Part_I_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I - 201902- Fordgobike Tripdata\n",
    "## by Falilou Niang\n",
    "\n",
    "## Introduction\n",
    "> This dataset includes information on individual rides made in a bike-sharing system covering the Greater San Francisco Bay Area during the month of February 2019.\n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "bikedata = pd.read_csv('201902-fordgobike-tripdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
     ]
    }
   ],
   "source": [
    "# high-level overview of data shape and composition\n",
    "print(bikedata.shape)\n",
    "print(bikedata.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>2019-03-01 08:01:55.9750</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>2019-03-01 06:42:03.0560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>2019-03-01 05:24:08.1460</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.0100</td>\n",
       "      <td>2019-03-01 04:02:36.8420</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.5490</td>\n",
       "      <td>2019-03-01 00:20:44.0740</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</td>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "3         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "4          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.393170     2535    Customer                NaN   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3         Other                      No  \n",
       "4          Male                     Yes  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                  0\n",
       "start_time                    0\n",
       "end_time                      0\n",
       "start_station_id            197\n",
       "start_station_name          197\n",
       "start_station_latitude        0\n",
       "start_station_longitude       0\n",
       "end_station_id              197\n",
       "end_station_name            197\n",
       "end_station_latitude          0\n",
       "end_station_longitude         0\n",
       "bike_id                       0\n",
       "user_type                     0\n",
       "member_birth_year          8265\n",
       "member_gender              8265\n",
       "bike_share_for_all_trip       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1878.0\n",
      "2001.0\n"
     ]
    }
   ],
   "source": [
    "print(bikedata.member_birth_year.min())\n",
    "print(bikedata.member_birth_year.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Issues\n",
    "Quality\n",
    "- start_time, end_time are object  should be datetime\n",
    "- start_station_id, end_station_id are float  should be str\n",
    "- bike_id is int should be str\n",
    "- member_birth_year is float should be int\n",
    "- user_type, member_gender should be categorical types\n",
    "- nan rows for stations should be deleted\n",
    "- rows where calculated 'age' is more than 90 should be deleted\n",
    "\n",
    " Tidyness\n",
    "- age field calculated on member_birth_year should be added\n",
    "- start_day and end_day "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define\n",
    "- Datatypes issues fixing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 start_time, end_time are object  should be datetime\n",
    "bikedata['start_time'] = pd.to_datetime(bikedata['start_time'])\n",
    "bikedata['end_time'] = pd.to_datetime(bikedata['end_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 start_station_id, end_station_id are float  should be str\n",
    "bikedata['start_station_id'] = bikedata['start_station_id'].astype(str)\n",
    "bikedata['end_station_id'] = bikedata['end_station_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikedata['start_station_id'] = bikedata['start_station_id'].str[:-2]\n",
    "bikedata['end_station_id'] = bikedata['end_station_id'].str[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 bike_id is int should be str\n",
    "bikedata['bike_id'] = bikedata['bike_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 member_birth_year is float should be int\n",
    "bikedata.member_birth_year.fillna(0,inplace=True)\n",
    "bikedata['member_birth_year'] = bikedata['member_birth_year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5 convert user_type, member_gender into categorical types\n",
    "ordinal_var_dict = {'user_type': ['Customer','Subscriber'],\n",
    "                    'member_gender': ['Male','Female','Other']}\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                categories = ordinal_var_dict[var])\n",
    "    bikedata[var] = bikedata[var].astype(ordered_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                   datetime64[ns]\n",
       "start_station_id                   object\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                     object\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                   int32\n",
       "member_gender                    category\n",
       "bike_share_for_all_trip            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define\n",
    "- Nan rows fixing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikedata.dropna(subset=['start_station_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                  0\n",
       "start_time                    0\n",
       "end_time                      0\n",
       "start_station_id              0\n",
       "start_station_name            0\n",
       "start_station_latitude        0\n",
       "start_station_longitude       0\n",
       "end_station_id                0\n",
       "end_station_name              0\n",
       "end_station_latitude          0\n",
       "end_station_longitude         0\n",
       "bike_id                       0\n",
