DA_Cyclistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb4fb77d",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importing libraries to use\n",
    "import pandas as pd \n",
    "from datetime import datetime\n",
    "import geopy.distance\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0c09a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cyclistic's dataframe has 258678 rows and 13 columns\n"
     ]
    }
   ],
   "source": [
    "df_bikes = pd.read_csv(\"Trip_Data_032023.csv\")\n",
    "\n",
    "# Let's take a look at the data size we're dealing with:\n",
    "print(\"Cyclistic's dataframe has\", df_bikes.shape[0], \"rows and\", df_bikes.shape[1], \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4f3df16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>6842AA605EE9FBB3</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-16 08:20:34</td>\n",
       "      <td>2023-03-16 08:22:52</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>13146</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>13193</td>\n",
       "      <td>41.918413</td>\n",
       "      <td>-87.636451</td>\n",
       "      <td>41.921822</td>\n",
       "      <td>-87.644140</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F984267A75B99A8C</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-04 14:07:06</td>\n",
       "      <td>2023-03-04 14:15:31</td>\n",
       "      <td>Public Rack - Kedzie Ave &amp; Argyle St</td>\n",
       "      <td>491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.970000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FF7CF57CFE026D02</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-31 12:28:09</td>\n",
       "      <td>2023-03-31 12:38:47</td>\n",
       "      <td>Orleans St &amp; Chestnut St (NEXT Apts)</td>\n",
       "      <td>620</td>\n",
       "      <td>Clark St &amp; Randolph St</td>\n",
       "      <td>TA1305000030</td>\n",
       "      <td>41.898203</td>\n",
       "      <td>-87.637536</td>\n",
       "      <td>41.884576</td>\n",
       "      <td>-87.631890</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6B61B916032CB6D6</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-22 14:09:08</td>\n",
       "      <td>2023-03-22 14:24:51</td>\n",
       "      <td>Desplaines St &amp; Kinzie St</td>\n",
       "      <td>TA1306000003</td>\n",
       "      <td>Sheffield Ave &amp; Kingsbury St</td>\n",
       "      <td>13154</td>\n",
       "      <td>41.888716</td>\n",
       "      <td>-87.644448</td>\n",
       "      <td>41.910522</td>\n",
       "      <td>-87.653106</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E55E61A5F1260040</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-09 07:15:00</td>\n",
       "      <td>2023-03-09 07:26:00</td>\n",
       "      <td>Walsh Park</td>\n",
       "      <td>18067</td>\n",
       "      <td>Sangamon St &amp; Lake St</td>\n",
       "      <td>TA1306000015</td>\n",
       "      <td>41.914480</td>\n",
       "      <td>-87.668013</td>\n",
       "      <td>41.885779</td>\n",
       "      <td>-87.651025</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type           started_at             ended_at  \\\n",
       "0  6842AA605EE9FBB3  electric_bike  2023-03-16 08:20:34  2023-03-16 08:22:52   \n",
       "1  F984267A75B99A8C  electric_bike  2023-03-04 14:07:06  2023-03-04 14:15:31   \n",
       "2  FF7CF57CFE026D02   classic_bike  2023-03-31 12:28:09  2023-03-31 12:38:47   \n",
       "3  6B61B916032CB6D6   classic_bike  2023-03-22 14:09:08  2023-03-22 14:24:51   \n",
       "4  E55E61A5F1260040  electric_bike  2023-03-09 07:15:00  2023-03-09 07:26:00   \n",
       "\n",
       "                     start_station_name start_station_id  \\\n",
       "0               Clark St & Armitage Ave            13146   \n",
       "1  Public Rack - Kedzie Ave & Argyle St              491   \n",
       "2  Orleans St & Chestnut St (NEXT Apts)              620   \n",
       "3             Desplaines St & Kinzie St     TA1306000003   \n",
       "4                            Walsh Park            18067   \n",
       "\n",
       "               end_station_name end_station_id  start_lat  start_lng  \\\n",
       "0     Larrabee St & Webster Ave          13193  41.918413 -87.636451   \n",
       "1                           NaN            NaN  41.970000 -87.710000   \n",
       "2        Clark St & Randolph St   TA1305000030  41.898203 -87.637536   \n",
       "3  Sheffield Ave & Kingsbury St          13154  41.888716 -87.644448   \n",
       "4         Sangamon St & Lake St   TA1306000015  41.914480 -87.668013   \n",
       "\n",
       "     end_lat    end_lng member_casual  \n",
       "0  41.921822 -87.644140        member  \n",
       "1  41.950000 -87.710000        member  \n",
       "2  41.884576 -87.631890        member  \n",
       "3  41.910522 -87.653106        member  \n",
       "4  41.885779 -87.651025        member  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a quick look at the first 5 rows of the dataframe:\n",
    "df_bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9af75f5",
   "metadata": {},
   "source": [
    "The types are correct in relation to info displayed in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cfa94e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The column 'rideable_type' has 3 different types of bikes registered, which are: electric_bike 148575 classic_bike 107083 and docked_bike 3020\n"
     ]
    }
   ],
   "source": [
    "# We have some columns that have different types of data in it that may be worth to look at.\n",
    "print(\"The column 'rideable_type' has\", df_bikes['rideable_type'].nunique(), \"different types of bikes registered, which are:\",\n",
    "     df_bikes['rideable_type'].unique()[0], df_bikes['rideable_type'].value_counts()[0],\n",
    "      df_bikes['rideable_type'].unique()[1], df_bikes['rideable_type'].value_counts()[1], \"and\",\n",
    "       df_bikes['rideable_type'].unique()[2], df_bikes['rideable_type'].value_counts()[2] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce19ec05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 different types of users registered, which are: member 196477 and casual 62201\n"
     ]
    }
   ],
   "source": [
    "# We can also see what types of members have been registered in this dataframe and how many for each:\n",
    "print(\"There are\", df_bikes['member_casual'].nunique(), \"different types of users registered, which are:\",\n",
    "     df_bikes['member_casual'].unique()[0], df_bikes['member_casual'].value_counts()[0], \"and\",\n",
    "      df_bikes['member_casual'].unique()[1], df_bikes['member_casual'].value_counts()[1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "005deabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Member users (%): 75.954 Percentage of Casual users (%): 24.046\n"
     ]
    }
   ],
   "source": [
    "# In percentage this is represented as:\n",
    "member_percentage = (df_bikes['member_casual'].value_counts()[0]/df_bikes['member_casual'].value_counts().sum())*100\n",
    "casual_percentage = (df_bikes['member_casual'].value_counts()[1]/df_bikes['member_casual'].value_counts().sum())*100\n",
    "print(\"Percentage of Member users (%):\", round(member_percentage, 3),\n",
    "      \"Percentage of Casual users (%):\", round(casual_percentage, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f6e264",
   "metadata": {},
   "source": [
    "From the data we can see that the majority of users are members with ~76% from the total registered and casual users with ~24%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0443c0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    258678\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if there is any duplicated value in the dataframe:\n",
    "df_bikes.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "185ee37d-ec1f-4129-8f89-5ae032568ff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 258678 entries, 0 to 258677\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   ride_id             258678 non-null  object \n",
      " 1   rideable_type       258678 non-null  object \n",
      " 2   started_at          258678 non-null  object \n",
      " 3   ended_at            258678 non-null  object \n",
      " 4   start_station_name  222768 non-null  object \n",
      " 5   start_station_id    222768 non-null  object \n",
      " 6   end_station_name    220240 non-null  object \n",
      " 7   end_station_id      220240 non-null  object \n",
      " 8   start_lat           258678 non-null  float64\n",
      " 9   start_lng           258678 non-null  float64\n",
      " 10  end_lat             258495 non-null  float64\n",
      " 11  end_lng             258495 non-null  float64\n",
      " 12  member_casual       258678 non-null  object \n",
      "dtypes: float64(4), object(9)\n",
      "memory usage: 25.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# We can also check for the type of data that there is in each column:\n",
    "# df_bikes.dtypes\n",
    "df_bikes.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f4d3498b",
   "metadata": {},
   "source": [
    "Columns 'started at' and 'ended at' are time data, so are not an object type, we should change that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2dbdc046",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the data type of the columns\n",
    "df_bikes['started_at'] = pd.to_datetime(df_bikes['started_at'])\n",
    "df_bikes['ended_at'] = pd.to_datetime(df_bikes['ended_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a98fe3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                       object\n",
       "rideable_type                 object\n",
       "started_at            datetime64[ns]\n",
       "ended_at              datetime64[ns]\n",
       "start_station_name            object\n",
       "start_station_id              object\n",
       "end_station_name              object\n",
       "end_station_id                object\n",
       "start_lat                    float64\n",
       "start_lng                    float64\n",
       "end_lat                      float64\n",
       "end_lng                      float64\n",
       "member_casual                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can now check again if it's changed or not:\n",
    "df_bikes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1fd067b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's find at what time of the day the customers were using the service:\n",
    "list =[]\n",
    "for i in df_bikes[\"started_at\"]:\n",
    "    if i.hour < 12: \n",
    "        list.append(\"morning\")\n",
    "    if i.hour >= 12 and i.hour < 16: \n",
    "        list.append(\"afternoon\")\n",
    "    if i.hour >= 16 and i.hour < 19: \n",
    "        list.append(\"evening\")\n",
    "    if i.hour >= 19 and i.hour < 24:\n",
