "      <td>581</td>\n",
       "      <td>2019-02-15 16:20:33.5910</td>\n",
       "      <td>2019-02-15 16:30:14.7540</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>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>5084</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105613</th>\n",
       "      <td>208</td>\n",
       "      <td>2019-02-13 22:09:11.5540</td>\n",
       "      <td>2019-02-13 22:12:40.1670</td>\n",
       "      <td>243.0</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>267.0</td>\n",
       "      <td>Derby St at College Ave</td>\n",
       "      <td>37.861804</td>\n",
       "      <td>-122.253569</td>\n",
       "      <td>4605</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25984</th>\n",
       "      <td>178</td>\n",
       "      <td>2019-02-25 14:57:50.0030</td>\n",
       "      <td>2019-02-25 15:00:48.8390</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Howard St at 2nd St</td>\n",
       "      <td>37.787522</td>\n",
       "      <td>-122.397405</td>\n",
       "      <td>284.0</td>\n",
       "      <td>Yerba Buena Center for the Arts (Howard St at ...</td>\n",
       "      <td>37.784872</td>\n",
       "      <td>-122.400876</td>\n",
       "      <td>5229</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100471</th>\n",
       "      <td>695</td>\n",
       "      <td>2019-02-14 18:17:35.2250</td>\n",
       "      <td>2019-02-14 18:29:10.8190</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>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>4648</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1992.0</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",
       "103696           447  2019-02-14 11:31:51.0640  2019-02-14 11:39:18.7000   \n",
       "94477            581  2019-02-15 16:20:33.5910  2019-02-15 16:30:14.7540   \n",
       "105613           208  2019-02-13 22:09:11.5540  2019-02-13 22:12:40.1670   \n",
       "25984            178  2019-02-25 14:57:50.0030  2019-02-25 15:00:48.8390   \n",
       "100471           695  2019-02-14 18:17:35.2250  2019-02-14 18:29:10.8190   \n",
       "\n",
       "        start_station_id                                start_station_name  \\\n",
       "103696             246.0                             Berkeley Civic Center   \n",
       "94477               21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "105613             243.0                       Bancroft Way at College Ave   \n",
       "25984               25.0                               Howard St at 2nd St   \n",
       "100471              81.0                                Berry St at 4th St   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "103696               37.869060              -122.270556           254.0   \n",
       "94477                37.789625              -122.400811            67.0   \n",
       "105613               37.869360              -122.254337           267.0   \n",
       "25984                37.787522              -122.397405           284.0   \n",
       "100471               37.775880              -122.393170             3.0   \n",
       "\n",
       "                                         end_station_name  \\\n",
       "103696                            Vine St at Shattuck Ave   \n",
       "94477   San Francisco Caltrain Station 2  (Townsend St...   \n",
       "105613                            Derby St at College Ave   \n",
       "25984   Yerba Buena Center for the Arts (Howard St at ...   \n",
       "100471       Powell St BART Station (Market St at 4th St)   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "103696             37.880222            -122.269592     5455  Subscriber   \n",
       "94477              37.776639            -122.395526     5084  Subscriber   \n",
       "105613             37.861804            -122.253569     4605  Subscriber   \n",
       "25984              37.784872            -122.400876     5229  Subscriber   \n",
       "100471             37.786375            -122.404904     4648    Customer   \n",
       "\n",
       "        member_birth_year member_gender bike_share_for_all_trip  \n",
       "103696             1995.0        Female                      No  \n",
       "94477              1973.0        Female                      No  \n",
       "105613             1998.0          Male                     Yes  \n",
       "25984              1975.0          Male                      No  \n",
       "100471             1992.0          Male                      No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "duration_sec               183412 non-null int64\n",
      "start_time                 183412 non-null object\n",
      "end_time                   183412 non-null object\n",
      "start_station_id           183215 non-null float64\n",
      "start_station_name         183215 non-null object\n",
      "start_station_latitude     183412 non-null float64\n",
      "start_station_longitude    183412 non-null float64\n",
      "end_station_id             183215 non-null float64\n",
      "end_station_name           183215 non-null object\n",
      "end_station_latitude       183412 non-null float64\n",
      "end_station_longitude      183412 non-null float64\n",
      "bike_id                    183412 non-null int64\n",
      "user_type                  183412 non-null object\n",
      "member_birth_year          175147 non-null float64\n",
      "member_gender              175147 non-null object\n",
      "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_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bike_data['bike_share_for_all_trip'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      130651\n",
       "Female     40844\n",
       "Other       3652\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.member_gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8265"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.member_gender.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    163544\n",
       "Customer       19868\n",
       "Name: user_type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data.user_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.user_type.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.duration_sec.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.start_station_name.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8265"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.member_birth_year.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(bike_data.end_time.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality issues\n",
    "bike_data table\n",
    "Rating denominator has values less than 10\n",
    "\n",
    "1. NaN values in Data to be removed (member_gender, member_birth_year and start_station_name columns)\n",
    "\n",
    "2. Erroneous datatypes (start_time and end_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "bike_data_clean = bike_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issue #1: NaN values in Data to be removed (member_gender, member_birth_year and start_station_name columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: \n",
    "Remove the rows that have have associating NaN values in the member_gender, member_birth_year and start_station_name  columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_data_clean = bike_data_clean.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    174952\n",
       "Name: start_station_name, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data_clean.start_station_name.isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>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>2</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>3</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1793</td>\n",
