Bike_dataset_exploration part I-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ford GoBike System Dataset Exploration\n",
    "## By Precious Okon\n",
    "\n",
    "## Introduction\n",
    "> Introduce the dataset\n",
    "\n",
    "This project is on the exploration of Ford GoBike Dataset to obtain various insights, findings and visualize on them\n",
    "\n",
    "his data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. It contains 183412 rows and 16 variables on each bike Id. \n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import datetime as dt\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Load in your dataset and describe its properties through the questions below. Try and motivate your exploration goals through this section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset into a pandas dataframe\n",
    "bike = pd.read_csv('fordgo-bike-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "<p>183412 rows × 16 columns</p>\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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "0               Male                      No  \n",
       "1                NaN                      No  \n",
       "2               Male                      No  \n",
       "3              Other                      No  \n",
       "4               Male                     Yes  \n",
       "...              ...                     ...  \n",
       "183407          Male                      No  \n",
       "183408          Male                      No  \n",
       "183409          Male                     Yes  \n",
       "183410          Male                      No  \n",
       "183411          Male                      No  \n",
       "\n",
       "[183412 rows x 16 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#opening the dataset\n",
    "bike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "#overview of data shape and composition\n",
    "print(bike.shape)\n",
    "print(bike.dtypes)\n",
    "print(bike.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "1. Convert start time and end time column from string to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the start_time and end_time column from string to datetime\n",
    "bike[['start_time','end_time']] = bike[['start_time','end_time']].apply(pd.to_datetime)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Checking for null values in member gender column and replacing the null values with the most frequent category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', nan, 'Other', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the unique value of member_gender column\n",
    "bike.member_gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values of member_gender column\n",
    "bike.member_gender.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the fillna function to replace the null values with the most frequent category which is Male\n",
    "#Since its a categorical data, I have to replace the null values with the most frequent category \n",
    "bike = bike.fillna(bike['member_gender'].value_counts().index[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Converting the column member gender, user type and bike share from string to ordered categorical datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert member_gender, user_type, and bike_share_for_all_trip columns into ordered categorical types\n",
    "ordinal_var_dict = {'member_gender': ['Male','Female','Other'],\n",
    "                    'user_type': ['Customer', 'Subscriber'],\n",
    "                    'bike_share_for_all_trip': ['No', 'Yes']}\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                categories = ordinal_var_dict[var])\n",
    "    bike[var] = bike[var].astype(ordered_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Converting the start staion id column, end station id column, member birth year from string to numeric datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing the start_station_id, end_station_id, member_birth_year column from string to numerical data\n",
    "bike[['start_station_id', 'end_station_id', 'member_birth_year']] = bike[['start_station_id', \n",
    "                        'end_station_id', 'member_birth_year']].apply(pd.to_numeric, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Dropping rows with null values in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows with null values\n",
    "bike.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if it was dropped\n",
    "bike.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change bike id datatype from float to string\n",
    "bike['bike_id'] =bike.bike_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Extracting the hour, day, month from the start and end date and putting them into a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using datetime as dt \n",
    "bike['start_day'] = bike['start_time'].dt.day_name()\n",
    "bike['start_month'] = bike['start_time'].dt.month_name()\n",
    "# bike['start_year'] = bike['start_time'].dt.year\n",
    "bike['start_hr'] = bike['start_time'].dt.hour\n",
    "bike['start_hr'] = bike.start_hr.astype(str)\n",
    "\n",
    "\n",
    "bike['end_day'] = bike['end_time'].dt.day_name()\n",
    "bike['end_month'] = bike['end_time'].dt.month_name()\n",
    "bike['end_hr'] = bike['end_time'].dt.hour\n",
