Exploration_FordGoBikes_Tash_Updated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Ford GoBikes System Data¶\n",
    "## By Tash Bettridge \n",
    "\n",
    "## Preliminary Wrangling\n",
    "\n",
    ">Formerly known as Ford GoBike, renamed Lyft Bikes is a regional public bicycle sharing system in the San Francisco Bay Area, California.\n",
    "\n",
    "### Data\n",
    "\n",
    "The data will be analysed from the following:\n",
    "\n",
    "* 2019 FordGo Bike and Baywheels Tripdata\n",
    "\n",
    "### Process\n",
    "\n",
    "The data wrangling steps that were involved in this project were:\n",
    "- Step 1. Assessing the Data \n",
    "- Step 2. Preliminary Wrangling\n",
    "- Step 3. Cleaning data\n",
    "- Step 4. Univariate Exploration\n",
    "- Step 5. Multivariate Exploration\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Assessing the Data \n",
    "\n",
    "Data Analysis, storing data and data visualization of the wrangled data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import glob\n",
    "import os\n",
    "import datetime\n",
    "import matplotlib.ticker as tick\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retrieve the datasets from the User directory \n",
    "path = r'C:\\Users\\tash_\\Desktop\\Udacity\\dataset'\n",
    "# detect all of the files ending with a .csv name within a specific working directory\n",
    "files = glob.glob(os.path.join(path, \"*.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DtypeWarning: Columns (14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DtypeWarning: Columns (13,14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DtypeWarning: Columns (13) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#use the pd.concat() method to stack every dataframe one on top of another.\n",
    "df = pd.concat((pd.read_csv(f) for f in files), ignore_index = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save a Copy To Master File\n",
    "df.to_csv('baywheels_master.csv', index = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Preliminary Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (1,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Read contents of Master File\n",
    "df = pd.read_csv('baywheels_master.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>bike_id</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2.506983e+06</td>\n",
       "      <td>2.506983e+06</td>\n",
       "      <td>2.424081e+06</td>\n",
       "      <td>2.506983e+06</td>\n",
       "      <td>2.506983e+06</td>\n",
       "      <td>2.426249e+06</td>\n",
       "      <td>2.506983e+06</td>\n",
       "      <td>2.506983e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2.789833e+04</td>\n",
       "      <td>8.076483e+02</td>\n",
       "      <td>1.427044e+02</td>\n",
       "      <td>3.776422e+01</td>\n",
       "      <td>-1.223459e+02</td>\n",
       "      <td>1.465047e+02</td>\n",
       "      <td>3.776506e+01</td>\n",
       "      <td>-1.223499e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.146067e+05</td>\n",
       "      <td>1.974714e+03</td>\n",
       "      <td>1.214296e+02</td>\n",
       "      <td>2.392885e-01</td>\n",
       "      <td>7.080417e-01</td>\n",
       "      <td>1.223171e+02</td>\n",
       "      <td>1.363698e-01</td>\n",
       "      <td>3.089648e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.000000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.225143e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.225143e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.952000e+03</td>\n",
       "      <td>3.590000e+02</td>\n",
       "      <td>4.300000e+01</td>\n",
       "      <td>3.777003e+01</td>\n",
       "      <td>-1.224117e+02</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>3.776931e+01</td>\n",
       "      <td>-1.224130e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.420000e+03</td>\n",
       "      <td>5.710000e+02</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>3.778076e+01</td>\n",
       "      <td>-1.223981e+02</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>3.778053e+01</td>\n",
       "      <td>-1.223983e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9.682000e+03</td>\n",
       "      <td>8.870000e+02</td>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>3.779587e+01</td>\n",
       "      <td>-1.222934e+02</td>\n",
       "      <td>2.430000e+02</td>\n",
       "      <td>3.779539e+01</td>\n",
       "      <td>-1.222914e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.999410e+05</td>\n",
       "      <td>9.121100e+05</td>\n",
       "      <td>4.980000e+02</td>\n",
       "      <td>4.551000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.980000e+02</td>\n",
       "      <td>4.551000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            bike_id  duration_sec  end_station_id  end_station_latitude  \\\n",
       "count  2.506983e+06  2.506983e+06    2.424081e+06          2.506983e+06   \n",
       "mean   2.789833e+04  8.076483e+02    1.427044e+02          3.776422e+01   \n",
       "std    1.146067e+05  1.974714e+03    1.214296e+02          2.392885e-01   \n",
       "min    4.000000e+00  6.000000e+01    3.000000e+00          0.000000e+00   \n",
       "25%    1.952000e+03  3.590000e+02    4.300000e+01          3.777003e+01   \n",
       "50%    4.420000e+03  5.710000e+02    1.010000e+02          3.778076e+01   \n",
       "75%    9.682000e+03  8.870000e+02    2.390000e+02          3.779587e+01   \n",
       "max    9.999410e+05  9.121100e+05    4.980000e+02          4.551000e+01   \n",
       "\n",
       "       end_station_longitude  start_station_id  start_station_latitude  \\\n",
       "count           2.506983e+06      2.426249e+06            2.506983e+06   \n",
       "mean           -1.223459e+02      1.465047e+02            3.776506e+01   \n",
       "std             7.080417e-01      1.223171e+02            1.363698e-01   \n",
       "min            -1.225143e+02      3.000000e+00            0.000000e+00   \n",
       "25%            -1.224117e+02      4.700000e+01            3.776931e+01   \n",
       "50%            -1.223981e+02      1.050000e+02            3.778053e+01   \n",
       "75%            -1.222934e+02      2.430000e+02            3.779539e+01   \n",
       "max             0.000000e+00      4.980000e+02            4.551000e+01   \n",
       "\n",
       "       start_station_longitude  \n",
       "count             2.506983e+06  \n",
       "mean             -1.223499e+02  \n",
       "std               3.089648e-01  \n",
       "min              -1.225143e+02  \n",
       "25%              -1.224130e+02  \n",
       "50%              -1.223983e+02  \n",
       "75%              -1.222914e+02  \n",
       "max               0.000000e+00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing the first five rows of the newly combined dataset\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_time</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_time</th>\n",
       "      <th>user_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1154512</td>\n",
       "      <td>896</td>\n",
       "      <td>No</td>\n",
       "      <td>451</td>\n",
       "      <td>115.0</td>\n",
       "      <td>37.765026</td>\n",
       "      <td>-122.398773</td>\n",
       "      <td>Jackson Playground</td>\n",
       "      <td>2019-06-14 09:36:16.6730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>2019-06-14 09:28:45.5450</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2176750</td>\n",
       "      <td>2216</td>\n",
       "      <td>No</td>\n",
