Part_I_exploration_template.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfcf660c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57185711",
   "metadata": {},
   "source": [
    "# Part I - Ford GoBike Data Exploration\n",
    "## by Tella Adetayo\n",
    "\n",
    "## Introduction\n",
    "Ford GoBike is the Bay Area's bike sharing system. Bay Area Bike Share was introduced in 2013 as a pilot program for the region, with 700 bikes and 70 stations across San Fransico and San Jose. \n",
    "\n",
    "The programs consists of bikes which are specially designed that can be locked in a network of docking station, it can be unlocked in one station and locked in another station. The bikes are available 24 hours per day, 7 days per week and 356 days a year.\n",
    "\n",
    "\n",
    "## Preliminary Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebc2a4f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f532078",
   "metadata": {},
   "source": [
    "## Data Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01d61294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset in to pandas dataframe\n",
    "GoBike_df = pd.read_csv(\"Downloads/201902-fordgobike-tripdata.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8d068f",
   "metadata": {},
   "source": [
    "## Accessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68243db6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183412, 16)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>2019-03-01 08:01:55.9750</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>2019-03-01 06:42:03.0560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>2019-03-01 05:24:08.1460</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.0100</td>\n",
       "      <td>2019-03-01 04:02:36.8420</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.5490</td>\n",
       "      <td>2019-03-01 00:20:44.0740</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</td>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "3         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "4          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.393170     2535    Customer                NaN   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3         Other                      No  \n",
       "4          Male                     Yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the shape of the dataframe and the first five rows of the dataframe\n",
    "print(GoBike_df.shape)\n",
    "GoBike_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97983c6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 183412 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration_sec             183412 non-null  int64  \n",
      " 1   start_time               183412 non-null  object \n",
      " 2   end_time                 183412 non-null  object \n",
      " 3   start_station_id         183215 non-null  float64\n",
      " 4   start_station_name       183215 non-null  object \n",
      " 5   start_station_latitude   183412 non-null  float64\n",
      " 6   start_station_longitude  183412 non-null  float64\n",
      " 7   end_station_id           183215 non-null  float64\n",
      " 8   end_station_name         183215 non-null  object \n",
      " 9   end_station_latitude     183412 non-null  float64\n",
      " 10  end_station_longitude    183412 non-null  float64\n",
      " 11  bike_id                  183412 non-null  int64  \n",
      " 12  user_type                183412 non-null  object \n",
      " 13  member_birth_year        175147 non-null  float64\n",
      " 14  member_gender            175147 non-null  object \n",
      " 15  bike_share_for_all_trip  183412 non-null  object \n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the general info of the dataset\n",
    "GoBike_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4d26116",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning the sum of the null values \n",
    "null_values = GoBike_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c02167f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station_id       197\n",
       "start_station_name     197\n",
       "end_station_id         197\n",
       "end_station_name       197\n",
       "member_birth_year     8265\n",
       "member_gender         8265\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values greater than 0\n",
    "null_values[null_values > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78cee5d4",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration_sec</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>726.078435</td>\n",
       "      <td>1794.389780</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>514.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>85444.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <td>183215.0</td>\n",
       "      <td>138.590427</td>\n",
       "      <td>111.778864</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_station_latitude</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>37.771223</td>\n",
       "      <td>0.099581</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>37.797280</td>\n",
       "      <td>37.880222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_station_longitude</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>-122.352664</td>\n",
       "      <td>0.117097</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>-122.412408</td>\n",
       "      <td>-122.398285</td>\n",
       "      <td>-122.286533</td>\n",
       "      <td>-121.874119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_station_id</th>\n",
       "      <td>183215.0</td>\n",
       "      <td>136.249123</td>\n",
       "      <td>111.515131</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_station_latitude</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>37.771427</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>37.880222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_station_longitude</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>-122.352250</td>\n",
       "      <td>0.116673</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>-122.288045</td>\n",
       "      <td>-121.874119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bike_id</th>\n",
       "      <td>183412.0</td>\n",
       "      <td>4472.906375</td>\n",
       "      <td>1664.383394</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>4958.000000</td>\n",
       "      <td>5502.000000</td>\n",
       "      <td>6645.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_birth_year</th>\n",
       "      <td>175147.0</td>\n",
       "      <td>1984.806437</td>\n",
       "      <td>10.116689</td>\n",
       "      <td>1878.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count         mean          std          min  \\\n",
       "duration_sec             183412.0   726.078435  1794.389780    61.000000   \n",
       "start_station_id         183215.0   138.590427   111.778864     3.000000   \n",
       "start_station_latitude   183412.0    37.771223     0.099581    37.317298   \n",
       "start_station_longitude  183412.0  -122.352664     0.117097  -122.453704   \n",
