{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn \n",
    "from pandas.api.types import CategoricalDtype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge all twelve csv files into one (January to december 2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Accessing the path to all the files. The files must all have a similar naming structure. All my files ended in \"data.csv\"\n",
    "joined_files = os.path.join(\"C:\\\\Users\\\\jeyjo\\\\Desktop\\\\GOOGLE DATA ANALYTICS CERTIFICATE PROGRAM\\\\CASE STUDY\\\\Bikeshare\\\\CYCLISTIC DATA\",\"*data.csv\") # merging the files\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of all the files is returned\n",
    "joined_list = glob.glob(joined_files) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E19E6F1B8D4C42ED</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 16:14:19</td>\n",
       "      <td>2021-01-23 16:24:44</td>\n",
       "      <td>California Ave &amp; Cortez St</td>\n",
       "      <td>17660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.900341</td>\n",
       "      <td>-87.696743</td>\n",
       "      <td>41.890000</td>\n",
       "      <td>-87.720000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC88F20C2C55F27F</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-27 18:43:08</td>\n",
       "      <td>2021-01-27 18:47:12</td>\n",
       "      <td>California Ave &amp; Cortez St</td>\n",
       "      <td>17660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.900333</td>\n",
       "      <td>-87.696707</td>\n",
       "      <td>41.900000</td>\n",
       "      <td>-87.690000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EC45C94683FE3F27</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-21 22:35:54</td>\n",
       "      <td>2021-01-21 22:37:14</td>\n",
       "      <td>California Ave &amp; Cortez St</td>\n",
       "      <td>17660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.900313</td>\n",
       "      <td>-87.696643</td>\n",
       "      <td>41.900000</td>\n",
       "      <td>-87.700000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4FA453A75AE377DB</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-07 13:31:13</td>\n",
       "      <td>2021-01-07 13:42:55</td>\n",
       "      <td>California Ave &amp; Cortez St</td>\n",
       "      <td>17660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.900399</td>\n",
       "      <td>-87.696662</td>\n",
       "      <td>41.920000</td>\n",
       "      <td>-87.690000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE5E8EB4E7263A0B</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 02:24:02</td>\n",
       "      <td>2021-01-23 02:24:45</td>\n",
       "      <td>California Ave &amp; Cortez St</td>\n",
       "      <td>17660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.900326</td>\n",
       "      <td>-87.696697</td>\n",
       "      <td>41.900000</td>\n",
       "      <td>-87.700000</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595058</th>\n",
       "      <td>847431F3D5353AB7</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-12 13:36:55</td>\n",
       "      <td>2021-12-12 13:56:08</td>\n",
       "      <td>Canal St &amp; Madison St</td>\n",
       "      <td>13341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.882289</td>\n",
       "      <td>-87.639752</td>\n",
       "      <td>41.890000</td>\n",
       "      <td>-87.610000</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595059</th>\n",
       "      <td>CF407BBC3B9FAD63</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-06 19:37:50</td>\n",
       "      <td>2021-12-06 19:44:51</td>\n",
       "      <td>Canal St &amp; Madison St</td>\n",
       "      <td>13341</td>\n",
       "      <td>Kingsbury St &amp; Kinzie St</td>\n",
       "      <td>KA1503000043</td>\n",
       "      <td>41.882123</td>\n",
       "      <td>-87.640053</td>\n",
       "      <td>41.889106</td>\n",
       "      <td>-87.638862</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595060</th>\n",
       "      <td>60BB69EBF5440E92</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-02 08:57:04</td>\n",
       "      <td>2021-12-02 09:05:21</td>\n",
       "      <td>Canal St &amp; Madison St</td>\n",
       "      <td>13341</td>\n",
       "      <td>Dearborn St &amp; Monroe St</td>\n",
       "      <td>TA1305000006</td>\n",
       "      <td>41.881956</td>\n",
       "      <td>-87.639955</td>\n",
       "      <td>41.880254</td>\n",
       "      <td>-87.629603</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595061</th>\n",
       "      <td>C414F654A28635B8</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-13 09:00:26</td>\n",
       "      <td>2021-12-13 09:14:39</td>\n",
       "      <td>Lawndale Ave &amp; 16th St</td>\n",
       "      <td>362.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.860000</td>\n",
       "      <td>-87.720000</td>\n",
       "      <td>41.850000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595062</th>\n",
       "      <td>37AC57E34B2E7E97</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-12-13 08:45:32</td>\n",
       "      <td>2021-12-13 08:49:09</td>\n",
       "      <td>Michigan Ave &amp; Jackson Blvd</td>\n",
       "      <td>TA1309000002</td>\n",
       "      <td>Dearborn St &amp; Monroe St</td>\n",
       "      <td>TA1305000006</td>\n",
       "      <td>41.877850</td>\n",
       "      <td>-87.624080</td>\n",
       "      <td>41.881320</td>\n",
       "      <td>-87.629521</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5595063 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id  rideable_type           started_at  \\\n",
       "0        E19E6F1B8D4C42ED  electric_bike  2021-01-23 16:14:19   \n",
       "1        DC88F20C2C55F27F  electric_bike  2021-01-27 18:43:08   \n",
       "2        EC45C94683FE3F27  electric_bike  2021-01-21 22:35:54   \n",
       "3        4FA453A75AE377DB  electric_bike  2021-01-07 13:31:13   \n",
       "4        BE5E8EB4E7263A0B  electric_bike  2021-01-23 02:24:02   \n",
       "...                   ...            ...                  ...   \n",
       "5595058  847431F3D5353AB7  electric_bike  2021-12-12 13:36:55   \n",
       "5595059  CF407BBC3B9FAD63  electric_bike  2021-12-06 19:37:50   \n",
       "5595060  60BB69EBF5440E92  electric_bike  2021-12-02 08:57:04   \n",
       "5595061  C414F654A28635B8  electric_bike  2021-12-13 09:00:26   \n",
       "5595062  37AC57E34B2E7E97   classic_bike  2021-12-13 08:45:32   \n",
       "\n",
       "                    ended_at           start_station_name start_station_id  \\\n",
       "0        2021-01-23 16:24:44   California Ave & Cortez St            17660   \n",
       "1        2021-01-27 18:47:12   California Ave & Cortez St            17660   \n",
       "2        2021-01-21 22:37:14   California Ave & Cortez St            17660   \n",
       "3        2021-01-07 13:42:55   California Ave & Cortez St            17660   \n",
       "4        2021-01-23 02:24:45   California Ave & Cortez St            17660   \n",
       "...                      ...                          ...              ...   \n",
       "5595058  2021-12-12 13:56:08        Canal St & Madison St            13341   \n",
       "5595059  2021-12-06 19:44:51        Canal St & Madison St            13341   \n",
       "5595060  2021-12-02 09:05:21        Canal St & Madison St            13341   \n",
       "5595061  2021-12-13 09:14:39       Lawndale Ave & 16th St            362.0   \n",
       "5595062  2021-12-13 08:49:09  Michigan Ave & Jackson Blvd     TA1309000002   \n",
       "\n",
       "                 end_station_name end_station_id  start_lat  start_lng  \\\n",
       "0                             NaN            NaN  41.900341 -87.696743   \n",
       "1                             NaN            NaN  41.900333 -87.696707   \n",
       "2                             NaN            NaN  41.900313 -87.696643   \n",
       "3                             NaN            NaN  41.900399 -87.696662   \n",