    "        list.append(\"night\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee6700f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>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",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6842AA605EE9FBB3</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-16 08:20:34</td>\n",
       "      <td>2023-03-16 08:22:52</td>\n",
       "      <td>Clark St &amp; Armitage Ave</td>\n",
       "      <td>13146</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>13193</td>\n",
       "      <td>41.918413</td>\n",
       "      <td>-87.636451</td>\n",
       "      <td>41.921822</td>\n",
       "      <td>-87.644140</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F984267A75B99A8C</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-04 14:07:06</td>\n",
       "      <td>2023-03-04 14:15:31</td>\n",
       "      <td>Public Rack - Kedzie Ave &amp; Argyle St</td>\n",
       "      <td>491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.970000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FF7CF57CFE026D02</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-31 12:28:09</td>\n",
       "      <td>2023-03-31 12:38:47</td>\n",
       "      <td>Orleans St &amp; Chestnut St (NEXT Apts)</td>\n",
       "      <td>620</td>\n",
       "      <td>Clark St &amp; Randolph St</td>\n",
       "      <td>TA1305000030</td>\n",
       "      <td>41.898203</td>\n",
       "      <td>-87.637536</td>\n",
       "      <td>41.884576</td>\n",
       "      <td>-87.631890</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6B61B916032CB6D6</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-22 14:09:08</td>\n",
       "      <td>2023-03-22 14:24:51</td>\n",
       "      <td>Desplaines St &amp; Kinzie St</td>\n",
       "      <td>TA1306000003</td>\n",
       "      <td>Sheffield Ave &amp; Kingsbury St</td>\n",
       "      <td>13154</td>\n",
       "      <td>41.888716</td>\n",
       "      <td>-87.644448</td>\n",
       "      <td>41.910522</td>\n",
       "      <td>-87.653106</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E55E61A5F1260040</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-09 07:15:00</td>\n",
       "      <td>2023-03-09 07:26:00</td>\n",
       "      <td>Walsh Park</td>\n",
       "      <td>18067</td>\n",
       "      <td>Sangamon St &amp; Lake St</td>\n",
       "      <td>TA1306000015</td>\n",
       "      <td>41.914480</td>\n",
       "      <td>-87.668013</td>\n",
       "      <td>41.885779</td>\n",
       "      <td>-87.651025</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  6842AA605EE9FBB3  electric_bike 2023-03-16 08:20:34 2023-03-16 08:22:52   \n",
       "1  F984267A75B99A8C  electric_bike 2023-03-04 14:07:06 2023-03-04 14:15:31   \n",
       "2  FF7CF57CFE026D02   classic_bike 2023-03-31 12:28:09 2023-03-31 12:38:47   \n",
       "3  6B61B916032CB6D6   classic_bike 2023-03-22 14:09:08 2023-03-22 14:24:51   \n",
       "4  E55E61A5F1260040  electric_bike 2023-03-09 07:15:00 2023-03-09 07:26:00   \n",
       "\n",
       "                     start_station_name start_station_id  \\\n",
       "0               Clark St & Armitage Ave            13146   \n",
       "1  Public Rack - Kedzie Ave & Argyle St              491   \n",
       "2  Orleans St & Chestnut St (NEXT Apts)              620   \n",
       "3             Desplaines St & Kinzie St     TA1306000003   \n",
       "4                            Walsh Park            18067   \n",
       "\n",
       "               end_station_name end_station_id  start_lat  start_lng  \\\n",
       "0     Larrabee St & Webster Ave          13193  41.918413 -87.636451   \n",
       "1                           NaN            NaN  41.970000 -87.710000   \n",
       "2        Clark St & Randolph St   TA1305000030  41.898203 -87.637536   \n",
       "3  Sheffield Ave & Kingsbury St          13154  41.888716 -87.644448   \n",
       "4         Sangamon St & Lake St   TA1306000015  41.914480 -87.668013   \n",
       "\n",
       "     end_lat    end_lng member_casual time_of_day  day_of_month  day_of_week  \n",
       "0  41.921822 -87.644140        member     morning            16            3  \n",
       "1  41.950000 -87.710000        member   afternoon             4            5  \n",
       "2  41.884576 -87.631890        member   afternoon            31            4  \n",
       "3  41.910522 -87.653106        member   afternoon            22            2  \n",
       "4  41.885779 -87.651025        member     morning             9            3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes[\"time_of_day\"] = pd.DataFrame(list)\n",
    "df_bikes[\"day_of_month\"] = df_bikes[\"started_at\"].dt.day    # Day of the month\n",
    "df_bikes[\"day_of_week\"] = df_bikes[\"started_at\"].dt.weekday # Day of the week\n",
    "\n",
    "df_bikes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0312c713",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes[\"started_hour\"] = df_bikes[\"started_at\"].dt.hour\n",
    "df_bikes[\"ended_hour\"] = df_bikes[\"ended_at\"].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7fdb10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                   0\n",
       "rideable_type             0\n",
       "started_at                0\n",
       "ended_at                  0\n",
       "start_station_name    35910\n",
       "start_station_id      35910\n",
       "end_station_name      38438\n",
       "end_station_id        38438\n",
       "start_lat                 0\n",
       "start_lng                 0\n",
       "end_lat                 183\n",
       "end_lng                 183\n",
       "member_casual             0\n",
       "time_of_day               0\n",
       "day_of_month              0\n",
       "day_of_week               0\n",
       "started_hour              0\n",
       "ended_hour                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's consider now the nan values that may have been registered, first we'll check if they exist in the dataframe:\n",
    "df_bikes.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "569958a0-82c3-4b5f-8b5a-60f8a1e60b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As percentages, the NaN values in the current data set would be:\n",
      " ride_id                0.000000\n",
      "rideable_type          0.000000\n",
      "started_at             0.000000\n",
      "ended_at               0.000000\n",
      "start_station_name    16.119910\n",
      "start_station_id      16.119910\n",
      "end_station_name      17.452779\n",
      "end_station_id        17.452779\n",
      "start_lat              0.000000\n",
      "start_lng              0.000000\n",
      "end_lat                0.070794\n",
      "end_lng                0.070794\n",
      "member_casual          0.000000\n",
      "time_of_day            0.000000\n",
      "day_of_month           0.000000\n",
      "day_of_week            0.000000\n",
      "started_hour           0.000000\n",
      "ended_hour             0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"As percentages, the NaN values in the current data set would be:\\n\",\n",
    "      (df_bikes.isna().sum()/df_bikes.count())*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59dd3cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "988"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.start_station_name.nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fad619ec-2a7b-4198-90ed-38b3b539b787",
   "metadata": {},
   "source": [
    "We have between 0% and ~15% of NaN values in the location columns. So far we've seen that according to the company's needs we're looking to have more annual member users rather than casual users, so it'd be good to consider how consistent the users are, through the time data and compare member users with casual users. \n",
    "\n",
    "For this reason, we're going to be considering the time columns and geolocation (long, lat) and disregard the location ones, besides this, as shown above in the previous line of code, there are over 900 station names so there isn't a pattern that can be easily identified from that data.\n",
    "\n",
    "And at the same time, regardless of the location, the user still wants to use the service for transportation, easy-to-use, money-saving and environmental reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "916729ce-5ba4-440f-b277-801bfc0d929d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rideable_type      0\n",
       "started_at         0\n",
       "ended_at           0\n",
       "start_lat          0\n",
       "start_lng          0\n",
       "end_lat          183\n",
       "end_lng          183\n",
       "member_casual      0\n",
       "time_of_day        0\n",
       "day_of_month       0\n",
       "day_of_week        0\n",
       "started_hour       0\n",
       "ended_hour         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing all NaN columns (columns with the location/stations data):\n",
    "df_bikes.drop(columns=[\"ride_id\", \"start_station_name\",\"start_station_id\",\"end_station_name\", \"end_station_id\"], inplace = True) \n",
    "# The ride ID is not a necessary feature in which our results may depend or vary\n",
    "df_bikes.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e93318d-afa6-4e3a-902f-a75e77b79223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rideable_type    0\n",
       "started_at       0\n",
       "ended_at         0\n",
       "start_lat        0\n",
       "start_lng        0\n",
       "end_lat          0\n",
       "end_lng          0\n",
       "member_casual    0\n",
       "time_of_day      0\n",
       "day_of_month     0\n",
       "day_of_week      0\n",
       "started_hour     0\n",
       "ended_hour       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Having few NaN values for the ending latitude and longitude, let's replace them for the mean, rather than dropping them,\n",
    "# since the rest of the information is available and could be useful to check:\n",
    "df_bikes[\"end_lat\"].fillna(value = df_bikes[\"end_lat\"].mean(), inplace = True)\n",
    "df_bikes[\"end_lng\"].fillna(value = df_bikes[\"end_lng\"].mean(), inplace = True)\n",
    "df_bikes.isna().sum() # Double checking if there is still a NaN value in any of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d5b8987-9e73-4a94-95af-0b9d22ea5c6c",
   "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>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</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",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>started_hour</th>\n",
       "      <th>ended_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-16 08:20:34</td>\n",
       "      <td>2023-03-16 08:22:52</td>\n",
       "      <td>41.918413</td>\n",
       "      <td>-87.636451</td>\n",