       "end_station_id           183215.0   136.249123   111.515131     3.000000   \n",
       "end_station_latitude     183412.0    37.771427     0.099490    37.317298   \n",
       "end_station_longitude    183412.0  -122.352250     0.116673  -122.453704   \n",
       "bike_id                  183412.0  4472.906375  1664.383394    11.000000   \n",
       "member_birth_year        175147.0  1984.806437    10.116689  1878.000000   \n",
       "\n",
       "                                 25%          50%          75%           max  \n",
       "duration_sec              325.000000   514.000000   796.000000  85444.000000  \n",
       "start_station_id           47.000000   104.000000   239.000000    398.000000  \n",
       "start_station_latitude     37.770083    37.780760    37.797280     37.880222  \n",
       "start_station_longitude  -122.412408  -122.398285  -122.286533   -121.874119  \n",
       "end_station_id             44.000000   100.000000   235.000000    398.000000  \n",
       "end_station_latitude       37.770407    37.781010    37.797320     37.880222  \n",
       "end_station_longitude    -122.411726  -122.398279  -122.288045   -121.874119  \n",
       "bike_id                  3777.000000  4958.000000  5502.000000   6645.000000  \n",
       "member_birth_year        1980.000000  1987.000000  1992.000000   2001.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics of the dataframe\n",
    "GoBike_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b731a2e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the duplicates values\n",
    "sum(GoBike_df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34308ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    163544\n",
       "Customer       19868\n",
       "Name: user_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the values counts of the user_trip column\n",
    "GoBike_df[\"user_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9c4a4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     166053\n",
       "Yes     17359\n",
       "Name: bike_share_for_all_trip, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the values counts of the bike_share_for_all_trip column\n",
    "GoBike_df[\"bike_share_for_all_trip\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23172fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Market St at 10th St                                         3904\n",
       "San Francisco Caltrain Station 2  (Townsend St at 4th St)    3544\n",
       "Berry St at 4th St                                           3052\n",
       "Montgomery St BART Station (Market St at 2nd St)             2895\n",
       "Powell St BART Station (Market St at 4th St)                 2760\n",
       "                                                             ... \n",
       "Willow St at Vine St                                            9\n",
       "Parker Ave at McAllister St                                     7\n",
       "Palm St at Willow St                                            4\n",
       "21st Ave at International Blvd                                  4\n",
       "16th St Depot                                                   2\n",
       "Name: start_station_name, Length: 329, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the value counts of the start_station_name column\n",
    "GoBike_df[\"start_station_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e17ec9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique values in the start_station_name column\n",
    "GoBike_df[\"start_station_name\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "901ab00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Francisco Caltrain Station 2  (Townsend St at 4th St)    4857\n",
       "Market St at 10th St                                         3973\n",
       "Montgomery St BART Station (Market St at 2nd St)             3647\n",
       "San Francisco Ferry Building (Harry Bridges Plaza)           3368\n",
       "Powell St BART Station (Market St at 4th St)                 2997\n",
       "                                                             ... \n",
       "Parker Ave at McAllister St                                     9\n",
       "Palm St at Willow St                                            7\n",
       "16th St Depot                                                   6\n",
       "21st Ave at International Blvd                                  6\n",
       "Willow St at Vine St                                            5\n",
       "Name: end_station_name, Length: 329, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the value counts of the end_station_name column\n",
    "GoBike_df[\"end_station_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "650d118f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique values in the end_station_name column\n",
    "GoBike_df[\"end_station_name\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d54377",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "\n",
    "###### `GoBike_df` table\n",
    "- Erroneous datatype in start_time, end_time, memeber_gender, bake_share_for_all_trip, user_type\n",
    "- Null values in the start_station_id, start_station_name, end_station_id, end_station_name, member_birth_year    member_gender\n",
    "- The week and day can be extracted from the start_time column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb526421",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "baf5fcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "GoBike_df_copy = GoBike_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9397eac6",
   "metadata": {},
   "source": [
    "### Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83887e87",
   "metadata": {},
   "source": [
    "##### `GoBike_df` table: Null values in the start_station_id, start_station_name, end_station_id, end_station_name, member_birth_year member_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbc0074",
   "metadata": {},
   "source": [
    "#### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b2095c",
   "metadata": {},
   "source": [
    "Drop the null values in the start_station_id, start_station_name, end_station_id, end_station_name, member_birth_year, member_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7a2d06",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d56b8573",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the null values\n",
    "GoBike_df_copy.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f204a5fd",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d96ff39",
   "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              0\n",
       "bike_share_for_all_trip    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to confirm if all the null values are dropped\n",
    "GoBike_df_copy.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3a2013",
   "metadata": {},
   "source": [
    "##### `tripdata` table: Erroneous datatype in start_time, end_time, member_gender, bake_share_for_all_trip, user_type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36199b4",
   "metadata": {},
   "source": [
    "#### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfea813d",