    "bike['end_hr'] = bike.end_hr.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Converting start day, end day, start month, end month, start hr, end hour to ordered categorical datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert day, and hour into ordered categorical types\n",
    "ordinal_var_dict = {'start_day': ['Monday','Tuesday','Wednesday','Thursday','Friday', 'Saturday', 'Sunday'],\n",
    "                    'end_day': ['Monday','Tuesday','Wednesday','Thursday','Friday', 'Saturday', 'Sunday'],\n",
    "                    'start_month': ['Februar'],\n",
    "                    'end_month': ['February', 'March'],\n",
    "                    'start_hr': [str(i) for i in range(0,24)],\n",
    "                    'end_hr': [str(i) for i in range(0,24)]}\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                categories = ordinal_var_dict[var])\n",
    "    bike[var] = bike[var].astype(ordered_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                   datetime64[ns]\n",
       "start_station_id                  float64\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                    float64\n",
       "end_station_name                   object\n",
       "end_station_latitude              float64\n",
       "end_station_longitude             float64\n",
       "bike_id                            object\n",
       "user_type                        category\n",
       "member_birth_year                 float64\n",
       "member_gender                    category\n",
       "bike_share_for_all_trip          category\n",
       "start_day                        category\n",
       "start_month                      category\n",
       "start_hr                         category\n",
       "end_day                          category\n",
       "end_month                        category\n",
       "end_hr                           category\n",
       "dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally checking our datatypes to check if the columns are all in their right datatype\n",
    "bike.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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_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>member_birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "      <td>174952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>704.002744</td>\n",
       "      <td>139.002126</td>\n",
       "      <td>37.771220</td>\n",
       "      <td>-122.351760</td>\n",
       "      <td>136.604486</td>\n",
       "      <td>37.771414</td>\n",
       "      <td>-122.351335</td>\n",
       "      <td>1984.803135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1642.204905</td>\n",
       "      <td>111.648819</td>\n",
       "      <td>0.100391</td>\n",
       "      <td>0.117732</td>\n",
       "      <td>111.335635</td>\n",
       "      <td>0.100295</td>\n",
       "      <td>0.117294</td>\n",
       "      <td>10.118731</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>1878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>323.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411901</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411647</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>510.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.397437</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>789.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.283093</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>37.797673</td>\n",
       "      <td>-122.286533</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84548.000000</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>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec  start_station_id  start_station_latitude  \\\n",
       "count  174952.000000     174952.000000           174952.000000   \n",
       "mean      704.002744        139.002126               37.771220   \n",
       "std      1642.204905        111.648819                0.100391   \n",
       "min        61.000000          3.000000               37.317298   \n",
       "25%       323.000000         47.000000               37.770407   \n",
       "50%       510.000000        104.000000               37.780760   \n",
       "75%       789.000000        239.000000               37.797320   \n",
       "max     84548.000000        398.000000               37.880222   \n",
       "\n",
       "       start_station_longitude  end_station_id  end_station_latitude  \\\n",
       "count            174952.000000   174952.000000         174952.000000   \n",
       "mean               -122.351760      136.604486             37.771414   \n",
       "std                   0.117732      111.335635              0.100295   \n",
       "min                -122.453704        3.000000             37.317298   \n",
       "25%                -122.411901       44.000000             37.770407   \n",
       "50%                -122.398279      101.000000             37.781010   \n",
       "75%                -122.283093      238.000000             37.797673   \n",
       "max                -121.874119      398.000000             37.880222   \n",
       "\n",
       "       end_station_longitude  member_birth_year  \n",
       "count          174952.000000      174952.000000  \n",
       "mean             -122.351335        1984.803135  \n",
       "std                 0.117294          10.118731  \n",