       "      <td>41.921822</td>\n",
       "      <td>-87.64414</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-04 14:07:06</td>\n",
       "      <td>2023-03-04 14:15:31</td>\n",
       "      <td>41.970000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-87.71000</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-31 12:28:09</td>\n",
       "      <td>2023-03-31 12:38:47</td>\n",
       "      <td>41.898203</td>\n",
       "      <td>-87.637536</td>\n",
       "      <td>41.884576</td>\n",
       "      <td>-87.63189</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rideable_type          started_at            ended_at  start_lat  \\\n",
       "0  electric_bike 2023-03-16 08:20:34 2023-03-16 08:22:52  41.918413   \n",
       "1  electric_bike 2023-03-04 14:07:06 2023-03-04 14:15:31  41.970000   \n",
       "2   classic_bike 2023-03-31 12:28:09 2023-03-31 12:38:47  41.898203   \n",
       "\n",
       "   start_lng    end_lat   end_lng member_casual time_of_day  day_of_month  \\\n",
       "0 -87.636451  41.921822 -87.64414        member     morning            16   \n",
       "1 -87.710000  41.950000 -87.71000        member   afternoon             4   \n",
       "2 -87.637536  41.884576 -87.63189        member   afternoon            31   \n",
       "\n",
       "   day_of_week  started_hour  ended_hour  \n",
       "0            3             8           8  \n",
       "1            5            14          14  \n",
       "2            4            12          12  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.head(3) # All okay ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebfe7618-cdbc-497e-b872-e7977a8bebc7",
   "metadata": {},
   "source": [
    "We can now take a deeper look into the user's preference when it comes to the type of bike and how much time they spend using the service.\n",
    "\n",
    "First, we're going to deal with the time length spent on each ride by the users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e29aee2-267d-416e-82a5-13ea2d715eba",
   "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>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</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",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>started_hour</th>\n",
       "      <th>ended_hour</th>\n",
       "      <th>time_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-16 08:20:34</td>\n",
       "      <td>2023-03-16 08:22:52</td>\n",
       "      <td>41.918413</td>\n",
       "      <td>-87.636451</td>\n",
       "      <td>41.921822</td>\n",
       "      <td>-87.64414</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0 days 00:02:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-03-04 14:07:06</td>\n",
       "      <td>2023-03-04 14:15:31</td>\n",
       "      <td>41.970000</td>\n",
       "      <td>-87.710000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-87.71000</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0 days 00:08:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-03-31 12:28:09</td>\n",
       "      <td>2023-03-31 12:38:47</td>\n",
       "      <td>41.898203</td>\n",
       "      <td>-87.637536</td>\n",
       "      <td>41.884576</td>\n",
       "      <td>-87.63189</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0 days 00:10:38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rideable_type          started_at            ended_at  start_lat  \\\n",
       "0  electric_bike 2023-03-16 08:20:34 2023-03-16 08:22:52  41.918413   \n",
       "1  electric_bike 2023-03-04 14:07:06 2023-03-04 14:15:31  41.970000   \n",
       "2   classic_bike 2023-03-31 12:28:09 2023-03-31 12:38:47  41.898203   \n",
       "\n",
       "   start_lng    end_lat   end_lng member_casual time_of_day  day_of_month  \\\n",
       "0 -87.636451  41.921822 -87.64414        member     morning            16   \n",
       "1 -87.710000  41.950000 -87.71000        member   afternoon             4   \n",
       "2 -87.637536  41.884576 -87.63189        member   afternoon            31   \n",
       "\n",
       "   day_of_week  started_hour  ended_hour     time_length  \n",
       "0            3             8           8 0 days 00:02:18  \n",
       "1            5            14          14 0 days 00:08:25  \n",
       "2            4            12          12 0 days 00:10:38  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the time spent (ride) by the user:\n",
    "df_bikes[\"time_length\"] = df_bikes[\"ended_at\"]-df_bikes[\"started_at\"]\n",
    "df_bikes.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9955a765-d973-4c07-98e4-bbc94e84dae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rideable_type             object\n",
       "started_at        datetime64[ns]\n",
       "ended_at          datetime64[ns]\n",
       "start_lat                float64\n",
       "start_lng                float64\n",
       "end_lat                  float64\n",
       "end_lng                  float64\n",
       "member_casual             object\n",
       "time_of_day               object\n",
       "day_of_month               int64\n",
       "day_of_week                int64\n",
       "started_hour               int64\n",
       "ended_hour                 int64\n",
       "time_length      timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "721e53e8-926a-4db1-8d2f-b3a6905c1307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                       258678\n",
       "mean     0 days 00:13:04.803017651\n",
       "std      0 days 01:38:11.450462766\n",
       "min                0 days 00:00:00\n",
       "25%                0 days 00:04:26\n",
       "50%                0 days 00:07:30\n",
       "75%                0 days 00:12:50\n",
       "max               11 days 16:08:04\n",
       "Name: time_length, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyzing what we got above:\n",
    "df_bikes[\"time_length\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0017dcf4",
   "metadata": {},
   "source": [
    "We can check time stats so far by the type of Cyclistic's costumer and also by type of bike:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "078e3541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time length and annual members:\n",
      " count                       196477\n",
      "mean     0 days 00:10:26.533273614\n",
      "std      0 days 00:26:47.888020927\n",
      "min                0 days 00:00:00\n",
      "25%                0 days 00:04:15\n",
      "50%                0 days 00:07:09\n",
      "75%                0 days 00:12:07\n",
      "max                1 days 01:59:40\n",
      "Name: time_length, dtype: object\n",
      "Time length and casual members:\n",
      " count                        62201\n",
      "mean     0 days 00:21:24.736531567\n",
      "std      0 days 03:14:15.595562775\n",
      "min                0 days 00:00:00\n",
      "25%                0 days 00:05:08\n",
      "50%                0 days 00:08:40\n",
      "75%                0 days 00:15:31\n",
      "max               11 days 16:08:04\n",
      "Name: time_length, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# For comparison, we can see the time_length stats as well for the annual customers only: \n",
    "print(\"Time length and annual members:\\n\", df_bikes[\"time_length\"][df_bikes[\"member_casual\"] == \"member\"].describe())\n",
    "# For comparison, we can see the time_length stats as well for the casual customers only:\n",
    "print(\"Time length and casual members:\\n\", df_bikes[\"time_length\"][df_bikes[\"member_casual\"] == \"casual\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97ed7fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time length and electric bike:\n",
      " count                       148575\n",
      "mean     0 days 00:09:46.513498233\n",
      "std      0 days 00:13:38.111896469\n",
      "min                0 days 00:00:00\n",
      "25%                0 days 00:04:10\n",
      "50%                0 days 00:06:58\n",
      "75%                0 days 00:11:39\n",
      "max                0 days 08:00:27\n",
      "Name: time_length, dtype: object\n",
      "Time length and classic bike:\n",
      " count                       107083\n",
      "mean     0 days 00:14:18.057095897\n",
      "std      0 days 00:56:54.949169450\n",
      "min                0 days 00:00:00\n",
      "25%                0 days 00:04:46\n",
      "50%                0 days 00:08:09\n",
      "75%                0 days 00:14:13\n",
      "max                1 days 01:59:56\n",
      "Name: time_length, dtype: object\n",
      "Time length and docked bike:\n",
      " count                         3020\n",
      "mean     0 days 02:12:22.617218543\n",
      "std      0 days 13:48:59.653208757\n",
      "min                0 days 00:00:05\n",
      "25%                0 days 00:12:06\n",
      "50%                0 days 00:21:31\n",
      "75%         0 days 00:38:14.500000\n",
      "max               11 days 16:08:04\n",
      "Name: time_length, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# In order to compare with the numbers that we have in our dataframe, we can see the time length stats for the 3 types of bike: \n",
    "print(\"Time length and electric bike:\\n\", df_bikes[\"time_length\"][df_bikes[\"rideable_type\"] == \"electric_bike\"].describe())\n",
    "print(\"Time length and classic bike:\\n\", df_bikes[\"time_length\"][df_bikes[\"rideable_type\"] == \"classic_bike\"].describe())\n",
    "print(\"Time length and docked bike:\\n\", df_bikes[\"time_length\"][df_bikes[\"rideable_type\"] == \"docked_bike\"].describe())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b58b4e2a",
   "metadata": {},
   "source": [
    "We can infere from this very quickly that **the docked bike takes longer than the classic bike. Being the electric the fastest with a mean of 14 minutes of time length in their trips.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d227df76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time of the day and annual members:\n",
      " count      196477\n",
      "unique          4\n",
      "top       morning\n",
      "freq        65838\n",
      "Name: time_of_day, dtype: object\n",
      "Time of the day and casual members:\n",
      " count         62201\n",
      "unique            4\n",
      "top       afternoon\n",
      "freq          18158\n",
      "Name: time_of_day, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"Time of the day and annual members:\\n\", df_bikes[\"time_of_day\"][df_bikes[\"member_casual\"] == \"member\"].describe())\n",
    "print(\"Time of the day and casual members:\\n\", df_bikes[\"time_of_day\"][df_bikes[\"member_casual\"] == \"casual\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6eb7c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time of the day and electric bike:\n",