       "      <td>2019-02-28 23:49:58.6320</td>\n",
       "      <td>2019-03-01 00:19:51.7600</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>323.0</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>5200</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1959.0</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",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "2         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "3          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "4          1793  2019-02-28 23:49:58.6320  2019-03-01 00:19:51.7600   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              86.0                           Market St at Dolores St   \n",
       "2             375.0                           Grove St at Masonic Ave   \n",
       "3               7.0                               Frank H Ogawa Plaza   \n",
       "4              93.0                      4th St at Mission Bay Blvd S   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.769305              -122.426826             3.0   \n",
       "2               37.774836              -122.446546            70.0   \n",
       "3               37.804562              -122.271738           222.0   \n",
       "4               37.770407              -122.391198           323.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "2                        Central Ave at Fell St             37.773311   \n",
       "3                         10th Ave at E 15th St             37.792714   \n",
       "4                            Broadway at Kearny             37.798014   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.404904     5905    Customer             1972.0   \n",
       "2            -122.444293     6638  Subscriber             1989.0   \n",
       "3            -122.248780     4898  Subscriber             1974.0   \n",
       "4            -122.405950     5200  Subscriber             1959.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1          Male                      No  \n",
       "2         Other                      No  \n",
       "3          Male                     Yes  \n",
       "4          Male                      No  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_data_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issue #2: Erroneous datatypes (start_time and end_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Convert start_time and end_time to datatype integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To datatime\n",
    "bike_data_clean.start_time = pd.to_datetime(bike_data_clean.start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_data_clean.end_time = pd.to_datetime(bike_data_clean.end_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 174952 entries, 0 to 174951\n",
      "Data columns (total 16 columns):\n",
      "duration_sec               174952 non-null int64\n",
      "start_time                 174952 non-null datetime64[ns]\n",
      "end_time                   174952 non-null datetime64[ns]\n",
      "start_station_id           174952 non-null float64\n",
      "start_station_name         174952 non-null object\n",
      "start_station_latitude     174952 non-null float64\n",
      "start_station_longitude    174952 non-null float64\n",
      "end_station_id             174952 non-null float64\n",
      "end_station_name           174952 non-null object\n",
      "end_station_latitude       174952 non-null float64\n",
      "end_station_longitude      174952 non-null float64\n",
      "bike_id                    174952 non-null int64\n",
      "user_type                  174952 non-null object\n",
      "member_birth_year          174952 non-null float64\n",
      "member_gender              174952 non-null object\n",
      "bike_share_for_all_trip    174952 non-null object\n",
      "dtypes: datetime64[ns](2), float64(7), int64(2), object(5)\n",
      "memory usage: 21.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#start_time and end_time should be in datetime\n",
    "bike_data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"bike_archive_master.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_data_clean.to_csv('bike_archive_master', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "bike = pd.read_csv('bike_archive_master')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 174952 entries, 0 to 174951\n",
      "Data columns (total 16 columns):\n",
      "duration_sec               174952 non-null int64\n",
      "start_time                 174952 non-null object\n",
      "end_time                   174952 non-null object\n",
      "start_station_id           174952 non-null float64\n",
      "start_station_name         174952 non-null object\n",
      "start_station_latitude     174952 non-null float64\n",
      "start_station_longitude    174952 non-null float64\n",
      "end_station_id             174952 non-null float64\n",
      "end_station_name           174952 non-null object\n",
      "end_station_latitude       174952 non-null float64\n",
      "end_station_longitude      174952 non-null float64\n",
      "bike_id                    174952 non-null int64\n",
      "user_type                  174952 non-null object\n",
      "member_birth_year          174952 non-null float64\n",
      "member_gender              174952 non-null object\n",
      "bike_share_for_all_trip    174952 non-null object\n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 21.4+ MB\n"
     ]
    }
   ],
   "source": [
    "bike.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> There were initially 183,412 entries in the dataset with 16 features (duration_sec, start_time, end_time, start_station_id, start_station_name, start_station_latitude, start_station_longitude, end_station_id, end_station_name, end_station_latitude, end_station_longitude, bike_id, user_type, member_birth_year, member_gender  and bike_share_for_all_trip) but after assessing and cleaning my dataset there are 174952 entries with 16 features in the dataset. Most variables are numeric in nature, but the variables start_time, end_time, start_station_name, end_station_name, member_gender and bike_share_for_all_trip are non-numeric.\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> I am most interested in figuring out the factors that determine or influence individual rides made in a bike-sharing system covering the greater San Francisco Bay area.\n",
    "\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> I expect that duration_sec, user_type and member_gender will have the main effect on individual rides made in the bike-sharing system of the dataset. I also think that bike_share_for_all_trip will have effects on the individual rides made in the bike-sharing system, though to a much smaller degree than the main effect of duration_sec, user_type and member_gender respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    ">I will start by looking at the distribution of the my main variable of interest: duration. Is the distribution skewed or symmetric? Is it unimodal or multimodal? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb4c1bf278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start with a standard-scaled plot\n",
    "binsize = 5\n",
    "bins = np.arange(0, bike['duration_sec'].max()+binsize, binsize)\n",
    "\n",
    "plt.figure(figsize=[8, 5])\n",
    "plt.hist(data = bike, x = 'duration_sec', bins = bins);\n",
    "plt.title('Distribution of the duration of bike ride')\n",