       "min              -122.453704        1878.000000  \n",
       "25%              -122.411647        1980.000000  \n",
       "50%              -122.397437        1987.000000  \n",
       "75%              -122.286533        1992.000000  \n",
       "max              -121.874119        2001.000000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the descriptive statistics of our dataset\n",
    "bike.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>...</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",
       "      <th>start_day</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hr</th>\n",
       "      <th>end_day</th>\n",
       "      <th>end_month</th>\n",
       "      <th>end_hr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</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>...</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>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>...</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>23</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1793</td>\n",
       "      <td>2019-02-28 23:49:58.632</td>\n",
       "      <td>2019-03-01 00:19:51.760</td>\n",
       "      <td>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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>23</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183407</th>\n",
       "      <td>480</td>\n",
       "      <td>2019-02-01 00:04:49.724</td>\n",
       "      <td>2019-02-01 00:12:50.034</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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183408</th>\n",
       "      <td>313</td>\n",
       "      <td>2019-02-01 00:05:34.744</td>\n",
       "      <td>2019-02-01 00:10:48.502</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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183409</th>\n",
       "      <td>141</td>\n",
       "      <td>2019-02-01 00:06:05.549</td>\n",
       "      <td>2019-02-01 00:08:27.220</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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183410</th>\n",
       "      <td>139</td>\n",
       "      <td>2019-02-01 00:05:34.360</td>\n",
       "      <td>2019-02-01 00:07:54.287</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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183411</th>\n",
       "      <td>271</td>\n",
       "      <td>2019-02-01 00:00:20.636</td>\n",
       "      <td>2019-02-01 00:04:52.058</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>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174952 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "0              52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "2              61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3              36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4               1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "5               1793 2019-02-28 23:49:58.632 2019-03-01 00:19:51.760   \n",
       "...              ...                     ...                     ...   \n",
       "183407           480 2019-02-01 00:04:49.724 2019-02-01 00:12:50.034   \n",
       "183408           313 2019-02-01 00:05:34.744 2019-02-01 00:10:48.502   \n",
       "183409           141 2019-02-01 00:06:05.549 2019-02-01 00:08:27.220   \n",
       "183410           139 2019-02-01 00:05:34.360 2019-02-01 00:07:54.287   \n",
       "183411           271 2019-02-01 00:00:20.636 2019-02-01 00:04:52.058   \n",
       "\n",
       "        start_station_id                                start_station_name  \\\n",
       "0                   21.0  Montgomery St BART Station (Market St at 2nd 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",
       "...                  ...                                               ...   \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",
       "0                    37.789625              -122.400811            13.0   \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",
       "...                        ...                      ...             ...   \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",
       "0                     Commercial St at Montgomery St             37.794231   \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",
       "...                                              ...                   ...   \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",
       "        ...   user_type member_birth_year member_gender  \\\n",
       "0       ...    Customer            1984.0          Male   \n",
       "2       ...    Customer            1972.0          Male   \n",
       "3       ...  Subscriber            1989.0         Other   \n",
       "4       ...  Subscriber            1974.0          Male   \n",
       "5       ...  Subscriber            1959.0          Male   \n",
       "...     ...         ...               ...           ...   \n",
       "183407  ...  Subscriber            1996.0          Male   \n",
       "183408  ...  Subscriber            1984.0          Male   \n",
       "183409  ...  Subscriber            1990.0          Male   \n",
       "183410  ...  Subscriber            1988.0          Male   \n",
       "183411  ...  Subscriber            1989.0          Male   \n",
       "\n",
       "        bike_share_for_all_trip start_day start_month start_hr end_day  \\\n",