   "metadata": {},
   "source": [
    "Change the datatype of start_time, end_time to datatime while member_gender, bake_share_for_all and user_type to category"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf1e6dc",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "170e0b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the datatypes of the columns to datetime and category\n",
    "GoBike_df_copy[\"start_time\"] = pd.to_datetime(GoBike_df_copy[\"start_time\"])\n",
    "GoBike_df_copy[\"end_time\"] = pd.to_datetime(GoBike_df_copy[\"end_time\"])\n",
    "GoBike_df_copy[\"bike_share_for_all_trip\"] = GoBike_df_copy[\"bike_share_for_all_trip\"].astype(\"category\")\n",
    "GoBike_df_copy[\"member_gender\"] = GoBike_df_copy[\"member_gender\"].astype(\"category\")\n",
    "GoBike_df_copy[\"user_type\"] = GoBike_df_copy[\"user_type\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddb4897",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d6f8de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 174952 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             174952 non-null  int64         \n",
      " 1   start_time               174952 non-null  datetime64[ns]\n",
      " 2   end_time                 174952 non-null  datetime64[ns]\n",
      " 3   start_station_id         174952 non-null  float64       \n",
      " 4   start_station_name       174952 non-null  object        \n",
      " 5   start_station_latitude   174952 non-null  float64       \n",
      " 6   start_station_longitude  174952 non-null  float64       \n",
      " 7   end_station_id           174952 non-null  float64       \n",
      " 8   end_station_name         174952 non-null  object        \n",
      " 9   end_station_latitude     174952 non-null  float64       \n",
      " 10  end_station_longitude    174952 non-null  float64       \n",
      " 11  bike_id                  174952 non-null  int64         \n",
      " 12  user_type                174952 non-null  category      \n",
      " 13  member_birth_year        174952 non-null  float64       \n",
      " 14  member_gender            174952 non-null  category      \n",
      " 15  bike_share_for_all_trip  174952 non-null  category      \n",
      "dtypes: category(3), datetime64[ns](2), float64(7), int64(2), object(2)\n",
      "memory usage: 19.2+ MB\n"
     ]
    }
   ],
   "source": [
    "GoBike_df_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93ad95dc",
   "metadata": {},
   "source": [
    "#### `GoBike_df`: The week and day can be extracted from the start_time column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8d8723",
   "metadata": {},
   "source": [
    "#### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85347e78",
   "metadata": {},
   "source": [
    "Extract the week and day from the start_time column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce378ffd",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e1eeda40",
   "metadata": {},
   "outputs": [],
   "source": [
    "days_of_week = {\n",
    "    0: \"Monday\",\n",
    "    1: \"Tuesday\",\n",
    "    2: \"Wednesday\",\n",
    "    3: \"Thursday\",\n",
    "    4: \"Friday\", \n",
    "    5: \"Saturday\",\n",
    "    6: \"Sunday\"\n",
    "}\n",
    "\n",
    "GoBike_df_copy[\"weekday_name\"] = GoBike_df_copy[\"start_time\"].dt.weekday.map(days_of_week)\n",
    "\n",
    "GoBike_df_copy[\"hour\"] = GoBike_df_copy[\"start_time\"].dt.hour\n",
    "GoBike_df_copy[\"minutes\"] = GoBike_df_copy[\"start_time\"].dt.minute"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a21d96",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "680979af",
   "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>weekday_name</th>\n",
       "      <th>hour</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-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",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>869.750000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-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",
       "      <td>Thursday</td>\n",
       "      <td>12</td>\n",
       "      <td>1030.900000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-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",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>608.166667</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-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",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>26.416667</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1793</td>\n",
       "      <td>2019-02-28 23:49:58.632</td>\n",
       "      <td>2019-03-01 00:19:51.760</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>323.0</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>5200</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>29.883333</td>\n",
       "      <td>49</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",
       "2         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3         36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4          1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "5          1793 2019-02-28 23:49:58.632 2019-03-01 00:19:51.760   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "5              93.0                      4th St at Mission Bay Blvd S   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "5               37.770407              -122.391198           323.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "5                            Broadway at Kearny             37.798014   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "5            -122.405950     5200  Subscriber             1959.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip weekday_name  hour  duration_min  \\\n",
       "0          Male                      No     Thursday    17    869.750000   \n",
       "2          Male                      No     Thursday    12   1030.900000   \n",
       "3         Other                      No     Thursday    17    608.166667   \n",
       "4          Male                     Yes     Thursday    23     26.416667   \n",
       "5          Male                      No     Thursday    23     29.883333   \n",
       "\n",
       "   minutes  \n",
       "0       32  \n",
       "2       13  \n",
       "3       54  \n",
       "4       54  \n",
       "5       49  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GoBike_df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78f66d89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(174952, 18)\n"
     ]
    }
   ],
   "source": [
    "# Checking the shape of dataset after premilinary wrangling\n",
    "print(GoBike_df_copy.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2ed5d4",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "The dataset have 174952 rows after some wrangling and 19 columns. The columns are duration_sec and bike_id which the datatype is int64, start_time and end_time which both their datatype are in datetime, user_type, member_gender and bike_share_for_all_trips are in category while start_station_id, start_station_name, start_station_latitude,start_station_longitude, end_station_id, end_station_name, end_station_latitude, end_station_longitude are float64, the weekday_name is in object and the hour column is in int64.\n",