       "4                             NaN            NaN  41.900326 -87.696697   \n",
       "...                           ...            ...        ...        ...   \n",
       "5595058                       NaN            NaN  41.882289 -87.639752   \n",
       "5595059  Kingsbury St & Kinzie St   KA1503000043  41.882123 -87.640053   \n",
       "5595060   Dearborn St & Monroe St   TA1305000006  41.881956 -87.639955   \n",
       "5595061                       NaN            NaN  41.860000 -87.720000   \n",
       "5595062   Dearborn St & Monroe St   TA1305000006  41.877850 -87.624080   \n",
       "\n",
       "           end_lat    end_lng member_casual  \n",
       "0        41.890000 -87.720000        member  \n",
       "1        41.900000 -87.690000        member  \n",
       "2        41.900000 -87.700000        member  \n",
       "3        41.920000 -87.690000        member  \n",
       "4        41.900000 -87.700000        casual  \n",
       "...            ...        ...           ...  \n",
       "5595058  41.890000 -87.610000        casual  \n",
       "5595059  41.889106 -87.638862        member  \n",
       "5595060  41.880254 -87.629603        member  \n",
       "5595061  41.850000 -87.710000        member  \n",
       "5595062  41.881320 -87.629521        member  \n",
       "\n",
       "[5595063 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This appends all the files into one dataframe\n",
    "mergeddata = pd.concat(map(pd.read_csv, joined_list), ignore_index=True) # Finally, the files are joined\n",
    "mergeddata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA CLEANING & WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5595063, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To know the number of rows and columns\n",
    "mergeddata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                    0\n",
       "rideable_type              0\n",
       "started_at                 0\n",
       "ended_at                   0\n",
       "start_station_name    690809\n",
       "start_station_id      690806\n",
       "end_station_name      739170\n",
       "end_station_id        739170\n",
       "start_lat                  0\n",
       "start_lng                  0\n",
       "end_lat                 4771\n",
       "end_lng                 4771\n",
       "member_casual              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for null values\n",
    "mergeddata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping columns that are not needed\n",
    "mergeddata.drop(mergeddata.iloc[:, 4:12], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
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       "      <td>E19E6F1B8D4C42ED</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 16:14:19</td>\n",
       "      <td>2021-01-23 16:24:44</td>\n",
       "      <td>member</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC88F20C2C55F27F</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-27 18:43:08</td>\n",
       "      <td>2021-01-27 18:47:12</td>\n",
       "      <td>member</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EC45C94683FE3F27</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-21 22:35:54</td>\n",
       "      <td>2021-01-21 22:37:14</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4FA453A75AE377DB</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-07 13:31:13</td>\n",
       "      <td>2021-01-07 13:42:55</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE5E8EB4E7263A0B</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 02:24:02</td>\n",
       "      <td>2021-01-23 02:24:45</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595058</th>\n",
       "      <td>847431F3D5353AB7</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-12 13:36:55</td>\n",
       "      <td>2021-12-12 13:56:08</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595059</th>\n",
       "      <td>CF407BBC3B9FAD63</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-06 19:37:50</td>\n",
       "      <td>2021-12-06 19:44:51</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595060</th>\n",
       "      <td>60BB69EBF5440E92</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-02 08:57:04</td>\n",
       "      <td>2021-12-02 09:05:21</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595061</th>\n",
       "      <td>C414F654A28635B8</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-13 09:00:26</td>\n",
       "      <td>2021-12-13 09:14:39</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595062</th>\n",
       "      <td>37AC57E34B2E7E97</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-12-13 08:45:32</td>\n",
       "      <td>2021-12-13 08:49:09</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5595063 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id  rideable_type           started_at  \\\n",
       "0        E19E6F1B8D4C42ED  electric_bike  2021-01-23 16:14:19   \n",
       "1        DC88F20C2C55F27F  electric_bike  2021-01-27 18:43:08   \n",
       "2        EC45C94683FE3F27  electric_bike  2021-01-21 22:35:54   \n",
       "3        4FA453A75AE377DB  electric_bike  2021-01-07 13:31:13   \n",
       "4        BE5E8EB4E7263A0B  electric_bike  2021-01-23 02:24:02   \n",
       "...                   ...            ...                  ...   \n",
       "5595058  847431F3D5353AB7  electric_bike  2021-12-12 13:36:55   \n",
       "5595059  CF407BBC3B9FAD63  electric_bike  2021-12-06 19:37:50   \n",
       "5595060  60BB69EBF5440E92  electric_bike  2021-12-02 08:57:04   \n",
       "5595061  C414F654A28635B8  electric_bike  2021-12-13 09:00:26   \n",
       "5595062  37AC57E34B2E7E97   classic_bike  2021-12-13 08:45:32   \n",
       "\n",
       "                    ended_at member_casual  \n",
       "0        2021-01-23 16:24:44        member  \n",
       "1        2021-01-27 18:47:12        member  \n",
       "2        2021-01-21 22:37:14        member  \n",
       "3        2021-01-07 13:42:55        member  \n",
       "4        2021-01-23 02:24:45        casual  \n",
       "...                      ...           ...  \n",
       "5595058  2021-12-12 13:56:08        casual  \n",
       "5595059  2021-12-06 19:44:51        member  \n",
       "5595060  2021-12-02 09:05:21        member  \n",
       "5595061  2021-12-13 09:14:39        member  \n",
       "5595062  2021-12-13 08:49:09        member  \n",
       "\n",
       "[5595063 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GETTING TO KNOW THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for duplicates\n",
    "mergeddata.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>member_casual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5595063</td>\n",
       "      <td>5595063</td>\n",
       "      <td>5595063</td>\n",
       "      <td>5595063</td>\n",
       "      <td>5595063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5595063</td>\n",
       "      <td>3</td>\n",
       "      <td>4677998</td>\n",
       "      <td>4671372</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>E19E6F1B8D4C42ED</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-07-16 16:59:57</td>\n",
       "      <td>2021-09-16 08:39:10</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>3251028</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>3066058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ride_id rideable_type           started_at  \\\n",
       "count            5595063       5595063              5595063   \n",
       "unique           5595063             3              4677998   \n",
       "top     E19E6F1B8D4C42ED  classic_bike  2021-07-16 16:59:57   \n",
       "freq                   1       3251028                    7   \n",
       "\n",
       "                   ended_at member_casual  \n",
       "count               5595063       5595063  \n",
       "unique              4671372             2  \n",
       "top     2021-09-16 08:39:10        member  \n",