      " count      148575\n",
      "unique          4\n",
      "top       morning\n",
      "freq        46946\n",
      "Name: time_of_day, dtype: object\n",
      "Time of the day and classic bike:\n",
      " count      107083\n",
      "unique          4\n",
      "top       morning\n",
      "freq        35298\n",
      "Name: time_of_day, dtype: object\n",
      "Time of the day and docked bike:\n",
      " count          3020\n",
      "unique            4\n",
      "top       afternoon\n",
      "freq           1209\n",
      "Name: time_of_day, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# In order to compare with the numbers that we have in our dataframe, we can see the time of the day stats for the 3 types of bike: \n",
    "print(\"Time of the day and electric bike:\\n\", df_bikes[\"time_of_day\"][df_bikes[\"rideable_type\"] == \"electric_bike\"].describe())\n",
    "print(\"Time of the day and classic bike:\\n\", df_bikes[\"time_of_day\"][df_bikes[\"rideable_type\"] == \"classic_bike\"].describe())\n",
    "print(\"Time of the day and docked bike:\\n\", df_bikes[\"time_of_day\"][df_bikes[\"rideable_type\"] == \"docked_bike\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20c89ca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day of the week and annual members:\n",
      " count    196477.00000\n",
      "mean          2.66647\n",
      "std           1.78586\n",
      "min           0.00000\n",
      "25%           1.00000\n",
      "50%           3.00000\n",
      "75%           4.00000\n",
      "max           6.00000\n",
      "Name: day_of_week, dtype: float64\n",
      "Day of the week and casual members:\n",
      " count    62201.000000\n",
      "mean         2.956512\n",
      "std          1.868702\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          3.000000\n",
      "75%          4.000000\n",
      "max          6.000000\n",
      "Name: day_of_week, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Day of the week and annual members:\\n\", df_bikes[\"day_of_week\"][df_bikes[\"member_casual\"] == \"member\"].describe())\n",
    "print(\"Day of the week and casual members:\\n\", df_bikes[\"day_of_week\"][df_bikes[\"member_casual\"] == \"casual\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18ec13d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day of the week and electric bike:\n",
      " count    148575.000000\n",
      "mean          2.747434\n",
      "std           1.803265\n",
      "min           0.000000\n",
      "25%           1.000000\n",
      "50%           3.000000\n",
      "75%           4.000000\n",
      "max           6.000000\n",
      "Name: day_of_week, dtype: float64\n",
      "Day of the week and classic bike:\n",
      " count    107083.000000\n",
      "mean          2.708506\n",
      "std           1.814200\n",
      "min           0.000000\n",
      "25%           1.000000\n",
      "50%           3.000000\n",
      "75%           4.000000\n",
      "max           6.000000\n",
      "Name: day_of_week, dtype: float64\n",
      "Day of the week and docked bike:\n",
      " count    3020.000000\n",
      "mean        3.166556\n",
      "std         1.957754\n",
      "min         0.000000\n",
      "25%         2.000000\n",
      "50%         3.000000\n",
      "75%         5.000000\n",
      "max         6.000000\n",
      "Name: day_of_week, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# In order to compare with the numbers that we have in our dataframe, we can see the day of the month stats for the 3 types of bike: \n",
    "print(\"Day of the week and electric bike:\\n\", df_bikes[\"day_of_week\"][df_bikes[\"rideable_type\"] == \"electric_bike\"].describe())\n",
    "print(\"Day of the week and classic bike:\\n\", df_bikes[\"day_of_week\"][df_bikes[\"rideable_type\"] == \"classic_bike\"].describe())\n",
    "print(\"Day of the week and docked bike:\\n\", df_bikes[\"day_of_week\"][df_bikes[\"rideable_type\"] == \"docked_bike\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e52e1da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day of the month and annual members:\n",
      " count    196477.000000\n",
      "mean         16.307018\n",
      "std           9.326074\n",
      "min           1.000000\n",
      "25%           8.000000\n",
      "50%          16.000000\n",
      "75%          24.000000\n",
      "max          31.000000\n",
      "Name: day_of_month, dtype: float64\n",
      "Day of the month and casual members:\n",
      " count    62201.000000\n",
      "mean        16.448417\n",
      "std          9.403356\n",
      "min          1.000000\n",
      "25%          8.000000\n",
      "50%         17.000000\n",
      "75%         25.000000\n",
      "max         31.000000\n",
      "Name: day_of_month, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Day of the month and annual members:\\n\", df_bikes[\"day_of_month\"][df_bikes[\"member_casual\"] == \"member\"].describe())\n",
    "print(\"Day of the month and casual members:\\n\", df_bikes[\"day_of_month\"][df_bikes[\"member_casual\"] == \"casual\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58c69c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day of the month and electric bike:\n",
      " count    148575.000000\n",
      "mean         16.550449\n",
      "std           9.313166\n",
      "min           1.000000\n",
      "25%           8.000000\n",
      "50%          17.000000\n",
      "75%          25.000000\n",
      "max          31.000000\n",
      "Name: day_of_month, dtype: float64\n",
      "Day of the month and classic bike:\n",
      " count    107083.000000\n",
      "mean         16.039474\n",
      "std           9.375897\n",
      "min           1.000000\n",
      "25%           7.000000\n",
      "50%          16.000000\n",
      "75%          24.000000\n",
      "max          31.000000\n",
      "Name: day_of_month, dtype: float64\n",
      "Day of the month and docked bike:\n",
      " count    3020.000000\n",
      "mean       16.729801\n",
      "std         9.507646\n",
      "min         1.000000\n",
      "25%         8.000000\n",
      "50%        17.000000\n",
      "75%        26.000000\n",
      "max        31.000000\n",
      "Name: day_of_month, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# In order to compare with the numbers that we have in our dataframe, we can see the day of the month stats for the 3 types of bike: \n",
    "print(\"Day of the month and electric bike:\\n\", df_bikes[\"day_of_month\"][df_bikes[\"rideable_type\"] == \"electric_bike\"].describe())\n",
    "print(\"Day of the month and classic bike:\\n\", df_bikes[\"day_of_month\"][df_bikes[\"rideable_type\"] == \"classic_bike\"].describe())\n",
    "print(\"Day of the month and docked bike:\\n\", df_bikes[\"day_of_month\"][df_bikes[\"rideable_type\"] == \"docked_bike\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f29a4fcb",
   "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>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>started_hour</th>\n",
       "      <th>ended_hour</th>\n",
       "      <th>time_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201.000000</td>\n",
       "      <td>62201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.900174</td>\n",
       "      <td>-87.650366</td>\n",
       "      <td>41.900803</td>\n",
       "      <td>-87.650720</td>\n",
       "      <td>16.448417</td>\n",
       "      <td>2.956512</td>\n",
       "      <td>14.029742</td>\n",
       "      <td>14.181010</td>\n",
       "      <td>0 days 00:21:24.736531567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.048452</td>\n",
       "      <td>0.032138</td>\n",
       "      <td>0.048488</td>\n",
       "      <td>0.032233</td>\n",
       "      <td>9.403356</td>\n",
       "      <td>1.868702</td>\n",
       "      <td>4.753383</td>\n",
       "      <td>4.800166</td>\n",
       "      <td>0 days 03:14:15.595562775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.648501</td>\n",
       "      <td>-87.830000</td>\n",
       "      <td>41.630000</td>\n",
       "      <td>-87.850000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.880000</td>\n",
       "      <td>-87.667435</td>\n",
       "      <td>41.880317</td>\n",
       "      <td>-87.667747</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0 days 00:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.895696</td>\n",
       "      <td>-87.645361</td>\n",
       "      <td>41.896617</td>\n",
       "      <td>-87.646453</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0 days 00:08:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.929426</td>\n",
       "      <td>-87.629767</td>\n",
       "      <td>41.929567</td>\n",
       "      <td>-87.629912</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0 days 00:15:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.070000</td>\n",
       "      <td>-87.528232</td>\n",
       "      <td>42.080000</td>\n",
       "      <td>-87.520000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>11 days 16:08:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          start_lat     start_lng       end_lat       end_lng  day_of_month  \\\n",
       "count  62201.000000  62201.000000  62201.000000  62201.000000  62201.000000   \n",
       "mean      41.900174    -87.650366     41.900803    -87.650720     16.448417   \n",
       "std        0.048452      0.032138      0.048488      0.032233      9.403356   \n",
       "min       41.648501    -87.830000     41.630000    -87.850000      1.000000   \n",
       "25%       41.880000    -87.667435     41.880317    -87.667747      8.000000   \n",
       "50%       41.895696    -87.645361     41.896617    -87.646453     17.000000   \n",
       "75%       41.929426    -87.629767     41.929567    -87.629912     25.000000   \n",
       "max       42.070000    -87.528232     42.080000    -87.520000     31.000000   \n",
       "\n",
       "        day_of_week  started_hour    ended_hour                time_length  \n",
       "count  62201.000000  62201.000000  62201.000000                      62201  \n",
       "mean       2.956512     14.029742     14.181010  0 days 00:21:24.736531567  \n",
       "std        1.868702      4.753383      4.800166  0 days 03:14:15.595562775  \n",
       "min        0.000000      0.000000      0.000000            0 days 00:00:00  \n",
       "25%        1.000000     11.000000     11.000000            0 days 00:05:08  \n",
       "50%        3.000000     15.000000     15.000000            0 days 00:08:40  \n",
       "75%        4.000000     17.000000     17.000000            0 days 00:15:31  \n",
       "max        6.000000     23.000000     23.000000           11 days 16:08:04  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In general, we have:\n",
    "df_bikes[df_bikes[\"member_casual\"]==\"casual\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "20149fec-00bd-4528-a55f-68422c48c96b",