       "user_type                     0\n",
       "member_birth_year             0\n",
       "member_gender              8263\n",
       "bike_share_for_all_trip       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define\n",
    "- age field calculated on member_birth_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikedata['age'] = 2019 - bikedata.member_birth_year\n",
    "bikedata.age.replace(2019,0,inplace= True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>age</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</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</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</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.789</td>\n",
       "      <td>2019-03-01 06:42:03.056</td>\n",
       "      <td>23</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</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</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3</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</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "1         42521 2019-02-28 18:53:21.789 2019-03-01 06:42:03.056   \n",
       "2         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "\n",
       "  start_station_id                                start_station_name  \\\n",
       "0               21  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1               23                     The Embarcadero at Steuart St   \n",
       "2               86                           Market St at Dolores St   \n",
       "\n",
       "   start_station_latitude  start_station_longitude end_station_id  \\\n",
       "0               37.789625              -122.400811             13   \n",
       "1               37.791464              -122.391034             81   \n",
       "2               37.769305              -122.426826              3   \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",
       "\n",
       "   end_station_longitude bike_id user_type  member_birth_year member_gender  \\\n",
       "0            -122.402923    4902  Customer               1984          Male   \n",
       "1            -122.393170    2535  Customer                  0           NaN   \n",
       "2            -122.404904    5905  Customer               1972          Male   \n",
       "\n",
       "  bike_share_for_all_trip  age  \n",
       "0                      No   35  \n",
       "1                      No    0  \n",
       "2                      No   47  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define\n",
    "- rows where calculated 'age' is more than 90 should be deleted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikedata = bikedata.query('age < 90')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(bikedata.age>90).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define\n",
    "- add start_day, end_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikedata['start_day'] = bikedata['start_time'].dt.day_name()\n",
    "bikedata['end_day'] = bikedata['end_time'].dt.day_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>age</th>\n",
       "      <th>start_day</th>\n",
       "      <th>end_day</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</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</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</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "\n",
       "  start_station_id                                start_station_name  \\\n",
       "0               21  Montgomery St BART Station (Market St at 2nd St)   \n",
       "\n",
       "   start_station_latitude  start_station_longitude end_station_id  \\\n",
       "0               37.789625              -122.400811             13   \n",
       "\n",
       "                 end_station_name  end_station_latitude  \\\n",
       "0  Commercial St at Montgomery St             37.794231   \n",
       "\n",
       "   end_station_longitude bike_id user_type  member_birth_year member_gender  \\\n",
       "0            -122.402923    4902  Customer               1984          Male   \n",
       "\n",
       "  bike_share_for_all_trip  age start_day end_day  \n",
       "0                      No   35  Thursday  Friday  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikedata.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183138 entries, 0 to 183411\n",
      "Data columns (total 19 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183138 non-null  int64         \n",
      " 1   start_time               183138 non-null  datetime64[ns]\n",
      " 2   end_time                 183138 non-null  datetime64[ns]\n",
      " 3   start_station_id         183138 non-null  object        \n",
      " 4   start_station_name       183138 non-null  object        \n",
      " 5   start_station_latitude   183138 non-null  float64       \n",
      " 6   start_station_longitude  183138 non-null  float64       \n",
      " 7   end_station_id           183138 non-null  object        \n",
      " 8   end_station_name         183138 non-null  object        \n",
      " 9   end_station_latitude     183138 non-null  float64       \n",
      " 10  end_station_longitude    183138 non-null  float64       \n",
      " 11  bike_id                  183138 non-null  object        \n",
      " 12  user_type                183138 non-null  category      \n",
      " 13  member_birth_year        183138 non-null  int32         \n",
      " 14  member_gender            174875 non-null  category      \n",
      " 15  bike_share_for_all_trip  183138 non-null  object        \n",
      " 16  age                      183138 non-null  int32         \n",
      " 17  start_day                183138 non-null  object        \n",