       "freq                     17       3066058  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['electric_bike', 'classic_bike', 'docked_bike'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.rideable_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['member', 'casual'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.member_casual.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id          object\n",
       "rideable_type    object\n",
       "started_at       object\n",
       "ended_at         object\n",
       "member_casual    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DATA WRANGLING AND MANIPULATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting into datetime datatypes for the respective columns\n",
    "mergeddata['started_at'] = pd.to_datetime(mergeddata['started_at'])\n",
    "mergeddata['ended_at'] = pd.to_datetime(mergeddata['ended_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mergeddata = mergeddata.rename(columns = {'member_casual':'membership'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Feature extraction\n",
    "mergeddata['day'] = mergeddata['started_at'].dt.day_name()\n",
    "mergeddata['month'] = mergeddata['ended_at'].dt.month_name()\n",
    "mergeddata['hour'] = mergeddata['started_at'].dt.hour\n",
    "mergeddata['ride_length'] = (mergeddata['ended_at'] - mergeddata['started_at']).dt.total_seconds()/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>membership</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E19E6F1B8D4C42ED</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 16:14:19</td>\n",
       "      <td>2021-01-23 16:24:44</td>\n",
       "      <td>member</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "      <td>16</td>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC88F20C2C55F27F</td>\n",
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       "      <td>2021-01-27 18:47:12</td>\n",
       "      <td>member</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>18</td>\n",
       "      <td>4.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EC45C94683FE3F27</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-21 22:35:54</td>\n",
       "      <td>2021-01-21 22:37:14</td>\n",
       "      <td>member</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "      <td>22</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4FA453A75AE377DB</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-07 13:31:13</td>\n",
       "      <td>2021-01-07 13:42:55</td>\n",
       "      <td>member</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "      <td>13</td>\n",
       "      <td>11.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE5E8EB4E7263A0B</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-01-23 02:24:02</td>\n",
       "      <td>2021-01-23 02:24:45</td>\n",
       "      <td>casual</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>January</td>\n",
       "      <td>2</td>\n",
       "      <td>0.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5595058</th>\n",
       "      <td>847431F3D5353AB7</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-12 13:36:55</td>\n",
       "      <td>2021-12-12 13:56:08</td>\n",
       "      <td>casual</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>December</td>\n",
       "      <td>13</td>\n",
       "      <td>19.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595059</th>\n",
       "      <td>CF407BBC3B9FAD63</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-06 19:37:50</td>\n",
       "      <td>2021-12-06 19:44:51</td>\n",
       "      <td>member</td>\n",
       "      <td>Monday</td>\n",
       "      <td>December</td>\n",
       "      <td>19</td>\n",
       "      <td>7.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595060</th>\n",
       "      <td>60BB69EBF5440E92</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-02 08:57:04</td>\n",
       "      <td>2021-12-02 09:05:21</td>\n",
       "      <td>member</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>December</td>\n",
       "      <td>8</td>\n",
       "      <td>8.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595061</th>\n",
       "      <td>C414F654A28635B8</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-12-13 09:00:26</td>\n",
       "      <td>2021-12-13 09:14:39</td>\n",
       "      <td>member</td>\n",
       "      <td>Monday</td>\n",
       "      <td>December</td>\n",
       "      <td>9</td>\n",
       "      <td>14.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5595062</th>\n",
       "      <td>37AC57E34B2E7E97</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-12-13 08:45:32</td>\n",
       "      <td>2021-12-13 08:49:09</td>\n",
       "      <td>member</td>\n",
       "      <td>Monday</td>\n",
       "      <td>December</td>\n",
       "      <td>8</td>\n",
       "      <td>3.616667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5595063 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id  rideable_type          started_at  \\\n",
       "0        E19E6F1B8D4C42ED  electric_bike 2021-01-23 16:14:19   \n",
       "1        DC88F20C2C55F27F  electric_bike 2021-01-27 18:43:08   \n",
       "2        EC45C94683FE3F27  electric_bike 2021-01-21 22:35:54   \n",
       "3        4FA453A75AE377DB  electric_bike 2021-01-07 13:31:13   \n",
       "4        BE5E8EB4E7263A0B  electric_bike 2021-01-23 02:24:02   \n",
       "...                   ...            ...                 ...   \n",
       "5595058  847431F3D5353AB7  electric_bike 2021-12-12 13:36:55   \n",
       "5595059  CF407BBC3B9FAD63  electric_bike 2021-12-06 19:37:50   \n",
       "5595060  60BB69EBF5440E92  electric_bike 2021-12-02 08:57:04   \n",
       "5595061  C414F654A28635B8  electric_bike 2021-12-13 09:00:26   \n",
       "5595062  37AC57E34B2E7E97   classic_bike 2021-12-13 08:45:32   \n",
       "\n",
       "                   ended_at membership        day     month  hour  ride_length  \n",
       "0       2021-01-23 16:24:44     member   Saturday   January    16    10.416667  \n",
       "1       2021-01-27 18:47:12     member  Wednesday   January    18     4.066667  \n",
       "2       2021-01-21 22:37:14     member   Thursday   January    22     1.333333  \n",
       "3       2021-01-07 13:42:55     member   Thursday   January    13    11.700000  \n",
       "4       2021-01-23 02:24:45     casual   Saturday   January     2     0.716667  \n",
       "...                     ...        ...        ...       ...   ...          ...  \n",
       "5595058 2021-12-12 13:56:08     casual     Sunday  December    13    19.216667  \n",
       "5595059 2021-12-06 19:44:51     member     Monday  December    19     7.016667  \n",
       "5595060 2021-12-02 09:05:21     member   Thursday  December     8     8.283333  \n",
       "5595061 2021-12-13 09:14:39     member     Monday  December     9    14.216667  \n",
       "5595062 2021-12-13 08:49:09     member     Monday  December     8     3.616667  \n",
       "\n",
       "[5595063 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5044266</th>\n",
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       "      <td>2021-11-07 01:00:06</td>\n",
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       "      <td>Sunday</td>\n",
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       "    <tr>\n",
       "      <th>5113400</th>\n",
       "      <td>508B09A5FB0737DC</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-11-07 01:54:50</td>\n",
       "      <td>2021-11-07 01:00:45</td>\n",
       "      <td>member</td>\n",
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       "      <td>November</td>\n",
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       "      <th>5248365</th>\n",
       "      <td>6F9E76F5EDAAC1B8</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-11-07 01:55:42</td>\n",
       "      <td>2021-11-07 01:01:55</td>\n",
       "      <td>member</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>November</td>\n",