    "plt.xlabel('Duration (s)')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  I performed log scaling of duration before plotting again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb4a0e8ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16,5])\n",
    "# Get the ticks for bins between [0 - maximum weight]\n",
    "bins = 10 ** np.arange(-1, 5+0.1, 0.1)\n",
    "\n",
    "# Generate the x-ticks you want to apply\n",
    "ticks = [0.1, 0.5, 1, 5, 10, 50, 100, 500, 1000,5000,10000,50000,100000]\n",
    "# Convert ticks into string values, to be displaye dlong the x-axis\n",
    "labels = ['{}'.format(v) for v in ticks]\n",
    "\n",
    "# Plot the histogram\n",
    "plt.hist(data=bike, x='duration_sec', bins=bins);\n",
    "plt.title('Distribution of the log duration of bike ride')\n",
    "plt.xlabel('Log_Duration (s)')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# The argument in the xscale() represents the axis scale type to apply.\n",
    "plt.xscale('log')\n",
    "\n",
    "# Apply x-ticks\n",
    "plt.xticks(ticks, labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's move on to exploring the other three categorical variables; user_type, member_ gender and bike_share_for_ all_ trip. For each of these variables does the data tend to you have a higher number or lower number in terms of the patronage of individual ridesmade in the bike sharing system?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V5BpgJQ/8mXvE5FUevIn6TPum/2vA+5KcC/z7gLYTvtvVpQHvA/xZVf11/4IkS3awLc0gz1nQTPkE8PBJ45Q/T+8Om4ckeXg7UnBkW/Zo4LFVdSm9YYVlbbW7gccAVNW3gLsmxlCB3wF2dJi13+XAi5Ls1/a5b5KhZ2GTZrMkT06ytK+0DPhX4CZ6R9QAfnPSascmeUSSxwFH0Dvq9pPA7VX1buA9wDOAzwH/OcnBbV/7MpwHbZ/enXZf1n7mSXLgxM+kZg+PLGhGtPMOXgD8z3Yo9Af0fmm9GrgIuBq4gfsPRT4GuCTJI+h983hNq18IvDvJq+gdqVgJ/FWSRwI3AidPoU/XJ/kj4LIkuwH/AZxK7xeqtKt7NPCOJHvTO5qwGVgF/AzwniR/AHx+0jpfAP4eeAJwdjshciXwuiT/AXwHOKmqtiZZBfxd+9m5HfjVIfr0oO0DtyT5GeBzbTjwO8BL6A0Fapbwds+SJKmTwxCSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIGnWaLf4/S/j7oekBzIsSJpxSbZ32fYSwLAgzTKGBUk71L7xX9v3/rVt0qBX5f7Jvi5syx6VZHWbGOhLSY5t9Zcm+WCSj9Kbt2CQNwK/1CYtek2Sf0pvdtKJ/f5Lkqe1fb9v2EmJJD003pRJ0kNxOnBwmwJ571b7Q+ATVfWyVvtCkn9sy54FPG3SXAKTt/faqvp1gCTbgJcCr07yJODhVXV1khcyhUmJquoz0/y5pXnFIwuSHoqrgb9N8hJ6dwmE3qRApye5it68BI+gd8c+gA0dQWGQDwK/3iYxehlwQd+yS6rq+1V1BzAxKdFR3D8p0ReBp9ALD5IeAu/g2CxcuLCWLFky7m5IkjQjNm3adEdVLRqmrcMQzZIlS9i4ceO4uyFJ0oxIMvQ8OA5DSJKkToYFSZLUybAgSZI6GRYkSVInT3CcBoe+bu24u6AR2XTuSePugiSN3ciOLLQ7uN0+6a5v5yb5Sruz2sUTN3Fpd4f7frtr21VJ/qpvnUOTXJNkc5K3J0mr75tkQ7t724Yk+7R6WrvNbT/PGNVnlCRpPhjlMMQFwIpJtQ3AU6vqacDXgDf0Lft6VS1rj9/rq58HrKJ3Y5Wlfds8Hbi8qpYCl7f3AEf3tV3V1pckSTtpZGGh3V5126TaZVU1cZe3K4DFXdtIcgCwV1V9rnp3j1oLHNcWHwusaa/XTKqvrZ4rgL3bdiRJ0k4Y5wmOLwM+3vf+4DbpzKeT/FKrHQhs6WuzpdUA9q+qWwHa835969y8nXUkSdIUjeUExyR/SO8+8n/bSrcCT6iqO5McCnwkyc/Smwhmsh3dn3rodZKsojdUwROe8IRBTSRJmvdm/MhCkpXArwO/3YYWqKp7qurO9noT8HXgSfSOCvQPVSwGbmmvb5sYXmjPt7f6FuCg7azzAFV1flUtr6rlixYNdXtsSZLmnRkNC0lWAK8Hnl9V3+urL0qyoL3+KXonJ97YhhfuTnJ4uwriJOCStto6YGV7vXJS/aR2VcThwLcmhiskSdLUjWwYIsn7gSOAhUm2AGfQu/rh4cCGdgXkFe3Kh+cAZyW5F7gP+L2+aWxfQe/Kij3pneMwcZ7DG4GLkpwCfBM4vtUvBY4BNgPfA04e1WeUJGk+GFlYqKoTB5Tfs522HwY+vJ1lG4GnDqjfCRw5oF7AqVPqrCRJ2i5v9yxJkjoZFiRJUifDgiRJ6mRYkCRJnQwLkiSpk2FBkiR1MixIkqROhgVJktTJsCBJkjoZFiRJUifDgiRJ6mRYkCRJnQwLkiSpk2FBkiR1MixIkqROIw0LSVYnuT3JtX21fZNsSHJDe96n1ZPk7Uk2J7k6yTP61lnZ2t+QZGVf/dAk17R13p4kXfuQJElTN+ojCxcAKybVTgcur6qlwOXtPcDRwNL2WAWcB70//MAZwDOBw4Az+v74n9faTqy3Ygf7kCRJUzTSsFBVnwG2TSofC6xpr9cAx/XV11bPFcDeSQ4AngdsqKptVXUXsAFY0ZbtVVWfq6oC1k7a1qB9SJKkKRrHOQv7V9WtAO15v1Y/ELi5r92WVuuqbxlQ79rHAyRZlWRjko1bt259SB9KkqS5ajad4JgBtdqJ+tCq6vyqWl5VyxctWjSVVSVJmjfGERZua0MItOfbW30LcFBfu8XALTuoLx5Q79qHJEmaonGEhXXAxBUNK4FL+uontasiDge+1YYQ1gNHJdmnndh4FLC+Lbs7yeHtKoiTJm1r0D4kSdIU7T7KjSd5P3AEsDDJFnpXNbwRuCjJKcA3geNb80uBY4DNwPeAkwGqaluSs4ErW7uzqmripMlX0LviYk/g4+1Bxz4kSdIUjTQsVNWJ21l05IC2BZy6ne2sBlYPqG8EnjqgfuegfUiSpKmbTSc4SpKkWciwIEmSOhkWJElSJ8OCJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCJEnqNFRYSHL5MDVJkjT3dM46meQRwCPpTTG9D5C2aC/g8SPumyRJmgV2dGTh5cAm4CnteeJxCfDOndlhkicnuarv8e0kr05yZpJ/66sf07fOG5JsTvLVJM/rq69otc1JTu+rH5zk80luSPKBJHvsTF8lSdIOwkJVva2qDgZeW1U/VVUHt8fTq+ovd2aHVfXVqlpWVcuAQ4HvARe3xW+dWFZVlwIkOQQ4AfhZYAXwriQLkiygF1iOBg4BTmxtAd7UtrUUuAs4ZWf6KkmSdjAMMaGq3pHkF4Al/etU1dqHuP8jga9X1b8m2V6bY4ELq+oe4BtJNgOHtWWbq+pGgCQXAscm+TLwXOC/tDZrgDOB8x5iXyVJmpeGPcHxfcCbgV8Efr49lk/D/k8A3t/3/rQkVydZ3c6RADgQuLmvzZZW2179ccC/V9W9k+oPkmRVko1JNm7duvWhfxpJkuagoY4s0AsGh1RVTdeO23kEzwfe0ErnAWcD1Z7fAryM+0+q7FcMDjrV0f7BxarzgfMBli9fPm2fTZKkuWTY+yxcC/zENO/7aOCLVXUbQFXdVlX3VdWPgHdz/1DDFuCgvvUWA7d01O8A9k6y+6S6JEnaCcOGhYXA9UnWJ1k38XiI+z6RviGIJAf0LXsBvYACsA44IcnDkxwMLAW+AFwJLG1XPuxBb0hjXTv68UngRW39lfSu3pAkSTth2GGIM6dzp0keCfwqvUszJ/x5kmX0hgxumlhWVdcluQi4HrgXOLWq7mvbOQ1YDywAVlfVdW1brwcuTPKnwJeA90xn/yVJmk+GvRri09O506r6Hr0TEftrv9PR/hzgnAH1S4FLB9Rv5P5hDEmS9BAMFRaS3M39JwnuATwM+G5V7TWqjkmSpNlh2CMLj+l/n+Q4/OYuSdK8sFOzTlbVR+jd+EiSJM1xww5DvLDv7W707rvgfQkkSZoHhr0a4jf6Xt9L72qFY6e9N5IkadYZ9pyFk0fdEUmSNDsNOzfE4iQXJ7k9yW1JPpxk8ag7J0mSxm/YExzfS+9Oio+nNynTR1tNkiTNccOGhUVV9d6qurc9LgAWjbBfkiRplhg2LNyR5CVJFrTHS4A7R9kxSZI0OwwbFl4GvBj4v8Ct9CZp8qRHSZLmgWEvnTwbWFlVdwEk2Rd4M70QIUmS5rBhjyw8bSIoAFTVNuDnRtMlSZI0mwwbFnZLss/Em3ZkYdijEpIkaRc2bFh4C/DZJGcnOQv4LPDnD2XHSW5Kck2Sq5JsbLV9k2xIckN73qfVk+TtSTYnuTrJM/q2s7K1vyHJyr76oW37m9u6eSj9lSRpvhoqLFTVWuA3gduArcALq+p907D/X66qZVW1vL0/Hbi8qpYCl7f3AEcDS9tjFXAe/PgIxxnAM+nNgnlG3xGQ81rbifVWTEN/JUmad4YeSqiq64HrR9gX6M03cUR7vQb4FPD6Vl9bVQVckWTvJAe0thvaORQk2QCsSPIpYK+q+lyrrwWOAz4+4v5LkjTn7NQU1dOkgMuSbEqyqtX2r6pbAdrzfq1+IHBz37pbWq2rvmVAXZIkTdE4T1J8dlXdkmQ/YEOSr3S0HXS+Qe1E/YEb7YWUVQBPeMITdtxjSZLmobEdWaiqW9rz7cDF9M45uK0NL9Ceb2/NtwAH9a2+GLhlB/XFA+qT+3B+VS2vquWLFnn3akmSBhlLWEjyqCSPmXgNHAVcS2+yqokrGlYCl7TX64CT2lURhwPfasMU64GjkuzTTmw8Cljflt2d5PB2FcRJfduSJElTMK5hiP2Bi9vVjLsD/7uq/iHJlcBFSU4Bvgkc39pfChwDbAa+R7vVdFVtS3I2cGVrd9bEyY7AK4ALgD3pndjoyY2SJO2EsYSFqroRePqA+p3AkQPqBZy6nW2tBlYPqG8EnvqQOytJ0jw3zqshJEnSLsCwIEmSOhkWJElSJyeDkqR54NDXrR13FzQim849aeT78MiCJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCJEnqZFiQJEmdDAuSJKnTjIeFJAcl+WSSLye5Lsnvt/qZSf4tyVXtcUzfOm9IsjnJV5M8r6++otU2Jzm9r35wks8nuSHJB5LsMbOfUpKkuWMcRxbuBf6fqvoZ4HDg1CSHtGVvrapl7XEpQFt2AvCzwArgXUkWJFkAvBM4GjgEOLFvO29q21oK3AWcMlMfTpKkuWbGw0JV3VpVX2yv7wa+DBzYscqxwIVVdU9VfQPYDBzWHpur6saq+iFwIXBskgDPBT7U1l8DHDeaTyNJ0tw31nMWkiwBfg74fCudluTqJKuT7NNqBwI39622pdW2V38c8O9Vde+k+qD9r0qyMcnGrVu3TsMnkiRp7hlbWEjyaODDwKur6tvAecATgWXArcBbJpoOWL12ov7gYtX5VbW8qpYvWrRoip9AkqT5Yfdx7DTJw+gFhb+tqr8DqKrb+pa/G/hYe7sFOKhv9cXALe31oPodwN5Jdm9HF/rbS5KkKRrH1RAB3gN8uar+oq9+QF+zFwDXttfrgBOSPDzJwcBS4AvAlcDSduXDHvROglxXVQV8EnhRW38lcMkoP5MkSXPZOI4sPBv4HeCaJFe12h/Qu5phGb0hg5uAlwNU1XVJLgKup3clxalVdR9AktOA9cACYHVVXde293rgwiR/CnyJXjiRJEk7YcbDQlX9M4PPK7i0Y51zgHMG1C8dtF5V3UjvaglJkvQQeQdHSZLUybAgSZI6GRYkSVInw4IkSepkWJAkSZ0MC5IkqZNhQZIkdTIsSJKkToYFSZLUybAgSZI6GRYkSVInw4IkSepkWJAkSZ0MC5IkqdOMT1E9U5KsAN4GLAD+pqreOOYuSUM79HVrx90Fjcimc08adxekKZuTRxaSLADeCRwNHAKcmOSQ8fZKkqRd05wMC8BhwOaqurGqfghcCBw75j5JkrRLSlWNuw/TLsmLgBVV9bvt/e8Az6yq0ya1WwWsam+fDHx1Rju6a1oI3DHuTmhO8d+Uppv/pobzk1W1aJiGc/WchQyoPSgVVdX5wPmj787ckWRjVS0fdz80d/hvStPNf1PTb64OQ2wBDup7vxi4ZUx9kSRplzZXw8KVwNIkByfZAzgBWDfmPkmStEuak8MQVXVvktOA9fQunVxdVdeNuVtzhcM2mm7+m9J089/UNJuTJzhKkqTpM1eHISRJ0jQxLEiSpE6GBQGQpJK8r+/97km2JvnYDtY7YkdtNL8kWZzkkiQ3JPl6krcl2SPJsiTH9LU7M8lrx9lXzT5J7ktyVd9jyQj39dIkfzmq7c8lhgVN+C7w1CR7tve/CvzbGPujXVCSAH8HfKSqlgJPAh4NnAMsA47pWH2q+1owXdvSrPL9qlrW97hp3B2SYUEP9HHg19rrE4H3TyxIcliSzyb5Unt+8uSVkzwqyeokV7Z23mJ7/nku8IOqei9AVd0HvAb4XeDPgd9q3xZ/q7U/JMmnktyY5FUTG0nykiRfaG3/eiIYJPlOkrOSfB541ox+Mo1NkgVJzm2/W65O8vJWPyLJp5NclORrSd6Y5Lfbv51rkjyxtfuNJJ9vv5f+Mcn+A/axKMmH2z6uTPLsmf6cs5lhQf0uBE5I8gjgacDn+5ZuYVy1AAAT1UlEQVR9BXhOVf0c8MfA/xiw/h8Cn6iqnwd+GTg3yaNG3GfNLj8LbOovVNW3gZuAPwU+0L4tfqAtfgrwPHrzuZyR5GFJfgb4LeDZVbUMuA/47db+UcC1VfXMqvrnkX8ajcOefUMQF7faKcC32u+Wnwf+a5