       "      <td>681</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>2019-11-27 15:14:28.6780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>37.795393</td>\n",
       "      <td>-122.404770</td>\n",
       "      <td>Washington St at Kearny St</td>\n",
       "      <td>2019-11-27 15:03:06.7740</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>503299</td>\n",
       "      <td>3405</td>\n",
       "      <td>No</td>\n",
       "      <td>424</td>\n",
       "      <td>121.0</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>2019-03-17 12:56:59.3760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.0</td>\n",
       "      <td>37.747998</td>\n",
       "      <td>-122.420219</td>\n",
       "      <td>Valencia St at Cesar Chavez St</td>\n",
       "      <td>2019-03-17 12:49:54.4390</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>885510</td>\n",
       "      <td>1228</td>\n",
       "      <td>No</td>\n",
       "      <td>622</td>\n",
       "      <td>122.0</td>\n",
       "      <td>37.760299</td>\n",
       "      <td>-122.418892</td>\n",
       "      <td>19th St at Mission St</td>\n",
       "      <td>2019-05-29 20:26:14.3490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>37.770030</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>11th St at Bryant St</td>\n",
       "      <td>2019-05-29 20:15:52.2210</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>941319</td>\n",
       "      <td>988</td>\n",
       "      <td>No</td>\n",
       "      <td>334</td>\n",
       "      <td>372.0</td>\n",
       "      <td>37.804037</td>\n",
       "      <td>-122.262409</td>\n",
       "      <td>Madison St at 17th St</td>\n",
       "      <td>2019-05-20 11:31:34.7140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.0</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>2019-05-20 11:26:00.5460</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>567674</td>\n",
       "      <td>6188</td>\n",
       "      <td>No</td>\n",
       "      <td>1087</td>\n",
       "      <td>15.0</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>2019-03-10 13:11:50.4590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>2019-03-10 12:53:42.8860</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350110</td>\n",
       "      <td>2193</td>\n",
       "      <td>No</td>\n",
       "      <td>730</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.799953</td>\n",
       "      <td>-122.398525</td>\n",
       "      <td>The Embarcadero at Vallejo St</td>\n",
       "      <td>2019-02-05 20:05:53.9530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>2019-02-05 19:53:43.8850</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2425905</td>\n",
       "      <td>3417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1295</td>\n",
       "      <td>146.0</td>\n",
       "      <td>37.742314</td>\n",
       "      <td>-122.423181</td>\n",
       "      <td>30th St at San Jose Ave</td>\n",
       "      <td>2019-12-11 16:53:20.6690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>37.777791</td>\n",
       "      <td>-122.406432</td>\n",
       "      <td>Victoria Manalo Draves Park</td>\n",
       "      <td>2019-12-11 16:31:44.8200</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>443231</td>\n",
       "      <td>5306</td>\n",
       "      <td>No</td>\n",
       "      <td>719</td>\n",
       "      <td>80.0</td>\n",
       "      <td>37.775235</td>\n",
       "      <td>-122.397437</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>2019-03-23 19:37:27.2450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>2019-03-23 19:25:27.4630</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3373</td>\n",
       "      <td>2124</td>\n",
       "      <td>No</td>\n",
       "      <td>519</td>\n",
       "      <td>67.0</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>2019-01-31 17:23:47.1020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.787522</td>\n",
       "      <td>-122.397405</td>\n",
       "      <td>Howard St at 2nd St</td>\n",
       "      <td>2019-01-31 17:15:07.8980</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bike_id bike_share_for_all_trip  duration_sec  end_station_id  \\\n",
       "1154512      896                      No           451           115.0   \n",
       "2176750     2216                      No           681            30.0   \n",
       "503299      3405                      No           424           121.0   \n",
       "885510      1228                      No           622           122.0   \n",
       "941319       988                      No           334           372.0   \n",
       "567674      6188                      No          1087            15.0   \n",
       "350110      2193                      No           730             8.0   \n",
       "2425905     3417                     NaN          1295           146.0   \n",
       "443231      5306                      No           719            80.0   \n",
       "3373        2124                      No           519            67.0   \n",
       "\n",
       "         end_station_latitude  end_station_longitude  \\\n",
       "1154512             37.765026            -122.398773   \n",
       "2176750             37.776598            -122.395282   \n",
       "503299              37.759210            -122.421339   \n",
       "885510              37.760299            -122.418892   \n",
       "941319              37.804037            -122.262409   \n",
       "567674              37.795392            -122.394203   \n",
       "350110              37.799953            -122.398525   \n",
       "2425905             37.742314            -122.423181   \n",
       "443231              37.775235            -122.397437   \n",
       "3373                37.776639            -122.395526   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "1154512                                 Jackson Playground   \n",
       "2176750     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "503299                                  Mission Playground   \n",
       "885510                               19th St at Mission St   \n",
       "941319                               Madison St at 17th St   \n",
       "567674   San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "350110                       The Embarcadero at Vallejo St   \n",
       "2425905                            30th St at San Jose Ave   \n",
       "443231                               Townsend St at 5th St   \n",
       "3373     San Francisco Caltrain Station 2  (Townsend St...   \n",
       "\n",
       "                         end_time rental_access_method  start_station_id  \\\n",
       "1154512  2019-06-14 09:36:16.6730                  NaN              60.0   \n",
       "2176750  2019-11-27 15:14:28.6780                  NaN              10.0   \n",
       "503299   2019-03-17 12:56:59.3760                  NaN             141.0   \n",
       "885510   2019-05-29 20:26:14.3490                  NaN              88.0   \n",
       "941319   2019-05-20 11:31:34.7140                  NaN             181.0   \n",
       "567674   2019-03-10 13:11:50.4590                  NaN              93.0   \n",
       "350110   2019-02-05 20:05:53.9530                  NaN              36.0   \n",
       "2425905  2019-12-11 16:53:20.6690                  NaN              62.0   \n",
       "443231   2019-03-23 19:37:27.2450                  NaN             121.0   \n",
       "3373     2019-01-31 17:23:47.1020                  NaN              25.0   \n",
       "\n",
       "         start_station_latitude  start_station_longitude  \\\n",
       "1154512               37.774520              -122.409449   \n",