       "      <td>1</td>\n",
       "      <td>-53.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5270218</th>\n",
       "      <td>7AECC76D1562B51C</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-11-07 01:54:58</td>\n",
       "      <td>2021-11-07 01:01:29</td>\n",
       "      <td>casual</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>November</td>\n",
       "      <td>1</td>\n",
       "      <td>-53.483333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ride_id  rideable_type          started_at  \\\n",
       "5044266  7CA158F5F050156E  electric_bike 2021-11-07 01:58:08   \n",
       "5129636  FD8AF7324ABAE9DA  electric_bike 2021-11-07 01:56:51   \n",
       "5113400  508B09A5FB0737DC   classic_bike 2021-11-07 01:54:50   \n",
       "5248365  6F9E76F5EDAAC1B8  electric_bike 2021-11-07 01:55:42   \n",
       "5270218  7AECC76D1562B51C   classic_bike 2021-11-07 01:54:58   \n",
       "\n",
       "                   ended_at membership     day     month  hour  ride_length  \n",
       "5044266 2021-11-07 01:00:06     casual  Sunday  November     1   -58.033333  \n",
       "5129636 2021-11-07 01:00:57     casual  Sunday  November     1   -55.900000  \n",
       "5113400 2021-11-07 01:00:45     member  Sunday  November     1   -54.083333  \n",
       "5248365 2021-11-07 01:01:55     member  Sunday  November     1   -53.783333  \n",
       "5270218 2021-11-07 01:01:29     casual  Sunday  November     1   -53.483333  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Sorting the ride length in ascending order\n",
    "mergeddata.sort_values(by=['ride_length'], axis = 0, inplace = True)\n",
    "mergeddata.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85233"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The total number of negative ride_lengths and those less than 60 seconds.\n",
    "mergeddata[mergeddata['ride_length']<1]['ride_length'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85233"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The number of negative values and those less than 60 seconds\n",
    "len(mergeddata[mergeddata['ride_length']<1])\n",
    "\n",
    "## 85233 is only 1.5% of the total data thus can be deleted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Assign the negative values and those less than 60 seconds to a variable\n",
    "Bad_Data = mergeddata[ (mergeddata['ride_length']<1)].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop the bad data\n",
    "mergeddata.drop(Bad_Data, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>membership</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>ride_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A18D0099C32F7239</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2021-08-13 13:40:35</td>\n",
       "      <td>2021-08-13 13:41:35</td>\n",
       "      <td>member</td>\n",
       "      <td>Friday</td>\n",
       "      <td>August</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5F884BFB1A6265FD</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-05-27 14:14:35</td>\n",
       "      <td>2021-05-27 14:15:35</td>\n",
       "      <td>member</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>May</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACD14297A4F64102</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-10-29 23:55:17</td>\n",
       "      <td>2021-10-29 23:56:17</td>\n",
       "      <td>member</td>\n",
       "      <td>Friday</td>\n",
       "      <td>October</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CFDB24DFFD0A4CA9</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-11-07 22:50:56</td>\n",
       "      <td>2021-11-07 22:51:56</td>\n",
       "      <td>member</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>November</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B6E3BD1790D2BC64</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2021-09-18 11:15:51</td>\n",
       "      <td>2021-09-18 11:16:51</td>\n",
       "      <td>member</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>September</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  A18D0099C32F7239   classic_bike 2021-08-13 13:40:35 2021-08-13 13:41:35   \n",
       "1  5F884BFB1A6265FD  electric_bike 2021-05-27 14:14:35 2021-05-27 14:15:35   \n",
       "2  ACD14297A4F64102  electric_bike 2021-10-29 23:55:17 2021-10-29 23:56:17   \n",
       "3  CFDB24DFFD0A4CA9  electric_bike 2021-11-07 22:50:56 2021-11-07 22:51:56   \n",
       "4  B6E3BD1790D2BC64  electric_bike 2021-09-18 11:15:51 2021-09-18 11:16:51   \n",
       "\n",
       "  membership       day      month  hour  ride_length  \n",
       "0     member    Friday     August    13          1.0  \n",
       "1     member  Thursday        May    14          1.0  \n",
       "2     member    Friday    October    23          1.0  \n",
       "3     member    Sunday   November    22          1.0  \n",
       "4     member  Saturday  September    11          1.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To reset the index\n",
    "mergeddata.reset_index(drop=True).head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5509830"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## To ascertain the right number of rows after dropping the bad data. i.e, 5595063 - 85233\n",
    "mergeddata.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id          0\n",
       "rideable_type    0\n",
       "started_at       0\n",
       "ended_at         0\n",
       "membership       0\n",
       "day              0\n",
       "month            0\n",
       "hour             0\n",
       "ride_length      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Inspect the data since new fields have been populated\n",
    "mergeddata.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                  object\n",
       "rideable_type            object\n",
       "started_at       datetime64[ns]\n",
       "ended_at         datetime64[ns]\n",
       "membership               object\n",
       "day                      object\n",
       "month                    object\n",
       "hour                      int64\n",
       "ride_length             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## A defined and ordered category for month and day ( This allows the months and days to appear in order when plotting)\n",
    "Months = [\"Januray\",'February','March', 'April', 'May', 'June','July','August','September','October','November','December']\n",
    "category1_type = CategoricalDtype(categories=Months, ordered=True) ## created an ordered category from the list \"months\"\n",
    "mergeddata['month'] = mergeddata['month'].astype(category1_type)\n",
    "\n",
    "Days = [\"Monday\",'Tuesday','Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday']\n",
    "category2_type = CategoricalDtype(categories=Days, ordered=True)\n",
    "mergeddata['day']=mergeddata['day'].astype(category2_type)\n",
    "\n",
    "mergeddata['ride_length'] = mergeddata['ride_length'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                  object\n",
       "rideable_type            object\n",
       "started_at       datetime64[ns]\n",
       "ended_at         datetime64[ns]\n",
       "membership               object\n",
       "day                    category\n",
       "month                  category\n",
       "hour                      int64\n",
       "ride_length               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANALYSIS AND VISUALIZATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANALYZING THE DIFFERENCE BETWEEN CASUAL RIDERS AND MEMBERS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. The difference in total rides per each member type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "membership\n",
       "casual    2494892\n",
       "member    3014938\n",
       "Name: ride_id, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeddata.groupby(['membership'])['ride_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.set_theme(style=\"darkgrid\")\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.countplot(y=\"membership\", data = mergeddata, hue=\"membership\")\n",