      " 18  end_day                  183138 non-null  object        \n",
      "dtypes: category(2), datetime64[ns](2), float64(4), int32(2), int64(1), object(8)\n",
      "memory usage: 24.1+ MB\n"
     ]
    }
   ],
   "source": [
    "bikedata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save clean data  \n",
    "bikedata.to_csv('clean_dataset.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> There are 183,138 bikes sharing entries from February 1st and 28th 2019 which records.\n",
    "\n",
    "- Duration of the trip in seconds\n",
    "- Start and End timestamps (date/hour)\n",
    "- Start and End Stations names, gps coordinates and id\n",
    "- Bike id number\n",
    "- Type of user (whether the user is a subscriber or customer)\n",
    "- Various personal data (Gender / Birth Year)\n",
    "- Bike share for all trip which is an affordable pricing plan option\n",
    "- Age (added column) of user \n",
    "- Start and End days (added columns)<br>\n",
    "> Most variables are numeric in nature, but the variables user_type, member_gender, and Bike_share_for_all_trip are categorical variables.\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> I'm most interested in figuring out how the duration is affected by other variables.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> I expect that weekdays, member_gender, user_type, age will have main effects on the duration. I also think that the hours, will have effects on duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll start by looking at the distribution of the main variable of interest: duration.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binsize=100\n",
    "bins = np.arange(0,bikedata['duration_sec'].max()+binsize, binsize)\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.hist(data=bikedata, x='duration_sec', bins=bins)\n",
    "\n",
    "plt.title('Distribution of trip length in second', size=(18))\n",
    "plt.xlabel('Trip length (in sec)', size=14)\n",
    "plt.ylabel('Number', size=14)\n",
    "plt.xlim(0,10000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of trip durations in second seems to be Right-skewed at first with a peak between 0 and 500.\n",
    "There's a long tail in the distribution, so let's put it on a log scale instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "log_binsize = 0.05\n",
    "bins = 10 ** np.arange(2.4, np.log10(bikedata['duration_sec'].max())+log_binsize, log_binsize)\n",
    "\n",
    "plt.figure(figsize=[8,5])\n",
    "plt.hist(data = bikedata, x = 'duration_sec', bins = bins)\n",
    "plt.xscale('log')\n",
    "tick_locs = [200, 500, 1e3, 2e3, 5e3] \n",
    "plt.xticks(tick_locs, tick_locs)\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Number of trips')\n",
    "plt.title('Distribution of trip length in second', size=(18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observations: \n",
    " By setting up a value limit of 5000 seconds for x and plot a second graph, we can see that duration_sec has a very long-tailed distribution, with a lot of bike rides on the short duration end, and few on the long duration end. Outliers at the high end skewed the data. I plotted on a log-scale. From this graph we can see that the peak is less than 600 seconds and  the majority of trips are between 250 and 2000 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up, the first predictor variable of interest: weekdays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of trips')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "color = sb.color_palette()[0]\n",
    "sb.countplot(data=bikedata, x='start_day', order=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], color=color)\n",
    "\n",
    "plt.title('Number of trips ber Weekday', size=(18))\n",
    "plt.xlabel('Weekday', size=12)\n",
    "plt.ylabel('Number of trips', size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation: \n",
    "This chart shows that users make significantly less trips in weekends than other days.\n",
    "Around 15000 trips in Saturdays and Sundays, we have at least 26000 trips a day (with a peak around 33000 on Thursdays)in the other days of the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next predictor variable of interest: member_gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,6])\n",
    "\n",
    "counts = bikedata['member_gender'].value_counts()\n",
    "plt.pie(counts, labels = counts.index, \n",
    "        autopct='%1.1f%%',shadow=False, startangle = 90,counterclock = False)\n",
    "plt.title('Gender (in %)', fontsize=14, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "The users are in majority Males around 3/4 of total (74,6%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next predictor variable of interest: user_type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,6])\n",
    "\n",
    "counts = bikedata['user_type'].value_counts()\n",
    "plt.pie(counts, labels = counts.index, \n",
    "        autopct='%1.1f%%',shadow=False, startangle = 90,counterclock = False)\n",
    "plt.title('User Types (in %)', fontsize=14, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "A vast majority (89,2%) of the users are Subscribers to GoBike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next predictor variable of interest: age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=np.arange(15,90+1,1)\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.hist(data=bikedata, x='age',bins=bins);\n",
    "plt.xlabel('Age during the trip', size=14)\n",
    "plt.ylabel('Number of trips', size=14)\n",
    "plt.title('Distribution of age ', size=(18))\n",
    "plt.xlim(0,90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "We can see that  a majority of the users are between 20 and 40 years old."