    "\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "The main features of interest in the dataset are:\n",
    "- user_type\n",
    "- member_birth_year\n",
    "- member_gender\n",
    "- bike_share_for_all_trip\n",
    "- hour\n",
    "- weekday_name\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "The duration_sec, start_time, end_time and bike_id should in supporting my investigation into my features of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f24dba",
   "metadata": {},
   "source": [
    "## Univariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c929c81",
   "metadata": {},
   "source": [
    "### How often is sharing of bikes for trips?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8cc9422b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for a countplot\n",
    "def plot_count(x, **kwargs):\n",
    "    base_color = sns.color_palette()[0]\n",
    "    sns.countplot(x=x, color = base_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "88c190b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the countplot of bike_share_for_all_trip column\n",
    "plot_count(GoBike_df_copy[\"bike_share_for_all_trip\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f01542",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- Sharing of bikes with other riders is not a common thing because from the visualization close to 160000 doesn't share rides."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd7557c3",
   "metadata": {},
   "source": [
    "### What type of user are the people who took a trip?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f036f7ca",
   "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": [
    "# Plot the user_type column\n",
    "plot_count(GoBike_df_copy[\"user_type\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9e338b",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- There are only two types of users which are the customer and subscriber and the most common user are the subscriber which are way above the customer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b65d70cb",
   "metadata": {},
   "source": [
    "### What is the average duration for every trips?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d5be527",
   "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": [
    "# Plot the average duration of every trips\n",
    "plt.hist(data = GoBike_df_copy, x = \"duration_sec\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0d4e53d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    174952.000000\n",
       "mean        704.002744\n",
       "std        1642.204905\n",
       "min          61.000000\n",
       "25%         323.000000\n",
       "50%         510.000000\n",
       "75%         789.000000\n",
       "max       84548.000000\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics of the duration_sec column\n",
    "GoBike_df_copy[\"duration_sec\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96130ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Av7b95wCPV9Xutr8NOKJtHwE8DNCOP9HKfz8+wTk/JMmGJFuSbNm5c+ccNkOSNFO9L5mT5NXAjqq6Pckr+7hnVW0ENgKsXr26+rin9p7L6UiL0yjWans58ItJTgYOBJ4FnA8cnGS/1qtZDmxv5bcDRwLbkuwHPBv45kB8zOA5kqR5qvehtqo6u6qWV9UKuskBN1TV64EbgVNbsfXA1W37mrZPO35DVVWLn9Zmva0EVgG39tQMSdIszafVqX8HuCLJu4AvABe3+MXAh5NsBXbRJSuq6r4kVwL3A7uBM6vqyf6rLUmaiZEmnqq6CbipbT/EBLPSquqfgddMcv65wLnDq6Ekaa65coEkqVcmHklSr0w8kqRemXgkSb2aT7PapGmb7I9L/cNSaf6zxyNJ6pWJR5LUKxOPJKlXfsfTTLUgpSRp7tjjkST1ysQjSeqVQ21aVHyHjzT/2eORJPXKxCNJ6pWJR5LUKxOPJKlXJh5JUq9MPJKkXvU+nTrJkcClwGFAARur6vwkhwAfBVYAXwFeW1WPJQlwPnAy8B3gDVV1R7vWeuD32qXfVVWX9NkWLSxOtZbmh1H0eHYDv1VVRwHHAWcmOQo4C7i+qlYB17d9gJOAVe2zAbgAoCWqc4CXAscC5yRZ2mdDJEkz13viqapHxnosVfUPwAPAEcA6YKzHcglwStteB1xanZuBg5McDpwIbK6qXVX1GLAZWNtfSyRJszHS73iSrABeBNwCHFZVj7RDj9INxUGXlB4eOG1bi00Wn+g+G5JsSbJl586dc9cASdKMjSzxJHkG8BfAb1TVtwaPVVXRff8zJ6pqY1WtrqrVy5Ytm6vLSpJmYSRrtSV5Kl3SuayqPt7CX09yeFU90obSdrT4duDIgdOXt9h24JXj4jcNs95avHyVttSf3ns8bZbaxcADVfUnA4euAda37fXA1QPx09M5DniiDcldB6xJsrRNKljTYpKkeWwUPZ6XA78M3JPkzhb7XeA84MokZwBfBV7bjl1LN5V6K9106jcCVNWuJO8Ebmvl3lFVu3ppgSRp1npPPFX1N0AmOXzCBOULOHOSa20CNs1d7SRJw+b7eKQp+Een0txzyRxJUq9MPJKkXjnUJs2Sw3DS7NjjkST1ysQjSeqVQ23SELgSgjQ5ezySpF6ZeCRJvXKoTeqRM+EkezySpJ7Z45HmCXtD2leYeKQFwFlyWkwcapMk9coej7SATTU8NxV7SholE4+0D/L7JI2SiUfSD5lNL8pkpZkw8Ujaa/agNBMmHklDNdvvoSZjIlv4FnziSbIWOB9YAlxUVeeNuEqShmiuE9lsmPz2zoJOPEmWAO8H/iOwDbgtyTVVdf9oayZpMZsPyQ8WbgJc0IkHOBbYWlUPASS5AlgHmHgkLXrzJQHO1EJPPEcADw/sbwNeOr5Qkg3Ahrb73ST39lC3UTkU+MaoKzEki7ltYPsWusXevufP1YUWeuKZlqraCGwESLKlqlaPuEpDs5jbt5jbBrZvodsX2jdX11roS+ZsB44c2F/eYpKkeWqhJ57bgFVJVibZHzgNuGbEdZIkTWFBD7VV1e4kbwGuo5tOvamq7tvDaRuHX7ORWsztW8xtA9u30Nm+aUpVzdW1JEnao4U+1CZJWmBMPJKkXu0ziSfJ2iRfTLI1yVmjrs9sJflKknuS3Dk2vTHJIUk2J3mw/Vza4kny3tbmu5McM9ra/6gkm5LsGPzbqtm0J8n6Vv7BJOtH0ZaJTNK+tyfZ3p7hnUlOHjh2dmvfF5OcOBCfd7+/SY5McmOS+5Pcl+TXW3xRPL8p2rdYnt+BSW5Ncldr3/9q8ZVJbml1/WibuEWSA9r+1nZ8xcC1Jmz3pKpq0X/oJh58CXgusD9wF3DUqOs1y7Z8BTh0XOz/AGe17bOAd7ftk4FPAQGOA24Zdf0naM8rgGOAe2fbHuAQ4KH2c2nbXjrqtk3RvrcD/3OCske1380DgJXtd3bJfP39BQ4HjmnbzwT+vrVhUTy/Kdq3WJ5fgGe07acCt7TnciVwWotfCPz3tv1m4MK2fRrw0anaPdW995Uez/eX1qmq7wFjS+ssFuuAS9r2JcApA/FLq3MzcHCSw0dQv0lV1eeAXePCM23PicDmqtpVVY8Bm4G1Q6/8NEzSvsmsA66oqu9W1ZeBrXS/u/Py97eqHqmqO9r2PwAP0K0msiie3xTtm8xCe35VVd9uu09tnwKOB65q8fHPb+y5XgWckCRM3u5J7SuJZ6Kldab6BZrPCvhMktvTLQUEcFhVPdK2HwUOa9sLtd0zbc9CbOdb2nDTprGhKBZw+9qwy4vo/qt50T2/ce2DRfL8kixJciewgy7hfwl4vKp2tyKDdf1+O9rxJ4DnMIv27SuJZzH5uao6BjgJODPJKwYPVtf3XTRz5Bdbe5oLgJ8AXgg8AvzxSGuzl5I8A/gL4Deq6luDxxbD85ugfYvm+VXVk1X1QrpVX44FfrKP++4riWfRLK1TVdvbzx3AX9L9snx9bAit/dzRii/Uds+0PQuqnVX19fYP/l+BD/KDYYkF174kT6X7P+XLqurjLbxont9E7VtMz29MVT0O3Ai8jG4IdGxxgcG6fr8d7fizgW8yi/btK4lnUSytk+TpSZ45tg2sAe6la8vYTKD1wNVt+xrg9Dab6DjgiYEhkPlspu25DliTZGkb9ljTYvPSuO/ZfonuGULXvtPa7KGVwCrgVubp728b378YeKCq/mTg0KJ4fpO1bxE9v2VJDm7bB9G91+wBugR0ais2/vmNPddTgRtaj3aydk9u1DMr+vrQzaj5e7oxzLeNuj6zbMNz6WaP3AXcN9YOunHW64EHgb8GDqkfzFp5f2vzPcDqUbdhgjZdTjdc8S90Y8NnzKY9wK/Qfam5FXjjqNu1h/Z9uNX/7vaP9vCB8m9r7fsicNJ8/v0Ffo5uGO1u4M72OXmxPL8p2rdYnt9PA19o7bgX+P0Wfy5d4tgKfAw4oMUPbPtb2/Hn7qndk31cMkeS1Kt9ZahNkjRPmHgkSb0y8UiSemXikST1ysQjSeqViUcakOTJtuLwfW3V3t9KMmf/TpK8Icm/Hdi/KMlRc3Ddw5J8otX5/iTX7u01pWFxOrU0IMm3q+oZbfvHgI8Af1tV58zgGkuq6slJjt1Et7Lxlrmo78B1/xy4v6rOb/s/XVV3z+U9pLlij0eaRHXLEm2gWxAyrbfyvrHjrYfxyrb97SR/nOQu4GVJfj/JbUnuTbKxnX8qsBq4rPWqDkpyU5LV7RqvS/eupXuTvHvgPt9Ocm7rzdyc5DB+1OF0f6A6Vve7B85/a6vL3WnvXGnx01vsriQfnqP/2aQ9MvFIU6iqh+jep/Jjeyj6dLr3y/xMVf0N8L6qeklVHQ0cBLy6qq4CtgCvr6oXVtU/jZ3cht/eTbck/QuBlyQ5ZeDaN1fVzwCfA940wf3fD1yc7sVlbxsbzkuyhm4Jk2PbdV+c5BVJXgD8HnB8u+6vz+h/GGkvmHikufEk3WKSY34+3Vsa76FLJi/Yw/kvAW6qqp3VLTl/Gd1L5AC+B3yibd8OrBh/clVdR7fUyQfpVhj+QpJldOueraFbGuWOdmxVq9PHquob7fzpvjNI2mv77bmItO9K8ly6pLID2M0P/8fagQPb/zz2vU6SA4EP0K1F9nCSt48rO1P/Uj/4MvZJJvl325LHR4CPJPkEXeIK8L+r6s/HtetX96I+0l6xxyNNovUYLqQbNiu6146/MMlTkhzJ5G9ZHEsy30j3LpdTB479A91rlMe7Ffj3SQ5NsgR4HfDZGdT1+CRPa9vPpHtfzNfoVnn+lVYPkhzRJk3cALwmyXNa/JDp3kvaW/Z4pB92ULo3Mj6VrofzYWBsSfy/Bb4M3E+3fPwdE12gqh5P8kG6FX8fpVsWf8yHgAuT/BPdu0/GznkkyVl0S9IH+GRVXc30vRh4X5KxXtlFVXUbQJKfAj7frfLPt4H/WlX3JTkX+GySJ+mG4t4wg/tJs+Z0aklSrxxqkyT1ysQjSeqViUeS1CsTjySpVyYeSVKvTDySpF6ZeCRJvfr/92K5oJeY+pEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of the duration sec\n",