    "plt.ticklabel_format(style='plain', axis=\"x\") ## to change from scientific notation\n",
    "plt.xlabel(\"Number of Rides\")\n",
    "plt.ylabel(\"Membership Type\")\n",
    "plt.title(\"TOTAL NUMBER OF RIDES BETWEEN MEMBERS IN 2021 \")\n",
    "plt.xticks(rotation = 45, horizontalalignment = \"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. The total ride lengths (Casual vs. Members) in 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = mergeddata.groupby(['membership'], as_index=False)['ride_length'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.barplot(data = df1, x='ride_length', y ='membership', hue = 'membership')\n",
    "plt.ticklabel_format(style='plain', axis=\"x\") ## to change from scientific notation\n",
    "plt.title('TOTAL RIDE LENGTH IN 2021 (CASUAL RIDERS VS. MEMBERS)')\n",
    "plt.xlabel('TOTAL RIDE LENGTH IN MINUTES')\n",
    "plt.ylabel('MEMBER TYPE')\n",
    "plt.xticks(rotation = 45, horizontalalignment = \"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Total number of rides in each month between casual riders and members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Januray</th>\n",
       "      <th>casual</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">February</th>\n",
       "      <th>casual</th>\n",
       "      <td>9957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>38684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">March</th>\n",
       "      <th>casual</th>\n",
       "      <td>83136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>142366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">April</th>\n",
       "      <th>casual</th>\n",
       "      <td>134894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>197459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">May</th>\n",
       "      <th>casual</th>\n",
       "      <td>253253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>269902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">June</th>\n",
       "      <th>casual</th>\n",
       "      <td>365040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>352661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">July</th>\n",
       "      <th>casual</th>\n",
       "      <td>435622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>373781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">August</th>\n",
       "      <th>casual</th>\n",
       "      <td>407903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>385494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">September</th>\n",
       "      <th>casual</th>\n",
       "      <td>359206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>385949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">October</th>\n",
       "      <th>casual</th>\n",
       "      <td>253857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>367474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">November</th>\n",
       "      <th>casual</th>\n",
       "      <td>105423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>248701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">December</th>\n",
       "      <th>casual</th>\n",
       "      <td>68703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>174880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Count</th>\n",
       "      <th></th>\n",
       "      <td>5414345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count\n",
       "                        ride_id\n",
       "month       membership         \n",
       "Januray     casual            0\n",
       "            member            0\n",
       "February    casual         9957\n",
       "            member        38684\n",
       "March       casual        83136\n",
       "            member       142366\n",
       "April       casual       134894\n",
       "            member       197459\n",
       "May         casual       253253\n",
       "            member       269902\n",
       "June        casual       365040\n",
       "            member       352661\n",
       "July        casual       435622\n",
       "            member       373781\n",
       "August      casual       407903\n",
       "            member       385494\n",
       "September   casual       359206\n",
       "            member       385949\n",
       "October     casual       253857\n",
       "            member       367474\n",
       "November    casual       105423\n",
       "            member       248701\n",
       "December    casual        68703\n",
       "            member       174880\n",
       "Total Count             5414345"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_1 = mergeddata.pivot_table(\n",
    "index=[\"month\",\"membership\"],\n",
    "values=\"ride_id\",\n",
    "aggfunc= ['count'],\n",
    "margins=True,\n",
    "margins_name= 'Total Count')\n",
    "pivot_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot1_df = mergeddata.groupby(['month','membership'], as_index=False)[['ride_id']].count()\n",
    "plt.figure(figsize=(18,6))\n",
    "sn.lineplot(data = pivot1_df , x = \"month\", y='ride_id', hue = \"membership\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel('No. of Rides')\n",
    "plt.title(\"Total Monthly Rides Between Casual Riders and Members\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. The most popular type of ride between the members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rideable_type</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">classic_bike</th>\n",
       "      <th>casual</th>\n",
       "      <td>1253395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>1955673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">docked_bike</th>\n",
       "      <th>casual</th>\n",
       "      <td>310390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">electric_bike</th>\n",
       "      <th>casual</th>\n",
       "      <td>931107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>1059264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Count</th>\n",
       "      <th></th>\n",
       "      <td>5509830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count\n",
       "                          ride_id\n",
       "rideable_type membership         \n",
       "classic_bike  casual      1253395\n",
       "              member      1955673\n",
       "docked_bike   casual       310390\n",
       "              member            1\n",
       "electric_bike casual       931107\n",
       "              member      1059264\n",
       "Total_Count               5509830"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_2 = mergeddata.pivot_table(\n",
    "    index=['rideable_type', 'membership'],\n",
    "    values=['ride_id'],\n",
    "    aggfunc=['count'],\n",
    "    margins=True,\n",
    "    margins_name= \"Total_Count\"\n",
    ")\n",
    "pivot_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot2_df = mergeddata.groupby(['rideable_type','membership'], as_index=False)['ride_id'].count()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.barplot(data = pivot2_df, x= \"rideable_type\", y='ride_id', hue='membership')\n",
    "plt.ticklabel_format(style='plain', axis=\"y\")\n",
    "plt.title('THE MOST POPULAR RIDE TYPES BETWEEN MEMBER TYPES')\n",
    "plt.xlabel('RIDE TYPE')\n",
    "plt.ylabel('NO. OF RIDES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Total usage per day between casual riders and members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Monday</th>\n",
       "      <th>casual</th>\n",
       "      <td>282545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>409354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tuesday</th>\n",
       "      <th>casual</th>\n",
       "      <td>270713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>458153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wednesday</th>\n",
       "      <th>casual</th>\n",