       "2176750               37.795393              -122.404770   \n",
       "503299                37.747998              -122.420219   \n",
       "885510                37.770030              -122.411726   \n",
       "941319                37.811377              -122.265192   \n",
       "567674                37.770407              -122.391198   \n",
       "350110                37.783830              -122.398870   \n",
       "2425905               37.777791              -122.406432   \n",
       "443231                37.759210              -122.421339   \n",
       "3373                  37.787522              -122.397405   \n",
       "\n",
       "                     start_station_name                start_time   user_type  \n",
       "1154512            8th St at Ringold St  2019-06-14 09:28:45.5450  Subscriber  \n",
       "2176750      Washington St at Kearny St  2019-11-27 15:03:06.7740  Subscriber  \n",
       "503299   Valencia St at Cesar Chavez St  2019-03-17 12:49:54.4390    Customer  \n",
       "885510             11th St at Bryant St  2019-05-29 20:15:52.2210  Subscriber  \n",
       "941319          Grand Ave at Webster St  2019-05-20 11:26:00.5460  Subscriber  \n",
       "567674     4th St at Mission Bay Blvd S  2019-03-10 12:53:42.8860    Customer  \n",
       "350110              Folsom St at 3rd St  2019-02-05 19:53:43.8850  Subscriber  \n",
       "2425905     Victoria Manalo Draves Park  2019-12-11 16:31:44.8200    Customer  \n",
       "443231               Mission Playground  2019-03-23 19:25:27.4630    Customer  \n",
       "3373                Howard St at 2nd St  2019-01-31 17:15:07.8980  Subscriber  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retrieve the first 10 samples of the data\n",
    "df.sample(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2506983 entries, 0 to 2506982\n",
      "Data columns (total 15 columns):\n",
      "bike_id                    2506983 non-null int64\n",
      "bike_share_for_all_trip    2263724 non-null object\n",
      "duration_sec               2506983 non-null int64\n",
      "end_station_id             2424081 non-null float64\n",
      "end_station_latitude       2506983 non-null float64\n",
      "end_station_longitude      2506983 non-null float64\n",
      "end_station_name           2424633 non-null object\n",
      "end_time                   2506983 non-null object\n",
      "rental_access_method       120838 non-null object\n",
      "start_station_id           2426249 non-null float64\n",
      "start_station_latitude     2506983 non-null float64\n",
      "start_station_longitude    2506983 non-null float64\n",
      "start_station_name         2426850 non-null object\n",
      "start_time                 2506983 non-null object\n",
      "user_type                  2506983 non-null object\n",
      "dtypes: float64(6), int64(2), object(7)\n",
      "memory usage: 286.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info(null_counts = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bike_id                          0\n",
       "bike_share_for_all_trip     243259\n",
       "duration_sec                     0\n",
       "end_station_id               82902\n",
       "end_station_latitude             0\n",
       "end_station_longitude            0\n",
       "end_station_name             82350\n",
       "end_time                         0\n",
       "rental_access_method       2386145\n",
       "start_station_id             80734\n",
       "start_station_latitude           0\n",
       "start_station_longitude          0\n",
       "start_station_name           80133\n",
       "start_time                       0\n",
       "user_type                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dataset for duplicated data\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the original data set and create a copy \n",
    "df_cleaned = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Cleaning Data \n",
    "There are some missing values in the dataset, such as start_station_id, start_station_name, end_station_id, end_station_name, rental_access_method. For more information please see below:\n",
    "\n",
    "* start_station_id\n",
    "* end_station_id\n",
    "* bike_id\n",
    "* user_type\n",
    "* rental_access_method\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    ">Calculate the total distance traveled using the latitude and longitude in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from math import radians, sin, cos, acos\n",
    "def distance(origin, destination):\n",
    "    lat1, long1 = origin\n",
    "    lat2, long2 = destination\n",
    "    radius = 6371 # this is in kms\n",
    "    dlat = math.radians(lat2 - lat1)\n",
    "    dlong = math.radians(long2 - long1)\n",
    "    x = (math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(math.radians(lat1)) \n",
    "         * math.cos(math.radians(lat2)) * math.sin(dlong / 2) * math.sin(dlong / 2))\n",
    "    y = 2 * math.atan2(math.sqrt(x), math.sqrt(1 - x))\n",
    "    z = radius * y\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the calcuated math on the columns for latidue and longitude\n",
    "df_cleaned['distance_km'] = df_cleaned.apply(lambda x: distance((x['start_station_latitude'], x['start_station_longitude']), (x['end_station_latitude'], x['end_station_longitude'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the kilometers to miles\n",
    "df_cleaned['distance_miles'] = df_cleaned['distance_km'] * 0.621371"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_time</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_time</th>\n",
       "      <th>user_type</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>distance_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1281537</td>\n",
       "      <td>9252</td>\n",
       "      <td>No</td>\n",
       "      <td>1438</td>\n",
       "      <td>280.0</td>\n",
       "      <td>37.337122</td>\n",
       "      <td>-121.883215</td>\n",
       "      <td>San Fernando St at 7th St</td>\n",
       "      <td>2019-07-24 23:01:44.2630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>2019-07-24 22:37:46.2200</td>\n",
       "      <td>Customer</td>\n",
       "      <td>0.256257</td>\n",
       "      <td>0.159231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>804818</td>\n",
       "      <td>6303</td>\n",
       "      <td>No</td>\n",
       "      <td>606</td>\n",
       "      <td>93.0</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>2019-04-08 08:39:35.3010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>2019-04-08 08:29:28.5180</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1428576</td>\n",
       "      <td>936255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.780504</td>\n",
       "      <td>-122.439246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-29 07:39:30</td>\n",
       "      <td>app</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746438</td>\n",
       "      <td>-122.420584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-29 07:19:51</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>4.128022</td>\n",
       "      <td>2.565033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2323427</td>\n",
       "      <td>11381</td>\n",
       "      <td>No</td>\n",
       "      <td>618</td>\n",
       "      <td>363.0</td>\n",
       "      <td>37.787492</td>\n",
       "      <td>-122.398285</td>\n",
       "      <td>Salesforce Transit Center (Natoma St at 2nd St)</td>\n",
       "      <td>2019-11-05 17:37:00.0100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>323.0</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>2019-11-05 17:26:41.5510</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.349995</td>\n",