   ]
  },
  {
   "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",
    "> Our analysis told us that most of the bike rides are around 600 seconds; more of go bike user are usually male, sbscriber to the service and below its 40's. Also we observe that more trips occur on weekends than Business days.\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",
    "> The distribution of trip durations had to be plotted on a logarithmic scale to  looks more like a normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin with, let's look at the relationship between the entities in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's filter trips duration under 5500s\n",
    "filtered  = bikedata.query('duration_sec < 5500')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1- duration vs weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "avg_min_duration_weekday = filtered.groupby('start_day')['duration_sec'].mean().reindex(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']).reset_index()\n",
    "\n",
    "plt.bar(avg_min_duration_weekday['start_day'],avg_min_duration_weekday['duration_sec'], color='#1e77b4')\n",
    "\n",
    "plt.title('Average trip duration by weekday', size=(18))\n",
    "plt.xlabel('Weekday', size=12)\n",
    "plt.ylabel('Average duration (in sec)', size=12)\n",
    "plt.grid(color='grey', axis='y', linestyle='-', linewidth=0.2);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "Though the amount of trips are more important in week days compared to weekends (as seen previously), they seem to be longer on average on Sundays and Saturdays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move on to looking at how duration correlate with the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:1209: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(UserWarning(msg))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def boxgrid(x, y, **kwargs):\n",
    "    \"\"\" Quick hack for creating box plots with seaborn's PairGrid. \"\"\"\n",
    "    default_color = sb.color_palette()[0]\n",
    "    sb.boxplot(x, y, color = default_color)\n",
    "\n",
    "plt.figure(figsize = [10, 10])\n",
    "g = sb.PairGrid(data = filtered, y_vars = ['duration_sec', ], x_vars = ['member_gender','user_type'],\n",
    "                size = 3, aspect = 1.5)\n",
    "g.map(boxgrid)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "The above plots show that all genders have a rather similar average trip durations. However, we saw earlier that users of the services were in 3/4 Males, but gender really doesn't affects duration.\n",
    "Plots also show that Customers have a Median of trip duration higher than the 3rd quartile of Subscribers. It suggests that user type have a impact on trip durations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's look at relationships between the categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [6, 6])\n",
    "sb.countplot(data = filtered, x = 'user_type', hue = 'member_gender', palette = 'Blues')\n",
    "plt.title('User Type vs member Gender', size=(18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For customers or subscribers, most of users are males"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look relationship between duration and bike_share_for_all_trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:1209: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(UserWarning(msg))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 10])\n",
    "g = sb.PairGrid(data = filtered.query('duration_sec <  3000'), y_vars = ['duration_sec', ], x_vars = ['bike_share_for_all_trip'],\n",
    "                size = 3, aspect = 1.5)\n",
    "g.map(boxgrid)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our plot show that we have longer trip duration for bikes not shared for all trip <br>\n",
    "Let's find relationship between bike_share_for_all_trip and user_type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [6, 6])\n",
    "\n",
    "sb.countplot(data = filtered, x = 'user_type', hue = 'bike_share_for_all_trip', palette = 'Greens')\n",
    "plt.title('User Type vs bike_share_for_all_trip', size=(18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "We can see that users generally opt for No bike share for all trip and only subscribers seem to have some Yes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's finally find how duration correlate with hours of trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Easy Services 2.0\\AppData\\Local\\Temp/ipykernel_3252/1210410044.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered['start_hour'] = filtered['start_time'].dt.hour\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered['start_hour'] = filtered['start_time'].dt.hour\n",
    "plt.figure(figsize=(15,6))\n",
    "sb.barplot(data = filtered, x = 'start_hour', y = 'duration_sec', \n",
    "            palette=  'Blues')\n",