   "metadata": {},
   "source": [
    "We can see a maximum of 11 days and over 16 hours and a minimum of 0 hours. **The mean of the time spent by users in Cyclistic's service is 21 minutes.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc6586b-8b29-4f8e-a47a-0ea571871981",
   "metadata": {},
   "source": [
    "Now that we know the time length for each ride, we're going to get these values in the same unit, that is, in minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c2bc02f-229b-40c5-8398-8a274a7d843e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_bikes[\"time_length (min)\"] = df_bikes[\"time_length\"].dt.total_seconds()/60\n",
    "# These results make all sense: ((11days*24h)+16h)*60m= ~16800 minutes\n",
    "\n",
    "# Dropping the previous columns with no unit transformation:\n",
    "df_bikes.drop(columns = [\"time_length\", \"started_at\", \"ended_at\"], inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "33c8de25-6d34-47ca-8793-2373d0d41883",
   "metadata": {},
   "source": [
    "Now we're going to be dealing with the geolocation, having the starting and ending lat and long data, we're going to obtain the total distance for each ride."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "861f22c4-6014-4e29-b41f-e9e69ae0a321",
   "metadata": {},
   "source": [
    "#### Transforming lat and long data to traveled distance by the user.\n",
    "For this, we'll be considering the geopy library since it implements more accurate ellipsoidal models (such as WGS-84).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "af51eb2a-d48a-4088-95c5-1092501dd4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Distance(0.7417368531814638),\n",
       " Distance(2.221450138584198),\n",
       " Distance(1.584415649104751)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the points in pairs:\n",
    "distance = []\n",
    "for i in range(len(df_bikes[\"start_lat\"])):\n",
    "    # distance.append(geopy.distance.geodesic((x1,y1), (x2,y2))\n",
    "    distance.append(geopy.distance.geodesic((df_bikes[\"start_lat\"][i], df_bikes[\"start_lng\"][i]), (df_bikes[\"end_lat\"][i], df_bikes[\"end_lng\"][i])))\n",
    "    \n",
    "distance[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eeb1e469",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the distance list as a dataframe and assinging it as a column to the df_bikes dataframe:\n",
    "df_bikes[\"distance (km)\"] = pd.DataFrame(distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87bc84c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the 'km' unit word (strings) in the distance list:\n",
    "df_bikes[\"distance (km)\"] = pd.to_numeric(df_bikes[\"distance (km)\"].astype(str).str.replace(\" km\", ''))\n",
    "# An alternative: df['col'] = pd.to_numeric(df['col'].astype(str).str[:-1], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d899696",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that we have the distance we can drop the longitude and latitude columns:\n",
    "df_bikes.drop(columns = [\"start_lat\", \"start_lng\", \"end_lat\", \"end_lng\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "735462ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rideable_type         object\n",
       "member_casual         object\n",
       "time_of_day           object\n",
       "day_of_month           int64\n",
       "day_of_week            int64\n",
       "started_hour           int64\n",
       "ended_hour             int64\n",
       "time_length (min)    float64\n",
       "distance (km)        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double checking data types to make sure that makes sense \n",
    "df_bikes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "df60913c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance traveled and annual members:\n",
      " count    196477.000000\n",
      "mean          1.845672\n",
      "std           1.689528\n",
      "min           0.000000\n",
      "25%           0.798039\n",
      "50%           1.346003\n",
      "75%           2.365784\n",
      "max          21.342916\n",
      "Name: distance (km), dtype: float64\n",
      "Distance traveled and casual members:\n",
      " count    62201.000000\n",
      "mean         1.783076\n",
      "std          1.619640\n",
      "min          0.000000\n",
      "25%          0.829545\n",
      "50%          1.385962\n",
      "75%          2.286190\n",
      "max         23.725628\n",
      "Name: distance (km), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# For comparison, we can see the distance stats as well for the annual customers only: \n",
    "print(\"Distance traveled and annual members:\\n\", df_bikes[\"distance (km)\"][df_bikes[\"member_casual\"] == \"member\"].describe())\n",
    "# For comparison, we can see the distance stats as well for the casual customers only:\n",
    "print(\"Distance traveled and casual members:\\n\", df_bikes[\"distance (km)\"][df_bikes[\"member_casual\"] == \"casual\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8ff7a0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance traveled and electric bike:\n",
      " count    148575.000000\n",
      "mean          1.917323\n",
      "std           1.738135\n",
      "min           0.000000\n",
      "25%           0.830451\n",
      "50%           1.405270\n",
      "75%           2.473232\n",
      "max          21.342916\n",
      "Name: distance (km), dtype: float64\n",
      "Distance traveled and classic bike:\n",
      " count    107083.000000\n",
      "mean          1.705464\n",
      "std           1.559457\n",
      "min           0.000000\n",
      "25%           0.758964\n",
      "50%           1.234264\n",
      "75%           2.104801\n",
      "max          21.485888\n",
      "Name: distance (km), dtype: float64\n",
      "Distance traveled and docked bike:\n",
      " count    3020.000000\n",
      "mean        2.002884\n",
      "std         1.996626\n",
      "min         0.000000\n",
      "25%         0.820614\n",
      "50%         1.575530\n",
      "75%         2.632775\n",
      "max        23.725628\n",
      "Name: distance (km), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# In order to compare with the numbers that we have in our dataframe, we can see the distance stats for the 3 types of bike: \n",
    "print(\"Distance traveled and electric bike:\\n\", df_bikes[\"distance (km)\"][df_bikes[\"rideable_type\"] == \"electric_bike\"].describe())\n",
    "print(\"Distance traveled and classic bike:\\n\", df_bikes[\"distance (km)\"][df_bikes[\"rideable_type\"] == \"classic_bike\"].describe())\n",
    "print(\"Distance traveled and docked bike:\\n\",df_bikes[\"distance (km)\"][df_bikes[\"rideable_type\"] == \"docked_bike\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5781a048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    258678.000000\n",
      "mean          1.830620\n",
      "std           1.673200\n",
      "min           0.000000\n",
      "25%           0.807003\n",
      "50%           1.356842\n",
      "75%           2.345643\n",
      "max          23.725628\n",
      "Name: distance (km), dtype: float64\n"
     ]
    },
    {
     "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>rideable_type</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>started_hour</th>\n",
       "      <th>ended_hour</th>\n",
       "      <th>time_length (min)</th>\n",
       "      <th>distance (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.741737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8.416667</td>\n",
       "      <td>2.221450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10.633333</td>\n",
       "      <td>1.584416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15.716667</td>\n",
       "      <td>2.526332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.485600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258673</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.383333</td>\n",
       "      <td>1.663205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258674</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>26.050000</td>\n",
       "      <td>0.353800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258675</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>evening</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>0.599241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258676</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>night</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>1.484985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258677</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>night</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>5.733333</td>\n",
       "      <td>1.400513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258678 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        rideable_type member_casual time_of_day  day_of_month  day_of_week  \\\n",
       "0       electric_bike        member     morning            16            3   \n",
       "1       electric_bike        member   afternoon             4            5   \n",
       "2        classic_bike        member   afternoon            31            4   \n",
       "3        classic_bike        member   afternoon            22            2   \n",
       "4       electric_bike        member     morning             9            3   \n",
       "...               ...           ...         ...           ...          ...   \n",
       "258673  electric_bike        member     morning             8            2   \n",
       "258674   classic_bike        member   afternoon            21            1   \n",
       "258675  electric_bike        member     evening            21            1   \n",
       "258676   classic_bike        member       night            16            3   \n",
       "258677  electric_bike        member       night            27            0   \n",
       "\n",
       "        started_hour  ended_hour  time_length (min)  distance (km)  \n",
       "0                  8           8           2.300000       0.741737  \n",
       "1                 14          14           8.416667       2.221450  \n",
       "2                 12          12          10.633333       1.584416  \n",
       "3                 14          14          15.716667       2.526332  \n",
       "4                  7           7          11.000000       3.485600  \n",
       "...              ...         ...                ...            ...  \n",
       "258673             0           0           6.383333       1.663205  \n",
       "258674            14          14          26.050000       0.353800  \n",
       "258675            16          16           2.583333       0.599241  \n",
       "258676            20          20           7.100000       1.484985  \n",