       "      <td>0.838848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1074516</td>\n",
       "      <td>1896</td>\n",
       "      <td>No</td>\n",
       "      <td>443</td>\n",
       "      <td>9.0</td>\n",
       "      <td>37.798572</td>\n",
       "      <td>-122.400869</td>\n",
       "      <td>Broadway at Battery St</td>\n",
       "      <td>2019-06-27 08:23:18.2310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.789756</td>\n",
       "      <td>-122.394643</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>2019-06-27 08:15:55.0040</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.122623</td>\n",
       "      <td>0.697565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bike_id bike_share_for_all_trip  duration_sec  end_station_id  \\\n",
       "1281537     9252                      No          1438           280.0   \n",
       "804818      6303                      No           606            93.0   \n",
       "1428576   936255                     NaN          1179             NaN   \n",
       "2323427    11381                      No           618           363.0   \n",
       "1074516     1896                      No           443             9.0   \n",
       "\n",
       "         end_station_latitude  end_station_longitude  \\\n",
       "1281537             37.337122            -121.883215   \n",
       "804818              37.770407            -122.391198   \n",
       "1428576             37.780504            -122.439246   \n",
       "2323427             37.787492            -122.398285   \n",
       "1074516             37.798572            -122.400869   \n",
       "\n",
       "                                        end_station_name  \\\n",
       "1281537                        San Fernando St at 7th St   \n",
       "804818                      4th St at Mission Bay Blvd S   \n",
       "1428576                                              NaN   \n",
       "2323427  Salesforce Transit Center (Natoma St at 2nd St)   \n",
       "1074516                           Broadway at Battery St   \n",
       "\n",
       "                         end_time rental_access_method  start_station_id  \\\n",
       "1281537  2019-07-24 23:01:44.2630                  NaN             310.0   \n",
       "804818   2019-04-08 08:39:35.3010                  NaN              93.0   \n",
       "1428576       2019-07-29 07:39:30                  app               NaN   \n",
       "2323427  2019-11-05 17:37:00.0100                  NaN             323.0   \n",
       "1074516  2019-06-27 08:23:18.2310                  NaN              22.0   \n",
       "\n",
       "         start_station_latitude  start_station_longitude  \\\n",
       "1281537               37.335885              -121.885660   \n",
       "804818                37.770407              -122.391198   \n",
       "1428576               37.746438              -122.420584   \n",
       "2323427               37.798014              -122.405950   \n",
       "1074516               37.789756              -122.394643   \n",
       "\n",
       "                   start_station_name                start_time   user_type  \\\n",
       "1281537     San Fernando St at 4th St  2019-07-24 22:37:46.2200    Customer   \n",
       "804818   4th St at Mission Bay Blvd S  2019-04-08 08:29:28.5180  Subscriber   \n",
       "1428576                           NaN       2019-07-29 07:19:51  Subscriber   \n",
       "2323427            Broadway at Kearny  2019-11-05 17:26:41.5510  Subscriber   \n",
       "1074516         Howard St at Beale St  2019-06-27 08:15:55.0040  Subscriber   \n",
       "\n",
       "         distance_km  distance_miles  \n",
       "1281537     0.256257        0.159231  \n",
       "804818      0.000000        0.000000  \n",
       "1428576     4.128022        2.565033  \n",
       "2323427     1.349995        0.838848  \n",
       "1074516     1.122623        0.697565  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "\n",
    "> Converting the Start_time and end_time to the timestamp format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the format to datetime\n",
    "df_cleaned.start_time = pd.to_datetime(df_cleaned.start_time)\n",
    "df_cleaned.end_time = pd.to_datetime(df_cleaned.end_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2506983 entries, 0 to 2506982\n",
      "Data columns (total 17 columns):\n",
      "bike_id                    2506983 non-null int64\n",
      "bike_share_for_all_trip    2263724 non-null object\n",
      "duration_sec               2506983 non-null int64\n",
      "end_station_id             2424081 non-null float64\n",
      "end_station_latitude       2506983 non-null float64\n",
      "end_station_longitude      2506983 non-null float64\n",
      "end_station_name           2424633 non-null object\n",
      "end_time                   2506983 non-null datetime64[ns]\n",
      "rental_access_method       120838 non-null object\n",
      "start_station_id           2426249 non-null float64\n",
      "start_station_latitude     2506983 non-null float64\n",
      "start_station_longitude    2506983 non-null float64\n",
      "start_station_name         2426850 non-null object\n",
      "start_time                 2506983 non-null datetime64[ns]\n",
      "user_type                  2506983 non-null object\n",
      "distance_km                2506983 non-null float64\n",
      "distance_miles             2506983 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(8), int64(2), object(5)\n",
      "memory usage: 325.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned.info(null_counts = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define \n",
    "\n",
    ">Converting the User type and rental_access_method for all to category format\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the user_type and rental_access_method to category format\n",
    "df_cleaned.user_type = df_cleaned.user_type.astype('category')\n",
    "df_cleaned.rental_access_method = df_cleaned.rental_access_method.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2506983 entries, 0 to 2506982\n",
      "Data columns (total 17 columns):\n",
      "bike_id                    2506983 non-null int64\n",
      "bike_share_for_all_trip    2263724 non-null object\n",
      "duration_sec               2506983 non-null int64\n",
      "end_station_id             2424081 non-null float64\n",
      "end_station_latitude       2506983 non-null float64\n",
      "end_station_longitude      2506983 non-null float64\n",
      "end_station_name           2424633 non-null object\n",
      "end_time                   2506983 non-null datetime64[ns]\n",
      "rental_access_method       120838 non-null category\n",
      "start_station_id           2426249 non-null float64\n",
      "start_station_latitude     2506983 non-null float64\n",
      "start_station_longitude    2506983 non-null float64\n",
      "start_station_name         2426850 non-null object\n",
      "start_time                 2506983 non-null datetime64[ns]\n",
      "user_type                  2506983 non-null category\n",
      "distance_km                2506983 non-null float64\n",
      "distance_miles             2506983 non-null float64\n",
      "dtypes: category(2), datetime64[ns](2), float64(8), int64(2), object(3)\n",
      "memory usage: 291.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned.info(null_counts = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define\n",
    "\n",