       "0                            No  Thursday    February       17  Friday   \n",
       "2                            No  Thursday    February       12  Friday   \n",
       "3                            No  Thursday    February       17  Friday   \n",
       "4                           Yes  Thursday    February       23  Friday   \n",
       "5                            No  Thursday    February       23  Friday   \n",
       "...                         ...       ...         ...      ...     ...   \n",
       "183407                       No    Friday    February        0  Friday   \n",
       "183408                       No    Friday    February        0  Friday   \n",
       "183409                      Yes    Friday    February        0  Friday   \n",
       "183410                       No    Friday    February        0  Friday   \n",
       "183411                       No    Friday    February        0  Friday   \n",
       "\n",
       "       end_month end_hr  \n",
       "0          March      8  \n",
       "2          March      5  \n",
       "3          March      4  \n",
       "4          March      0  \n",
       "5          March      0  \n",
       "...          ...    ...  \n",
       "183407  February      0  \n",
       "183408  February      0  \n",
       "183409  February      0  \n",
       "183410  February      0  \n",
       "183411  February      0  \n",
       "\n",
       "[174952 rows x 22 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final dataset\n",
    "bike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "\n",
    "The Ford Gobike Dataset contains records from 183412 rides and 16 variables which was later dropped to 174952 rides and 22 variables. \n",
    "\n",
    "The columns include:\n",
    "start_time and end_time column which are datetime64 datatype\n",
    "duration_sec which is int64 datatype\n",
    "\n",
    "start_station_id, start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude, end station id and member birth year which are float64 datatype\n",
    "\n",
    "start station name, bike id and end station name which are string datatype\n",
    "\n",
    "user type, member gender and bike for all share trip which are category datatype\n",
    "\n",
    "New Columns:\n",
    "start_day, start month, start hour, end day, end month and end hour column all category datatype\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "My most interest id to find out the duration each gender takes on rides and which particular day each gender like going on rides\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "The duration features, Gender features, start day and End Day features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    " In this section I will explore individual variables. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "#### Checking the distribution of duration per seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph of distribition of gender\n",
    "binsize = 20\n",
    "bins = np.arange(20, 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.xlabel('Duration (Sec)', fontsize = 12)\n",
    "plt.ylabel('Count', fontsize = 12)\n",
    "plt.title ('Distribution of Duration Per Second', fontsize = 18)\n",
    "plt.xlim(0, 6000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "It can be observed that the duration has a long tailed distribution and most of the rides took place betwwen 0 - 1000 seconds which are short rides and they are fewer ride with long duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "#### Checking the distribution of End months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph to show the dictribution of End Month\n",
    "ax = sns.countplot(data = bike, x = 'end_month',  color=sns.color_palette()[0], order=bike.end_month.value_counts().index)\n",
    "\n",
    "total = len(bike)\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.3f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width()/2\n",
    "    y = p.get_height()+.05\n",
    "    ax.annotate(percentage, (x, y),ha='center')\n",
    "plt.title('Distribution of End Month', fontsize = 18)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Most ride ended in February with over 99% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "Checking the distribution of Start Day and end Day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the distribution of start day\n",
    "default_color = sns.color_palette()[0]\n",
    "sns.countplot(data = bike, x = 'start_day',  color=default_color)\n",
    "plt.title('Distribution of Start Day', fontsize = 