       "      <td>275188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>469601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thursday</th>\n",
       "      <th>casual</th>\n",
       "      <td>282311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>444440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Friday</th>\n",
       "      <th>casual</th>\n",
       "      <td>359117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>438997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Saturday</th>\n",
       "      <th>casual</th>\n",
       "      <td>550484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>425175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sunday</th>\n",
       "      <th>casual</th>\n",
       "      <td>474534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>369218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <th></th>\n",
       "      <td>5509830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count\n",
       "                      ride_id\n",
       "day       membership         \n",
       "Monday    casual       282545\n",
       "          member       409354\n",
       "Tuesday   casual       270713\n",
       "          member       458153\n",
       "Wednesday casual       275188\n",
       "          member       469601\n",
       "Thursday  casual       282311\n",
       "          member       444440\n",
       "Friday    casual       359117\n",
       "          member       438997\n",
       "Saturday  casual       550484\n",
       "          member       425175\n",
       "Sunday    casual       474534\n",
       "          member       369218\n",
       "Total                 5509830"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_3 = mergeddata.pivot_table(\n",
    "    index=['day','membership'],\n",
    "    values=['ride_id'],\n",
    "    aggfunc=['count'],\n",
    "    margins=True,\n",
    "    margins_name= \"Total\"\n",
    ")\n",
    "pivot_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot3_df=mergeddata.groupby(['day','membership'], as_index=False)['ride_id'].count()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.lineplot(data = pivot3_df, x=\"day\", y='ride_id', hue='membership', markers=True)\n",
    "plt.title('TOTAL RIDES FOR EACH DAY OF THE WEEK (CASUAL VS. MEMBERS)')\n",
    "plt.xlabel('DAYS OF THE WEEK')\n",
    "plt.ylabel('TOTAL RIDES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. The average / total number of rides per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>casual</th>\n",
       "      <td>53099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>32481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>casual</th>\n",
       "      <td>38659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>21442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>casual</th>\n",
       "      <td>25179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>casual</th>\n",
       "      <td>13875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>7026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>casual</th>\n",
       "      <td>9801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>8006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>casual</th>\n",
       "      <td>12358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>29604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>casual</th>\n",
       "      <td>25406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>80649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>casual</th>\n",
       "      <td>46152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>147619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>casual</th>\n",
       "      <td>62780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>170673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>casual</th>\n",
       "      <td>75191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>128767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>casual</th>\n",
       "      <td>103332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>127090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
       "      <th>casual</th>\n",
       "      <td>134131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>152778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
       "      <th>casual</th>\n",
       "      <td>159795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>177628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
       "      <th>casual</th>\n",
       "      <td>171071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>174798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
       "      <th>casual</th>\n",
       "      <td>176047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>171589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15</th>\n",
       "      <th>casual</th>\n",
       "      <td>186070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>197620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
       "      <th>casual</th>\n",
       "      <td>202590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>253307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
       "      <th>casual</th>\n",
       "      <td>233544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>315080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">18</th>\n",
       "      <th>casual</th>\n",
       "      <td>210980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>267020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">19</th>\n",
       "      <th>casual</th>\n",
       "      <td>164053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>191027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>casual</th>\n",
       "      <td>120428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>129219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">21</th>\n",
       "      <th>casual</th>\n",
       "      <td>102392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>95424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">22</th>\n",
       "      <th>casual</th>\n",
       "      <td>94922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>73217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
       "      <th>casual</th>\n",
       "      <td>73037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>50687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Count</th>\n",
       "      <th></th>\n",
       "      <td>5509830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count\n",
       "                        ride_id\n",
       "hour        membership         \n",
       "0           casual        53099\n",
       "            member        32481\n",
       "1           casual        38659\n",
       "            member        21442\n",
       "2           casual        25179\n",
       "            member        12187\n",
       "3           casual        13875\n",
       "            member         7026\n",
       "4           casual         9801\n",
       "            member         8006\n",
       "5           casual        12358\n",
       "            member        29604\n",
       "6           casual        25406\n",
       "            member        80649\n",
       "7           casual        46152\n",
       "            member       147619\n",
       "8           casual        62780\n",
       "            member       170673\n",
       "9           casual        75191\n",
       "            member       128767\n",
       "10          casual       103332\n",
       "            member       127090\n",
       "11          casual       134131\n",
       "            member       152778\n",
       "12          casual       159795\n",
       "            member       177628\n",
       "13          casual       171071\n",
       "            member       174798\n",
       "14          casual       176047\n",
       "            member       171589\n",
       "15          casual       186070\n",
       "            member       197620\n",