    "> Convertng the bike_id, start_station_id, and end_station_id to object\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertng the bike_id, start_station_id, and end_station_id to object\n",
    "df_cleaned.bike_id = df_cleaned.bike_id.astype(str)\n",
    "df_cleaned.start_station_id = df_cleaned.start_station_id.astype(str)\n",
    "df_cleaned.end_station_id = df_cleaned.end_station_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2506983 entries, 0 to 2506982\n",
      "Data columns (total 17 columns):\n",
      "bike_id                    2506983 non-null object\n",
      "bike_share_for_all_trip    2263724 non-null object\n",
      "duration_sec               2506983 non-null int64\n",
      "end_station_id             2506983 non-null object\n",
      "end_station_latitude       2506983 non-null float64\n",
      "end_station_longitude      2506983 non-null float64\n",
      "end_station_name           2424633 non-null object\n",
      "end_time                   2506983 non-null datetime64[ns]\n",
      "rental_access_method       120838 non-null category\n",
      "start_station_id           2506983 non-null object\n",
      "start_station_latitude     2506983 non-null float64\n",
      "start_station_longitude    2506983 non-null float64\n",
      "start_station_name         2426850 non-null object\n",
      "start_time                 2506983 non-null datetime64[ns]\n",
      "user_type                  2506983 non-null category\n",
      "distance_km                2506983 non-null float64\n",
      "distance_miles             2506983 non-null float64\n",
      "dtypes: category(2), datetime64[ns](2), float64(6), int64(1), object(6)\n",
      "memory usage: 291.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cleaned.info(null_counts = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Define\n",
    "\n",
    ">Adding columns for the start month, days of the week to create data visualisations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start time month from January through to December 2019\n",
    "df_cleaned['start_time_month'] = df_cleaned['start_time'].dt.strftime('%B')\n",
    "df_cleaned['start_time_month_number'] = df_cleaned['start_time'].dt.month.astype(int)\n",
    "df_cleaned['start_time_weekday'] = df_cleaned['start_time'].dt.strftime('%a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_time</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_time</th>\n",
       "      <th>user_type</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>distance_miles</th>\n",
       "      <th>start_time_month</th>\n",
       "      <th>start_time_month_number</th>\n",
       "      <th>start_time_weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2256523</td>\n",
       "      <td>12258</td>\n",
       "      <td>No</td>\n",
       "      <td>531</td>\n",
       "      <td>239.0</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>2019-11-14 20:30:57.315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.0</td>\n",
       "      <td>37.870139</td>\n",
       "      <td>-122.268422</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>2019-11-14 20:22:05.497</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0.860499</td>\n",
       "      <td>0.534689</td>\n",
       "      <td>November</td>\n",
       "      <td>11</td>\n",
       "      <td>Thu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1930081</td>\n",
       "      <td>9817</td>\n",
       "      <td>No</td>\n",
       "      <td>3823</td>\n",
       "      <td>377.0</td>\n",
       "      <td>37.771951</td>\n",
       "      <td>-122.453705</td>\n",
       "      <td>Fell St at Stanyan St</td>\n",
       "      <td>2019-09-01 14:02:45.440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.782405</td>\n",
       "      <td>-122.439446</td>\n",
       "      <td>O'Farrell St at Divisadero St</td>\n",
       "      <td>2019-09-01 12:59:02.288</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.709274</td>\n",
       "      <td>1.062093</td>\n",
       "      <td>September</td>\n",
       "      <td>9</td>\n",
       "      <td>Sun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1829101</td>\n",
       "      <td>400</td>\n",
       "      <td>No</td>\n",
       "      <td>1309</td>\n",
       "      <td>350.0</td>\n",
       "      <td>37.771431</td>\n",
       "      <td>-122.405787</td>\n",
       "      <td>8th St at Brannan St</td>\n",
       "      <td>2019-09-15 19:51:33.722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386.0</td>\n",
       "      <td>37.752105</td>\n",
       "      <td>-122.419724</td>\n",
       "      <td>24th St at Bartlett St</td>\n",
       "      <td>2019-09-15 19:29:44.691</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2.473688</td>\n",
       "      <td>1.537078</td>\n",
       "      <td>September</td>\n",
       "      <td>9</td>\n",
       "      <td>Sun</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        bike_id bike_share_for_all_trip  duration_sec end_station_id  \\\n",
       "2256523   12258                      No           531          239.0   \n",
       "1930081    9817                      No          3823          377.0   \n",
       "1829101     400                      No          1309          350.0   \n",
       "\n",
       "         end_station_latitude  end_station_longitude  \\\n",
       "2256523             37.868813            -122.258764   \n",
       "1930081             37.771951            -122.453705   \n",
       "1829101             37.771431            -122.405787   \n",
       "\n",
       "                      end_station_name                end_time  \\\n",
       "2256523  Bancroft Way at Telegraph Ave 2019-11-14 20:30:57.315   \n",
       "1930081          Fell St at Stanyan St 2019-09-01 14:02:45.440   \n",
       "1829101           8th St at Brannan St 2019-09-15 19:51:33.722   \n",
       "\n",
       "        rental_access_method start_station_id  start_station_latitude  \\\n",
       "2256523                  NaN            245.0               37.870139   \n",
       "1930081                  NaN             29.0               37.782405   \n",
       "1829101                  NaN            386.0               37.752105   \n",
       "\n",
       "         start_station_longitude             start_station_name  \\\n",
       "2256523              -122.268422         Downtown Berkeley BART   \n",
       "1930081              -122.439446  O'Farrell St at Divisadero St   \n",
       "1829101              -122.419724         24th St at Bartlett St   \n",
       "\n",
       "                     start_time   user_type  distance_km  distance_miles  \\\n",
       "2256523 2019-11-14 20:22:05.497  Subscriber     0.860499        0.534689   \n",
       "1930081 2019-09-01 12:59:02.288    Customer     1.709274        1.062093   \n",
       "1829101 2019-09-15 19:29:44.691  Subscriber     2.473688        1.537078   \n",
       "\n",
       "        start_time_month  start_time_month_number start_time_weekday  \n",
       "2256523         November                       11                Thu  \n",
       "1930081        September                        9                Sun  \n",
       "1829101        September                        9                Sun  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Define\n",
    "\n",
    ">Adding columns for the  of the week to create data visualisations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start and end time hour and # Duration in seconds to duration in minutes\n",
    "df_cleaned['start_time_hour'] = df_cleaned['start_time'].dt.hour\n",
    "df_cleaned['end_time_hour'] = df_cleaned['end_time'].dt.hour\n",