18)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM6vEgcTMzCppZa8ts6Z40Emzvs1nJGZmVokDiZmZVeJAYmZmlTiQmJlZJW5sN9uOuaOD9QSfkZiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXisbbweENmZlW07IxE0n6SfiHpHklLJH06p0+W9EdJC/P0jkKZ0yQtk3SvpLcV0kdJWpSXnS1JOX0nSZfn9NslDW/V/piZWblWXtraAHw2Il4BHAacIungvOysiBiZp2sB8rJxwCHAWOA8Sf1y/vOBScCIPI3N6ROBxyPiIOAs4IwW7o+ZmZVoWSCJiJURcVeeXwfcAwxtUORo4LKIWB8RDwLLgNGS9gUGRsS8iAhgBnBMocz0PH8FMKZ2tmJmZr2jVxrb8yWn1wC356RPSrpb0sWS9shpQ4GHC8VW5LSheb4+vVOZiNgAPAnsVbL9SZLmS5q/evXqntkpMzMDeiGQSNoVuBI4NSKeIl2mOhAYCawEzqxlLSkeDdIblemcEDE1IjoiomPIkCHd2wEzM2uopYFE0o6kIPKDiPgRQEQ8GhEbI+J54AJgdM6+AtivUHwY8EhOH1aS3qmMpP7AIGBta/bGzMzKtLLXloCLgHsi4puF9H0L2Y4FFuf52cC43BPrAFKj+h0RsRJYJ+mwvM7xwNWFMhPy/HHATbkdxczMekkr7yM5HPgIsEjSwpz2ReADkkaSLkEtBz4OEBFLJM0ClpJ6fJ0SERtzuZOBacAA4Lo8QQpUMyUtI52JjGvh/piZWYmWBZKI+DXlbRjXNigzBZhSkj4fOLQk/Tng+ArVNDOzijxEipmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpW0LJBI2k/SLyTdI2mJpE/n9D0l3Sjp/vy4R6HMaZKWSbpX0tsK6aMkLcrLzpaknL6TpMtz+u2Shrdqf8zMrFwrz0g2AJ+NiFcAhwGnSDoY+AIwJyJGAHPyc/KyccAhwFjgPEn98rrOByYBI/I0NqdPBB6PiIOAs4AzWrg/ZmZWomWBJCJWRsRdeX4dcA8wFDgamJ6zTQeOyfNHA5dFxPqIeBBYBoyWtC8wMCLmRUQAM+rK1NZ1BTCmdrZiZma9o1faSPIlp9cAtwP7RMRKSMEG2DtnGwo8XCi2IqcNzfP16Z3KRMQG4Elgr5LtT5I0X9L81atX99BemZkZ9EIgkbQrcCVwakQ81ShrSVo0SG9UpnNCxNSI6IiIjiFDhmypymZm1g0tDSSSdiQFkR9ExI9y8qP5chX5cVVOXwHsVyg+DHgkpw8rSe9URlJ/YBCwtuf3xMzMutJUIJE0p5m0uuUCLgLuiYhvFhbNBibk+QnA1YX0cbkn1gGkRvU78uWvdZIOy+scX1emtq7jgJtyO4qZmfWS/o0WStoZeDEwOHfTrV1KGgi8ZAvrPhz4CLBI0sKc9kXgv4FZkiYCfwCOB4iIJZJmAUtJPb5OiYiNudzJwDRgAHBdniAFqpmSlpHORMZtoU5mZtbDGgYS4OPAqaSgsYBNgeQp4NxGBSPi15S3YQCM6aLMFGBKSfp84NCS9OfIgcjMzNqjYSCJiG8B35L0rxFxTi/VyczM+pAtnZEAEBHnSHoDMLxYJiJmtKheZmbWRzQVSCTNBA4EFgK1dovazYFmZrYdayqQAB3Awe4RZWZm9Zq9j2Qx8HetrIiZmfVNzZ6RDAaWSroDWF9LjIj/1ZJamZlZn9FsIJncykqYmVnf1WyvrV+2uiJmZtY3Ndtrax2bBkN8EbAj8OeIGNiqipmZWd/Q7BnJbsXnko4BRreiQmZm1rds1ei/EfFj4MierYqZmfVFzV7aek/h6Q6k+0p8T4mZmTXda+vdhfkNwHLS39yamdl2rtk2khNbXREzM+ubmv1jq2GSrpK0StKjkq6UNGzLJc3M7IWu2cb275H+jfAlwFDgJznNzMy2c80GkiER8b2I2JCnacCQFtbLzMz6iGYDyRpJH5bUL08fBh5rZcXMzKxvaDaQfBR4H/AnYCVwHOAGeDMza7r771eACRHxOICkPYFvkAKMmZltx5o9I3lVLYgARMRa4DWtqZKZmfUlzQaSHSTtUXuSz0gans1Iujh3F15cSJss6Y+SFubpHYVlp0laJuleSW8rpI+StCgvO1uScvpOki7P6bdLGt7kvpiZWQ9qNpCcCdwq6SuS/hO4FfjaFspMA8aWpJ8VESPzdC2ApIOBccAhucx5kvrl/OcDk4AReaqtcyLweEQcBJwFnNHkvpiZWQ9qKpBExAzgvcCjwGrgPRExcwtlbgbWNlmPo4HLImJ9RDwILANGS9oXGBgR8/L/xc8AjimUmZ7nrwDG1M5WzMys9zTb2E5ELAWW9sA2PylpPDAf+GxuexkK3FbIsyKn/TXP16eTHx/Oddsg6UlgL2BND9TRzMyatFXDyFdwPnAgMJLUjfjMnF52JhEN0huV2YykSZLmS5q/evXqblXYzMwa69VAEhGPRsTGiHgeuIBNf461AtivkHUY8EhOH1aS3qmMpP7AILq4lBYRUyOiIyI6hgzxDflmZj2pVwNJbvOoORao9eiaDYzLPbEOIDWq3xERK4F1kg7L7R/jgasLZSbk+eOAm3I7ipmZ9aKm20i6S9KlwBHAYEkrgNOBIySNJF2CWg58HCAilkiaRWqD2QCcEhEb86pOJvUAGwBclyeAi4CZkpaRzkTGtWpfzGzbNOrzM9pdhVILvj6+qXx9vf41LQskEfGBkuSLGuSfAkwpSZ8PHFqS/hxwfJU6mplZdb3d2G5mZi8wDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWScsCiaSLJa2StLiQtqekGyXdnx/3KCw7TdIySfdKelshfZSkRXnZ2ZKU03eSdHlOv13S8Fbti5mZda2VZyTTgLF1aV8A5kTECGBOfo6kg4FxwCG5zHmS+uUy5wOTgBF5qq1zIvB4RBwEnAWc0bI9MTOzLrUskETEzcDauuSjgel5fjpwTCH9sohYHxEPAsuA0ZL2BQZGxLyICGBGXZnauq4AxtTOVszMrPf0dhvJPhGxEiA/7p3ThwIPF/KtyGlD83x9eqcyEbEBeBLYq2yjkiZJmi9p/urVq3toV8zMDLadxvayM4lokN6ozOaJEVMjoiMiOoYMGbKVVTQzszK9HUgezZeryI+rcvoKYL9CvmHAIzl9WEl6pzKS+gOD2PxSmpmZtVhvB5LZwIQ8PwG4upA+LvfEOoDUqH5Hvvy1TtJhuf1jfF2Z2rqOA27K7ShmZtaL+rdqxZIuBY4ABktaAZwO/DcwS9JE4A/A8QARsUTSLGApsAE4JSI25lWdTOoBNgC4Lk8AFwEzJS0jnYmMa9W+mJlZ11oWSCLiA10sGtNF/inAlJL0+cChJenPkQORmZm1z7bS2G5mZn2UA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV0pZAImm5pEWSFkqan9P2lHSjpPvz4x6F/KdJWibpXklvK6SPyutZJulsSWrH/piZbc/aeUbylogYGREd+fkXgDkRMQKYk58j6WBgHHAIMBY4T1K/XOZ8YBIwIk9je7H+ZmbGtnVp62hgep6fDhxTSL8sItZHxIPAMmC0pH2BgRExLyICmFEoY2ZmvaRdgSSAGyQtkDQpp+0TESsB8uPeOX0o8HCh7IqcNjTP16dvRtIkSfMlzV+9enUP7oaZmfVv03YPj4hHJO0N3Cjpdw3ylrV7RIP0zRMjpgJTATo6OkrzmJnZ1mnLGUlEPJIfVwFXAaOBR/PlKvLjqpx9BbBfofgw4JGcPqwk3czMelGvBxJJu0jarTYPvBVYDMwGJuRsE4Cr8/xsYJyknSQdQGpUvyNf/lon6bDcW2t8oYyZmfWSdlza2ge4KvfU7Q9cEhE/k3QnMEvSROAPwPEAEbFE0ixgKbABOCUiNuZ1nQxMAwYA1+XJzMx6Ua8Hkoh4AHh1SfpjwJguykwBppSkzwcO7ek6mplZ87al7r9mZtYHOZCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJQ4kZmZWiQOJmZlV4kBiZmaVOJCYmVklDiRmZlaJA4mZmVXiQGJmZpU4kJiZWSUOJGZmVokDiZmZVeJAYmZmlTiQmJlZJX0+kEgaK+leScskfaHd9TEz29706UAiqR9wLvB24GDgA5IObm+tzMy2L306kACjgWUR8UBE/AW4DDi6zXUyM9uuKCLaXYetJuk4YGxEfCw//wjwuoj4ZF2+ScCk/PRlwL0trNZgYE0L199qrn/79OW6g+vfbq2u//4RMaRsQf8WbrQ3qCRts8gYEVOBqa2vDkiaHxEdvbGtVnD926cv1x1c/3ZrZ/37+qWtFcB+hefDgEfaVBczs+1SXw8kdwIjJB0g6UXAOGB2m+tkZrZd6dOXtiJig6RPAtcD/YCLI2JJm6vVK5fQWsj1b5++XHdw/dutbfXv043tZmbWfn390paZmbWZA4mZmVXiQAJICkkzC8/7S1ot6ac9tP7Jkj7XE+uqW+9ekhbm6U+S/lh4/qIe3M4RW/NaSDpL0qmF59dLurDw/ExJn2liPcMlLe7u9rtD0tNN5uvqNX9C0tJW1jFv/wRJ327xNjYW9nGhpOElea6VtHtJeks+6yXb+Q9JSyTdnev4ugZ5T5D0kh7Y5nJJg6uup4t1N70/3Vhnr7wX0Mcb23vQn4FDJQ2IiGeBfwb+2OY6bVFEPAaMhPShAZ6OiG+0s051bgWOB/5H0g6kG6YGFpa/ATi1DfXaal295vnHdqsPPCT1j4gNPVHHHvBsRIwsWyBJpLbVd/RulTrV4fXAu4DXRsT6/OPe6MDpBGAx3bg1oDffj63Yn22Oz0g2uQ54Z57/AHBpbYGkPSX9OB8t3CbpVTl9sqSLJc2V9ICkTxXK/EceTPLnpLvpa+knSbpT0m8lXSnpxZJ2k/SgpB1znoH56GfH7u6EpGn5jv/a86cL85/P275b0pdz2i6Srsn1WSzp/Tl9rKTfSfo18J7COkZLulXSb/Ljy3L6rySNLOS7BVhLChYAh5C+zOsk7SFpJ+AVOe8vJS3IZyz75rRRuU7zgFMK6z1B0o8k/UzS/ZK+Vlj2VknzJN0l6YeSds3p/y1pad7vb+S0A3LeOyV9pbCOXSXNyetYJOnonP4VSZ8u5JsC1B819pN0QT6yvEHSgJx3rqSOPD9Y0vLCvvxQ0k+AGyTtK+nmfES6WNKbcr4TJd0n6ZfA4YU6vFvS7fm9+LmkfSTtkF+XITnPDkoDmm71kbTSGeE9ks4D7gL2U+HovA2f9X2BNRGxHiAi1kTEI5K+lLe3WNJUJccBHcAP8us6oK7uHZLm5vnJudwNwAyls88b8uv7XQo3QCv9HizI7/WknDZR0ll1+//NCvvTqJ7b1O8OEbHdT8DTwKuAK4CdgYXAEcBP8/JzgNPz/JHAwjw/mXTUvRPpaPsxYEdgFLAIeDHpCHwZ8LlcZq/Cdv8f8K95/nvAMXl+EnBmN/dhMvA5YBpwXHHf8uNbSd0DRTqA+CnwZuC9wAWF/IPya/AwMCLnn1V4LQYC/fP8UcCVeX4C8D95/qXA/Dy/HPh74OPAJ4CvAO8g/SDOy6/fkJz3/aQu3AB3A/+U578OLM7zJwAPFOr5EOmm1MHAzcAuOd+/A18C9iQNiVProbh7fpwNjM/zpxRep/7AwDw/OL93AoYDd+X0HYDfA2cU3tfhwAZgZH4+C/hwnp8LdBTWubywLyuAPfPzzwL/kef7AbuRfmT+AAwhHaXeAnw759mjsF8fI39mgNOBUwvv+5Xd/CxtJH0HFgJX5X17HjiskGd53pd2fNZ3zXW7Dziv8DnZs5BnJvDu+te/WPc83wHMLXyHFgAD8vOzgS/l+XeSRs0YXNwWMIB0gLQXsEv+XOyYl90KvLLC/jSq5zbxu1ObfEaSRcTdpC/MB4Br6xa/kfTBJCJuAvaSNCgvuyYi1kfEGmAVsA/wJuCqiHgmIp6i802ShyodvS8CPkQ6Uge4EDgxz59IeoN70lvz9BvSUeXLSYFiEXCUpDMkvSkinszLHoyI+yN9wr5fWM8g4IdKbRZnFer/Q+Bd+Wjmo6SABumH7w15mpen2vM/AocCN0paCPwfYFh+bXePiF/mdfyt/SqbExFPRsRzwFJgf+Aw0gjQt+R1TcjpTwHPARdKeg/wTF7H4Ww66yyuX8B/Sbob+DkwFNgnIpYDj0l6TeF1fLauXg9GxMI8v4D0edqSGyNibZ6/EzhR6ZLZKyNiHemsZ25ErI40MOnlhbLDgOvzZ+nzbHovLgbG5/mP0v3P0rMRMTJPx+a0hyLitpK8vf5Zj4inST+ak4DVwOWSTgDeks/QFpEO+A7pei1dmh3p8jakA63v521eAzxeyPcpSb8FbiMdyIyIiD8DN5G+By8nBZRFFfankW3qd8dtJJ3NBr5BOhvZq5DeaEyv9YW0jWx6Tbu6QWca6Qjgt/nDcgRARNySLyH8E9AvIra2cXkD+ZKlJLHpWquAr0bEd+sLSBpFOkv4aj6tn92g/l8BfhERxyq1C8zN9X9G0o2k0ZffRzqCgnTk9AbglaQjt4dJR95Pkb50QyPi9XX12b3B9qH8NRfpR/kDJfs3GhhDGvngk6QfGbrYxodIR/+jIuKvSpehds7LLiSdRfwd6cd69BbqNSDP/+09Kayr5s+1mYi4WdKbSUe/MyV9nfQ6dfVanAN8MyJmSzqCdKRKRDws6VFJR5IC0Ye6KN8df26wrNc/6xGxkfTZm5t/HD9OuqrQkfd/Mpu/1jVNvR+1TdUXzq/1UcDr8+d+Lp0/I18Efkc3fpRL9mfCFuq5Tf3u+Iyks4uB/yw5iriZ/GXMH6I1OeJ35Wbg2Hw9djfg3YVluwEr85F7/Rd8BukoucrZyHLS0Q2kH/Xa9c7rgY9qU7vBUEl7K/VmeSYivk8Koq8lfQkOkHRgLlv8cR7Epo4IJ9Rt+0LS5YA7C0fZt5AaEtdGxMacvjvwetLR9RClxkYk7SjpkIh4AnhS0hvzOpr5IbwNOFzSQXldL5b00ry/gyLiWlLD/shCvcaVrH8QsCoHkbeQzmpqrgLGAv9Iej2btZxN78lxXWWStH/e9gXARaT34nbgiHy9fkdS54ViXWvvxYS61V1IOpqelX+kWqXXP+uSXiZpRCFpJJtG9F6T3/Pi67wu16VmOZvej/c22FTxe/920qVESK/74zmIvJx0NgxARNxOOkP5IIV21q3Yn4e6Uc9ifdvyu+MzkoKIWAF8q2TRZOB7+XLHM2z+pa1fz12SLidd93wI+FVh8f8l/Tg8RLqsVPyA/4B0/bKpD2AXLgCulnQHMId8hBURN0h6BTAvnajwNPBh4CDg65KeB/4KnBwRzyk1IF4jaQ3wa9IlKICvAdOVuu3eVLffCyQ9RecP5CLSddxL6tJ2jYhVSo2hZ+fLWf2B/wGWkE6zL5b0DE38aEfE6nykdalSQz6kS2Xr8uuxM+ms5d/ysk8Dlyg1oF9ZWNUPgJ9Imk96/35X2MZfJP0CeCIiNubXsRnfAGYp/c3BTQ3yHQF8XtJfSe/P+IhYmY+u5wErSZcl++X8k0mXGf9ICqQHFNY1m/Q+9PQl0k7a9FnfFTgnn7luILUFTAKeyNtZTrpMWDMN+I6kZ0kHMF8GLpL0xVy/rnyZ9Hm6C/glqa0K4GfAJ/Lvwb2k175oFqmt7HGa09X+vKLJegLt/d3xECnbkPyjenREfKTdddka+exmLvDyiHi+zdXpcUpdmO8Cjo+I+9tdn0aUeomdFRFvanddyvT1z3ojSvdcnRURc9pdl2b0xHvhM5JthKRzSH8Z3Lb++VVIGg9MAT7zAg0iB5N6ul3VB4LIF4CT6Zm2kR7X1z/rXclnFHcAv+1DQaRH3gufkZiZWSVubDczs0ocSMzMrBIHEjMzq8SBxMzMKnEgMWsTNTl0fc7baTBOs22JA4mZmVXiQGJWgaQPS7pDaYjy70rqJ+lpSVOUhuy+TdI+Oe8BKhm6vov1StK3lYa/vwbYu7CsbLj0A/Md2LU8IyQtaNmOmxU4kJhtpTzkzPuBwyP9EdRG0k2AuwC3RcSrSeMfnZSLfAs4PyL+EfjTFlZ/LOn/JF6Zy7+hsOzbEfGPEXEoaWDId0XE70njk43MeU5k0wjMZi3lQGK29caQBtW7U2no+jHAPwB/YdO/JRaHk+9q6PoybwYuzQNdPkLnMbreovLh0i8kDUPfjxTgLsGsF3iIFLOtJ2B6RJzWKVH6XGwaMqI4xDc0Hh6/XtkQ5juT/vyobLj0K0l/anUTsCDS3wKbtZzPSMy23hzgOEl7w9/+knn/Bvm7Grq+zM3AuNzmsi/wlpxeCxqbDZee/+jreuB8Wjzqr1mRA4nZVoqIpaSh6m/IQ4rfSPpr3K58GjhF0p2k/7Ro5CrgftKQ3+eThjEn/1fLBTn9x3QeLh3SkOAB3NCNXTGrxIM2mr2ASPoc6Y+8/m+762LbD7eRmL1ASLoKOJBNfyVs1it8RmLWRpJeyeY9uNZHxOvaUR+zreFAYmZmlbix3czMKnEgMTOzShxIzMysEgcSMzOr5P8DunMtlKybRwwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the distribution of end Day\n",
    "default_color = sns.color_palette()[0]\n",
    "sns.countplot(data = bike, x = 'end_day',  color=default_color)\n",
    "plt.title('Distribution of End Day', fontsize = 18)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Most ride took place on Thurdays which is and most ride took place during the week days while less ride took place during weekend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "Checking the distribution of Start hour and end hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the distribution of Start Hour\n",
    "default_color = sns.color_palette()[0]\n",
    "sns.countplot(data = bike, x = 'start_hr',  color=default_color)\n",
    "plt.title('Distribution of Start Hour', fontsize = 18)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the distribution of End Hour\n",
    "default_color = sns.color_palette()[0]\n",
    "sns.countplot(data = bike, x = 'end_hr',  color=default_color)\n",
    "plt.title('Distribution of End Hour', fontsize = 18)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "As observed in the 2 plot above the 8th, 9th have high trips which indicate morning rush to work and the 17th and 18th hour which indicated rushing hours home from work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "To know the most gender that took the most ride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Gender\n",
    "ax = sns.countplot(data = bike, x = 'member_gender',  color=sns.color_palette()[0], order=bike.member_gender.value_counts().index)\n",
    "\n",
    "total = len(bike)\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.3f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width()/2\n",
    "    y = p.get_height()+.05\n",
    "    ax.annotate(percentage, (x, y),ha='center')\n",
    "plt.title ('Distribution of Gender', fontsize = 18)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "The male Gender took the most ride with over 74% frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "Which user type took the most ride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of User Type\n",
    "ax = sns.countplot(data = bike, x = 'user_type',  color=sns.color_palette()[0], order=bike.user_type.value_counts().index)\n",
    "\n",
    "total = len(bike)\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.3f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width()/2\n",
    "    y = p.get_height()+.05\n",
    "    ax.annotate(percentage, (x, y),ha='center')\n",
    "plt.title ('Distribution of User Type', fontsize = 18)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Subcribers took the most ride than Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "To know the Distribution of Bike Share for all Trip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Bike share for all Trip\n",