    "plt.ylabel('Avg. duration in sec')\n",
    "plt.title('Average duration by Start hours', size=(18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Average ride duration in an hour increases from midnight and reaches the peak at 2am. It's kind of unexpected to see a peak at 2am in the morning though. Then it decreases and rises again from 5am and reach another peak at 2pm with gradual decrease after that until mid-night."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> Daily average trip duration during the weekend is longer than that of the weekdays. By comparing average trip duration for subscribers and customers, we found that although subscribers make a lot more trips than customers, customers generally take longer bike trip than subscribers. It's surprising to see that the average ride duration in an hour increases from midnight and reaches the peak at 2am in the morning. Most longer trips also don't opt for bike share for all trip\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> We see that bikes are used more on weekdays than on weekends, the majority of users are subscribers. And bike share option is sometimes chosen by subscribers"
   ]
  },
  {
   "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": [
    "let's Create faceted heat maps on weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "xbin = np.arange(0,24+1,1)\n",
    "ybin = np.arange(0,2000+250,250)\n",
    "g = sb.FacetGrid (data = filtered , col='start_day',col_wrap=3,height=3)\n",
    "g.map(plt.hist2d, 'start_hour', 'duration_sec', cmin=0.5, cmap = 'inferno_r', bins=[xbin,ybin])\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this heatmap we can differences between weekdays and weekends. We can see that durations are more longer for weekdays beetween 8am and 17am when they more longer for weelends form 14am to 17am"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How the duration of trips varies according to weekdays for users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "fig = plt.figure(figsize = [8,6])\n",
    "ax = sb.pointplot(data = filtered, x = 'start_day', y = 'duration_sec', hue = 'user_type',\n",
    "           palette = 'Blues', linestyles = '--', dodge = 0.4,order=[ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])\n",
    "plt.ylabel('Avg. duration')\n",
    "plt.yscale('log')\n",
    "plt.yticks([4e2, 6e2, 1e3,2e3], ['400', '600', '1000','2000'])\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.title('Average duration on weekdays per user Type', size=(18))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    " Daily trip duration for customers are longer than subscribers.Both subscribers and customers take longer trips during weekend than weekdays. Customers take longer trips during the weekend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to also how the duration of trips varies according to hours for users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "ax = sb.barplot(data = filtered, x = 'start_hour', y = 'duration_sec', hue = 'user_type', errwidth = 0)\n",
    "ax.legend(loc = 8, ncol = 3, framealpha = 1, title = 'user_type')\n",
    "plt.ylabel('Avg. duration')\n",
    "plt.yscale('log')\n",
    "plt.yticks([4e2, 6e2, 1e3,2e3], ['400', '600', '1000','2000'])\n",
    "ax.set_yticklabels([],minor = True)\n",
    "plt.title('Hourly Average Duration of trips in a Day per User Type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Observation:\n",
    "Average duration for customers are higher between 10am-5pm reaching its peak at 2pm.Average duration for subscribers are higher between 0am-5am reaching its peak at 2am."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> Daily average trip duration during the weekend is longer than that of the weekdays.Users take most of their trips around 8am and 17pm during the weekdays , typical commute hours to go to work/school and come back home and around 14pm to 17pm during weekends. There more customers taking their trips during weekends for longer duration than subscribers. \n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> Accordind to our boxplot, member_gender doesn't affects the duration.We saw that the hourly average duration increases from midnight and reaches the peak at 2am in the morning. And we observe that only subscriber have some bike share option for all trip."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    ">Given the results of our analyses we were able to conclude:\n",
    " - the duration of the trips is longer during the weekends \n",
    " - the majority of Go users are subscribers but the most trips are made by customers who apparently often make trips at night \n",
    " - the trips are mostly made by people aged between 20 and 40 \n",
    " - and the gender of members does not really affect the duration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}