    "df_cleaned['duration_min'] = df_cleaned['duration_sec']/60\n",
    "df_cleaned['duration_min'] = df_cleaned['duration_min'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_time</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>...</th>\n",
       "      <th>start_time</th>\n",
       "      <th>user_type</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>distance_miles</th>\n",
       "      <th>start_time_month</th>\n",
       "      <th>start_time_month_number</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>end_time_hour</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>501973</td>\n",
       "      <td>6279</td>\n",
       "      <td>No</td>\n",
       "      <td>315</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.785377</td>\n",
       "      <td>-122.396906</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>2019-03-17 15:04:31.728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-03-17 14:59:16.219</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0.997356</td>\n",
       "      <td>0.619728</td>\n",
       "      <td>March</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104829</td>\n",
       "      <td>5097</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1871</td>\n",
       "      <td>99.0</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>2019-01-17 21:51:37.325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-01-17 21:20:26.283</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0.414484</td>\n",
       "      <td>0.257548</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Thu</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1350344</td>\n",
       "      <td>785</td>\n",
       "      <td>No</td>\n",
       "      <td>406</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>2019-07-12 10:08:15.490</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-12 10:01:28.500</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.359125</td>\n",
       "      <td>0.844521</td>\n",
       "      <td>July</td>\n",
       "      <td>7</td>\n",
       "      <td>Fri</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        bike_id bike_share_for_all_trip  duration_sec end_station_id  \\\n",
       "501973     6279                      No           315           37.0   \n",
       "104829     5097                     Yes          1871           99.0   \n",
       "1350344     785                      No           406           27.0   \n",
       "\n",
       "         end_station_latitude  end_station_longitude         end_station_name  \\\n",
       "501973              37.785377            -122.396906      2nd St at Folsom St   \n",
       "104829              37.767037            -122.415442     Folsom St at 15th St   \n",
       "1350344             37.788059            -122.391865  Beale St at Harrison St   \n",
       "\n",
       "                       end_time rental_access_method start_station_id  ...  \\\n",
       "501973  2019-03-17 15:04:31.728                  NaN             16.0  ...   \n",
       "104829  2019-01-17 21:51:37.325                  NaN            112.0  ...   \n",
       "1350344 2019-07-12 10:08:15.490                  NaN             81.0  ...   \n",
       "\n",
       "                     start_time   user_type distance_km distance_miles  \\\n",
       "501973  2019-03-17 14:59:16.219  Subscriber    0.997356       0.619728   \n",
       "104829  2019-01-17 21:20:26.283  Subscriber    0.414484       0.257548   \n",
       "1350344 2019-07-12 10:01:28.500  Subscriber    1.359125       0.844521   \n",
       "\n",
       "        start_time_month  start_time_month_number  start_time_weekday  \\\n",
       "501973             March                        3                 Sun   \n",
       "104829           January                        1                 Thu   \n",
       "1350344             July                        7                 Fri   \n",
       "\n",
       "        start_time_hour  end_time_hour duration_min  \n",
       "501973               14             15            5  \n",
       "104829               21             21           31  \n",
       "1350344              10             10            6  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store data into Master file ('baywheels_master_clean.csv)\n",
    "df_cleaned.to_csv('baywheels_master_clean.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "The FordGoBike and BayWheels Datasets provide a valuable collection of data to work with in order draw data findings and a conclusion. This notebook uses data collected from January 2019 which the organisation was still using the naming structure FordGoBike until May 2019 and then changed its naming structure December 2019. There have been some anomity changes in the dataset and can no longer be distinguish by genderm work or ethnicity data. \n",
    "\n",
    "\n",
    "> The dataset has 25000+ bike rides that had happened in 2019. \n",
    "\n",
    "### The dataset contained features about:\n",
    "\n",
    "* trip duration\n",
    "* start time/end time\n",
    "* stations: start/end station, name, geolocation (latitude/longitude)\n",
    "* anonymized customer data: The users are divided into two user types: Subscriber and Customer\n",
    "* rented bikes: bike_id \n",
    "\n",
    "\n",
    "### The following columns that were added in the data cleaning phase:\n",
    "\n",
    "\n",
    "* Start Time Month\n",
    "* Start Time Month Number\n",
    "* Start Time Day of the Week\n",
    "* Start Time Hour\n",
    "* Distance (km)\n",
    "* Distance (miles)\n",
    "  \n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> I am interested in investigate the main features which include data that is related to the start time and end time of a ride. \n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> The features in the dataset that I think will ehance my investigation would be looking at the start times and end times as well as any related time-based information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Univariate Exploration\n",
    "\n",
    "> In this section, investigate distributions of individual variables. If\n",
    "you see unusual points or outliers, take a deeper look to clean things up\n",
    "and prepare yourself to look at relationships between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x270590c5788>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display mFord GoBikes System Monthly Usage in 2019\n",
    "graph = sb.catplot(data = df_cleaned, x = 'start_time_month', \n",
    "                   kind = 'count', aspect = 1.8);\n",
    "graph.set_axis_labels('Months in 2019','Number of Trips'),\n",
    "graph.fig.suptitle('Ford GoBikes System Monthly Usage in 2019',\n",
    "                   y = 1.07, fontsize = 18, fontweight = 'bold');\n",
    "graph.set_xticklabels(rotation=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBikes System Monthly Usage in 2019\n",
    "\n",
    "> In the Bay Area, July which is in Summer in the Northen Hemispher is the most popular time to use the Ford Go Bike sharing system. March is also the second most popular time to use the Ford Go Bike sharing system in the Bay Area. \n",
    "\n",
    ">While December which is Winter time in the Northern Hemisphere, is the least popular time to use the Ford Go Bike Sharing System. Therefore, there will be more bikes available due to the usuage of bikes not been used around the Christmas Holidays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ford GoBikes System Wekkly Usage in 2019\n",
    "days_of_the_week = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "graph = sb.catplot(data=df_cleaned, x='start_time_weekday', \n",