       "16          casual       202590\n",
       "            member       253307\n",
       "17          casual       233544\n",
       "            member       315080\n",
       "18          casual       210980\n",
       "            member       267020\n",
       "19          casual       164053\n",
       "            member       191027\n",
       "20          casual       120428\n",
       "            member       129219\n",
       "21          casual       102392\n",
       "            member        95424\n",
       "22          casual        94922\n",
       "            member        73217\n",
       "23          casual        73037\n",
       "            member        50687\n",
       "Total Count             5509830"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_4 = mergeddata.pivot_table(\n",
    "    index=['hour','membership'],\n",
    "    values=['ride_id'],\n",
    "    aggfunc=['count'],\n",
    "    margins=True,\n",
    "    margins_name=\"Total Count\"\n",
    ")\n",
    "pivot_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot4_df = mergeddata.groupby(['hour','membership'], as_index=False)['ride_id'].count()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.lineplot(data=pivot4_df, x=\"hour\",y='ride_id', hue='membership')\n",
    "plt.title('NO. OF RIDES PER HOUR (CASUAL VS. MEMBERS)')\n",
    "plt.xlabel('HOUR')\n",
    "plt.ylabel('NO. OF RIDES')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Average ride length per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>casual</th>\n",
       "      <td>34.199288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.401496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>casual</th>\n",
       "      <td>37.131638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>14.591036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>casual</th>\n",
       "      <td>43.410858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>15.347994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>casual</th>\n",
       "      <td>44.989766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>16.209081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>casual</th>\n",
       "      <td>46.986124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.251187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>casual</th>\n",
       "      <td>23.533662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>11.510505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>casual</th>\n",
       "      <td>21.044753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>11.865367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>casual</th>\n",
       "      <td>20.712754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>11.991573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>casual</th>\n",
       "      <td>23.822029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>11.869681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">9</th>\n",
       "      <th>casual</th>\n",
       "      <td>28.320796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.311291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.256397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.243843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">11</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.803051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.437772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">12</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.372064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.185686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">13</th>\n",
       "      <th>casual</th>\n",
       "      <td>34.431452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.413260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14</th>\n",
       "      <th>casual</th>\n",
       "      <td>34.729322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.912110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">15</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.977154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.793695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16</th>\n",
       "      <th>casual</th>\n",
       "      <td>31.500844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.835843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">17</th>\n",
       "      <th>casual</th>\n",
       "      <td>29.669514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>14.002295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">18</th>\n",
       "      <th>casual</th>\n",
       "      <td>29.807636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.861512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">19</th>\n",
       "      <th>casual</th>\n",
       "      <td>30.249048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.648652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>casual</th>\n",
       "      <td>32.024513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.576881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">21</th>\n",
       "      <th>casual</th>\n",
       "      <td>32.172982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.229376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">22</th>\n",
       "      <th>casual</th>\n",
       "      <td>32.355745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.406040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">23</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.459534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.930534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean\n",
       "                ride_length\n",
       "hour membership            \n",
       "0    casual       34.199288\n",
       "     member       13.401496\n",
       "1    casual       37.131638\n",
       "     member       14.591036\n",
       "2    casual       43.410858\n",
       "     member       15.347994\n",
       "3    casual       44.989766\n",
       "     member       16.209081\n",
       "4    casual       46.986124\n",
       "     member       13.251187\n",
       "5    casual       23.533662\n",
       "     member       11.510505\n",
       "6    casual       21.044753\n",
       "     member       11.865367\n",
       "7    casual       20.712754\n",
       "     member       11.991573\n",
       "8    casual       23.822029\n",
       "     member       11.869681\n",
       "9    casual       28.320796\n",
       "     member       12.311291\n",
       "10   casual       33.256397\n",
       "     member       13.243843\n",
       "11   casual       33.803051\n",
       "     member       13.437772\n",
       "12   casual       33.372064\n",
       "     member       13.185686\n",
       "13   casual       34.431452\n",
       "     member       13.413260\n",
       "14   casual       34.729322\n",
       "     member       13.912110\n",
       "15   casual       33.977154\n",
       "     member       13.793695\n",
       "16   casual       31.500844\n",
       "     member       13.835843\n",
       "17   casual       29.669514\n",
       "     member       14.002295\n",
       "18   casual       29.807636\n",
       "     member       13.861512\n",
       "19   casual       30.249048\n",
       "     member       13.648652\n",
       "20   casual       32.024513\n",
       "     member       13.576881\n",
       "21   casual       32.172982\n",
       "     member       13.229376\n",
       "22   casual       32.355745\n",
       "     member       13.406040\n",
       "23   casual       33.459534\n",
       "     member       13.930534"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_5 = mergeddata.pivot_table(\n",
    "    index=['hour','membership'],\n",
    "    values=['ride_length'],\n",
    "    aggfunc=['mean']\n",
    ")\n",
    "pivot_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot5_df = mergeddata.groupby(['hour','membership'], as_index=False)['ride_length'].mean()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.lineplot(data = pivot5_df, x='hour', y='ride_length', hue='membership')\n",