    "# bins_edges are the bins created to add to the plot\n",
    "bin_edges = np.arange(0, GoBike_df_copy[\"duration_sec\"].max()+60, 60)\n",
    "plt.hist(data = GoBike_df_copy, x = \"duration_sec\", bins = bin_edges)\n",
    "plt.xlim(0, 3000)\n",
    "plt.xlabel(\"Duration Sec\")\n",
    "plt.ylabel(\"Count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4df53bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.242919\n",
       "mean     2.847574\n",
       "std      3.215427\n",
       "min      1.785330\n",
       "25%      2.509203\n",
       "50%      2.707570\n",
       "75%      2.897077\n",
       "max      4.927103\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logarithm of the descriptive statistics of the duration_sec column\n",
    "np.log10(GoBike_df_copy[\"duration_sec\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "505d5e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the duration_sec by 60 and assigning it to the duration_min\n",
    "GoBike_df_copy[\"duration_min\"] = GoBike_df_copy[\"duration_sec\"]/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6491423",
   "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": [
    "# Plot the histogram of the duration_min which contains bins\n",
    "bin_edges = np.arange(0, GoBike_df_copy[\"duration_min\"].max(), 1)\n",
    "plt.hist(data = GoBike_df_copy, x = \"duration_min\", bins = bin_edges)\n",
    "plt.xlim(0, 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0349d7a",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The first graph of the duration_sec column is difficult to understand. So it is plot with different bins in the second plot and the distribution of the data is left skewed, then I divide the duration_sec by 60 and assign it to the duration_min which resulted in the third plot. From the third plot the duration peak is around 10 minutes and the data is unimodal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf83169",
   "metadata": {},
   "source": [
    "### What Gender took the most trips throughout the year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b03a519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign the value counts of the member_gender column to a new varibale\n",
    "# Plot the piechart of the member_gender \n",
    "sorted_columns = GoBike_df_copy[\"member_gender\"].value_counts()\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sorted_columns, labels = sorted_columns.index, explode = (0, 0.1, 0), autopct = \"%.0f%%\")\n",
    "plt.title(\"Member Gender\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10538b84",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The most common gender in the datasets are the male is 75%, followed by female which are 23% and other which they are 2%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da32cff0",
   "metadata": {},
   "source": [
    "### What day of the week has the highest starting time per rides?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2cd1b440",
   "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": [
    "# Plot the countplot of the highest starting time\n",
    "plt.figure(figsize=(8, 5))\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data = GoBike_df_copy, y = \"weekday_name\", color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c48784",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- Thursday is the day with highest rides followed by Tuesday and Wednesday which are all weekdays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a714d2",
   "metadata": {},
   "source": [
    "### What is/are the peak of the starting time for every rides?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c9a51e49",
   "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": [
    "# Plot the barchart of the hour\n",
    "plt.figure(figsize = (8, 5))\n",
    "plot_count(GoBike_df_copy[\"hour\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92071518",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The peak hours are 8 and 17 hours which means there is always a peak when people are going to work and coming back home from work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32210954",
   "metadata": {},
   "source": [
    "### Insights Gained during Univariate Exploration\n",
    "\n",
    "- Sharing of bikes for every trips is not a common thing and there are a lot of people that doesn't share bikes with others.\n",
    "- The most common users are the subscribers which are followed by the customers.\n",
    "- The duration_sec column was plotted at first which is unable to understood and then the bins were changed to minutes which shows that the distribution of the data are left skewed.\n",
    "- The most common genders for every trips are the male, followed by the females and others which are unspecified genders.\n",
    "- Thursday is the day with the most common rides and it shows that there are a lot rides during the weekdays.\n",
    "- The peak hours for every rides are the 8 and 17 hours which are time people leave home in the morning and in the evening people get back home."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce005d",
   "metadata": {},
   "source": [
    "## Bivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf122561",
   "metadata": {},
   "source": [
    "### How common is sharing of trips among genders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "104a1733",
   "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": [
    "# Plot the barchart of the member gender \n",
    "plt.figure(figsize = (8, 5))\n",
    "sns.countplot(data = GoBike_df_copy, x = \"member_gender\", hue = \"bike_share_for_all_trip\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4bb842",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- All gender that are not likely share no rides are not likely to share no rides but the females are more open to sharing rides "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0830e214",
   "metadata": {},
   "source": [
    "### What are the most user type for every days of the week? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7199b747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the clustered barchart of the user type \n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.countplot(data = GoBike_df_copy, x = \"weekday_name\", hue = \"user_type\")\n",
    "plt.xticks(rotation = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1925a58",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The most common users are the subscribers while there are few customers for the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2d0ec7",
   "metadata": {},
   "source": [
    "### How distributed are the age of the each genders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "030707b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the boxplot of the age of the genders\n",