    "                   kind='count', order = days_of_the_week)\n",
    "graph.set_axis_labels('Days of the Week', 'Number of Trips')\n",
    "graph.fig.suptitle('Ford GoBikes System Weekly Usage in 2019',\n",
    "                   y=1.07, fontsize=16, fontweight='bold');\n",
    "graph.set_xticklabels(rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBikes System Weekly Usage in 2019\n",
    "\n",
    "In 2019, Monday through Friday has heavy usage in hiring bikes from the Ford Go Bike sharing system in the Bay Area. \n",
    "The usage drops significantly on the weekend where Saturday and Sunday. The data suggests that the Ford Go Bike sharing system is mainly used during the week. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.506983e+06\n",
       "mean     1.296946e+01\n",
       "std      3.291311e+01\n",
       "min      1.000000e+00\n",
       "25%      5.000000e+00\n",
       "50%      9.000000e+00\n",
       "75%      1.400000e+01\n",
       "max      1.520100e+04\n",
       "Name: duration_min, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the duration \n",
    "df_cleaned.duration_min.describe(percentiles = [.95])\n",
    "df_cleaned.duration_min.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.969462497352396"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find out the average trip\n",
    "df_cleaned.duration_min.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "# Display the duration bike rides in minutes \n",
    "bins = 10**np.arange(0 , 3 + 0.1 , 0.1)\n",
    "ticks =  [ 1 , 3, 10, 30, 100, 300, 1000]\n",
    "labels = ['{}'.format(t) for t in ticks]\n",
    "plt.hist(data = df_cleaned , x = 'duration_min' , bins = bins);\n",
    "plt.xscale('log');\n",
    "plt.xticks(ticks,labels);\n",
    "plt.xlabel('Duration in Minutes');\n",
    "plt.title('2019 Ford GoBikes Average Bike Trip Duration in Minutes');\n",
    "plt.ylabel('Number of Trips');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2019 Duration of Bike Trips in Ford GoBikes System\n",
    "\n",
    "> In 2019, the average duration of bike trips was under 12.96 minutes. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ford GoBike System Hourly Usage in 2019\n",
    "graph = sb.catplot(data = df_cleaned, x='start_time_hour', kind='count', aspect = 1.7)\n",
    "graph.set_axis_labels('Number of Hours', 'Number of Trips')\n",
    "graph.fig.suptitle('Ford GoBikes System Hourly Usage in 2019', \n",
    "                   y=1.07, fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBikes Hourly Usage in 2019\n",
    "\n",
    "> In 2019, through observing the peak-times of when users use the Ford GoBike System. The hours are mainly high between the morning from 8-9am and from 1700-1800 (5-6 pm) in the afternoon.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> There were six observations that were made with the duration of the bike ride data. As the data was cleaned, through the data cleaning phase. The data visualisation of the duration of the bike ride and the distance helped to form a story. The trip duration takes a large amount of values and is concentrated.\n",
    "\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> Looking at the cleaned dataset, the average duration of bike trips was under 12.96 minutes. I want to look at the comparison between Subscriber and Customer. In 2019, through observing the peak-times of when users use the Ford GoBike System. The hours are mainly high between the morning from 8-9am and from 1700-1800 (5-6 pm) in the afternoon. I would like further investigate if that varies by user type. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, I investigate relationships between pairs of variables in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the User Type data for data visualisation\n",
    "customer = df_cleaned.query('user_type == \"Customer\"')['bike_id'].count()\n",
    "subscriber = df_cleaned.query('user_type == \"Subscriber\"')['bike_id'].count()\n",
    "customer_prop = customer / df_cleaned['bike_id'].count()\n",
    "subscriber_prop = subscriber / df_cleaned['bike_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "# Plot Number of Customers vs Subscribers in the Ford GoBike System to make a comparison.\n",
    "plt.figure(figsize = [10, 5])\n",
    "graph = sb.countplot(data = df_cleaned, x = \"user_type\", \n",
    "                     order = df_cleaned.user_type.value_counts().index);\n",
    "graph.set_xlabel('User Type');\n",
    "graph.set_ylabel('Number of Bike Trips');\n",
    "plt.suptitle('Ford GoBikes Sharing System Customers vs. Subscribers', \n",
    "             y=1.07, fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBike Sharing System Customers vs. Subscribers\n",
    "\n",
    "> There are more than 200,000 Subscribers as displayed in the Customers vs. Subscribers in Ford GoBike Sharing System chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a pie chart to compare the percentage of Customers vs Subscribers that are using the platform\n",
    "plt.figure(figsize = [10, 5])\n",
    "labels = ['Customer', 'Subscriber']\n",
    "size = [customer_prop, subscriber_prop]\n",
    "explode = (0, 0.3)\n",
    "plt.pie(size, explode=explode, labels = labels, autopct='%1.1f%%', \n",
    "        shadow=True, startangle=30, textprops={'color':'black'})\n",
    "plt.axis('equal')\n",
    "plt.suptitle('Ford GoBike Customers vs. Subscribers in 2019', y=1.07,\n",
    "             fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBikes System Customers vs. Subscribers in 2019\n",
    "\n",
    "> In 2019, 80.6% of the user types in the Ford GoBikes sharing platform were Subscribers. 19.4% of the user types in the Ford GoBikes Sharing Platform were Customers. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot comparison of usage between Customers vs Subscribersin 2019\n",
    "plt.figure(figsize = [12, 5])\n",
    "graph = sb.catplot(data=df_cleaned, x='start_time_month', \n",
    "                   col=\"user_type\", kind='count', sharey = True);\n",
    "graph.set_axis_labels(\"Month\", \"Number of Bike Trips\");\n",
    "graph.set_titles(\"{col_name}\");\n",
    "graph.fig.suptitle('Ford GoBike System Customers vs. Subscribers in 2019',\n",
    "                   y=1.07, fontsize=16, fontweight='bold');\n",
    "graph.set_xticklabels(rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customers vs. Subscribers Usage in Ford GoBikes Sharing\n",
    "\n",
    "> There are more than 200,000 Subscribers as displayed in the Customers vs. Subscribers in Ford GoBike Sharing System chart. \n",
    "\n",
    "Customers: The demand is high during December. However, the frequency is high betwen the months of July - October 2019.\n",
    "\n",
    "Subscribers: The demand is highest in March 2019. THis is followed by July 2019 which is Summer time in the Bay Area and April which is Spring. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "df_cleaned_user_week = df_cleaned.groupby(['start_time_weekday', 'user_type']).size().reset_index()\n",
    "weekday = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "ax = sb.pointplot(data=df_cleaned_user_week, x='start_time_weekday', \n",
    "                  y=0, hue = 'user_type', scale=.7, order = weekday);\n",
    "plt.title('Ford GoBikes Sharing Customer vs. Subscribers Trends in 2019', y=1.07, \n",
    "          fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Weekdays')\n",
    "plt.ylabel('Number of Bike Trips');\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ford GoBikes Sharing Customer vs. Subscribers Trends in 2019\n",
    " \n",