KD27KnA78P/Cfgd4AnVdVhwN8Ar2xt/hk4vP3+uhD4fwfs923AW9s+frOtr2ZO3mdBO6eqrm7jgycCl05a/FhgTZKl9G6d/bABmzgKeH7fOPQjgCcAXx5JhzUbhQG3Vu+o/31V3QPck+R2YH/gSOBQ4MreqAZ7Are39vcBH57uTmtW+X4Lif2OAp7W5v2B3u+jpcAPgSur6laAJF8HLmttrqH3pQV6d/H9QJIDgD2AbwzY76/QO9I18X6vJI+pqrun4TPt8gwLmmwd8GbgCOBxffWzgU9W1QtaoPjUgHUD/GZVOSHX/HUdvW9lP5ZkL3q3X79vQPt7+l7fR+93UoA1VfWGAe1/0IY2NL8EeGVVrX9AMTmCB/4b+lHf+x9x/9+4dwB/UVXr2jpnDtjHbsCzqur709ftucNhCE22Gjirqq6ZVH8s95/w+NLtrLseeGU7yY0kPzeSHmo2uxx4ZJKT4McnIb4FuAC4DXjMkNt4UZL92jb2TfKTo+mudhHrgVckeRhAkidNcYiz//fXyu20uQz48czESSYf3ZjXDAt6gKraUlVvG7Doz4E/S/Iv9G6hPcjZ9IYnrk5ybXuveaR6t4R9AXB8khuArwE/AP4A+CS9w7z9JzgO2sb1wB8BlyW5GtgAHDDyzms2+xvgeuCL7XfLXzO1I+NnAh9M8k9sf+rqVwHL2wmU1wO/9xD6O+d4u2dJktTJIwuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCpIesTQa1fNz9GNau1l9p3AwLksYqyay/7bzTYWu+MyxIc1ySJUm+kuRvklyb5G+T/EqSf0lyQ5t+fOD04klemuQjST6a5BtJTkvy31ubK5Ls27erl7Tpy69Nclhbv2u7H0zyUe6f+Gdyv3dL8q4k1yX5WJJLJyYSSnJom5p4U5L1bYKgiSMGb2pTFH8tyS+1+p5JLmx35/sAvcmpJvZzVJLPJfli69OjW/2mJH+c5J+B46f7/4u0K/EOjs3ChQtryZIl4+6GJEkzYtOmTXdU1aJh2s76w38zZcmSJWzcuHHc3ZAkaUYk+ddh2zoMIUmSOhkWJElSJ8OCJEnqZFiQJEmdPMFxxA593dpxd0GaFpvOPWncXZA0Jh5ZkCRJnQwLkiSp08jCQrtr2+1Jru2rnZnk35Jc1R7H9C17Q5LNSb6a5Hl99RWttjnJ6X31g5N8vt2B7gNJ9mj1h7f3m9vyJaP6jJIkzQejPLJwAbBiQP2tVbWsPS4FSHIIcALws22ddyVZ0O7H/k7gaOAQ4MTWFuBNbVtLgbuAU1r9FOCuqvpp4K2tnSRJ2kkjCwtV9Rlg25DNjwUurKp7quobwGbgsPbYXFU3VtUPgQuBY5MEeC7wobb+GuC4vm2taa8/BBzZ2kuSpJ0wjnMWTmuTuaxOsk+rHQjc3NdmS6ttr/444N+r6t5J9Qdsqy3/Vmv/IElWJdmYZOPWrVsf+ieTJGkOmumwcB7wRGAZcCvwllYf9M2/dqLeta0HF6vOr6rlVbV80aKh5tKQJGnemdGwUFW3VdV9VfUj4N30hhmgd2TgoL6mi4FbOup3AHsn2X1S/QHbassfy/DDIZIkaZIZDQsTc843LwAmrpRYB5zQrmQ4GFgKfAG4EljarnzYg95JkOuqN6/2J4EXtfVXApf0bWtle/0i4BPlPNySJO20kd3BMcn7gSOAhUm2AGcARyRZRm9Y4Cbg5QBVdV2Si4DrgXuBU6vqvrad04D1wAJgdVVd13bxeuDCJH8KfAl4T6u/B3hfks30jiicMKrPKEnSfDCysFBVJw4ov2dAbaL9OcA5A+qXApcOqN/I/cMY/fUfAMdPqbOSJGm7vIOjJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE6GBUmS1MmwIEmSOhkWJElSJ8OCJEnqZFiQJEmdDAuSJKmTYUGSJHUyLEiSpE4jDQtJVie5Pcm1fbV9k2xIckN73qfVk+TtSTYnuTrJM/rWWdna35BkZV/90CTXtHXeniRd+5AkSVM36iMLFwArJtVOBy6vqqXA5e09wNHA0vZYBZwHvT/8wBnAM4HDgDP6/vif19pOrLdiB/uQJElTNNKwUFWfAbZNKh8LrGmv1wDH9dXXVs8VwN5JDgCeB2yoqm1VdRewAVjRlu1VVZ+rqgLWTtrWoH1IkqQpGsc5C/tX1a0A7Xm/Vj8QuLmv3ZZW66pvGVDv2ockSZqi2XSCYwbUaifqw+8wWZVkY5KNW7duncqqkiTNG+MIC7e1IQTa8+2tvgU4qK/dYuCWHdQXD6h37eMBqur8qlpeVcsXLVr0kD6UJElz1VBhIcnlw9SGtA6YuKJhJXBJX/2kdlXE4cC32hDCeuCoJPu0ExuPAta3ZXcnObxdBXHSpG0N2ockSZqi3bsWJnkE8EhgYftDPXHofy/g8TvaeJL3A0e09bfQu6rhjcBFSU4Bvgkc35pfChwDbAa+B5wMUFXbkpwNXNnanVVVEydNvoLeFRd7Ah9vDzr2IUmSpqgzLAAvB15NLxhs4v6w8G3gnTvaeFWduJ1FRw5oW8Cp29nOamD1gPpG4KkD6ncO2ockSZq6zrBQVW8D3pbklVX1jhnqkyRJmkV2dGQBgKp6R5JfAJb0r1NVa0fUL0mSNEsMFRaSvA94InAVcF8rT9wISZIkzWFDhQVgOXBIO69AkiTNI8PeZ+Fa4CdG2RFJkjQ7DXtkYSFwfZIvAPdMFKvq+SPplSRJmjWGDQtnjrITkiRp9hr2aohPj7ojkiRpdhr2aoi7uX+Spj2AhwHfraq9RtUxSZI0Owx7ZOEx/e+THAccNpIeSZKkWWWnZp2sqo8Az53mvkiSpFlo2GGIF/a93Y3efRe854IkSfPAsFdD/Ebf63uBm4Bjp703kiRp1hn2nIWTR90RSZI0Ow11zkKSxUkuTnJ7ktuSfDjJ4lF3TpIkjd+wJzi+F1gHPB44EPhoq0mSpDlu2LCwqKreW1X3tscFwKIR9kuSJM0Sw4aFO5K8JMmC9ngJcOcoOyZJkmaHYcPCy4AXA/8XuBV4EeBJj5IkzQPDXjp5NrCyqu4CSLIv8GZ6IUKSJM1hwx5ZeNpEUACoqm3Az42mS5IkaTYZNizslmSfiTftyMKwRyUkSdIubNg/+G8BPpvkQ/Ru8/xi4JyR9UqSJM0aw97BcW2SjfQmjwrwwqq6fqQ9kyRJs8LQQwktHBgQJEmaZ3ZqimpJkjR/GBYkSVInw4IkSepkWJAkSZ0MC5IkqdPYwkKSm5Jck+SqdlkmSfZNsiHJDe15n1ZPkrcn2Zzk6iTP6NvOytb+hiQr++qHtu1vbutm5j+lJEm7vnEfWfjlqlpWVcvb+9OBy6tqKXB5ew9wNLC0PVYB58GP7yR5BvBM4DDgjL47TZ7X2k6st2L0H0eSpLln3GFhsmOBNe31GuC4vvra6rkC2DvJAcDzgA1Vta3NXbEBWNGW7VVVn6uqAtb2bUuSJE3BOMNCAZcl2ZRkVavtX1W3ArTn/Vr9QODmvnW3tFpXfcuA+gMkWZVkY5KNW7dunYaPJEnS3DPOyaCeXVW3JNkP2JDkKx1tB51vUDtRf2Ch6nzgfIDly5c/aLkkSRrjkYWquqU93w5cTO+cg9vaEALt+fbWfAtwUN/qi4FbdlBfPKAuSZKmaCxhIcmjkjxm4jVwFHAtsA6YuKJhJXBJe70OOKldFXE48K02TLEeOCrJPu3ExqOA9W3Z3UkOb1dBnNS3LUmSNAXjGobYH7i4Xc24O/C/q+ofklwJXJTkFOCbwPGt/aXAMcBm4HvAyQBVtS3J2cCVrd1ZVbWtvX4FcAGwJ/Dx9pAkSVM0lrBQVTcCTx9QvxM4ckC9gFO3s63VwOoB9Y3AUx9yZyVJmudm26WTkiRpljEsSJKkToYFSZLUybAgSZI6GRYkSVInw4IkSepkWJAkSZ0MC5IkqZNhQZIkdTIsSJKkToYFSZLUybAgSZI6GRYkSVInw4IkSepkWJAkSZ0MC5IkqZNhQZIkdTIsSJKkToYFSZLUafdxd0CSRuXQ160ddxekh2zTuSeNuwseWZAkSd0MC5IkqZNhQZIkdTIsSJKkToYFSZLUybAgSZI6GRYkSVInw4IkSepkWJAkSZ0MC5IkqdOcDQtJViT5apLNSU4fd38kSdpVzcmwkGQB8E7gaOAQ4MQkh4y3V5Ik7ZrmZFgADgM2V9WNVfVD4ELg2DH3SZKkXdJcnXXyQODmvvdbgGdObpRkFbCqvf1Okq/OQN80GguBO8bdibksb1457i5odvJnb8RG+LP3k8M2nKthIQNq9aBC1fnA+aPvjkYtycaqWj7ufkjzjT9788NcHYbYAhzU934xcMuY+iJJ0i5troaFK4GlSQ5OsgdwArBuzH2SJGmXNCeHIarq3iSnAeuBBcDqqrpuzN3SaDmcJI2HP3vzQKoeNJQvSZL0Y3N1GEKSJE0Tw4IkSepkWNAuJUkleUvf+9cmOXOMXZLmrPT8c5Kj+2ovTvIP4+yXZp5hQbuae4AXJlk47o5Ic131Tmr7PeAvkjwiyaOAc4BTx9szzTTDgnY199I7+/o1kxck+ckklye5uj0/Yea7J80tVXUt8FHg9cAZwNqq+nqSlUm+kOSqJO9KsluS3ZO8L8k1Sa5N8qrx9l7TZU5eOqk5753A1Un+fFL9L+n9IluT5GXA24HjZrx30tzzJ8AXgR8Cy5M8FXgB8AvtUvXz6d3P5uvAwqr6TwBJ9h5XhzW9DAva5VTVt5OsBV4FfL9v0bOAF7bX7wMmhwlJO6GqvpvkA8B3quqeJL8C/DywMQnAnvTm41kPPDnJ24BLgcvG1WdNL8OCdlX/k943nfd2tPEmItL0+VF7QG/+ndVV9f9NbpTkacDR9ML8b3L/ZH3ahXnOgnZJVbUNuAg4pa/8WXqHQgF+G/jnme6XNE/8I/DiiRONkzwuyROSLKJ3s78P0ju/4Rnj7KSmj0cWtCt7C3Ba3/tXAauTvA7YCpw8ll5Jc1xVXZPkT4B/TLIb8B/0rpq4D3hPemMTRe+kSM0B3u5ZkiR1chhCkiR1MixIkqROhgVJktTJsCBJkjoZFiRJUifDgrQLSLIkybUD6n+T5JD2+jsj2vdNMzlxV5JXJflykr8d0fZ//N8yyRFJPtbR9ogkv9Cx/PlJTh9FP6XZxPssSLuwqvrdcfehS5Ldq+reKa7234Cjq+obI9zHsI4AvkPvhl+D9rsOWDeifUuzhkcWpF3H7knWtFk1P5TkkUk+lWR5f6MkC5N8LsmvtfevS3JlW+9PtrfxJI9K8vdJ/k+bMfC3+ha/MskX22yCT2ntD0vy2SRfas9PbvWXJvlgko/S5gaYQh/+CvgpYF2S1yTZN8lH2npXtFsJk+TMJOcnuQxYu51tLUnyT63fX+w6QrC99endaOg1bWbFX0pyQZK/SPJJ4E3ts/5la39Bkr9q+/xakl+fyv6k2cwjC9Ku48nAKVX1L0lW0/sG/gBJ9qf3TfePqmpDkqOApcBh9O7nvy7Jc6rqMwO2vwK4paomQsZj+5bdUVXPSPLfgNcCvwt8BXhOm3XwV4D/QW8uAOhN6vW0qto2lT5U1e8lWQH8clXdkeQdwJeq6rgkz6UXDJa15ocCv1hV35+8neZ24Fer6gdJlgLvB5Zvp+2DVNVNLbx8p6re3P6bnAI8CfiVqrovyUsnrbYE+M/AE4FPJvnpqvrBsPuUZivDgrTruLmq/qW9/l/0bm/d72HA5cCpVfXpVjuqPb7U3j+a3h/uQWHhGuDNSd4EfKyq/qlv2d+1503cP7PnY4E17Q9xtf1P2NDm75hqHyb7RVoAqapPtDkIJkLMuo6gQOvPXyZZRu82xE8aYn/D+GBV3bedZRdV1Y+AG5LcCDwFuGqa9iuNjWFB2nVMvjf75Pf30vtj/jxgIiwE+LOq+usdbrzqa0kOBY4B/izJZVV1Vlt8T3u+j/t/b5wNfLKqXtAO2X+qb3Pf7Xs9dB8GyKCuDtjHIK8BbgOeTm/Idbq+4Xftd0f/j6RdkucsSLuOJyR5Vnt9Ig+eVbOAlwFP6TtDfz3wsiSPBkhyYJL9Bm08yeOB71XV/wLezI5nDHws8G/t9Us72g3dhwE+Q28GUZIcQW845NtDrvtY4Nb2Tf93gAVDrtfvbuAxU2h/fJLdkjyR3rkXX92JfUqzjkcWpF3Hl4GVSf4auAE4D/iN/gZtHP0E4KNJvl1V70ryM8DnehMB8h3gJfTG8yf7T8C5SX5EbxbBV+ygP39ObxjivwOf2F6jqrpsCn2Y7EzgvUmuBr4HrBxinQnvAj6c5Hjgk+z4SMQgHwU+lORY4JVDtP8qvaM6+wO/5/kKmiucdVKSpkGSC+id6/GhcfdFmm4OQ0iSpE4eWZDmmSSPo3fVxGRHVtWdu1ofkjwPeNOk8jeq6gVT2MbJwO9PKv9LVZ06lb5Ic5VhQZIkdXIYQpIkdTIsSJKkToYFSZLUybAgSZI6GRYkSVKn/x/cnxrZNYKUOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb47f31518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot all three together to get an idea of each ordinal variable's distribution.\n",
    "\n",
    "fig, ax = plt.subplots(nrows=3, figsize = [8,8])\n",
    "\n",
    "default_color = sb.color_palette()[0]\n",
    "\n",
    "sb.countplot(data = bike, x = 'user_type', color = default_color, ax = ax[0])\n",
    "sb.countplot(data = bike, x = 'member_gender', color = default_color, ax = ax[1])\n",
    "sb.countplot(data = bike, x = 'bike_share_for_all_trip', color = default_color, ax = ax[2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "> Duration distribution comment:I created a histogram chart for duration since it is a numeric variable. The initial plot shows that duration follows a highly skewed distribution. This called for a need of log scaling.\n",
    "> Duration distribution comment (log scaling): Under a log scale, I observed that the data for the duration is roughly unimodal with a large peak somewhere between 400 and 600. The duration distribution also seems to just cut off at its maximum, rather than declining in a smooth tail.\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",