    "plt.figure(figsize = (12, 8))\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.boxplot(data = GoBike_df_copy, x = \"member_gender\", y = \"member_birth_year\", color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a7b417c",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- From the visualizations the mean, median are both similar across all gender and there are a lot of outliers among the female gender which show that are people in the that gender are born before the the year 1980 that took a trip same with the male gender."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c421deea",
   "metadata": {},
   "source": [
    "### What are the correlation between the variables in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33927d70",
   "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": [
    "# Assign the numeric variables to new variable \n",
    "# plot the heatmap of the correlation\n",
    "GoBike_stats = ['duration_sec', 'start_station_id', 'start_station_latitude',\n",
    "       'start_station_longitude', 'end_station_id',\n",
    "       'end_station_latitude', 'end_station_longitude', 'bike_id']\n",
    "\n",
    "sns.heatmap(GoBike_df_copy[GoBike_stats].corr(), cmap = \"rocket_r\", annot = True, fmt = \".2f\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6410c437",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- There are strong correlation end_station_latitude and start_station_longitude, same with end_station_latitude and start_station_latitude which are only geographical positions so as one increase other also increase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63ee030",
   "metadata": {},
   "source": [
    "### How distributed are the bike id amongst genders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4dc4a682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the facetgrid which uses the bike id\n",
    "g = sns.FacetGrid(data = GoBike_df_copy, col = \"member_gender\", sharey = False)\n",
    "g.map(plt.hist, \"bike_id\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce9718b",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The bike_id are unimodal across a gender though male took more trips which resulted in more bike_id."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322339f4",
   "metadata": {},
   "source": [
    "### What are the top 10 station for starting trips?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e252a3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top 10 station name for starting rides\n",
    "plt.figure(figsize = (12, 8))\n",
    "base_color = sns.color_palette()[0]\n",
    "top10_start_station = GoBike_df_copy[\"start_station_name\"].value_counts()[:10].sort_values()\n",
    "top10_start_station.plot(kind = \"barh\", color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f99815",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The start station for every ride are mostly at the market station followed by San Fransico Caltrain station 2 for most starting rides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8c464fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the top 10 station name of the ending ride\n",
    "plt.figure(figsize = (12, 8))\n",
    "base_color = sns.color_palette()[0]\n",
    "top10_end_station = GoBike_df_copy[\"end_station_name\"].value_counts()[:10].sort_values()\n",
    "top10_end_station.plot(kind = \"barh\",  color = base_color);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb4ff2bb",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The end station rides are mostly at the San Fransico Caltrain Station 2 and Market Station which is the same as the starting station for every rides "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d5e410",
   "metadata": {},
   "source": [
    "### Insights Gained From Bivariate Exploration\n",
    "- No genders are more likely to share rides with other but females are more likely to.\n",
    "- The subscribers are the most common users every day of the week while the customers represent a small part.\n",
    "- The mean and median of all the genders are a bit similar but females have a lot of riders who are born earlier than 1980 same with the male but the female contain a rider which is born around the year 1880\n",
    "- The Market station and San Fransico Caltrain 1 are the most common station where trips are started and trips are ended."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e85516",
   "metadata": {},
   "source": [
    "## Multivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1354c49c",
   "metadata": {},
   "source": [
    "### What is the average duration per minutes for every rides by user?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7178abbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting of barplot of the average duration per minutes\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.barplot(data = GoBike_df_copy, x = \"hour\", y = \"minutes\", hue = \"user_type\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9275da6",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- The customer duration for every rides last longer than the subscribers almost every hour of the day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb36fdf",
   "metadata": {},
   "source": [
    "### What is the average per minutes during the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c18f1008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33mEvJ6z87ne/W76zs/Ox/gwrnZ2dDB3a185cL7WgAfbdg+ovm8e/+TkZ2LGX5d/NzE2bfwYVSZIkaT6mTp26zNChQ/Owww7rrvBniy22eLqzszO23XbbdbuX7bvvvmsec8wxKwEceOCBq6+zzjobjh8/fuL+++8/9pxzzln6L3/5y/Kf/exnx06YMGHiddddt+TFF188fJNNNpkwfvz4iW9961vXmTVr1hCAyZMnr/+hD31ojY022miDtddee8O///3vI3bYYYd11lprrY0OOeSQ1bpf74c//OGKG2+88QYTJkyYuM8++6zV2dkJwIgRIzbbb7/9xq6//voTzz333JGv5r3PN+Zk5h+aH68HjgDGtTwngZ/N57nnR8S4V1M4SZIkabC75pprhm+yySZP9XX9mTNnDjnrrLNWuPXWW6d1dHTw4IMPDhk1atQL22+//aNTpkx57AMf+MAjAOPHj5/43e9+986dd975iUMPPXS1ww8/fLWTTjrpLoAllliia9q0adO/8pWvrLznnnuu+69//Wv6yiuv3Dlu3LiNjzjiiPvvvffeYaeffvqKl1122Ywll1wy3/Oe96x53HHHrXTQQQc99PTTT3e84Q1vePKEE064+9W+9762yfwc+BRwLeXGLa/GQRGxL6Vl5hOZ+cir3J4kSZKkxkorrfTCkksu2bXXXnuNmzJlyqN77bXXYz3Xeeihh4bMnj17yM477/wEwH777ffQnnvuuXb347vvvvujAJtsssnT66677tNrrbXW8wBrrLHGs7feeusS55133shp06aN2GSTTTYAeOaZZzpWXnnlToAhQ4bw/ve/f6Fc4/f1PiuzMvPMzLwtM+/o/vcKXu9HwDrApsB9wFHzWjEi9o+IyyLislmzZs1rNUmSJGmxtvHGGz999dVXj+i5fNiwYdnVNacd4dlnn41mOVddddX0PfbY45GpU6cuv80226z3cl9zqaWWSoCOjg6WXHLJF8eud3R00NnZGZkZe+6550MzZsy4fsaMGdfffvvt044++uh7obTKvJpxKq36Gla+EBE/joi9I+Lt3f9e7otl5v2Z+UJmdgEnAJPns+7xmTkpMyeNHj365b6UJEmStFjYZZddZj/33HPxne98Z1T3sksvvXR4ZnLzzTcPf/rpp+PBBx8ccuGFFy4L8Nhjj3U8/PDDQ/baa6/HjjvuuLtmzJgxAmDkyJEvPP744x1QWl+WXXbZF84+++yRACeeeOJKW2yxxRN9LdOOO+74+NSpU1e45557hgLcf//9Q2688cYlFub7hr53A/sAMAEYxpxuYC/7PisRMSYz72t+3R2Y9nKeL0mSJA20vkw1vDB1dHRw5pln3nLggQeu8f3vf3/VJZdcMseOHfvssccee9cuu+zyyIQJEzYcO3bssxtuuOFTAI8++uiQKVOmrNvd0vKVr3zlLoB3v/vdDx9wwAHjjjvuuFVOP/30W37yk5/cdsABB6x1yCGHdKy55prP/uIXv7i9r2XafPPNn/nsZz97z3bbbTe+q6uLYcOG5THHHHPn+PHjn1uY772vYeX1mbn+y9lwRPwC2AYYFRF3A18AtomITSlB53bgIy9nm5IkSdJgNG7cuOfPOuusW3suP+644+4GXjKQ/dprr53ec9kOO+zw5C233HJd67Krr756Rs/1/vnPf97Q/fOUKVNmT5kyZXZvj+23336P7Lfffi8Zm/LUU09duYC302d9DSsXR8TEzLy+rxvOzL17WXxiX58vSZIkaXDra1h5I3BVRNwGPAsEkJn52raVTJIkSdKg1tew0tvNHSVJkiSpbfoUVl7hNMWSJEmS9Ir1depiSZIkSepXhhVJkiRJVVo4t5aUJEmSBok7v7zx5gtze2t+/toF3rfl8MMPX/WMM85YqaOjIzs6OvjhD394x1ve8pYne1v34x//+GojR4584ctf/vL9C6uM559//oiTTjpppZNPPvmudmx/XgwrkiRJUsX+8pe/LP3nP/95+Wuvvfb64cOH53333Te0+4aP/eH5559n6623fmrrrbd+6tVuq6uri8xkyJAhfVrfbmCSJElSxe65555hK664Yufw4cMTYMyYMZ3jxo17fvXVV9/4vvvuGwql5WPy5Mkv3sT9mmuuGbHppptOWGuttTY66qijRgHccccdwyZNmrT+hAkTJq633nobnn322SMBTj/99GUnTpy4wfrrrz9xiy22GA+ldWa33XZ7zete97oJb3/7218zderUZbbddtt157d9gM997nOrbLTRRhuMHz9+4sc+9rHVAG644YYlxo0bt9Huu+8+bvz48RvecsstS/T1vduyIkmSJFVst912e/wb3/jGauPGjdtoq622enzvvfd+eOedd35ifs+ZPn368Msvv3z67Nmzh2y22WYT3/GOdzz2k5/8ZMXtttvusW9961szOzs7mT17dse999479KCDDhp33nnnzZgwYcJz999//4tNHjfddNNSl1566YyRI0fm1KlTl1nQ9q+44orhN99881LXXHPN9Mxk++23X/dPf/rTyLXXXvu5O++8c8kTTzzxtu222+72l/PeDSuSJElSxZZbbrmuadOmXX/22Wcvc+655y7zvve9b53Pf/7zd8/vOTvttNOjI0eOzJEjR3ZuscUWj19wwQVLv/GNb3zyIx/5yLjnn3++Y4899nhkyy23fPpPf/rTMpMnT549YcKE5wBWWWWVF7q3seOOOz46cuTI7Ov2L7jggpHnn3/+shMnTpwI8NRTT3XMmDFjqbXXXvu5MWPGPLfddtv1OsZmfgwrkiRJUuWGDh3KlClTZk+ZMmX2a1/72qdPOeWUlYYMGZJdXV0APP3003MN74iYe0hLRLDTTjs9cf75599wxhlnLPfBD37wNQcddND9K664Yue8XnPppZfumtdjvW0/Mzn00EPv+9SnPvVg62M33HDDEiNGjJjntubHMSuSJElSxa6++uolr7322iW7f7/yyiuHjx079rmxY8c+d9FFF40AOO2001Zofc6f/vSn5Z966qmYOXPmkH/84x/LbLXVVk/eeOONS4wdO/b5T3ziEw/uu+++s6644ooR22yzzZP//Oc/l5kxY8YSAK3dwOant+3vtNNOj59yyimjHnvssQ6A2267bdg999zzqhpHbFmRJEmSXoa+TDW8MD3++ONDDjnkkDUff/zxIUOGDMlx48Y9+9Of/vSOq6++eqn//M//HPflL3/5hS233HJ263M22GCDp7bccsv1H3nkkaGf/OQn7xs3btzzxx577ErHHHPMqkOHDs0RI0a8cOqpp9622mqrdR5zzDG377777ut2dXWx0korPX/xxRfftKAy9bb9cePGPX/dddct9frXv34CwIgRI7pOPfXU24YOHdprV7K+MKxIkiRJFXvzm9/81JVXXjmj5/IxY8Y8cfvtt0/rufzoo4++t7ftHHzwwQ8dfPDBD/Vc/s53vvPxd77zndfPbxvdXdDmt32Az33ucw987nOfe6Dn8ptuuum6eT1nfuwGJkmSJKlKhhVJkiRJVTKsSJIkSfPX1dXV1W93jB9smn3b62xhhhVJkiRp/qbNmjVrOQPLwtfV1RWzZs1aDnjJ2BtwgL0kSZI0X52dnR+eOXPmj2fOnLkRVvYvbF3AtM7Ozg/39qBhRZIkSZqPzTff/AFg14Eux2BkMpQkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqpkWJEkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpXaFlYi4qSIeCAiprUsWzEizomIm5r/V2jX60uSJElatLWzZeVkYMceyz4NnJuZ6wHnNr9LkiRJ0ku0Laxk5vnAwz0Wvw34afPzT4Hd2vX6kiRJkhZt/T1mZZXMvK/5eSawyrxWjIj9I+KyiLhs1qxZ/VM6SZIkSdUYsAH2mZlAzufx4zNzUmZOGj16dD+WTJIkSVIN+jus3B8RYwCa/x/o59eXJEmStIjo77ByJvC+5uf3Ab/v59eXJEmStIho59TFvwAuAdaPiLsj4kPAN4G3RsRNwPbN75IkSZL0EkPbteHM3HseD23XrteUJEmStPjwDvaSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqpkWJEkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqpkWJEkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqDR2IF42I24HZwAtAZ2ZOGohySJIkSarXgISVxraZ+eAAvr4kSZKkitkNTJIkSVKVBiqsJPD/IuLyiNi/txUiYv+IuCwiLps1a1Y/F0+SJEnSQBuosLJVZr4O2An4aERs3XOFzDw+Mydl5qTRo0f3fwklSZIkDagBCSuZeU/z/wPAb4HJA1EOSZIkSfXq97ASEUtHxDLdPwM7ANP6uxySJEmS6jYQs4GtAvw2Irpf//8y8+wBKIckSZKkivV7WMnMW4FN+vt1JUmSJC1anLpYkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqpkWJEkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqpkWJEkSZJUJcOKJEmSpCoZViRJkiRVybAiSZIkqUqGFUmSJElVMqxIkiRJqpJhRZIkSVKVDCuSJEmSqmRYkSRJklQlw4okSZKkKhlWJEmSJFXJsCJJkiSpSoYVSZIkSVUyrEiSJEmqkmFFkiRJUpUMK5IkSZKqZFiRJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSJIkSaqSYUWSJElSlQwrkiRJkqo0IGElInaMiBsi4uaI+PRAlEGSJElS3fo9rETEEOAHwE7ARGDviJjY3+WQJEmSVLeBaFmZDNycmbdm5nPAL4G3DUA5JEmSJFUsMrN/XzBiD2DHzPxw8/t7gTdk5kE91tsf2L/5dX3ghn4t6MI1CnhwoAsxSLnvB5b7f2C5/weO+35guf8H1qK+/9fKzNEDXQgVQwe6APOSmccDxw90ORaGiLgsMycNdDkGI/f9wHL/Dyz3/8Bx3w8s9//Acv9rYRqIbmD3AGu0/D62WSZJkiRJLxqIsPIvYL2IeE1ELAG8CzhzAMohSZIkqWL93g0sMzsj4iDgz8AQ4KTMvK6/y9HPFovubIso9/3Acv8PLPf/wHHfDyz3/8By/2uh6fcB9pIkSZLUF97BXpIkSVKVDCuSJEmSqmRYWQREhH+nARQRMdBlkAaaxyENRh7/+5/HGvXkB6JCPQ+Omdk1UGUZjFoPlBERmZnNzHXSoNHzgsHjkAaDXs6/Duxts4gY0vJzZGaXIVGtDCsViOLFv0XPg2NEbBYRb+5et7/LtziLiCE9wklH64GyCSoHA18fsEJqviJiZESsPdDlWNRFREeP41BXj8ffExHj+79ki6fuY0xz/O/+eXTrY2qvZtcPifmff9eNiKMiYmL/l3Dx0xxnWsNJR2a+0P17c879MPC/3Y8PQDFVGT8EAyAilo+IJZufI4uulsfXiohtW56yC7Bvf5dzcdTs+yMiYnOAzHyhe99HxMpNUNkDOCkium9e+hSwcrOO35l+0vMiomX5EhGxUvPzcOBI4PX9Xb5FWXPBMFfwyMyuHseh/4iITVpWeQ+wVfOYF9MvQy+VIkOai7KRzfE/I2JD4GKwNr+dIuKAiFgfyn7uPgd0X0BHxNoR8fmI2Kl5yuuAFTLzej/3L19EjI2IvVv2eVd3OGk+/10RsWNE/L7laUOAu7vX7/9SqzZeePWT7hNVRGwHfAvovljOpubmw81FMsBrge9GxJiIGAaMBM7oXr//S7/oa7lQeAJYDdimWb5FRPw+Iq4BvhMR2wB/BK4HvtA8Z3ngQvDA2S4tNcubRcR18JIg+caIWLVZ/TDg6xGxfGY+DbwRuKl1O+pdS43mjsBXI2LdZvlSTTg5MiKmNOtMAg6NiA2aypXrgDvB49CCRMToiDgkIt4Gcz7LTWiZkJkvNK0o0yNi5eZpawJndFdkaeFpWlC67yu3E7B3s3yViPjviLgQ+E1E7JyZtwIPAZ9s1r8PWKffC72IaznWLA28FdigWT45Ik6OiEuBYyJideASoDMiuitltwH+3s9FVsUMK23S86Kp5SJ3GvAkMKZZ73DgRGBdYOuIeG9m/gE4AfhCZj5PuRi7v7/KvqjrrUa+uVBYHvh3yr6c0P0QcBQlPP4G+E5ZPb8NvCEiNgD+A7i52bYXw23QffGbmVcC4yJiWYCm+8UVwJeAb0bEJpn5VWA08K7mAuSS5ncvontoWlBe/My2dLe4A7gXWLe5qPgSpeXkLsrnfr/M/DIlpH+J8j3ZgrKvxVwBe+WeyzJzFrAGMK5ZvnlE/Aa4AjgqInZt1jmOEgiXp4TDhzPzWY8zr04vn/tsbkg9HLga6O7StRwQlPByBOXieUXKDQ1viYiPUlrVL4iIJTy+9G4e59zuY81qwMPA6s3vawKnA1sDt1H2+3PAfwN7RcRGQBewYrNtr1NlWFmYmtqbDui13+uaEXE05eQ0FnhN89CplFqH04CNgD0iYvXM/AEwNiI+QPlCd3d78STWQ28HyZZazFHNOq8H/gG8G9gEeE1ELJOZ/6Bc6P4V+AQwCtiu2dQ3gX0otWrD++XNLOZavyM9li8bER+MiHcAjwLbNw+dSjmp/Qfl73Bws/wEYG3gcMqF9FXtLfmiobdKktZjUURsGRFnUz7bY4HxzUXFkZR9+xwl0O/aPP9EYFXKd2Lp5t+gFzHXxBs/iIil4cWW8jUiYh9gCWDliFgGeBb4WmZuAvwO2C8iXpeZXwOGAR8F7qH8TaBcQOtl6BFOupq/xYpNa+1SEXEe8GfKPt4+IkZk5o3AL4FPAycBywL/3lQSfg2YDHwbuDYzn/PCuejlOPNiK3jLOjs3reSHAFsCGzTn3NMp156/Ad4LrAds1fwt/gZ8lvLdebDZtr0ZZFh5NeKlA1JfHHsSEZtExISWL/UhlC/gYZRuRRtFxCrATEqA+RLlgvkh4M3Nc74DvJ3SZ/bK7tdo9/uq0fxCWi8HyT0jYiql28quzeLPAl/MzH0oLVlPU2qQRwA7AEdm5puBPzEnrPwfcAOllueO5rUG5f5/pXrWuHV/R5qLhy0iYq3moU9TxmUtTWn56v673Q38CLigWT4pStfIcyi1cx8AtsvMQd3y2FKj3xpMVoiId0XEt6L0w18KOJTyuT6Achya0LROLQGcTKkw+QawVDTjuoAvU0Lh/cAz/fKGKtOEvK9GxHowV0vgc8BelP1HRGwB/IoytmcVStBeMzOnAaMi4l/AfpSAvWOz+ROAN1EqUi5qtusF2jzM61zQhJPu7tY7RcRXgd8De1LGfU7PzK2BAymh/N+ap34IeCIz30A5F2/VBJk7KMGyizmVi4NOU8E0zxnSmoqmd0TEHyLii1G6dUEJIt/OzN2BXwMrAGtHxGrAuyiT1mxJOcdu1Dznx8DlwM7YiqsWhpU+ijLjUOtg094GpG4YER+KiN8CP6HMZrFD8/BEYGpm3gR8D5hN6Qq2DbB2Zu7SdG9Zmzn9Yy+gXDismpkPte3NVSwilomIHXtpqerugrF6RBwYER+JiBWah7cD/i8zJ2TmSc2yh2i6CgH/pHQF2JDSJL0OcGkTHtdiTq1yZubPKYP9bmzfu1x89axxi9KX/wfAtZQL51HNBeBmwK6Z+TNKd4B/b56yKbBkZm6ZmXtQ/j4bZWZnZv6TUjt3RXcL2mDVXKhFRGwVEZ+KiJHAVMrx5zZKOB9G+cyfk5l3U7o/DqUch94N3JiZB1MqRkZRapXJzL9Qap+XyszHB1PtckR8JSLupFQyjQAeb5ZvGBFfaFpQvkSplQfYFrguMw+khLw7gYlNC8wBwP6ZOZlyDFoLoKlRPoISbG7utze3iImIpaOM6xzRY3l3QBkH/DUilqN0v9sTOCQzj6CEwZsAmnFup1N6NEDprndT8/MylL9h9/i4c4CzKWMYB02IjIg3NZ/9vwLb93L+fUPTgrgVpfJvd+CHlO7VRzSrPUg57gCc1fw+hhJa/iMzL2oeewOlqzuZ+SjwA8r5drk2vT0tggbNSWchuAb4XTT9k5ua4SkR8aOI2L1ZZzXgI8BFmfk64P8B721qEqYB3QMnr6McDMcCMyjNo9tFxFcoF9XrRMTQpnvGLOCPUfo0DwotB8oLKc3CH+l+/80Ja5Xm4mwr4FjKvhwHfLSpDX4SeF9Tq7x1RIylDNbbAqAJfusAm2TmjOY1/kI5Kf0S+EbTIjA0SrekS4Gl+m0HLEK6Wxd7q+2MiOUi4tCIODUiPt0sXhnYMDPXy8y9MvNyygDWSc2F8BKZ+WdKzf4qlH7LTzUXh++gfIe2bHmZm4CHMvPB+bW+Lcrm976imWEnyuyBp1G6MnYCUyi19Edl5nGZeR/l+PQXSmABeIAS4MdQLg4mRpmm+whgOnMPKr6eEnoGzQVbYzpwZmbulpkfz8z7I+IjlGNGJ+W4chawetNC9QKlphhK8LiVUhn1HCU4PhFl3MTrKft7AkBmXkUJ3iszyLVURA1p/exn5pOUUP2OKDN6LRelBb17YprZlPPnJMpYqysp4QPgdmDTlqB9I3PCytmUc8xFlHPJSc22oASjSc22BoWIeCfwVUrr05HAdRGxUXOM6Z6BcT/gfZTrmhHA9Zn5J+B/KJ/r1YBHKK2LZOYNlMqPjTLzOuBvUbrlnUsJjqdH6eVAs93/R5kMRwJKrZp6iIivU/rMvyszb21q7C+h9K2cTKmx3JfS/P9HYOcos3x9jBJEHm029TtKc2YXpbvXO4DfZ+YdEbEpcHtmTo2Ig4BPUfrdfzIzb2vKsRnwU+CnTY3DYi8ifk25QH2a0u3ksMy8JebclPHblBP6HpTa+Isp4xqOpIxF+RulKX8nSs3lByl9wQ8DvhZldp4xNE37ETE6M78WEX/IzGt6lGU9ykXfzzPz8Ta+7UVKROxKuQD7Xo9Wk+UprSD3Rxm0vRelduxHwJsj4ijKDGvrRsTPKBfAD2bmjyPi9oh4R2ae0YSU54EpmXli8135I+X7tA9lsGtQBmzuA5wJi0cXvSjjG3YG1srMb8FLulyMAoZn5l3NoqkR8Z+UILEx8OXM/L8oXTG2o8wqeAdlzMRvKBfPH6aElqGUY9rbMvMzzYXc3sBvKRfoTzWvuSulUmC/9r77Kv0F+FTzd/kApZviWZQZGr/dhJB7okyl3X2Btn6UmeoejTLb2rDm+/BxyoXw6pTa44spExoQZSB3B6U1fdCJiDdRusW9idKb4JycM73tCMrxYDhl330T+Bel2+L1lG7Tp1ECxlXAWyhdjB6g/E2gHDu2BQ6MiH9QKrfWbyq+/i8iHgHuyMzrexTtO5RJER5Z2O+5Rs1xdRvgF5l5fMvyrYHPUI4xz1JanN7WfMan0eyfzLwwygQFy1ACx/ci4urm96Ccc1eifJdeC1zVem6NiMmUng0nZeaz7X6/WnTEYnB+X+giYjfKif3gzPxBRLyO0oR/F+VkdQRwCnBCZv6tuaj9I+Xi+XDKzBfHNH3zL6UMXL2RcnAdQhksfzXly3x20yzdWzlWB1ZvursMChGxVGY+E+Umf++h7K91gOMz8/woUwv/MDMnRsSPKLWV0yg1nL/JzNsjyuDXZnv/RukfezClS9F/UYLK9yh9mJ/psX4Ac92kSnMGFDc/7wp8NjMnN61WWwNvo4TFm4H3U2o4L6Z0DViTEiqupdSarUsZL7E2JUT+D+W79R7Kd6eT0p1gRmYeGhHDe/uORLkvxVuBkxflMN9yobYt5YLsaUof7k0pY6V2oHQtujsivk/5/P5389k9CnguM/87Io6l1HD+qAkekWWK3E0p/cefoISOkygtKh2U49h04K+9dPUY0jx/daAzB+m4oIh4klIJdRklqJxH+Wy/s2mZpanhP40S9E6mdP+6mfK9uIVyzLo2IsY0rVyt21+eMk3utZn5q354S1VpavIPoIzZ/BelF0NQunG9i/J5P54yFujtlC5E726euzXw3czcvGnZ+nfggMycEhGfp7Syf7c5F4+ndNlbk1KBcn1mXhHNjYBbyjOEUkcwmFoQgRff++GUUHImpSX7Ikr3x/OBN2bmI1FmyfwRpWLlPZReIsdm5gMR8Tvg3Mw8NiL2ohzzn6KMzbo4M2f3eE3PuVogW1Z6dz5lIGlnlDupnk4Z5H4KcGhzAl8PuDVKt5WbIuIFykHwespFxhqUC42ZwC6Z+bmIOLB57NqWmlFgzhcWeHH2nsy8h9IqMGhkZvcA3icoB8rHKAe65ZrHz4vSV7Z7IPYJmfnN7uc3F1bDIuI9lFaRkZQpoDspFxvv7eU1s8fPg/ag2R0MonRD7Oxe3hJUNqZcPAxv/gaTgC8CX8nMvSLiLErt/O8oLYz7U8Zv7Z6ZNzah57pmW5dQQsmwzDwrIu6lnPjOprQwdjav/XSz/hDKwORsXEe5iFxktbQkPkM5VhzWtOYeCRxD6YqxHmVM1ScoF8MfpXyun6FUeHyp2dwtNAOBm4uzVZtazE0o4edLWbrLfZDScnN5L+V5MeR0Xzw0x6HB7BfArzLznO4FEXEZJWDOaBb9FXhvZv4sIg6jBPb1KLXRl7Uc0+9rnt/BnM/xo5QJQAad+dTkr0459k+hVGz8gPL9+D2wf0S8lhI2zo8ynnS95jz8GmDNpnX2HspnfxXgvub4897W4xrM6dbYXSEzmC+am2ub4yi9FT5EabH9BvB9ypi3TSm9F56hnCc3poTLzYHxlNascynjUgBOm1cAb9nfg/qcq74xrPQiMx+OiKeY06VoOuUL+AIwpGnmvB7YLTO/3zztVkr3ojspXZDGUcLKf1Nqd8jMeyn3NgDKCav7QOkXdm5NDc2jlAvjDmBMRIzKzAcp+3VHSs3OCc2F3cOU5uM/Ui7yLgX+mOW+HXMZzDVnPUXEsMx8vqmV/DQlJH6v5wm9efyXlFaucylTfE6m1DDfSvMZp8z6sinlAuMiYIlsJjmIiDWBFZua6l9RgsfVwNHwYr/9q1pec64az8X0IuK9TQvJOMosRWtT9uetlP75a1I+/8cC76TM5PVFYNWIeJjSWjW66aY0A9gkIsZmGUA/ntI19YHm+ZcCNN+hB2HO4OSW49Cg/0704heU2uFzImLJLN1TLqa0Fn6vWefF8SaZeQvwudYNtLZMNuu4n4sOyox/20bEbMqsav+iBO+plOmD39Cst01mnh0Rs4DX5Jxuu2cBn24qDLsoLVpr0YyFyMzHul8sy71WOigtN109/iZ2M6Fc/wA7RZl1cTSlK/oUSljZnnJdtC4lkOxK6YJ9B804n8w8tmVb3ZVcLznnur/1chhW5m0q5cv4G0qz/s8p3Y3uo0xLeQxwcET8D6UJdFZmds9E8qUsd8Ele/SBbT1pecLqXcs+uh1YnxJE1qFcID9IaZL+cJbxDR+jjF9ZmdI97/xmv7bWgg6Gi96XLSK+TRk/8qEmoG8InB2l2+OBlIvd4ykBZAdKd6PNotywcSXK7F1/a2qZu6cgvohyAutsnvfzKGO+JlJOfB+jBP09ur8jPcr04sXzYPh+tLQkPkm5gFqHMk7i75Ta9mzC5JXA+k2r1x3A1pk5LcoA+xUp/fSvptRGj6JcAF6Umecv4PUX+328EPyD0joIZewElNb3TbtXyMzbKWH/Ra0XaF6Y9W4eNflfB35GGbh9V2Z+JCL2BHaPMsvdeZSL6Z0p3Y0+FuV+ZGsBpzRhcX6v6We+D7Lca+beiHia0h3vq5Qb9t5NmXDgSMo4n0cpofJFnnO1sBlW5u0U4IPNgXIV4N7MfCwibqUMvN+n6RO7L+Xg+WeAphbnsdYN9QgonrT6bjqlGf9OytiV30TEXZSZXrovam8BvtXbk1uamT05ARGxC2UWoh9n5p2UGc7eTBnU+gdKzdi/UY4Lv6fUUO5Hqd0/nzmzQz1Dma7yyOb3O4ANoEzD2oSZTTPzFxGxIyVM/iozL2wpzq1NmYYwd9fHQfm3ysxZETGT0oK4bGZOj4jbKH+fv1PCx4ZR7ktzHGX2ooMoNfz/CzzZXDAf2rLN7gHKtiS+Cpn5ZERkRKyQmd0DiW8EDmpdr7VrV7OOF2h90EtN/smUsT4HM6d73AaUroxvobSof5ASFm9utvGT1m32vFhW3zVd8yZSxuBuRDk/3JyZ/9t0/f1Rc/54yfMG+3Fc7WNYmbfW2rRvUpqXodw08BqA5gv71QVtyIDy8rTsr9sos4jcTanhvxu4vLkom4vNzH1yDeXmaFMoA98vppzw3xAR51Jao75Oac36BSWorEXpsnUT5W7cS2S5k/PKlHukrEIJHjtGxAaZOZ0yYH4avDhN9P92F6CX7jCD/oKuZZ/cSnMTQcr+u4wygcDfKf3vRwBbNiFwFqVF9/yeFwbu47ZYu2kF6DkZB16gLRw9avL3oHSle2uU8Yc3UcZOTG8+zyf0fH5rxYd/i1cuMzMinqdM+HEb8F+ZeUXz2JM0XX576ULq+VZt42xg89F0t9i0uzZtHusEZT96cGyDlnEqPZcP8SLs5Ysy887hWWbP2ZcyccFwyv18hlC6e21FuZPwHzPz0pbn/pXSX/mflBPZnpT7evyVUhN3FfBsz5OW35H5674AjoiJlID498z8XXORdkiWWdeWpAyevzXLdLlzPR9n02m7niFQC8d8avI/0Hwnns5mOv8ez7PFUBokDCvzEXOm7pzrJOVJa+C471+9iJhO6UaxC6X74qOU7oyrUYLIzpTpPv/YrL8zpZa/i3LT050os/Ocn2Ugd2+vYTeMl6npYvF1ypTNP4pyf4nlssdUty3r+13QYiHKtMKHU2ryz+quye+xzlzd7NR+PbvpSgPFsLIAXhBocRMRn6Hc2Gt7ys23TouIQyndLD5N6Qf+QcqYlrUos0wd0fTTVxvNqyVRkqTByjErC2BQ0WLoDGA3yjS5E5tl51KmLd49M7eJiCsoc+dfmD1uuBgt9+LotxIPEgYVDWbW5EvqjS0r0iAUEVtRZvj6RmZ+plm2JXB7lvsBta4710BiSZKk/mJYkQapiPgocGZm3jWPx+0CKUmSBpRhRRqE5hVEDCiSJKkmHQNdAEn9r+VeER29LZckSaqBLSuSJEmSqmTLiiRJkqQqGVYkSZIkVcmwIkmSJKlKhhVJkiRJVTKsSFJFIuK8iJi0gHXeHxH/019lkiRpoBhWJEmSJFXJsCJJr0JEfCoiDml+/m5E/LX5+S0RcWpE7BARl0TEFRHx64gY2Ty+eUT8PSIuj4g/R8SYHtvtiIiTI+Krze8fiIgbI+KfwJta1tslIi6NiCsj4i8RsUrz3JsiYnTLtm7u/r2X93ByRBwTERdHxK0RsUezfGREnNuU/dqIeFuzfFxEzGied2PzPrePiIua153crLd0RJwUEf9syve2hbz7JUmLOcOKJL06FwBvbn6eBIyMiGHNsmuAzwLbZ+brgMuAjzePHwvskZmbAycBX2vZ5lDgVOCmzPxsE2S+RAkpWwETW9a9EHhjZm4G/BI4LDO7gJ8D727W2R64OjNnzed9jGm2PQX4ZrPsGWD3puzbAkdFRDSPrQscBUxo/u3TPP+TwBHNOp8B/pqZk5vnfzsilp5PGSRJmsvQgS6AJC3iLgc2j4hlgWeBKyih5c3AmZRgcVFzjb8EcAmwPrARcE6zfAhwX8s2/xc4LTO7A8wbgPO6w0ZE/AoY3zw2FvhVE2iWAG5rlp8E/B74HvBB4CcLeB+/a0LO9RGxSrMsgK9HxNZAF7A60P3YbZl5bVOe64BzMzMj4lpgXLPODsCuEfHJ5velgDWB6QsoiyRJgGFFkl6VzHw+Im4D3g9cTGlN2ZbS8nAbcE5m7t36nIjYGLguM7eYx2YvBraNiKMy85kFFOFY4OjMPDMitgG+2JTrroi4PyLeAkxmTivLvDzbWsTm/3cDo4HNm/d5OyVw9Fy/q+X3LuacWwJ4R2besIDXliSpV3YDk6RX7wJK96fzm5//E7gS+AfwpohYF14cwzEeuAEYHRFbNMuHRcSGLds7ETgLOC0ihgKXAv8WESs1Xcj2bFl3OeCe5uf39SjXjyndwX6dmS+8gve1HPBAE1S2BdZ6mc//M3Bwd9exiNjsFZRBkjSIGVYk6dW7gDLm45LMvJ8y1uOCptvW+4FfRMQ1lC5gEzLzOWAP4FsRcTVwFbBl6wYz82hK4DkFuJ/SYnIJcBFzd6P6IvDriLgceLBHuc4ERrLgLmDzciowqenatS8w42U+/yvAMOCapqvYV15hOSRJg1Rk5kCXQZLUBs39Wr6bmW9e4MqSJFXIMSuStBiKiE8DB7DgsSqSJFXLlhVJGiQi4jPMPd4FyniWr/W2viRJA82wIkmSJKlKDrCXJEmSVCXDiiRJkqQqGVYkSZIkVcmwIkmSJKlK/x8NJFcEqiHSvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the pointplot of the average per minutes\n",
    "plt.figure(figsize = (12, 8))\n",
    "days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "sns.barplot(data = GoBike_df_copy, y = \"minutes\", x = \"weekday_name\", hue = \"user_type\", order = days)\n",
    "plt.xticks(rotation = 15)\n",
    "plt.legend(bbox_to_anchor=(1.0, 0.5));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53899e10",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "- There is no much difference between the customers and subscribers on the amount of rides taken on a weekly basis"
   ]
  },
  {
   "cell_type": "markdown",
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    "### Insights Gained From Multivariate Exploration\n",
    "- The customer last longer on every trip than the subscribers which means these are the people that are used to the products and enjoyed using it unlike the subscribers that are just giving it a try\n",
    "- The customer also last longer on every day of the week than the subscribers;"
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    "## Conclusions\n",
    "- The Male genders are more likely to take a rides than any other genders\n",
    "- The Rides Peak at 8 which is in the morning and 17 hours which is in the evening which are the times people are communiting to go away from home and come back home\n",
    "- Subscriber are the most users which means there are low customer retention and a lot of effort needs to be put to turn maybe all or some subscribers to customers.\n",
    "- Market Station is the most common place for every starting rides and also the second common place to end rides which means people are mostly likely to pick a rides at the market, same as the San Fransico Caltrain 2. "
   ]
  }
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