    "ax = sns.countplot(data = bike, x = 'bike_share_for_all_trip',  color=sns.color_palette()[0], order=bike.bike_share_for_all_trip.value_counts().index)\n",
    "\n",
    "total = len(bike)\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.3f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width()/2\n",
    "    y = p.get_height()+.05\n",
    "    ax.annotate(percentage, (x, y),ha='center')\n",
    "plt.title ('Distribution of Bike Share for all trip', fontsize = 18)\n",
    "plt.show();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Over 90% of users were not allowed to share their ride"
   ]
  },
  {
   "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",
    "For Duaration per second visualization, I had to change it to a smaller bin size of 20 and also set the xlimit from 0 to 6000 and in other to clearly depict the data.\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",
    "I change some datatype of the data from object to category, from object to datetime and to other forms in other to enable easy visualization according to the datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section,  I will investigate relationships between pairs of variables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What is the relationship between user type and start hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between User Type and Start Hour')"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and start hr\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'start_hr', hue = 'user_type', palette = 'autumn')\n",
    "plt.title ('Relationship Between User Type and Start Hour', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Observation\n",
    "More subcribers started ride at 8th and 17th hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What is the relationship between start day and User type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between Start Day and User Type')"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and start hr\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'start_day', hue = 'user_type', palette = 'Accent')\n",
    "plt.title ('Relationship Between Start Day and User Type', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Subscribers mostly started ride thursday which is weekdays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What is the relationship between end month and user type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between End Month and User type')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and start hr\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'end_month', hue = 'user_type', palette = 'Oranges')\n",
    "plt.title ('Relationship Between End Month and User type', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Subscriber generally had more ride than customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What is the relationship between member gender and user type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between Member Gender and User Type')"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and start hr\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'member_gender', hue = 'user_type', palette = 'bone')\n",
    "plt.title ('Relationship Between Member Gender and User Type', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "More Subscribers were Male than female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question\n",
    "\n",
    "What is the relationship between user type and bike share for all trip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between User Type and Bike share for all trip')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and bike share for all trip\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'user_type', hue = 'bike_share_for_all_trip', palette = 'viridis_r')\n",
    "plt.title ('Relationship Between User Type and Bike share for all trip', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "More Subscribers shared their bike and no customer shared theirs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question\n",
    "\n",
    "What is the relationship between member gender and start hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship Between Member Gender and Start Hour')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot to know the relationship between user type and start hr\n",
    "plt.figure(figsize = [12, 8])\n",
    "sns.countplot(data = bike, x = 'start_hr', hue = 'member_gender', palette = 'Blues')\n",
    "plt.title ('Relationship Between Member Gender and Start Hour', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "Male alway start rides earlier and more than female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question\n",
    "\n",
    "What is the relationship between start day and end day as regards to duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='end_day', ylabel='duration_sec'>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting 2 subplots to know the relationship between start day and end day as regards to duration\n",
    "plt.figure(figsize = [8, 8])\n",
    "\n",
    "# subplot 1: color vs cut\n",
    "plt.subplot(2, 1, 1)\n",
    "sns.boxplot(data = bike, x = 'start_day', y = 'duration_sec')\n",
    "\n",
    "# subplot 2: clarity vs. cut\n",
    "ax = plt.subplot(2, 1, 2)\n",