    "> General categorical feature comment: I produced bad chart for all of the variables the because the datas are categorical.\n",
    "\n",
    ">User_type distribution: The bar chart shows that subscribers have the highest number of count making use of the bike sharing system while the customer type had the lowest number of count of users using the bike sharing system.\n",
    "\n",
    ">Member_gender distribution: The bar chart shows that the male gender have the highest number of count making use of the bike sharing system followed by the female gender with a relatively smaller count of users using the bike sharing system. The other gender had the lowest number of count of users using the bike sharing system.\n",
    "\n",
    ">Bike_share_for_all_trip distribution: The bar chart shows that the answer No has the highest number of count of individuals who refused to share their bike for all trip while using the bike sharing system while the answer Yes had the lowest number of count of individuals who shared thier bike for all trip while using the bike sharing system.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, I investigated the relationships between pairs of variables in my data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I begun by categorizing the data types\n",
    "numeric_vars = ['duration_sec']\n",
    "categoric_vars = ['user_type', 'member_gender', 'bike_share_for_all_trip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb476ef898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THREE BOX PLOTS IN ONE FIGURE\n",
    "plt.figure(figsize = [16, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# LEFT plot: box plot\n",
    "plt.subplot(1, 3, 1)\n",
    "#Let's return the axes object\n",
    "ax1 = sb.boxplot(data=bike, x='user_type', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "\n",
    "# Middle plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.boxplot(data=bike, x='member_gender', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot\n",
    "\n",
    "# RIGHT plot: box plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.boxplot(data=bike, x='bike_share_for_all_trip', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I then performed a log transformation of duration again and made a new plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb49ffa048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THREE VIOLIN PLOTS IN ONE FIGURE\n",
    "plt.figure(figsize = [16, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# LEFT plot: box plot\n",
    "plt.subplot(1, 3, 1)\n",
    "#Let's return the axes object\n",
    "ax1 = sb.violinplot(data=bike, x='user_type', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "\n",
    "# Middle plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.violinplot(data=bike, x='member_gender', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot\n",
    "\n",
    "# RIGHT plot: box plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.violinplot(data=bike, x='bike_share_for_all_trip', y='duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I then performed a log transformation of duration again and made a new plot.\n",
    "# compute the logarithm of price to make multivariate plotting easier\n",
    "def log_trans(x, inverse = False):\n",
    "    \"\"\" quick function for computing log and power operations \"\"\"\n",
    "    if not inverse:\n",
    "        return np.log10(x)\n",
    "    else:\n",
    "        return np.power(10, x)\n",
    "\n",
    "bike['log_duration_sec'] = bike['duration_sec'].apply(log_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb49907b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [16, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# LEFT plot: box plot\n",
    "plt.subplot(1, 3, 1)\n",
    "#Let's return the axes object\n",
    "ax1 = sb.violinplot(data=bike, x='user_type', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "\n",
    "# Middle plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.violinplot(data=bike, x='member_gender', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot\n",
    "\n",
    "# RIGHT plot: box plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.violinplot(data=bike, x='bike_share_for_all_trip', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Violin Plot:The violin plot shows the relationship of the categorical variables to the log transformation of duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb493a0f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THREE BOX PLOTS IN ONE FIGURE\n",
    "plt.figure(figsize = [16, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# LEFT plot: box plot\n",
    "plt.subplot(1, 3, 1)\n",
    "#Let's return the axes object\n",
    "ax1 = sb.boxplot(data=bike, x='user_type', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "\n",
    "# Middle plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.boxplot(data=bike, x='member_gender', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot\n",
    "\n",
    "# RIGHT plot: box plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.boxplot(data=bike, x='bike_share_for_all_trip', y='log_duration_sec', color=base_color);\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylim(ax1.get_ylim()); # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The plot of the full data using a violin plot and variable transformations reveals much more than the earlier box plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I will then look at the relationship between the three categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb48f6fc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# since there's only three subplots to create, using the full data should be fine.\n",
    "plt.figure(figsize = [8, 8])\n",
    "\n",
    "# subplot 1: user_type vs member_gender\n",
    "plt.subplot(3, 1, 1)\n",
    "sb.countplot(data = bike, x = 'user_type', hue = 'member_gender', palette = 'Blues')\n",
    "\n",
    "# subplot 2: user_type vs. bike_share_for_all_trip\n",
    "ax = plt.subplot(3, 1, 2)\n",
    "sb.countplot(data = bike, x = 'user_type', hue = 'bike_share_for_all_trip', palette = 'Blues')\n",
    "ax.legend(ncol = 2) # re-arrange legend to reduce overlapping\n",
    "\n",
    "# subplot 3: member_gender vs. bike_share_for_all_trip, use different color palette\n",
    "ax = plt.subplot(3, 1, 3)\n",
    "sb.countplot(data = bike, x = 'member_gender', hue = 'bike_share_for_all_trip', palette = 'Greens')\n",
    "ax.legend(loc = 1, ncol = 2) # re-arrange legend to remove overlapping\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "> Duration vs User_type: Interestingly it appears that there are no relationships between the categorical variables and the numeric value of interest which is duration. I then performed a log transformation of duration again and made a new plot.\n",
    "\n",
    "> Log_duration vs User_type: The box plot elements show that the median log duration for subscribers is lower than that of the customers when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration of both the customer and subscriber are highly concentrated around the median. The subscriber has a more elongated distribution at one end while that of the customer has a more elongated distribution at both ends.\n",
    "\n",
    "> Log_duration vs Member_gender: The box plot elements show that the median log duration for males is the lowest followed by that of the other gender and then than that of the female gender when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration for the male gender, other gender and female gender are highly concentrated around the median. The other gender has a more elongated distribution at one end which indicates that this particular gender had individuals recording a much higher duration while riding their bikes.\n",