    "plt.title('AVERAGE RIDE LENGTH PER HOUR (CASUAL VS. MEMBERS)')\n",
    "plt.xlabel('HOUR')\n",
    "plt.ylabel('AVERAGE RIDE LENGTH')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. The average ride length per day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Monday</th>\n",
       "      <th>casual</th>\n",
       "      <td>31.811113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.971457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tuesday</th>\n",
       "      <th>casual</th>\n",
       "      <td>27.855685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.495476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wednesday</th>\n",
       "      <th>casual</th>\n",
       "      <td>27.538279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.529075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thursday</th>\n",
       "      <th>casual</th>\n",
       "      <td>27.574328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.482677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Friday</th>\n",
       "      <th>casual</th>\n",
       "      <td>30.271313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.052720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Saturday</th>\n",
       "      <th>casual</th>\n",
       "      <td>34.683591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>15.048752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sunday</th>\n",
       "      <th>casual</th>\n",
       "      <td>37.590295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>15.451665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Average</th>\n",
       "      <th></th>\n",
       "      <td>21.777986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean\n",
       "                         ride_length\n",
       "day           membership            \n",
       "Monday        casual       31.811113\n",
       "              member       12.971457\n",
       "Tuesday       casual       27.855685\n",
       "              member       12.495476\n",
       "Wednesday     casual       27.538279\n",
       "              member       12.529075\n",
       "Thursday      casual       27.574328\n",
       "              member       12.482677\n",
       "Friday        casual       30.271313\n",
       "              member       13.052720\n",
       "Saturday      casual       34.683591\n",
       "              member       15.048752\n",
       "Sunday        casual       37.590295\n",
       "              member       15.451665\n",
       "Total Average              21.777986"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_6 = mergeddata.pivot_table(\n",
    "    index=['day','membership'],\n",
    "    values=['ride_length'],\n",
    "    aggfunc=['mean'],\n",
    "    margins=True,\n",
    "    margins_name=\"Total Average\"\n",
    ")\n",
    "pivot_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot6_df = mergeddata.groupby(['day','membership'], as_index=False)['ride_length'].mean()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.lineplot(data = pivot6_df, x='day', y='ride_length', hue='membership')\n",
    "plt.title('AVERAGE RIDE LENGTH PER DAY (CASUAL VS. MEMBERS)')\n",
    "plt.xlabel('DAY')\n",
    "plt.ylabel('AVERAGE RIDE LENGTH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Average ride length per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ride_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th>membership</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">February</th>\n",
       "      <th>casual</th>\n",
       "      <td>47.169529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>17.923560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">March</th>\n",
       "      <th>casual</th>\n",
       "      <td>37.004282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.671298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">April</th>\n",
       "      <th>casual</th>\n",
       "      <td>37.498169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>14.398336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">May</th>\n",
       "      <th>casual</th>\n",
       "      <td>36.609130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>14.399063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">June</th>\n",
       "      <th>casual</th>\n",
       "      <td>36.323291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>14.444180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">July</th>\n",
       "      <th>casual</th>\n",
       "      <td>33.448024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.982372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">August</th>\n",
       "      <th>casual</th>\n",
       "      <td>30.148324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.850942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">September</th>\n",
       "      <th>casual</th>\n",
       "      <td>27.644040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>13.450091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">October</th>\n",
       "      <th>casual</th>\n",
       "      <td>27.982116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>12.212848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">November</th>\n",
       "      <th>casual</th>\n",
       "      <td>24.151210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>11.047925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">December</th>\n",
       "      <th>casual</th>\n",
       "      <td>23.556206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member</th>\n",
       "      <td>10.690502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Average</th>\n",
       "      <th></th>\n",
       "      <td>21.891146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                mean\n",
       "                         ride_length\n",
       "month         membership            \n",
       "February      casual       47.169529\n",
       "              member       17.923560\n",
       "March         casual       37.004282\n",
       "              member       13.671298\n",
       "April         casual       37.498169\n",
       "              member       14.398336\n",
       "May           casual       36.609130\n",
       "              member       14.399063\n",
       "June          casual       36.323291\n",
       "              member       14.444180\n",
       "July          casual       33.448024\n",
       "              member       13.982372\n",
       "August        casual       30.148324\n",
       "              member       13.850942\n",
       "September     casual       27.644040\n",
       "              member       13.450091\n",
       "October       casual       27.982116\n",
       "              member       12.212848\n",
       "November      casual       24.151210\n",
       "              member       11.047925\n",
       "December      casual       23.556206\n",
       "              member       10.690502\n",
       "Total Average              21.891146"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_7 = mergeddata.pivot_table(\n",
    "    index=['month','membership'],\n",
    "    values=['ride_length'],\n",
    "    aggfunc=['mean'],\n",
    "    margins=True,\n",
    "    margins_name=\"Total Average\"\n",
    ")\n",
    "pivot_7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot7_df = mergeddata.groupby(['month','membership'], as_index=False)['ride_length'].mean()\n",
    "plt.figure(figsize=(12,6))\n",
    "sn.barplot(data = pivot7_df, x='month', y='ride_length', hue='membership')\n",
    "plt.title('AVERAGE RIDE LENGTH PER MONTH (CASUAL VS. MEMBERS)')\n",
    "plt.xlabel('MONTH')\n",
    "plt.ylabel('AVERAGE RIDE LENGTH')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "bbe66a7b688c3f95ead82791a7e89cd6d331fad3da6b1302ac5098f8988afc3c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}