    "sns.boxplot(data = bike, x = 'end_day', y = 'duration_sec')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "There is a positive relationship between start and end day and its duration. As start day increases, end day tends to decrease and vice versa "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some of the relationships I observed in this part of the investigation. \n",
    "\n",
    "I checked the between:\n",
    "\n",
    "start day and end day as regards to duration\n",
    "\n",
    "member gender and start hour\n",
    "\n",
    "user type and start hour and other relationships to see if one relationship affect the other or not\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "Which Gender has the longer ride duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to show the relationship of duration across member gender and user type\n",
    "fig = plt.figure(figsize = [8,6])\n",
    "ax = sns.pointplot(data = bike, x = 'user_type', y = 'duration_sec', hue = 'member_gender',\n",
    "           palette = 'Blues', linestyles = '', dodge = 0.4)\n",
    "plt.title('Trip duration across Gender and User Type', fontsize =18)\n",
    "plt.ylabel('Trip Duration(sec)')\n",
    "#plt.yscale('log')\n",
    "#plt.yticks([2e3, 4e3, 6e3, 1e4], ['2k', '4k', '6k', '10k'])\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "It can be observed that Customers who were identified as Other gender had longer ride duration than male and female customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What time of the day do other gender start their ride and the duration of the ride at that time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to show the relationship of duration across member gender and user type\n",
    "fig = plt.figure(figsize = [8,6])\n",
    "ax = sns.pointplot(data = bike, x = 'start_hr', y = 'duration_sec', hue = 'member_gender',\n",
    "           palette = 'viridis', linestyles = '', dodge = 0.4)\n",
    "plt.title('Trip duration across Start hour and Gender', fontsize = 18)\n",
    "plt.ylabel('Trip Duration(sec)')\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "It can be observed that other gender has long duration when the start their ride with 2nd or 3rd hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question \n",
    "\n",
    "What time of the day do other gender end their ride"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to show the relationship of duration across member gender and user type\n",
    "fig = plt.figure(figsize = [8,6])\n",
    "ax = sns.pointplot(data = bike, x = 'end_hr', y = 'duration_sec', hue = 'member_gender',\n",
    "           palette = 'viridis', linestyles = '', dodge = 0.4)\n",
    "plt.title('Trip duration across End hour and Gender', fontsize = 18)\n",
    "plt.ylabel('Trip Duration(sec)')\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "It can be observed that other gender ends their trip by the 2nd hour or 4th hour "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question\n",
    "\n",
    "Which day of the weeks do most Gender like going and ride and how long does the ride takes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to show the relationship of duration across member gender and user type\n",
    "fig = plt.figure(figsize = [8,6])\n",
    "ax = sns.pointplot(data = bike, x = 'start_day', y = 'duration_sec', hue = 'member_gender',\n",
    "           palette = 'Pastel1', linestyles = '', dodge = 0.4)\n",
    "plt.title('Trip duration across start Day and Gender', fontsize = 18)\n",
    "plt.ylabel('Trip Duration(sec)')\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "\n",
    "It can be observed that other genders prefer going on ride during weekends especially on Sunday and they usually have ride duration of about 1800 per seconds during weekends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Some of the relationships I observed in this part of the investigation. \n",
    "\n",
    "I observed the relationship of Duration across otheir variables like start hour, end hour, start day, gender and User type\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "Most ride ended in February with over 99% \n",
    "As observed in the 2 plot above the 8th, 9th have high trips which indicate morning rush to work and the 17th and 18th hour which indicated rushing hours home from work\n",
    "Most ride took place on Thurdays which is and most ride took place during the week days \n",
    "Subcribers took the most ride than Customers\n",
    "More Subscribers shared their bike and no customer shared theirs\n",
    "More subcribers started ride at 8th and 17th hour\n",
    "Subscribers mostly started ride thursday which is weekdays\n",
    "More Users were male than female\n",
    "Male alway start rides earlier and more than female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> Remove all Tips mentioned above, before you convert this notebook to PDF/HTML\n",
    "\n",
    "\n",
    "> At the end of your report, make sure that you export the notebook as an\n",
    "html file from the `File > Download as... > HTML or PDF` menu. Make sure you keep\n",
    "track of where the exported file goes, so you can put it in the same folder\n",
    "as this notebook for project submission. Also, make sure you remove all of\n",
    "the quote-formatted guide notes like this one before you finish your report!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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