    "In 2019, customers usage is quite low and consistant staying under 10,000 bike trips during the week. While Subscribers frequently used the the Ford GoBikes Sharing platform during Monday - Thursday with a drop of the number of bike trips on Friday and a significant drop through to Saturday and Sunday. \n"
   ]
  },
  {
   "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",
    "> The importance of including the user type to the data analysis revealed that there were some differences in the behaviour of the customers and subscribers using the Ford GoBikes Sharing platform. \n",
    "\n",
    "> The dataset highlights the usage trends between customers and subscribers. This suggests that Customers are casual users and the Subscribers are daily commuters that maybe working, studying etc. \n",
    "\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> Through Observation, there is a difference in usage between customers and subscribers. The popularity of using Ford GoBikes for Customers is usually in December and increases more on Saturday as opposed to Subscribers where it decreases significantly on Saturday and during the weekend. \n",
    "> There were some limitations in this analysis, due to the anomity of customer data shighuch as gender. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "# Number of bike trips Customers vs. Subscriber Usage by Duration\n",
    "plt.figure(figsize = [8, 5])\n",
    "base_color = sb.color_palette()[1]\n",
    "sb.boxplot(data = df, x = 'user_type', y = 'duration_sec')\n",
    "plt.title('Ford GoBikes Customer vs. Subscribers Duration in Seconds', y=1.07, fontsize=16, fontweight='bold')\n",
    "plt.xlabel('User Type')\n",
    "plt.ylabel('Duration in sec')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customers vs. Subscribers Ride Duration in Seconds\n",
    "\n",
    "> In 2019, the graph above highlights that the values are vey widespread to see a box plot. In the next observation, will have a look at the box plot with the duration in minutes as opposed to seconds. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df_cleaned.query('duration_min < 30')\n",
    "x = sb.catplot(data=data, y='duration_min', \n",
    "               col=\"user_type\", kind='box')\n",
    "x.set_titles(col_template = '{col_name}')\n",
    "x.set_axis_labels(\"\", \"Duration in Minutes\")\n",
    "x.fig.suptitle('Ford GoBikes Duration in Minutes by Customers vs. Subcribers',\n",
    "               y=1.05, fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Duration in Minutes by User Type \n",
    "\n",
    "> The Duration in Minutes by User Type plot highlights that Customers normally have longer trip durations. Customers normally ride between 8 minutes and 18 minutes. Subscriber trips are normally shorter and last anywhere between 5-13 minutes. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Multivariate Exploration\n",
    "\n",
    "> In this section, I plot more graphs in order to investigate the duration of the Ford Go Bike data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ford GoBikes Customer \n",
    "weekday = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "df_cleaned['start_time_weekday'] = pd.Categorical(df_cleaned['start_time_weekday'], \n",
    "                                                  categories= ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'], ordered=True);\n",
    "df_customer = df_cleaned.query('user_type == \"Customer\"').groupby(['start_time_hour', 'start_time_weekday'])['bike_id'].size().reset_index();\n",
    "df_customer = df_customer.pivot('start_time_hour', 'start_time_weekday', 'bike_id')\n",
    "heat_map = sb.heatmap(df_customer, cmap = 'twilight')\n",
    "plt.title('Ford GoBikes Customer Weekly Trends in 2019', y=1.07, fontweight='bold')\n",
    "plt.xlabel('Days of the Week', labelpad = 17)\n",
    "plt.ylabel('Start Time in Hour',  labelpad = 17)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat Map for Ford GoBikes Subscriber Information\n",
    "df_subscriber = df_cleaned.query('user_type == \"Subscriber\"').groupby(['start_time_hour', 'start_time_weekday'])['bike_id'].size().reset_index();\n",
    "df_subscriber = df_subscriber.pivot('start_time_hour', 'start_time_weekday', 'bike_id')\n",
    "hot_maps = sb.heatmap(df_subscriber, cmap = 'twilight')\n",
    "plt.title(\"Ford GoBike Subscribers Weekly Trends in 2019\", y=1.07, fontweight='bold')\n",
    "plt.xlabel('Days of the Week', labelpad = 17)\n",
    "plt.ylabel('Start Time in Hours', labelpad = 17);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ford GoBike HeatMap\n",
    "\n",
    "> As per the heat map above, Customers who use the Ford Gobikes sharing system mainly on the weekends. While, Subscriber normally ride bikes during the weekday from Monday through to Friday, around 8-9am and 4-6pm. In the weekends, there is a significant drop in the number of rented bikes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly usage by user type \n",
    "graph = sb.catplot(data=df_cleaned, x='start_time_month', col=\"user_type\", kind='count', sharey = False, height=5, aspect=2);\n",
    "graph.set_axis_labels(\"Month\", \"Number of Bike Trips\")\n",
    "graph.set_titles(\"{col_name}\")\n",
    "graph.fig.suptitle('Ford GoBike System Monthly Usage Per User Type', y=1.07, fontsize=16, fontweight='bold');\n",
    "graph.set_xticklabels(rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ford GoBike Monthly Usage Per User Type\n",
    "\n",
    "> As per the graph above, in 2019 the Ford GoBike Monthly Usage per User Type. There are two types of users who use the Ford GoBike which is between Customer and Subscriber. \n",
    "\n",
    "> Customers saw an increase of usage in December while Subscribers would frequently use the the Ford GoBike system in March, July and April. While the Monthly usage for Subscribers would decrease in December. \n"
   ]
  },
  {
   "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",
    "> Through further observation between Customer and Supplier behaviours, Customers saw an increase of usage in December while Subscribers would frequently use the the Ford GoBike system in March, July and April. While the Monthly usage for Subscribers would decrease in December. \n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> Through observation of the heatmap, Customers who use the Ford Gobikes sharing system mainly on the weekends. While, Subscriber normally ride bikes during the weekday from Monday through to Friday, around 8-9am and 4-6pm. In the weekends, there is a significant drop in the number of bikes used in the weekends from the subscribers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "1. How To Combine Multiple CSV Files In Python: https://sempioneer.com/python-for-seo/how-to-combine-multiple-csv-files-in-python/\n",
    "2. FordGoBike Data Set: https://www.lyft.com/bikes/bay-wheels/system-data\n",
    "3. Github: https://github.com/ijdev/Ford-GoBike-System-Data---Data-Analysis/blob/master/exploration_template.ipynb, \n",
    "4. Github: https://github.com/satyarajreddy/Udacity-Data-Visualization/blob/master/Visualize_Data.ipynb \n",
    "5. Github: https://gist.github.com/rochacbruno/2883505\n",
    "6. Github: https://github.com/chelseymarie6/Communicate-Data-Findings\n",
    "7. Github: https://github.com/rishusingh121/fordgobike/blob/master/slide_deck_template.ipynb\n",
    "8. Keggle: https://www.kaggle.com/chirag02/ford-gobike-data-analysis\n",
    "9. seaborn catplot: https://seaborn.pydata.org/generated/seaborn.catplot.html\n",
    "10. matplotlib Documentation: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html\n",
    "\n"
   ]
  }
 ],
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