    "\n",
    "> Log_duration vs Bike_share_for_all_trip: The box plot elements show that the median log duration for the answer Yes is lower than that of the No when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration of both the Yes and No are highly concentrated around the median. They both have a more elongated distribution at one end.\n",
    "\n",
    "> Box plot: The box plots are relatively short, that means that the data is more compact and they do have some outliers.\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> User_type vs Member_gender: I observed that the subscribers have the most members with the highest count spread across the various genders. The male gender is the most dominating gender by count or frequency for both the user types followed by the female gender and the other gender.\n",
    "\n",
    "> User_type vs Bike_share_for_all_trip: I observed that for the customer type all of the customers do not share thier ride for all trips while it is also observed that most subscriber members also do not share their rides but the difference is that a few subscriber members do share their ride for all trips.\n",
    "\n",
    "> Bike_share_for_all_trip vs  Member_gender: I observed in this plot that majority of the three classified genders do not share their bikes for all trips. The male gender has a count of over 100,000 members who do not share their ride and a count lesss than 25,000 for those who share their ride."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> The main thing I want to explore in this part of the analysis is how the three categorical measures of quality play into the relationship between duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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681d0ZnaKmbUws51JzkL/ouQrDYmZnUZyuegftTE/JWCR+Iyk+f0Lkibod0m5xlqHXEpy6egjkmuH2d6vIFJnmdlUkptir0j7JUf+5qkmaBERkdqnM2AREZEIlIBFREQiqLu9RGyj9u3be5cuXWKHISIidUhRUdFyd+9Qfc38a7AJuEuXLsycOTN2GCIiUoeYWbaPuM0bNUGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRNNjOGETquuHDh1NcXMyuu+7KnXfeGTscEallSsAikRQXF7N48eLYYYhIJGqCFhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEIlIBFREQiUAIWERGJQAlYREQkAiVgERGRCJSARUREIlACFhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEIlIBFREQiUAIWERGJQAlYREQkAiVgERGRCJSARUREIlACFhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEIlIBFREQiUAIWERGJIK8J2Mz2MrOXzOxdM3vbzK4O5e3MbLKZzQ3/dw7lZmb3m9mHZjbbzHqmTGtYqD/XzIblM24REZF8y/cZ8CbgOnc/AOgPXGFm3YEbgSnu3hWYEt4DHAd0DX+XAL+FJGEDtwL9gL7ArWVJW0REpD7KawJ29yXu/mZ4vRp4F9gDGAKMC9XGASeH10OAhz3xKtDWzHYDjgEmu/sKd/8CmAwcm8/YRURE8qnWrgGbWRfgEOA1YBd3XwJJkgY6hmp7AAtTRlsUyiorFxERqZdqJQGbWWvgaeAad/9vVVUzlHkV5enzucTMZprZzGXLlm1dsCIiIrUg7wnYzJqTJN/H3P3Pofiz0LRM+L80lC8C9koZfU/g0yrKK3D30e7e2917d+jQIbcLIiIikkP5vgvagP8D3nX3X6YMmgCU3ck8DHgupXxouBu6P7AqNFG/CAwys53DzVeDQpmIiEi91CzP0z8MOA94y8xmhbIfAqOAJ83sQmABcEYY9gIwGPgQWAdcAODuK8zsduCNUO82d1+R59hFRETyJq8J2N2nk/n6LcCRGeo7cEUl0xoDjMlddCIiIvHoSVgiIiIRKAGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRKAGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRKAGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRKAGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRNIsdgEhd1OuGh/M+jzbLV9MUWLB8dd7nV3TX0LxOX0SypzNgERGRCJSARUREIlACFhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEI8pqAzWyMmS01szkpZSPMbLGZzQp/g1OG3WRmH5rZ+2Z2TEr5saHsQzO7MZ8xi4jUpuHDhzN06FCGDx8eOxSpZfl+FOVY4NdA+nP27nH3u1MLzKw7cBbQA9gd+LuZdQuDHwCOBhYBb5jZBHd/J5+Bi4jUhuLiYhYvXhw7DIkgrwnY3aeZWZcaVh8CjHf3DcA8M/sQ6BuGfejuHwOY2fhQVwlYRETqrVjXgK80s9mhiXrnULYHsDClzqJQVlm5iIhIvRUjAf8W2BcoBJYAvwjllqGuV1G+BTO7xMxmmtnMZcuW5SJWERGRvKj1BOzun7l7ibuXAg+yuZl5EbBXStU9gU+rKM807dHu3tvde3fo0CH3wYuIiORIrSdgM9st5e0pQNkd0hOAs8xsOzPbG+gKvA68AXQ1s73NrAXJjVoTajNmERGRXMvrTVhm9jgwEGhvZouAW4GBZlZI0ow8H7gUwN3fNrMnSW6u2gRc4e4lYTpXAi8CTYEx7v52PuMWERHJt3zfBX12huL/q6L+SGBkhvIXgBdyGJqIiEhUehKWiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISAT57oxBRKTe6nVDej8yuddm+WqaAguWr877/IruGprX6Ut2dAYsIiISQY0TsJn1N7M2Ke/bmFm//IQlIiLSsGVzBvxbYE3K+7WhTERERLKUTQI2dy/vhSh0pqBryCIiIlshmwT8sZldZWbNw9/VwMf5CkxERKQhyyYBXwb8D7CYpIvAfsAl+QhKRESkoatxE7K7LyXpClBERES2UTZ3QXczsylmNie8LzCzm/MXmoiISMOVTRP0g8BNwEYAd5+NzohFRES2SjYJeHt3fz2tbFMugxEREWkssknAy81sX8ABzOx0YEleohIREWngsvkd7xXAaODrZrYYmAecm5eoREREGrhs7oL+GDjKzHYAmrj76vyFJSLSOJS22KHCf2k8apyAw4M3HgJWAw+aWU/gRneflK/gREQaurVdB8UOQSLJ5hrwd9z9v8AgoCNwATAqL1E1YMOHD2fo0KEMHz48digiIhJRNteALfwfDDzk7v8xM6tqBNlScXExixcvjh2GiIhEls0ZcJGZTSJJwC+GrglL8xOWiIhIw5bNGfCFQCHwsbuvM7OvkTRDA2BmPdz97VwHKCIi0hBlcxd0KfBmyvvPgc9TqjwC9MxdaCIiIg1XNk3Q1dH1YBERkRrKZQL2HE5LRESkQctlAhYREZEaymUC/iqH0xIREWnQsrkLGjPbA+icOp67Twv/++c2tNrX64aH8z6PNstX0xRYsHx13udXdNfQvE5fRES2XjaPovw5cCbwDlASih2Yloe4REREGrRszoBPBvZ39w35CkZERKSxyOYa8MdA83wFIiIi0phkcwa8DphlZlOA8rNgd78q51GJiIg0cNkk4AnhT0RERLZRNo+iHGdmLYBuoeh9d9+Yn7BEREQatmzugh4IjAPmkzx2ci8zG1b2MyQRERGpuWyaoH8BDHL39wHMrBvwONArH4GJiIg0ZNkk4OZlyRfA3T8wM90VnaXSFjtU+C8iIo1TNgl4ppn9H0m3gwDnAEW5D6lhW9t1UOwQRESkDsgmAX8XuAK4iuQa8DTgN/kISkREpKHL5i7oDcAvw5+IiIhsg2oTsJk96e7fNrO3yNDnr7sX5CUyERGRBqwmZ8BXh/8n5DMQERGRxqTaZ0G7+5Lw8nJ3/yT1D7g8v+GJiIg0TNl0xnB0hrLjchWIiIhIY1KTa8DfJTnT3cfMZqcMagO8kq/AREREGrKaXAP+I/BX4A7gxpTy1e6+Ii9RiYiINHDVJmB3XwWsAs4GMLOOQEugtZm1dvcF+Q1RRESk4anxNWAzO9HM5gLzgH+SdMrw1zzFJSIi0qBlcxPWT4H+wAfuvjdwJLoGLCIislWyScAb3f1zoImZNXH3l4DCPMUlIiLSoGXzLOiVZtaa5BnQj5nZUmBTfsISERFp2LI5Ax4CrAOuBf4GfAScmI+gREREGroanQGbWVPgOXc/CigFxuU1KhERkQauRgnY3UvMbJ2Z7RR+liQi26i0xQ4V/otI45LNNeD1wFtmNhlYW1bo7lflPCqRRmBt10GxQxCRiLJJwM+HPxEREdlGNU7A7q7rviIiIjlS4wRsZvMATy93931yGpGI1EvDhw+nuLiYXXfdlTvvvDN2OCJ1XjY/Q+oN9Al/hwP3A49WNYKZjTGzpWY2J6WsnZlNNrO54f/OodzM7H4z+9DMZptZz5RxhoX6c81sWDYLKCK1o7i4mMWLF1NcXBw7FJF6ocYJ2N0/T/lb7O73AkdUM9pY4Ni0shuBKe7eFZjC5h6WjgO6hr9LgN9CkrCBW4F+QF/g1rKkLSIiUl9l0wTdM+VtE5Iz4jZVjePu08ysS1rxEGBgeD0OmAr8IJQ/7O4OvGpmbc1st1B3clnXh+Eu7GOBx2sau4iISF2TzV3Qv2DzNeBNJL0hnbEV89zF3ZcAuPuS0L0hwB7AwpR6i0JZZeVbMLNLSM6e6dSp01aEJiIiUjuqTcBm9v3wciJJArbw3oETgF/mKBbLUOZVlG9Z6D4aGA3Qu3fvjHVERETqgppcA24T/noB3wV2A3YHLgO6b8U8PwtNy4T/S0P5ImCvlHp7Ap9WUS4iIlJvVZuA3f0n7v4ToD3Q092vd/frSBLynlsxzwlA2Z3Mw4DnUsqHhruh+wOrQlP1i8AgM9s53Hw1KJSJiIjUW9lcA+4EfJXy/iugS1UjmNnjJDdRtTezRSR3M48CnjSzC4EFbL6O/AIwGPiQpNelCwDcfYWZ3Q68EerdVnZDloiISH2VTQJ+BHjdzJ4huQZ7CtX0iuTuZ1cy6MgMdR24opLpjAHGZBGriIhInZbNoyhHmtlfSR7CAXCBu/87P2GJiIg0bNmcAePubwJv5ikWERGRRiObR1GKiIhIjigBi4iIRKAELCIiEoESsIiISARZ3YQlIvXTgtsOyvs8Nq1oBzRj04pP8j6/Tre8ldfpi9QGnQGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRKAGLiIhEoN6QRCQn2rcsBTaF/yJSHSVgEcmJ6wtWxg5BpF5RE7SIiEgESsAiIiIRKAGLiIhEoAQsIiISgRKwiIhIBErAIiIiESgBi4iIRKAELCIiEoESsIiISARKwCIiIhEoAYuIiESgZ0GLiEjODB8+nOLiYnbddVfuvPPO2OHUaUrAIiKSM8XFxSxevDh2GPWCmqBFREQi0BmwiEgjseC2g/I+j00r2gHN2LTik7zPr9Mtb+V1+vmmM2AREZEIlIBFREQiUBO0iIjkTPuWpcCm8F+qogQsIiI5c33Bytgh1BtqghYREYlACVhERCQCJWAREZEIlIBFREQi0E1Ysk303FcRka2jBCzbRM99FRHZOkrADVhDe+wc1P9Hz4mIlNE1YBERkQh0BizbRE+9ERHZOkrAsk301BsRka2jJmgREZEIlIBFREQiUAIWERGJQAlYREQkAiVgERGRCJSARUREIlACFhERiUAJWEREJIJoCdjM5pvZW2Y2y8xmhrJ2ZjbZzOaG/zuHcjOz+83sQzObbWY9Y8UtIiKSC7HPgL/l7oXu3ju8vxGY4u5dgSnhPcBxQNfwdwnw21qPVEREJIdiJ+B0Q4Bx4fU44OSU8oc98SrQ1sx2ixGgiIhILsRMwA5MMrMiM7sklO3i7ksAwv+OoXwPYGHKuItCmYiISL0UszOGw9z9UzPrCEw2s/eqqGsZynyLSkkivwSgU6dOuYlSREQkD6KdAbv7p+H/UuAZoC/wWVnTcvi/NFRfBOyVMvqewKcZpjna3Xu7e+8OHTrkM3wREZFtEiUBm9kOZtam7DUwCJgDTACGhWrDgOfC6wnA0HA3dH9gVVlTtYiISH0Uqwl6F+AZMyuL4Y/u/jczewN40swuBBYAZ4T6LwCDgQ+BdcAFtR+yiIhI7kRJwO7+MXBwhvLPgSMzlDtwRS2EJiIiUivq2s+QREREGgUlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEIlIBFREQiUAIWERGJQAlYREQkAiVgERGRCJSARUREIlACFhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYlACVhERCQCJWAREZEIlIBFREQiUAIWERGJQAlYREQkAiVgERGRCJSARUREIlACFhERiaBZ7ACk8SlpuTOrD/0BG1vtAmZZjbusqChPUVU0+qwetTKf2rKMsbFDyKm6uh24w9K1G/nFlI9Z+eWmPEUlDYUSsNS61Yf+gA77HMwuu+1OkyZqhJG80vCsAAASr0lEQVSGo7S0lMVLirkO+PHED2KHI3Wcjn5S6za22kXJVxqkJk2asMduu9Jxh+axQ5F6QEdAqX1mSr7SYDVp0iTbKyvSSOkoKNKIzJ8/nz/+8Y+xwxARlIBFGqRNmzLfAKQELFJ3KAGL1AHz58/nwAMPLH9/9913M2LECO6//366d+9OQUEBZ511FgBr167lO9/5Dn369OGQQw7hueeeA2Ds2LGcccYZnHjiiQwaNCjjfG688UZefvllCgsLueeeezj88MOZNWtW+fDDDjuM2bNnM2LECM477zyOOOIIunbtyoMPPlhe56677qJPnz4UFBRw66235mN1iDQKugtapA4bNWoU8+bNY7vttmPlypUAjBw5kiOOOIIxY8awcuVK+vbty1FHHQXAjBkzmD17Nu3atat0enfffTcTJ04EoF27dowdO5Z7772XDz74gA0bNlBQUMCf//xnZs+ezauvvsratWs55JBDOP7445kzZw5z587l9ddfx9056aSTmDZtGgMGDKidFSLSgOgMWKQOKygo4JxzzuHRRx+lWbPk+/KkSZMYNWoUhYWFDBw4kPXr17NgwQIAjj766EqTbyZnnHEGEydOZOPGjYwZM4bzzz+/fNiQIUNo1aoV7du351vf+havv/46kyZNYtKkSRxyyCH07NmT9957j7lz5+Z0mUUaC50Bi9QBzZo1o7S0tPz9+vXrAXj++eeZNm0aEyZM4Pbbb+ftt9/G3Xn66afZf//9K0zjtddeY4cddshqvttvvz1HH300zz33HE8++SQzZ84sH2Zpt/KaGe7OTTfdxKWXXprtIopIGp0Bi9QBu+yyC0uXLuXzzz9nw4YNTJw4kdLSUhYuXMi3vvUt7rzzTlauXMmaNWs45phj+NWvfoW7A/Dvf/+7xvNp06YNq1evrlB20UUXcdVVV9GnT58KZ8/PPfcc69ev5/PPP2fq1Kn06dOHY445hjFjxrBmzRoAFi9ezNKlS3OwBkQaH50Bi9QBzZs355ZbbqFfv37svffefP3rX6ekpIRzzz2XVatW4e5ce+21tG3blh//+Mdcc801FBQU4O506dKl/JpudQoKCmjWrBkHH3ww559/Ptdeey29evVixx135IILLqhQt2/fvhx//PEsWLCAH//4x+y+++7svvvuvPvuuxx66KEAtG7dmkcffZSOHTvmfJ2INHRKwCJ1xFVXXcVVV11Vbb1WrVrx+9//fovy888/v8I13EyaN2/OlClTKpR9+umnlJaWbnHndLdu3Rg9evQW07j66qu5+uqrq41TRKqmJmiRRuzhhx+mX79+jBw5Uk8nE6llOgMWaYDeeustzjvvvApl2223Ha+99lqFsqFDhzJ06NAtxh8xYkQ+wxMRlIBFGqSDDjqowgM2RKTuUZuTiIhIBErAIiIiESgBi4iIRKAELCIiEoESsDRKxcXFnHXWWey77750796dwYMH88EHH2Q1jWeffZZ33nknTxE2fCNHjqRHjx4UFBRQWFi4xR3aqUaMGMHdd9+d0/nPnDmz/HfX+Zi+SHV0F7RE1+uGh3M6vaK7tvxZTSp355RTTmHYsGGMHz8egFmzZvHZZ5/RrVu3Gs/n2Wef5YQTTqB79+7bFG82SkpKaNq0aU6nueC2g3I6vU63vFVtnRkzZjBx4kTefPNNtttuO5YvX85XX32V0ziqsmnTJnr37k3v3r23eVrujrvrd9SSNW0x0ui89NJLNG/enMsuu6y8rLCwkJKSEk444YTysiuvvJKxY8cCST+6Zf3yXn/99fzrX/9iwoQJ3HDDDRQWFvLRRx8xa9Ys+vfvT0FBAaeccgpffPEFAAMHDuTaa69lwIABHHDAAbzxxhuceuqpdO3alZtvvrl8fo8++ih9+/alsLCQSy+9lJKSEiB53GPZYypnzJhRC2so/5YsWUL79u3ZbrvtAGjfvj277747Xbp0Yfny5UByhjpw4MDycf7zn/9s0T/xkiVLGDBgAIWFhRx44IG8/PLLAPztb3+jZ8+eHHzwwRx55JFAcpZ7ySWXMGjQIIYOHcrUqVMrfN6Zpg+Z+z+eP38+BxxwAJdffjk9e/Zk4cKF+VtZ0mDpDFganTlz5tCrV68a11+xYgXPPPMM7733HmbGypUradu2LSeddBInnHACp59+OpA8Z/lXv/oV3/zmN7nlllv4yU9+wr333gtAixYtmDZtGvfddx9DhgyhqKiIdu3ase+++3LttdeydOlSnnjiCV555RWaN2/O5ZdfzmOPPcbQoUNZu3YtBx54ILfddlte1kcMgwYN4rbbbqNbt24cddRRnHnmmXzzm9+scpxM/RM//vjjHHPMMfzoRz+ipKSEdevWsWzZMi6++GKmTZvG3nvvzYoVK8qnUVRUxPTp02nVqhVTp06tdvqV9X/cqVMn3n//fR566CF+85vf5GMVSSOgBCxSjR133JGWLVty0UUXcfzxx1c4ayqzatUqVq5cWZ5Ehg0bxhlnnFE+/KSTTgKSB2T06NGD3XbbDYB99tmHhQsXMn36dIqKiujTpw8AX375ZXkHB02bNuW0007L6zLWttatW1NUVMTLL7/MSy+9xJlnnsmoUaOqHKesf+JWrVqV90/cp08fvvOd77Bx40ZOPvlkCgsLmTp1KgMGDGDvvfcGqNDD00knnUSrVq1qPP3p06eX938MsGbNGubOnUunTp3o3Lkz/fv3z9EakcZICVganR49evDUU09tUV5Zn7zNmjXj9ddfZ8qUKYwfP55f//rX/OMf/8hqnmVNrU2aNCl/XfZ+06ZNuDvDhg3jjjvu2GLcli1b5vy6b13QtGlTBg4cyMCBAznooIMYN25chc+gbP2XydQ/8YABA5g2bRrPP/885513HjfccANt27bdom6ZqvpLzqb/4/nz52fd97JIOl0DlkbniCOOYMOGDRWu873xxhuUlJTwzjvvsGHDBlatWlXea9CaNWtYtWoVgwcP5t577y1/xGNq37o77bQTO++8c/k1yEceeaTaJtVURx55JE899VR537orVqzgk08+ycny1kXvv/8+c+fOLX8/a9YsOnfuTJcuXSgqKgLg6aefrjBOpv6JP/nkEzp27MjFF1/MhRdeyJtvvsmhhx7KP//5T+bNmwdQoQm6Kur/WGqbzoCl0TEznnnmGa655hpGjRpFy5Yt6dKlC/feey/f/va3KSgooGvXruXNjqtXr2bIkCGsX78ed+eee+4B4KyzzuLiiy/m/vvv56mnnmLcuHFcdtllrFu3jn322YeHHnqoxjF1796dn/70pwwaNIjS0lKaN2/OAw88QOfOnfOyDmJbs2YN3/ve91i5ciXNmjVjv/32Y/To0bz77rtceOGF/OxnP6Nfv34VxsnUP/G4ceO46667aN68Oa1bt+bhhx+mQ4cOjB49mlNPPZXS0lI6duzI5MmTq40pm/6PG2KLhNQ+c/fYMeRF7969febMmVmNk+ufw8T2TJu7YoeQ0bLjx2Z1E5RIfVNUVMQl49+OHcYW6uoxYWvV5Cdv6cysyN23/fdnOaAmaBERkQiUgEVERCJQAhYREYlACVhqn3uFn/uINCSlpaU00FtrJMeUgKXWNf/yMz5bslhJWBqc0tJSFi8pZunajbFDkXpAP0OSWtdmxs9Zxg/4dMlnUMkDE0TqI3dYunYjv5jycexQpB6oVwnYzI4F7gOaAn9w96qfXSd1UtP1X9D2pRu3atyt+dnB1tBP0uo2bQfSENSbJmgzawo8ABwHdAfONrPa6wdOREQkh+pNAgb6Ah+6+8fu/hUwHhgSOSYREZGtUp8S8B5Aaqebi0KZiIhIvVOfrgFnulunws3+ZnYJcEl4u8bM3s97VHVYZ2gPLI8dR07dqpu2tkaD2xa0HWwVbQcA1JkHrNenBLwI2Cvl/Z7Ap6kV3H00MLo2g6rLzGxmXXnmqcSlbUFA20FdU5+aoN8AuprZ3mbWAjgLmBA5JhERka1Sb86A3X2TmV0JvEjyM6Qx7l73uhsRERGpgXqTgAHc/QXghdhx1CNqjpcy2hYEtB3UKQ22P2AREZG6rD5dAxYREWkwlIDrITNzM3sk5X0zM1tmZhOrGW9gdXWk7jOzPc3sOTOba2Yfmdl9ZtbCzArNbHBKvRFmdn3MWCV3zKzEzGal/HXJ47zON7Nf52v6klACrp/WAgeaWavw/mhgccR4pJaYmQF/Bp51965AN6A1MBIoBAZXMXq282qaq2lJTnzp7oUpf/NjByTbRgm4/vorcHx4fTbweNkAM+trZv8ys3+H//unj2xmO5jZGDN7I9TTYz3rhyOA9e7+EIC7lwDXAhcBdwJnhrOjM0P97mY21cw+NrOryiZiZuea2euh7u/Lkq2ZrTGz28zsNeDQWl0yyZqZNTWzu8J+PNvMLg3lA83sn2b2pJl9YGajzOyc8Jm/ZWb7hnonmtlr4RjwdzPbJcM8OpjZ02Eeb5jZYbW9nA2VEnD9NR44y8xaAgXAaynD3gMGuPshwC3AzzKM/yPgH+7eB/gWcJeZ7ZDnmGXb9QCKUgvc/b/AfOCnwBPh7OiJMPjrwDEkz1K/1cyam9kBwJnAYe5eCJQA54T6OwBz3L2fu0/P+9JINlqlND8/E8ouBFaF/bgPcLGZ7R2GHQxcDRwEnAd0c/e+wB+A74U604H+4VgxHhieYb73AfeEeZwWxpccqFc/Q5LN3H12uAZ0Nlv+NGsnYJyZdSV5XGfzDJMYBJyUco2wJdAJeDcvAUuuGGmPYK2m/Hl33wBsMLOlwC7AkUAv4I2kRZtWwNJQvwR4OtdBS058Gb4wpRoEFJjZ6eH9TkBX4CvgDXdfAmBmHwGTQp23SL50Q/JEwSfMbDegBTAvw3yPImlJKXu/o5m1cffVOVimRk0JuH6bANwNDAS+llJ+O/CSu58SkvTUDOMacJq7N+rnZddDb5OchZQzsx1JHtNakqH+hpTXJST7vAHj3P2mDPXXh2ZtqR8M+J67v1ih0GwgFT/70pT3pWw+9v8K+KW7TwjjjMgwjybAoe7+Ze7CFlATdH03BrjN3dN7J9+JzTdlnV/JuC8C3ws39WBmh+QlQsm1KcD2ZjYUym+U+gUwFvgMaFPDaZxuZh3DNNqZWZ15QL1k5UXgu2bWHMDMumV5KSn1WDGskjqTgCvL3phZ+lm4bCUl4HrM3Re5+30ZBt0J3GFmr5A8tjOT20mapmeb2ZzwXuo4T56ccwpwhpnNBT4A1gM/BF4iaSpMvQkr0zTeAW4GJpnZbGAysFveg5d8+APwDvBm2I9/T3YtmyOAP5nZy1TeS9JVQO9wk9c7wGXbEK+k0JOwREREItAZsIiISARKwCIiIhEoAYuIiESgBCwiIhKBErCIiEgESsAiIiIRKAGL1AOhQ4XeseOoqfoWr0gMSsAiDZyZ1flHzqrrQ2mMlIBFtpGZdTGz98zsD2Y2x8weM7OjzOwVM5sbuofM2P1j6Pj8WTP7i5nNM7Mrzez7oc6rZtYuZVbnhu4l55hZ3zB+VdP9k5n9hc0P4U+Pu4mZ/cbM3jaziWb2QtlD/c2sV+jOrsjMXgwP6y87s/156NbuAzM7PJS3MrPx4WlJT5B08FA2n0FmNsPM3gwxtQ7l883sFjObDpyR689FpK5TAhbJjf1Ium0rIOkC8H+BbwDXkzwmsqruHw8M9fsCI4F1oXu4GcDQlHns4O7/A1xO8hxwqpnuocAwdz+ikphPBbqQdFd3UahPeK7wr4DT3b1XmNfIlPGahW7trgFuDWXfDXEXhLq9wrTakzz28ih37wnMBL6fMq317v4Ndx9fSYwiDVadb5oSqSfmlXWKYWZvA1Pc3c3sLZIktyeZu3+EpOeq1cBqM1sF/CWUv0WS0Ms8DuDu08xsRzNrS+XdSgJMdvcVVcT8DeBP7l4KFJvZS6F8f5IvBZNDXx1NgSUp4/05/C8KywYwALg/xDc7PGMaoD/QHXglTKsFyReLMk8g0kgpAYvkRnVdv5WQoftHM+tXg3HLpD+43amkW8kw3bXVxGxVlL/t7odWMrwsvrLuDSuLr2xak9397EqmVV2MIg2WmqBFakcuun88M4z7DWCVu6/axulOB04L14J3IelXGuB9oIOZlTdJm1mPaqY1DTgn1D+QzWfurwKHmdl+Ydj2ZtYtixhFGiwlYJHakYvuH78ws38BvwMuzMF0nwYWAWXd2L1Gkti/Ak4Hfm5m/wFmAf9TzbR+C7QOTc/DgdcB3H0ZSZ/Uj4dhr5JcIxdp9NQdoUgjZmat3X2NmX2NJGke5u7FseMSaQx0DVikcZsYbuZqAdyu5CtSe3QGLNLAmdlBwCNpxRvcvV+MeEQkoQQsIiISgW7CEhERiUAJWEREJAIlYBERkQiUgEVERCJQAhYREYng/wEQVT6+RH4VaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb48f98940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A clustered bar chart\n",
    "plt.figure(figsize = [7,6])\n",
    "ax = sb.barplot(data = bike, x = 'member_gender', y = 'duration_sec', hue = 'user_type');\n",
    "ax.legend(loc = 8, ncol = 7, framealpha = 1, title = 'user_type');\n",
    "plt.title('Clustered bar chart of duration against gender of members and user type');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb49374550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A clustered bar chart\n",
    "plt.figure(figsize = [7,6])\n",
    "ax = sb.barplot(data = bike, x = 'member_gender', y = 'duration_sec', hue = 'bike_share_for_all_trip');\n",
    "ax.legend(loc = 8, ncol = 7, framealpha = 1, title = 'bike_share_for_all_trip');\n",
    "plt.title('Clustered bar chart of duration against gender of members and bike sharing for all trips');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb48b8dda0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.pointplot(data = bike, x = 'member_gender', y = 'duration_sec', hue = 'user_type',\n",
    "                  dodge = 0.3, linestyles = \"\")\n",
    "plt.title('Point plot of duration against gender of members and user type');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb48b601d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.pointplot(data = bike, x = 'member_gender', y = 'duration_sec', hue = 'bike_share_for_all_trip',\n",
    "                  dodge = 0.3, linestyles = \"\")\n",
    "plt.title('Point plot of duration against gender of members and bike sharing for all trips');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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",
    "> From the bar plot of durations, user_type and member_gender; I observed that all the genders in the customer category that patronize the bike system record the highest number of duration riding their bike. \n",
    "\n",
    "> The other gender have the highest frequency or count for the duration recorded for both the customers and subscribers.\n",
    "\n",
    "> It is also observed that the other gender has the highest frequency for the duration spent for riders who share their bike for all trip and those that do not share their bike for all trip. The male gender has the lowest frequency for both riders who share their bike for all trip and those that do not share their bike for all trip.\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> From the graph I observed that the three gender categories the highest number occuring frequency for those that do not share their ride for all trip is greater than those that do share their bike for all trip."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "> I first created a histogram chart for duration since it is a numeric variable. The initial plot shows that duration follows a highly skewed distribution. This called for a need of log scaling. I performed log scaling of duration before plotting again. Under a log scale, I observed that the data for the duration is roughly unimodal with a large peak somewhere between 400 and 600. The duration distribution also seems to just cut off at its maximum, rather than declining in a smooth tail.\n",
    "\n",
    "> I moved on to explore the other three categorical variables; user_type, member_ gender and bike_share_for_ all_ trip. From the bar chart of the user_type distribution it shows that subscribers have the highest number of count making use of the bike sharing system while the customer type had the lowest number of count of users using the bike sharing system.The bar chart of the member_gender distribution shows that the male gender have the highest number of count making use of the bike sharing system followed by the female gender with a relatively smaller count of users using the bike sharing system. The other gender had the lowest number of count of users using the bike sharing system.The bar chart of the bike_share_for_all_trip distribution:shows that the answer No has the highest number of count of individuals who refused to share their bike for all trip while using the bike sharing system while the answer Yes had the lowest number of count of individuals who shared thier bike for all trip while using the bike sharing system.\n",
    "\n",
    "> For the bivariate, I investigated the relationships between pairs of variables in my data.  The initial plot of duration vs user_type showed that there appeared to beno relationships between the categorical variables and the numeric value of interest which is duration. I then performed a log transformation of duration again and made a new plot. For the violin plot of Log_duration vs User_type it showed that the box plot elements show that the median log duration for subscribers is lower than that of the customers when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration of both the customer and subscriber are highly concentrated around the median. The subscriber has a more elongated distribution at one end while that of the customer has a more elongated distribution at both ends. For the violin plot of Log_duration vs Member_gender it showed that the box plot elements show that the median log duration for males is the lowest followed by that of the other gender and then than that of the female gender when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration for the male gender, other gender and female gender are highly concentrated around the median. The other gender has a more elongated distribution at one end which indicates that this particular gender had individuals recording a much higher duration while riding their bikes. For the violin plot of Log_duration vs Bike_share_for_all_trip it showed that the box plot elements show that the median log duration for the answer Yes is lower than that of the No when compared with each other. The shape of the distribution (extremely skinny on each end and wide in the middle) indicates that the duration of both the Yes and No are highly concentrated around the median. They both have a more elongated distribution at one end. The box plots of the Log_duration vs categorical variables indicate that the box elements are relatively short, that means that the data is more compact and they do have some outliers. The plot of the full data using a violin plot and variable transformation of the duration reveals much more than the earlier box plots.\n",
    "\n",
    "> I then proceeded to look at the relationship between the three categorical variables on bivariate plots. From the bar plot of user_type vs member_gender, I observed that the subscribers have the most members with the highest count spread across the various genders. The male gender is the most dominating gender by count or frequency for both the user types followed by the female gender and the other gender. From the bar plot of user_type vs bike_share_for_all_trip; I observed that for the customer type all of the customers do not share thier ride for all trips while it is also observed that most subscriber members also do not share their rides but the difference is that a few subscriber members do share their ride for all trips. From the bar plot of bike_share_for_all_trip vs  member_gender; I observed in this plot that majority of the three classified genders do not share their bikes for all trips. The male gender has a count of over 100,000 members who do not share their ride and a count lesss than 25,000 for those who share their ride.\n",
    "\n",
    "> I then went on to explore the three categorical variables and their relationship with duration using multivariate exploration. From the bar plot of durations, user_type and member_gender; I observed that all the genders in the customer category that patronize the bike system record the highest number of duration riding their bike. The other gender have the highest frequency or count for the duration recorded for both the customers and subscribers.From the bar plot of durations,  member_gender and bike_share_for_all_trip; It is also observed that the other gender has the highest frequency for the duration spent for riders who share their bike for all trip and those that do not share their bike for all trip. The male gender has the lowest frequency for both riders who share their bike for all trip and those that do not share their bike for all trip.\n",
    "\n",
    "> From the graph I observed that the three gender categories the highest number occuring frequency for those that do not share their ride for all trip is greater than those that do share their bike for all trip.\n",
    "\n"
   ]
  }
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