       "258677            19          20           5.733333       1.400513  \n",
       "\n",
       "[258678 rows x 9 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting basic statistics in general from the distance column:\n",
    "print(df_bikes[\"distance (km)\"].describe())\n",
    "df_bikes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d44e6966",
   "metadata": {},
   "source": [
    "We can see that the minimum distance traveled is 0.0 km, the maximum is 23.73 km and **on average, users traveled around 1.83 km.** We'll be seeing now the relationship between all these features."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b4f7878",
   "metadata": {},
   "source": [
    "#### **Analyzing the graphs**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7701f45a",
   "metadata": {},
   "source": [
    "For this data, histograms and bar graphs were chosen, due to the fact that we have categorical features and is easier to make comparisons between features with this type of plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5c4c9caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the time of day feature:\n",
    "\n",
    "plt.hist(df_bikes[\"time_of_day\"])\n",
    "plt.title(\"Frequency of time of the day by users\")\n",
    "plt.xlabel(\"Time of the day\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"images/time_freq_users.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "685b076c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the time of day feature:\n",
    "\n",
    "plt.hist(df_bikes[\"day_of_week\"], bins = df_bikes[\"day_of_week\"].nunique(), edgecolor='w', linewidth=1.1)\n",
    "plt.title(\"Frequency of day of the week by users\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"images/dayweek_freq_users.png\")\n",
    "plt.show()\n",
    "# 0: Monday, 6: Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a28667ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Day of the week feature:\n",
    "\n",
    "plt.hist(df_bikes[\"day_of_month\"], edgecolor='w', linewidth=1.1)\n",
    "plt.title(\"Frequency of day of the month by users\")\n",
    "plt.xlabel(\"Day of the month\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"images/daymonth_freq_users.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "efb3e5d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the distance feature:\n",
    "\n",
    "plt.hist(df_bikes[\"distance (km)\"], bins = 24, edgecolor='w', linewidth=1.1)\n",
    "plt.xticks(np.arange(0, 24, 2))\n",
    "plt.title(\"Frequency of distance traveled by users\")\n",
    "plt.xlabel(\"Distance traveled (km)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"images/distance_freq_users.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9b90e1e9",
   "metadata": {},
   "source": [
    "We can tell **the majority of users have a traveled distance of around 1.83 km or less**, this can be confirmed in the mean obtained ran in the previous piece of code.\n",
    "\n",
    "Now for the other features we're going the get the histogram as well, see the behaviour and explore more graphs to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b8857b54",
   "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>time_length (min)</th>\n",
       "      <th>distance (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>258678.000000</td>\n",
       "      <td>258678.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.080050</td>\n",
       "      <td>1.830620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>98.190841</td>\n",
       "      <td>1.673200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.433333</td>\n",
       "      <td>0.807003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.356842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.833333</td>\n",
       "      <td>2.345643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16808.066667</td>\n",
       "      <td>23.725628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time_length (min)  distance (km)\n",
       "count      258678.000000  258678.000000\n",
       "mean           13.080050       1.830620\n",
       "std            98.190841       1.673200\n",
       "min             0.000000       0.000000\n",
       "25%             4.433333       0.807003\n",
       "50%             7.500000       1.356842\n",
       "75%            12.833333       2.345643\n",
       "max         16808.066667      23.725628"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.describe() # Considers all features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58db2bce",
   "metadata": {},
   "source": [
    "Let's take a look at the following plots and see what's the behaviour between features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a11eff70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_bikes[\"member_casual\"])\n",
    "plt.title(\"Frequency of the type of Cyclistic's users\")\n",
    "plt.xlabel(\"Type of users\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"images/users_freq_users.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50a1937a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member    75.954275\n",
       "casual    24.045725\n",
       "Name: member_casual, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_bikes[\"member_casual\"].value_counts()/df_bikes[\"member_casual\"].value_counts().sum())*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c303c4",
   "metadata": {},
   "source": [
    "For this particular set of data available we have more members (~76%) registered than casual users (24%). We could be splitting these two types of Cyclistic's user data to obtain a comparison for both and see more clearly what we can do in Cyclistic to improve the service for the casual users to make them turn into members!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b086a644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wQIMGDSp8/hW9QGr7SnQiXfG4u8j27t0rABBmzZpV4XIAwujRo4UlS5YILVq0EAwMDIQ2bdoIa9asKVd77do1ITw8XGjWrJlgYGAgWFpaCh4eHsIXX3whFBQUPLbH0tJSYfbs2ULr1q0FAwMDwdraWvjggw+ErKwsSV1V7p4TBEEoLi4WYmJihFdeeUVQKpWCUqkUXnnlFSEmJkYoLi6W1E6YMEHo0KGDYGFhISiVSqF58+bCJ598Ily/fl1Sd/z4cSEoKEiwsbERDAwMBDs7O+GNN94Qli1bJtZUdrfXvXv3hI8//lho27atYG5uLhgbGwtOTk7C5MmThTt37sg6pp07dwoABADCmTNnKqx5Hj2WlZUJc+bMEZo3by4YGRkJHTp0EP74449yd8999dVXgre3t2BtbS0YGhoKTZs2FYYNGyZcuHBB1vE+rteHlz1699wrr7xSrrayu9oe/D1/WEZGhjB06FDhpZdeEgwMDITGjRsL3t7ewvTp05/Yr4ODg/jf6NHXw3ccrly5UvD09BRMTU0FY2NjoUWLFsLgwYMld/1VdixDhgwpdwdcWlqa0LNnT8HIyEiwtLQUhg0bJvz4448CAOH48eNi3c2bN4V3331XaNSokaBQKIQHH5kP7p6bO3duhT+jyZMnP/HYqW5SCIIgPN+YRlT3REZGYunSpcjKyqrwYlOFQoHRo0dj0aJFtdAdET2rESNGYN26dbhx40aNnGqk+oGn54geIykpCWfOnMGSJUswcuTICgMTEdUt06ZNg1qtRvPmzVFQUIAtW7bg+++/x5dffsnARI/F0ET0GA+u0/H398f06dNrux0iqgYGBgaYO3cuLl26hPv376NVq1aYP38+xo4dW9utkY7j6TkiIiIiGfhwSyIiIiIZGJqIiIiIZGBoIiIiIpKBF4JXo7KyMly5cgVmZmYVfuUCERER6R5BEHD79m2o1epyD8J9GENTNbpy5Qrs7e1ruw0iIiJ6CllZWZV+KwPA0FStzMzMAPzzQzc3N6/lboiIiEiO/Px82Nvbi5/jlWFoqkYPTsmZm5szNBEREdUxT7q0hheCExEREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQz6td0AERH9f44TttZ2C0Q668KsvrW6f840EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMtRqa9u/fj379+kGtVkOhUODXX38tV5Oeno6AgACoVCqYmZmhc+fOuHjxori8qKgIYWFhsLa2hqmpKQICAnDp0iXJNvLy8qDRaKBSqaBSqaDRaHDr1i1JzcWLF9GvXz+YmprC2toa4eHhKC4uronDJiIiojqoVkPTnTt30K5dOyxatKjC5efPn8drr72GNm3aYO/evTh+/DgmTpwIIyMjsSYiIgIbN25EXFwcEhISUFBQAH9/f5SWloo1wcHBSE1NRXx8POLj45GamgqNRiMuLy0tRd++fXHnzh0kJCQgLi4OGzZsQGRkZM0dPBEREdUpCkEQhNpuAgAUCgU2btyIwMBAcez999+HgYEBVq9eXeE6Wq0WjRs3xurVqzFgwAAAwJUrV2Bvb49t27bBz88P6enpcHFxQVJSEjw9PQEASUlJ8PLywqlTp+Dk5ITt27fD398fWVlZUKvVAIC4uDiEhIQgNzcX5ubmso4hPz8fKpUKWq1W9jpERA9znLC1tlsg0lkXZvWtke3K/fzW2WuaysrKsHXrVrRu3Rp+fn6wsbGBp6en5BReSkoKSkpK4OvrK46p1Wq4urri0KFDAIDExESoVCoxMAFA586doVKpJDWurq5iYAIAPz8/FBUVISUlpYaPlIiIiOoCnQ1Nubm5KCgowKxZs9CrVy/s3LkTb731Ft5++23s27cPAJCTkwNDQ0NYWFhI1rW1tUVOTo5YY2NjU277NjY2khpbW1vJcgsLCxgaGoo1FSkqKkJ+fr7kRURERPWTfm03UJmysjIAQP/+/fHJJ58AANq3b49Dhw5h2bJl6NatW6XrCoIAhUIhvn/4z89S86iZM2di6tSpTz4YIiIiqvN0dqbJ2toa+vr6cHFxkYw7OzuLd8/Z2dmhuLgYeXl5kprc3Fxx5sjOzg5Xr14tt/1r165Jah6dUcrLy0NJSUm5GaiHRUdHQ6vViq+srKyqHygRERHVCTobmgwNDdGxY0ecPn1aMn7mzBk4ODgAADw8PGBgYIBdu3aJy7Ozs5GWlgZvb28AgJeXF7RaLZKTk8Waw4cPQ6vVSmrS0tKQnZ0t1uzcuRNKpRIeHh6V9qhUKmFubi55ERERUf1Uq6fnCgoKcO7cOfF9RkYGUlNTYWlpiaZNm+LTTz/FgAED0LVrV3Tv3h3x8fHYvHkz9u7dCwBQqVQYNmwYIiMjYWVlBUtLS0RFRcHNzQ09e/YE8M/MVK9evTB8+HAsX74cADBixAj4+/vDyckJAODr6wsXFxdoNBrMnTsXN2/eRFRUFIYPH84gRERERABqeabp6NGjcHd3h7u7OwBg3LhxcHd3x6RJkwAAb731FpYtW4Y5c+bAzc0N33//PTZs2IDXXntN3MaCBQsQGBiIoKAgdOnSBSYmJti8eTP09PTEmjVr1sDNzQ2+vr7w9fVF27ZtJY8x0NPTw9atW2FkZIQuXbogKCgIgYGBmDdv3nP6SRAREZGu05nnNNUHfE4TET0rPqeJqHJ8ThMRERFRHcDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMtRqa9u/fj379+kGtVkOhUODXX3+ttHbkyJFQKBRYuHChZLyoqAhhYWGwtraGqakpAgICcOnSJUlNXl4eNBoNVCoVVCoVNBoNbt26Jam5ePEi+vXrB1NTU1hbWyM8PBzFxcXVdKRERERU19VqaLpz5w7atWuHRYsWPbbu119/xeHDh6FWq8sti4iIwMaNGxEXF4eEhAQUFBTA398fpaWlYk1wcDBSU1MRHx+P+Ph4pKamQqPRiMtLS0vRt29f3LlzBwkJCYiLi8OGDRsQGRlZfQdLREREdZp+be68d+/e6N2792NrLl++jDFjxmDHjh3o27evZJlWq8UPP/yA1atXo2fPngCAn376Cfb29vj999/h5+eH9PR0xMfHIykpCZ6engCAFStWwMvLC6dPn4aTkxN27tyJkydPIisrSwxmX331FUJCQjBjxgyYm5vXwNETERFRXaLT1zSVlZVBo9Hg008/xSuvvFJueUpKCkpKSuDr6yuOqdVquLq64tChQwCAxMREqFQqMTABQOfOnaFSqSQ1rq6ukpksPz8/FBUVISUlpaYOj4iIiOqQWp1pepLZs2dDX18f4eHhFS7PycmBoaEhLCwsJOO2trbIyckRa2xsbMqta2NjI6mxtbWVLLewsIChoaFYU5GioiIUFRWJ7/Pz8+UdGBEREdU5OjvTlJKSgq+//hqxsbFQKBRVWlcQBMk6Fa3/NDWPmjlzpnhxuUqlgr29fZX6JCIiorpDZ0PTgQMHkJubi6ZNm0JfXx/6+vrIzMxEZGQkHB0dAQB2dnYoLi5GXl6eZN3c3Fxx5sjOzg5Xr14tt/1r165Jah6dUcrLy0NJSUm5GaiHRUdHQ6vViq+srKxnOWQiIiLSYTobmjQaDf7880+kpqaKL7VajU8//RQ7duwAAHh4eMDAwAC7du0S18vOzkZaWhq8vb0BAF5eXtBqtUhOThZrDh8+DK1WK6lJS0tDdna2WLNz504olUp4eHhU2qNSqYS5ubnkRURERPVTrV7TVFBQgHPnzonvMzIykJqaCktLSzRt2hRWVlaSegMDA9jZ2cHJyQkAoFKpMGzYMERGRsLKygqWlpaIioqCm5ubeDeds7MzevXqheHDh2P58uUAgBEjRsDf31/cjq+vL1xcXKDRaDB37lzcvHkTUVFRGD58OIMQERERAajlmaajR4/C3d0d7u7uAIBx48bB3d0dkyZNkr2NBQsWIDAwEEFBQejSpQtMTEywefNm6OnpiTVr1qyBm5sbfH194evri7Zt22L16tXicj09PWzduhVGRkbo0qULgoKCEBgYiHnz5lXfwRIREVGdphAEQajtJuqL/Px8qFQqaLVazlAR0VNxnLC1tlsg0lkXZvV9ctFTkPv5rbPXNBERERHpEoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGSo1dC0f/9+9OvXD2q1GgqFAr/++qu4rKSkBJ999hnc3NxgamoKtVqNwYMH48qVK5JtFBUVISwsDNbW1jA1NUVAQAAuXbokqcnLy4NGo4FKpYJKpYJGo8GtW7ckNRcvXkS/fv1gamoKa2trhIeHo7i4uKYOnYiIiOqYWg1Nd+7cQbt27bBo0aJyy+7evYtjx45h4sSJOHbsGH755RecOXMGAQEBkrqIiAhs3LgRcXFxSEhIQEFBAfz9/VFaWirWBAcHIzU1FfHx8YiPj0dqaio0Go24vLS0FH379sWdO3eQkJCAuLg4bNiwAZGRkTV38ERERFSnKARBEGq7CQBQKBTYuHEjAgMDK605cuQIOnXqhMzMTDRt2hRarRaNGzfG6tWrMWDAAADAlStXYG9vj23btsHPzw/p6elwcXFBUlISPD09AQBJSUnw8vLCqVOn4OTkhO3bt8Pf3x9ZWVlQq9UAgLi4OISEhCA3Nxfm5uayjiE/Px8qlQparVb2OkRED3OcsLW2WyDSWRdm9a2R7cr9/K5T1zRptVooFAo0atQIAJCSkoKSkhL4+vqKNWq1Gq6urjh06BAAIDExESqVSgxMANC5c2eoVCpJjaurqxiYAMDPzw9FRUVISUmptJ+ioiLk5+dLXkRERFQ/1ZnQdO/ePUyYMAHBwcFiCszJyYGhoSEsLCwktba2tsjJyRFrbGxsym3PxsZGUmNraytZbmFhAUNDQ7GmIjNnzhSvk1KpVLC3t3+mYyQiIiLdVSdCU0lJCd5//32UlZVhyZIlT6wXBAEKhUJ8//Cfn6XmUdHR0dBqteIrKyvrib0RERFR3aTzoamkpARBQUHIyMjArl27JOca7ezsUFxcjLy8PMk6ubm54syRnZ0drl69Wm67165dk9Q8OqOUl5eHkpKScjNQD1MqlTA3N5e8iIiIqH7S6dD0IDCdPXsWv//+O6ysrCTLPTw8YGBggF27dolj2dnZSEtLg7e3NwDAy8sLWq0WycnJYs3hw4eh1WolNWlpacjOzhZrdu7cCaVSCQ8Pj5o8RCIiIqoj9Gtz5wUFBTh37pz4PiMjA6mpqbC0tIRarca7776LY8eOYcuWLSgtLRVngywtLWFoaAiVSoVhw4YhMjISVlZWsLS0RFRUFNzc3NCzZ08AgLOzM3r16oXhw4dj+fLlAIARI0bA398fTk5OAABfX1+4uLhAo9Fg7ty5uHnzJqKiojB8+HDOHhERERGApwhNFy5cwIEDB3DhwgXcvXsXjRs3hru7O7y8vGBkZFSlbR09ehTdu3cX348bNw4AMGTIEEyZMgWbNm0CALRv316y3p49e+Dj4wMAWLBgAfT19REUFITCwkL06NEDsbGx0NPTE+vXrFmD8PBw8S67gIAAybOh9PT0sHXrVoSGhqJLly4wNjZGcHAw5s2bV6XjISIiovpL9nOa1q5di2+++QbJycmwsbHBSy+9BGNjY9y8eRPnz5+HkZERBg0ahM8++wwODg413bdO4nOaiOhZ8TlNRJWr7ec0yZppevXVV9GgQQOEhITgP//5D5o2bSpZXlRUhMTERMTFxaFDhw5YsmQJ3nvvvWc7AiIiIiIdIis0/etf/0LfvpWnO6VSCR8fH/j4+GD69OnIyMiotgaJiIiIdIGs0PS4wPQoa2trWFtbP3VDRERERLroqe6eKysrw7lz55Cbm4uysjLJsq5du1ZLY0RERES6pMqhKSkpCcHBwcjMzMSj15ArFAqUlpZWW3NEREREuqLKoenjjz9Ghw4dsHXrVjRp0uSxXzNCREREVF9UOTSdPXsWP//8M1q2bFkT/RARERHppCp/jYqnp6fkKd5EREREL4IqzzSFhYUhMjISOTk5cHNzg4GBgWR527Ztq605IiIiIl1R5dD0zjvvAACGDh0qjikUCgiCwAvBiYiIqN6qcmjigyuJiIjoRVTl0PSifq8cERERvdhkhaZNmzahd+/eMDAwwKZNmx5bGxAQUC2NEREREekSWaEpMDAQOTk5sLGxQWBgYKV1vKaJiIiI6itZoenhr0p59GtTiIiIiF4EVX5OExEREdGL6Km+sDc5ORl79+6t8At758+fXy2NEREREemSKoemmJgYfPnll3BycoKtra3ku+f4PXRERERUX1U5NH399ddYuXIlQkJCaqAdIiIiIt1U5WuaGjRogC5dutREL0REREQ6q8qh6ZNPPsHixYtrohciIiIinVXl03NRUVHo27cvWrRoARcXl3Jf2PvLL79UW3NEREREuqLKoSksLAx79uxB9+7dYWVlxYu/iYiI6IVQ5dD073//Gxs2bEDfvn1roh8iIiIinVTla5osLS3RokWLmuiFiIiISGdVOTRNmTIFkydPxt27d2uiHyIiIiKdVOXTc9988w3Onz8PW1tbODo6lrsQ/NixY9XWHBEREZGuqHJoCgwMrIE2iIiIiHRblUPT5MmTa6IPIiIiIp1W5Wua5BAEoSY2S0RERFRrZIUmZ2dnrF27FsXFxY+tO3v2LEaNGoXZs2dXS3NEREREukJWaFq8eDEWLFgAW1tbDBgwAHPnzsWaNWuwYcMGfP/99xg3bhw6deoEd3d3qFQqhIaGytr5/v370a9fP6jVaigUCvz666+S5YIgYMqUKVCr1TA2NoaPjw9OnDghqSkqKkJYWBisra1hamqKgIAAXLp0SVKTl5cHjUYDlUoFlUoFjUaDW7duSWouXryIfv36wdTUFNbW1ggPD39iSCQiIqIXh6zQ9MYbb+DIkSPYunUr7OzssHbtWowZMwaDBg3ClClTcPbsWQwePBiXLl3CrFmzYG5uLmvnd+7cQbt27bBo0aIKl8+ZMwfz58/HokWLcOTIEdjZ2eHNN9/E7du3xZqIiAhs3LgRcXFxSEhIQEFBAfz9/VFaWirWBAcHIzU1FfHx8YiPj0dqaio0Go24vLS0FH379sWdO3eQkJCAuLg4bNiwAZGRkbKOg4iIiOo/haAjFyApFAps3LhRvDtPEASo1WpERETgs88+A/DPrJKtrS1mz56NkSNHQqvVonHjxli9ejUGDBgAALhy5Qrs7e2xbds2+Pn5IT09HS4uLkhKSoKnpycAICkpCV5eXjh16hScnJywfft2+Pv7IysrC2q1GgAQFxeHkJAQ5Obmyg6B+fn5UKlU0Gq1stchInqY44Sttd0Ckc66MKtmvo1E7ud3jVwIXh0yMjKQk5MDX19fcUypVKJbt244dOgQACAlJQUlJSWSGrVaDVdXV7EmMTERKpVKDEwA0LlzZ6hUKkmNq6urGJgAwM/PD0VFRUhJSam0x6KiIuTn50teREREVD/pbGjKyckBANja2krGbW1txWU5OTkwNDSEhYXFY2tsbGzKbd/GxkZS8+h+LCwsYGhoKNZUZObMmeJ1UiqVCvb29lU8SiIiIqordDY0PaBQKCTvBUEoN/aoR2sqqn+amkdFR0dDq9WKr6ysrMf2RURERHWXzoYmOzs7ACg305ObmyvOCtnZ2aG4uBh5eXmPrbl69Wq57V+7dk1S8+h+8vLyUFJSUm4G6mFKpRLm5uaSFxEREdVPOhuamjVrBjs7O+zatUscKy4uxr59++Dt7Q0A8PDwgIGBgaQmOzsbaWlpYo2Xlxe0Wi2Sk5PFmsOHD0Or1Upq0tLSkJ2dLdbs3LkTSqUSHh4eNXqcREREVDdU+WtUAKCsrAznzp1Dbm4uysrKJMu6du0qezsFBQU4d+6c+D4jIwOpqamwtLRE06ZNERERgZiYGLRq1QqtWrVCTEwMTExMEBwcDABQqVQYNmwYIiMjYWVlBUtLS0RFRcHNzQ09e/YE8M+DOXv16oXhw4dj+fLlAIARI0bA398fTk5OAABfX1+4uLhAo9Fg7ty5uHnzJqKiojB8+HDOHhERERGApwhNSUlJCA4ORmZmZrmvS1EoFJLnIz3J0aNH0b17d/H9uHHjAABDhgxBbGwsxo8fj8LCQoSGhiIvLw+enp7YuXMnzMzMxHUWLFgAfX19BAUFobCwED169EBsbCz09PTEmjVr1iA8PFy8yy4gIEDybCg9PT1s3boVoaGh6NKlC4yNjREcHIx58+ZV7YdDRERE9VaVn9PUvn17tG7dGlOnTkWTJk3KXSitUqmqtcG6hM9pIqJnxec0EVWutp/TVOWZprNnz+Lnn39Gy5Ytn6lBIiIiorqkyheCe3p6Sq5DIiIiInoRyJpp+vPPP8U/h4WFITIyEjk5OXBzc4OBgYGktm3bttXbIREREZEOkBWa2rdvD4VCIbnwe+jQoeKfHyyr6oXgRERERHWFrNCUkZFR030QERER6TRZocnBwUH88/79++Ht7Q19femq9+/fx6FDhyS1RERERPVFlS8E7969O27evFluXKvVSp65RERERFSfVDk0VfYltjdu3ICpqWm1NEVERESka2Q/p+ntt98G8M9F3yEhIVAqleKy0tJS/Pnnn+J3uRERERHVN7JD04MnfQuCADMzMxgbG4vLDA0N0blzZwwfPrz6OyQiIiLSAbJD06pVqwAAjo6OiIqK4qk4IiIieqFU+WtUJk+eXBN9EBEREem0Kocmd3f3Ci8EVygUMDIyQsuWLRESEsI76YiIiKheqfLdc7169cLff/8NU1NTdO/eHT4+PmjYsCHOnz+Pjh07Ijs7Gz179sRvv/1WE/0SERER1YoqzzRdv34dkZGRmDhxomR8+vTpyMzMxM6dOzF58mT861//Qv/+/autUSIiIqLaVOWZpv/85z8YOHBgufH3338f//nPfwAAAwcOxOnTp5+9OyIiIiIdUeXQZGRkhEOHDpUbP3ToEIyMjAAAZWVlkuc4EREREdV1VT49FxYWho8//hgpKSno2LEjFAoFkpOT8f333+Pzzz8HAOzYsQPu7u7V3iwRERFRbVEIgiBUdaU1a9Zg0aJF4ik4JycnhIWFITg4GABQWFgo3k33IsnPz4dKpYJWq4W5uXltt0NEdZDjhK213QKRzrowq2+NbFfu53eVZ5oAYNCgQRg0aFClyx9+WjgRERFRffBUoQkAiouLkZubi7KyMsl406ZNn7kpIiIiIl1T5dB09uxZDB06tNzF4IIgQKFQoLS0tNqaIyIiItIVVQ5NISEh0NfXx5YtW9CkSZMKnw5OREREVN9UOTSlpqYiJSUFbdq0qYl+iIiIiHRSlZ/T5OLiguvXr9dEL0REREQ6q8qhafbs2Rg/fjz27t2LGzduID8/X/IiIiIiqo+qfHquZ8+eAIAePXpIxnkhOBEREdVnVQ5Ne/bsqYk+iIiIiHRalUNTt27daqIPIiIiIp1W5WuaAODAgQP44IMP4O3tjcuXLwMAVq9ejYSEhGptjoiIiEhXVDk0bdiwAX5+fjA2NsaxY8dQVFQEALh9+zZiYmKqvUEiIiIiXVDl0DR9+nQsW7YMK1asgIGBgTju7e2NY8eOVWtz9+/fx5dffolmzZrB2NgYzZs3x7Rp0yRf3SIIAqZMmQK1Wg1jY2P4+PjgxIkTku0UFRUhLCwM1tbWMDU1RUBAAC5duiSpycvLg0ajgUqlgkqlgkajwa1bt6r1eIiIiKjuqnJoOn36NLp27Vpu3NzcvNpDxuzZs7Fs2TIsWrQI6enpmDNnDubOnYtvv/1WrJkzZw7mz5+PRYsW4ciRI7Czs8Obb76J27dvizURERHYuHEj4uLikJCQgIKCAvj7+0vu9AsODkZqairi4+MRHx+P1NRUaDSaaj0eIiIiqruqfCF4kyZNcO7cOTg6OkrGExIS0Lx58+rqCwCQmJiI/v37o2/fvgAAR0dHrFu3DkePHgXwzyzTwoUL8cUXX+Dtt98GAPz444+wtbXF2rVrMXLkSGi1Wvzwww9YvXq1+LiEn376Cfb29vj999/h5+eH9PR0xMfHIykpCZ6engCAFStWwMvLC6dPn4aTk1O1HhcRERHVPVWeaRo5ciTGjh2Lw4cPQ6FQ4MqVK1izZg2ioqIQGhparc299tpr2L17N86cOQMAOH78OBISEtCnTx8AQEZGBnJycuDr6yuuo1Qq0a1bN/ELhVNSUlBSUiKpUavVcHV1FWsSExOhUqnEwAQAnTt3hkqlKvfFxA8rKiriwz2JiIheEFWeaRo/fjy0Wi26d++Oe/fuoWvXrlAqlYiKisKYMWOqtbnPPvsMWq0Wbdq0gZ6eHkpLSzFjxgwMHDgQAJCTkwMAsLW1laxna2uLzMxMscbQ0BAWFhblah6sn5OTAxsbm3L7t7GxEWsqMnPmTEydOvXpD5CIiIjqjKd65MCMGTNw/fp1JCcnIykpCdeuXcOkSZNw8eLFam1u/fr1+Omnn7B27VocO3YMP/74I+bNm4cff/xRUqdQKCTvHzyd/HEeramo/knbiY6OhlarFV9ZWVlyDouIiIjqoCrPND1gYmKCDh06iO+PHz+OV199tVq/RuXTTz/FhAkT8P777wMA3NzckJmZiZkzZ2LIkCGws7MD8M9MUZMmTcT1cnNzxdknOzs7FBcXIy8vTzLblJubC29vb7Hm6tWr5fZ/7dq1crNYD1MqlVAqlc9+oERERKTznmqm6Xm5e/cuGjSQtqinpyc+cqBZs2aws7PDrl27xOXFxcXYt2+fGIg8PDxgYGAgqcnOzkZaWppY4+XlBa1Wi+TkZLHm8OHD0Gq1Yg0RERG92J56pul56NevH2bMmIGmTZvilVdewf/+9z/Mnz8fQ4cOBfDPKbWIiAjExMSgVatWaNWqFWJiYmBiYoLg4GAAgEqlwrBhwxAZGQkrKytYWloiKioKbm5u4t10zs7O6NWrF4YPH47ly5cDAEaMGAF/f3/eOUdEREQAdDw0ffvtt5g4cSJCQ0ORm5sLtVqNkSNHYtKkSWLN+PHjUVhYiNDQUOTl5cHT0xM7d+6EmZmZWLNgwQLo6+sjKCgIhYWF6NGjB2JjY6GnpyfWrFmzBuHh4eJddgEBAVi0aNHzO1giIiLSaQpBEAQ5hX/++edjl586dQoDBw6s1mua6pr8/HyoVCpotVqYm5vXdjtEVAc5Ttha2y0Q6awLs/rWyHblfn7Lnmlq3749FAoFKspYD8afdMcaERERUV0lOzRlZGTUZB9EREREOk12aHJwcKjJPoiIiIh0mk4/coCIiIhIVzA0EREREcnA0EREREQkA0MTERERkQxPFZru37+P33//HcuXL8ft27cBAFeuXEFBQUG1NkdERESkK6r8RPDMzEz06tULFy9eRFFREd58802YmZlhzpw5uHfvHpYtW1YTfRIRERHVqirPNI0dOxYdOnRAXl4ejI2NxfG33noLu3fvrtbmiIiIiHRFlWeaEhIScPDgQRgaGkrGHRwccPny5WprjIiIiEiXVHmmqaysrMLvl7t06ZLkS3KJiIiI6pMqh6Y333wTCxcuFN8rFAoUFBRg8uTJ6NOnT3X2RkRERKQzqnx6bsGCBejevTtcXFxw7949BAcH4+zZs7C2tsa6detqokciIiKiWlfl0KRWq5Gamop169bh2LFjKCsrw7BhwzBo0CDJheFERERE9UmVQxMAGBsbY+jQoRg6dGh190NERESkk54qNF2+fBkHDx5Ebm4uysrKJMvCw8OrpTEiIiIiXVLl0LRq1Sp8/PHHMDQ0hJWVFRQKhbhMoVAwNBEREVG9VOXQNGnSJEyaNAnR0dFo0IBfXUdEREQvhiqnnrt37+L9999nYCIiIqIXSpWTz7Bhw/Df//63JnohIiIi0llVPj03c+ZM+Pv7Iz4+Hm5ubjAwMJAsnz9/frU1R0RERKQrqhyaYmJisGPHDjg5OQFAuQvBiYiIiOqjKoem+fPnY+XKlQgJCamBdoiIiIh0U5WvaVIqlejSpUtN9EJERESks6ocmsaOHYtvv/22JnohIiIi0llVPj2XnJyMP/74A1u2bMErr7xS7kLwX375pdqaIyIiItIVVQ5NjRo1wttvv10TvRARERHprKf6GhUiIiKiFw0f601EREQkg6yZpldffRW7d++GhYUF3N3dH/s8pmPHjlVbc0RERES6QtZMU//+/aFUKgEAgYGB6N+/f6Wv6nb58mV88MEHsLKygomJCdq3b4+UlBRxuSAImDJlCtRqNYyNjeHj44MTJ05ItlFUVISwsDBYW1vD1NQUAQEBuHTpkqQmLy8PGo0GKpUKKpUKGo0Gt27dqvbjISIiorpJIQiCIKdw6NCh+Prrr2FmZlbTPYny8vLg7u6O7t27Y9SoUbCxscH58+fh6OiIFi1aAABmz56NGTNmIDY2Fq1bt8b06dOxf/9+nD59Wux11KhR2Lx5M2JjY2FlZYXIyEjcvHkTKSkp0NPTAwD07t0bly5dwnfffQcAGDFiBBwdHbF582bZ/ebn50OlUkGr1cLc3LyafxpE9CJwnLC1tlsg0lkXZvWtke3K/fyWHZr09PSQnZ0NGxubamvySSZMmICDBw/iwIEDFS4XBAFqtRoRERH47LPPAPwzq2Rra4vZs2dj5MiR0Gq1aNy4MVavXo0BAwYAAK5cuQJ7e3ts27YNfn5+SE9Ph4uLC5KSkuDp6QkASEpKgpeXF06dOiV+ZcyTMDQR0bNiaCKqXG2HJtkXgsvMVtVq06ZN6NChA9577z3Y2NjA3d0dK1asEJdnZGQgJycHvr6+4phSqUS3bt1w6NAhAEBKSgpKSkokNWq1Gq6urmJNYmIiVCqVGJgAoHPnzlCpVGJNRYqKipCfny95ERERUf1UpbvnnvcX8v79999YunQpWrVqhR07duDjjz9GeHg4/v3vfwMAcnJyAAC2traS9WxtbcVlOTk5MDQ0hIWFxWNrKppBs7GxEWsqMnPmTPEaKJVKBXt7+6c/WCIiItJpVXpOU+vWrZ8YnG7evPlMDT2srKwMHTp0QExMDADA3d0dJ06cwNKlSzF48GCx7tGeBEF4Yp+P1lRU/6TtREdHY9y4ceL7/Px8BiciIqJ6qkqhaerUqVCpVDXVSzlNmjSBi4uLZMzZ2RkbNmwAANjZ2QH4Z6aoSZMmYk1ubq44+2RnZ4fi4mLk5eVJZptyc3Ph7e0t1ly9erXc/q9du1ZuFuthSqVSvKuQiIiI6rcqhab333//uV4I3qVLF5w+fVoydubMGTg4OAAAmjVrBjs7O+zatQvu7u4AgOLiYuzbtw+zZ88GAHh4eMDAwAC7du1CUFAQACA7OxtpaWmYM2cOAMDLywtarRbJycno1KkTAODw4cPQarVisCIiIqIXm+zQ9LyvZwKATz75BN7e3oiJiUFQUBCSk5Px3XffiY8FUCgUiIiIQExMDFq1aoVWrVohJiYGJiYmCA4OBgCoVCoMGzYMkZGRsLKygqWlJaKiouDm5oaePXsC+Gf2qlevXhg+fDiWL18O4J9HDvj7+8u+c46IiIjqN9mhqTbunuvYsSM2btyI6OhoTJs2Dc2aNcPChQsxaNAgsWb8+PEoLCxEaGgo8vLy4OnpiZ07d0qeJ7VgwQLo6+sjKCgIhYWF6NGjB2JjY8VnNAHAmjVrEB4eLt5lFxAQgEWLFj2/gyUiIiKdJvs5TfRkfE4TET0rPqeJqHJ15jlNRERERC8yhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZKhToWnmzJlQKBSIiIgQxwRBwJQpU6BWq2FsbAwfHx+cOHFCsl5RURHCwsJgbW0NU1NTBAQE4NKlS5KavLw8aDQaqFQqqFQqaDQa3Lp16zkcFREREdUFdSY0HTlyBN999x3atm0rGZ8zZw7mz5+PRYsW4ciRI7Czs8Obb76J27dvizURERHYuHEj4uLikJCQgIKCAvj7+6O0tFSsCQ4ORmpqKuLj4xEfH4/U1FRoNJrndnxERESk2+pEaCooKMCgQYOwYsUKWFhYiOOCIGDhwoX44osv8Pbbb8PV1RU//vgj7t69i7Vr1wIAtFotfvjhB3z11Vfo2bMn3N3d8dNPP+Gvv/7C77//DgBIT09HfHw8vv/+e3h5ecHLywsrVqzAli1bcPr06Vo5ZiIiItItdSI0jR49Gn379kXPnj0l4xkZGcjJyYGvr684plQq0a1bNxw6dAgAkJKSgpKSEkmNWq2Gq6urWJOYmAiVSgVPT0+xpnPnzlCpVGJNRYqKipCfny95ERERUf2kX9sNPElcXBxSUlJw9OjRcstycnIAALa2tpJxW1tbZGZmijWGhoaSGaoHNQ/Wz8nJgY2NTbnt29jYiDUVmTlzJqZOnVq1AyIiIqI6SadnmrKysjB27FisWbMGRkZGldYpFArJe0EQyo096tGaiuqftJ3o6GhotVrxlZWV9dh9EhERUd2l06EpJSUFubm58PDwgL6+PvT19bFv3z5888030NfXF2eYHp0Nys3NFZfZ2dmhuLgYeXl5j625evVquf1fu3at3CzWw5RKJczNzSUvIiIiqp90OjT16NEDf/31F1JTU8VXhw4dMGjQIKSmpqJ58+aws7PDrl27xHWKi4uxb98+eHt7AwA8PDxgYGAgqcnOzkZaWppY4+XlBa1Wi+TkZLHm8OHD0Gq1Yg0RERG92HT6miYzMzO4urpKxkxNTWFlZSWOR0REICYmBq1atUKrVq0QExMDExMTBAcHAwBUKhWGDRuGyMhIWFlZwdLSElFRUXBzcxMvLHd2dkavXr0wfPhwLF++HAAwYsQI+Pv7w8nJ6TkeMREREekqnQ5NcowfPx6FhYUIDQ1FXl4ePD09sXPnTpiZmYk1CxYsgL6+PoKCglBYWIgePXogNjYWenp6Ys2aNWsQHh4u3mUXEBCARYsWPffjISIiIt2kEARBqO0m6ov8/HyoVCpotVpe30RET8VxwtbaboFIZ12Y1bdGtiv381unr2kiIiIi0hUMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJoNOhaebMmejYsSPMzMxgY2ODwMBAnD59WlIjCAKmTJkCtVoNY2Nj+Pj44MSJE5KaoqIihIWFwdraGqampggICMClS5ckNXl5edBoNFCpVFCpVNBoNLh161ZNHyIRERHVETodmvbt24fRo0cjKSkJu3btwv379+Hr64s7d+6INXPmzMH8+fOxaNEiHDlyBHZ2dnjzzTdx+/ZtsSYiIgIbN25EXFwcEhISUFBQAH9/f5SWloo1wcHBSE1NRXx8POLj45GamgqNRvNcj5eIiIh0l0IQBKG2m5Dr2rVrsLGxwb59+9C1a1cIggC1Wo2IiAh89tlnAP6ZVbK1tcXs2bMxcuRIaLVaNG7cGKtXr8aAAQMAAFeuXIG9vT22bdsGPz8/pKenw8XFBUlJSfD09AQAJCUlwcvLC6dOnYKTk5Os/vLz86FSqaDVamFubl4zPwQiqtccJ2yt7RaIdNaFWX1rZLtyP791eqbpUVqtFgBgaWkJAMjIyEBOTg58fX3FGqVSiW7duuHQoUMAgJSUFJSUlEhq1Go1XF1dxZrExESoVCoxMAFA586doVKpxJqKFBUVIT8/X/IiIiKi+qnOhCZBEDBu3Di89tprcHV1BQDk5OQAAGxtbSW1tra24rKcnBwYGhrCwsLisTU2Njbl9mljYyPWVGTmzJniNVAqlQr29vZPf4BERESk0+pMaBozZgz+/PNPrFu3rtwyhUIheS8IQrmxRz1aU1H9k7YTHR0NrVYrvrKysp50GERERFRH1YnQFBYWhk2bNmHPnj14+eWXxXE7OzsAKDcblJubK84+2dnZobi4GHl5eY+tuXr1arn9Xrt2rdws1sOUSiXMzc0lLyIiIqqfdDo0CYKAMWPG4JdffsEff/yBZs2aSZY3a9YMdnZ22LVrlzhWXFyMffv2wdvbGwDg4eEBAwMDSU12djbS0tLEGi8vL2i1WiQnJ4s1hw8fhlarFWuIiIjoxaZf2w08zujRo7F27Vr89ttvMDMzE2eUVCoVjI2NoVAoEBERgZiYGLRq1QqtWrVCTEwMTExMEBwcLNYOGzYMkZGRsLKygqWlJaKiouDm5oaePXsCAJydndGrVy8MHz4cy5cvBwCMGDEC/v7+su+cIyIiovpNp0PT0qVLAQA+Pj6S8VWrViEkJAQAMH78eBQWFiI0NBR5eXnw9PTEzp07YWZmJtYvWLAA+vr6CAoKQmFhIXr06IHY2Fjo6emJNWvWrEF4eLh4l11AQAAWLVpUswdIREREdUadek6TruNzmojoWfE5TUSV43OaiIiIiOoAhiYiIiIiGXT6mib6/zhlT/R4NTVtT0T0AGeaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGpkcsWbIEzZo1g5GRETw8PHDgwIHabomIiIh0AEPTQ9avX4+IiAh88cUX+N///ofXX38dvXv3xsWLF2u7NSIiIqplDE0PmT9/PoYNG4aPPvoIzs7OWLhwIezt7bF06dLabo2IiIhqGUPT/ykuLkZKSgp8fX0l476+vjh06FAtdUVERES6Qr+2G9AV169fR2lpKWxtbSXjtra2yMnJqXCdoqIiFBUVie+1Wi0AID8/v9r7Kyu6W+3bJKpPauL3rjbwd52ocjX1e/5gu4IgPLaOoekRCoVC8l4QhHJjD8ycORNTp04tN25vb18jvRFR5VQLa7sDIqppNf17fvv2bahUqkqXMzT9H2tra+jp6ZWbVcrNzS03+/RAdHQ0xo0bJ74vKyvDzZs3YWVlVWnQorovPz8f9vb2yMrKgrm5eW23Q0Q1hL/rLw5BEHD79m2o1erH1jE0/R9DQ0N4eHhg165deOutt8TxXbt2oX///hWuo1QqoVQqJWONGjWqyTZJh5ibm/N/pEQvAP6uvxgeN8P0AEPTQ8aNGweNRoMOHTrAy8sL3333HS5evIiPP/64tlsjIiKiWsbQ9JABAwbgxo0bmDZtGrKzs+Hq6opt27bBwcGhtlsjIiKiWsbQ9IjQ0FCEhobWdhukw5RKJSZPnlzu1CwR1S/8XadHKYQn3V9HRERERHy4JREREZEcDE1EREREMjA0EREREcnA0ERUjfbu3QuFQoFbt27VditEpGMUCgV+/fXX2m6DngFDExEREZEMDE1EdUBxcXFtt0BE9MJjaKJ6wcfHB2FhYYiIiICFhQVsbW3x3Xff4c6dO/jwww9hZmaGFi1aYPv27eI6J0+eRJ8+fdCwYUPY2tpCo9Hg+vXrz7TNBw4ePIh27drByMgInp6e+OuvvyTLDx06hK5du8LY2Bj29vYIDw/HnTt3xOWOjo6YPn06QkJCoFKpMHz48Br4qRHVL2VlZZg9ezZatmwJpVKJpk2bYsaMGQCAzz77DK1bt4aJiQmaN2+OiRMnoqSkRFz3+PHj6N69O8zMzGBubg4PDw8cPXoUADBlyhS0b99esq+FCxfC0dFRfH/kyBG8+eabsLa2hkqlQrdu3XDs2LEaP2Z6vhiaqN748ccfYW1tjeTkZISFhWHUqFF477334O3tjWPHjsHPzw8ajQZ3795FdnY2unXrhvbt2+Po0aOIj4/H1atXERQU9NTbfNinn36KefPm4ciRI7CxsUFAQID4P+i//voLfn5+ePvtt/Hnn39i/fr1SEhIwJgxYyTbmDt3LlxdXZGSkoKJEyfW7A+PqB6Ijo7G7NmzMXHiRJw8eRJr164Vv3DdzMwMsbGxOHnyJL7++musWLECCxYsENcdNGgQXn75ZRw5cgQpKSmYMGECDAwMZO/79u3bGDJkCA4cOICkpCS0atUKffr0we3bt6v9OKkWCUT1QLdu3YTXXntNfH///n3B1NRU0Gg04lh2drYAQEhMTBQmTpwo+Pr6SraRlZUlABBOnz79VNsUBEHYs2ePAECIi4sTa27cuCEYGxsL69evFwRBEDQajTBixAjJvg8cOCA0aNBAKCwsFARBEBwcHITAwMBn+pkQvUjy8/MFpVIprFixQlb9nDlzBA8PD/G9mZmZEBsbW2Ht5MmThXbt2knGFixYIDg4OFS6/fv37wtmZmbC5s2bxTEAwsaNG2X1R7qJX6NC9Ubbtm3FP+vp6cHKygpubm7i2IN/cebm5iIlJQV79uxBw4YNy23n/PnzaN26dZW3+TAvLy/xz5aWlnByckJ6ejoAICUlBefOncOaNWvEGkEQUFZWhoyMDDg7OwMAOnToUMWfANGLKz09HUVFRejRo0eFy3/++WcsXLgQ586dQ0FBAe7fvw9zc3Nx+bhx4/DRRx9h9erV6NmzJ9577z20aNFC9v5zc3MxadIk/PHHH7h69SpKS0tx9+5dXLx48ZmPjXQHQxPVG49OpSsUCsmYQqEA8M91D2VlZejXrx9mz55dbjtNmjR5qm0+ycO1I0eORHh4eLmapk2bin82NTV94jaJ6B/GxsaVLktKSsL777+PqVOnws/PDyqVCnFxcfjqq6/EmilTpiA4OBhbt27F9u3bMXnyZMTFxeGtt95CgwYNIDzyjWMPXw8FACEhIbh27RoWLlwIBwcHKJVKeHl58SaOeoahiV5Ir776KjZs2ABHR0fo61f/r0FSUpIYgPLy8nDmzBm0adNG3PeJEyfQsmXLat8v0YuqVatWMDY2xu7du/HRRx9Jlh08eBAODg744osvxLHMzMxy22jdujVat26NTz75BAMHDsSqVavw1ltvoXHjxsjJyYEgCOI/flJTUyXrHjhwAEuWLEGfPn0AAFlZWZIbS6h+4IXg9EIaPXo0bt68iYEDByI5ORl///03du7ciaFDh6K0tPSZtz9t2jTs3r0baWlpCAkJgbW1NQIDAwH8cxdPYmIiRo8ejdTUVJw9exabNm1CWFjYM++X6EVlZGSEzz77DOPHj8e///1vnD9/HklJSfjhhx/QsmVLXLx4EXFxcTh//jy++eYbbNy4UVy3sLAQY8aMwd69e5GZmYmDBw/iyJEj4qlyHx8fXLt2DXPmzMH58+exePHicnfNtmzZEqtXr0Z6ejoOHz6MQYMGPXb2i+omhiZ6IanVahw8eBClpaXw8/ODq6srxo4dC5VKhQYNnv3XYtasWRg7diw8PDyQnZ2NTZs2wdDQEMA/10nt27cPZ8+exeuvvw53d3dMnDhRclqQiKpu4sSJiIyMxKRJk+Ds7IwBAwYgNzcX/fv3xyeffIIxY8agffv2OHTokOSOVD09Pdy4cQODBw9G69atERQUhN69e2Pq1KkAAGdnZyxZsgSLFy9Gu3btkJycjKioKMm+V65ciby8PLi7u0Oj0SA8PBw2NjbP9fip5imER0/UEhEREVE5nGkiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiGQSBAEjRoyApaUlFApFua/SIKL6jaGJiJ47hULx2FdISEhtt1ih+Ph4xMbGYsuWLcjOzoarq2ttt0REzxG/sJeInrvs7Gzxz+vXr8ekSZNw+vRpcUxXv7Pr/PnzaNKkCby9vWu7FYmSkhIYGBjUdhtE9R5nmojoubOzsxNfKpUKCoUCdnZ2sLW1xWuvvYYVK1ZI6tPS0tCgQQOcP38ewD8zVUuXLkXv3r1hbGyMZs2a4b///a9kncuXL2PAgAGwsLCAlZUV+vfvjwsXLjy2r3379qFTp05QKpVo0qQJJkyYgPv37wMAQkJCEBYWhosXL0KhUMDR0bHCbUyZMgXt27eXjC1cuFBSv3fvXnTq1AmmpqZo1KgRunTpgszMTHH55s2b4eHhASMjIzRv3hxTp04V+3hw/MuWLUP//v1hamqK6dOnIy8vD4MGDULjxo1hbGyMVq1aYdWqVY89XiKqGoYmItIZCoUCQ4cOLfdhv3LlSrz++uto0aKFODZx4kS88847OH78OD744AMMHDgQ6enpAIC7d++ie/fuaNiwIfbv34+EhAQ0bNgQvXr1QnFxcYX7vnz5Mvr06YOOHTvi+PHjWLp0KX744QdMnz4dAPD1119j2rRpePnll5GdnY0jR4481THev38fgYGB6NatG/78808kJiZixIgRUCgUAIAdO3bggw8+QHh4OE6ePInly5cjNjYWM2bMkGxn8uTJ6N+/P/766y8MHToUEydOxMmTJ7F9+3akp6dj6dKlsLa2fqoeiagSAhFRLVq1apWgUqnE91euXBH09PSEw4cPC4IgCMXFxULjxo2F2NhYsQaA8PHHH0u24+npKYwaNUoQBEH44YcfBCcnJ6GsrExcXlRUJBgbGws7duyosI/PP/+83DqLFy8WGjZsKJSWlgqCIAgLFiwQHBwcHns8kydPFtq1aycZe3i9GzduCACEvXv3Vrj+66+/LsTExEjGVq9eLTRp0kR8D0CIiIiQ1PTr10/48MMPH9sbET0bzjQRkU5p0qQJ+vbti5UrVwIAtmzZgnv37uG9996T1Hl5eZV7/2CmKSUlBefOnYOZmRkaNmyIhg0bwtLSEvfu3RNP8T0qPT0dXl5e4owPAHTp0gUFBQW4dOlStR2fpaUlQkJC4Ofnh379+uHrr7+WXOOVkpKCadOmiX03bNgQw4cPR3Z2Nu7evSvWdejQQbLdUaNGIS4uDu3bt8f48eNx6NChauuZiP7B0EREOuejjz5CXFwcCgsLsWrVKgwYMAAmJiZPXO9B4CkrK4OHhwdSU1MlrzNnziA4OLjCdQVBkASmB2MPb1eOBg0aiOs9UFJSInm/atUqJCYmwtvbG+vXr0fr1q2RlJQk9j516lRJ33/99RfOnj0LIyMjcRumpqaSbfbu3RuZmZmIiIjAlStX0KNHD0RFRcnum4iejHfPEZHO6dOnD0xNTbF06VJs374d+/fvL1eTlJSEwYMHS967u7sDAF599VWsX78eNjY2MDc3l7VPFxcXbNiwQRKeDh06BDMzM7z00kuye2/cuDFycnIk26noeU7u7u5wd3dHdHQ0vLy8sHbtWnTu3BmvvvoqTp8+jZYtW8re58P7DgkJQUhICF5//XV8+umnmDdvXpW3Q0QV40wTEekcPT09hISEIDo6Gi1btix3Kg4A/vvf/2LlypU4c+YMJk+ejOTkZIwZMwYAMGjQIFhbW6N///44cOAAMjIysG/fPowdO7bSU22hoaHIyspCWFgYTp06hd9++w2TJ0/GuHHj0KCB/P9V+vj44Nq1a5gzZw7Onz+PxYsXY/v27eLyjIwMREdHIzExEZmZmdi5cyfOnDkDZ2dnAMCkSZPw73//G1OmTMGJEyeQnp6O9evX48svv3zsfidNmoTffvsN586dw4kTJ7BlyxZxm0RUPRiaiEgnDRs2DMXFxRg6dGiFy6dOnYq4uDi0bdsWP/74I9asWQMXFxcAgImJCfbv34+mTZvi7bffhrOzM4YOHYrCwsJKZ55eeuklbNu2DcnJyWjXrh0+/vhjDBs27Ilh5VHOzs5YsmQJFi9ejHbt2iE5OVlymszExASnTp3CO++8g9atW2PEiBEYM2YMRo4cCQDw8/PDli1bsGvXLnTs2BGdO3fG/Pnz4eDg8Nj9GhoaIjo6Gm3btkXXrl2hp6eHuLi4KvVORI+nEB49+U5EpAMOHjwIHx8fXLp0Cba2tpJlCoUCGzduRGBgYO00R0QvJF7TREQ6paioCFlZWZg4cSKCgoLKBSYiotrC03NEpFPWrVsHJycnaLVazJkzp7bbISIS8fQcERERkQycaSIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikuH/AXhnvl79MHSxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=df_bikes[\"member_casual\"], height=df_bikes[\"time_length (min)\"])\n",
    "plt.title(\"Type of Users versus Time Length\")\n",
    "plt.xlabel(\"Type of users\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172f7322",
   "metadata": {},
   "source": [
    "We can see from the bar plot above that **casual users tend to have larger trips**, spending more time in them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9819297e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=df_bikes[\"rideable_type\"], height=df_bikes[\"time_length (min)\"])\n",
    "plt.title(\"Type of Bikes versus Time Length\")\n",
    "plt.xlabel(\"Type of bikes\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6bb32c24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time length for electric bikes is:\n",
      " count    148575.000000\n",
      "mean          9.775225\n",
      "std          13.635198\n",
      "min           0.000000\n",
      "25%           4.166667\n",
      "50%           6.966667\n",
      "75%          11.650000\n",
      "max         480.450000\n",
      "Name: time_length (min), dtype: float64\n",
      "The time length for classic bikes is:\n",
      " count    107083.000000\n",
      "mean         14.300952\n",
      "std          56.915819\n",
      "min           0.000000\n",
      "25%           4.766667\n",
      "50%           8.150000\n",
      "75%          14.216667\n",
      "max        1559.933333\n",
      "Name: time_length (min), dtype: float64\n",
      "The time length for docked bikes is:\n",
      " count     3020.000000\n",
      "mean       132.376954\n",
      "std        828.994220\n",
      "min          0.083333\n",
      "25%         12.100000\n",
      "50%         21.516667\n",
      "75%         38.241667\n",
      "max      16808.066667\n",
      "Name: time_length (min), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Checking the bar plot above in numbers:\n",
    "print('The time length for electric bikes is:\\n', df_bikes[\"time_length (min)\"][df_bikes[\"rideable_type\"]=='electric_bike'].describe())\n",
    "print('The time length for classic bikes is:\\n', df_bikes[\"time_length (min)\"][df_bikes[\"rideable_type\"]=='classic_bike'].describe())\n",
    "print('The time length for docked bikes is:\\n', df_bikes[\"time_length (min)\"][df_bikes[\"rideable_type\"]=='docked_bike'].describe())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c989d511",
   "metadata": {},
   "source": [
    "And here we have a big difference. We can see a very strong variation among these three types of bikes, docked being the one in which users tend to spend more time in their bike trips. Hence, we can say that the **electric bike can definitely be the fastest option offered by Cyclistic (and besides is sustainable!)**.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec89e62",
   "metadata": {},
   "source": [
    "Now let's get on to analyzing this data for member users and casual users separately so we find more clues as to what to suggest in our business task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cc324749",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting data frames for both user types:\n",
    "df_member = df_bikes[df_bikes[\"member_casual\"]==\"member\"]\n",
    "df_casual = df_bikes[df_bikes[\"member_casual\"]==\"casual\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8fbf1372",
   "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>rideable_type</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>time_length (min)</th>\n",
       "      <th>distance (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>morning</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.741737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8.416667</td>\n",
       "      <td>2.221450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>member</td>\n",
       "      <td>afternoon</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>10.633333</td>\n",
       "      <td>1.584416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rideable_type member_casual time_of_day  day_of_month  day_of_week  \\\n",
       "0  electric_bike        member     morning            16            3   \n",
       "1  electric_bike        member   afternoon             4            5   \n",
       "2   classic_bike        member   afternoon            31            4   \n",
       "\n",
       "   time_length (min)  distance (km)  \n",
       "0           2.300000       0.741737  \n",
       "1           8.416667       2.221450  \n",
       "2          10.633333       1.584416  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_member.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "bbe14e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the time of day feature:\n",
    "\n",
    "plt.hist([df_member[\"time_of_day\"], df_casual[\"time_of_day\"]], bins=df_member[\"time_of_day\"].nunique(), edgecolor='w', linewidth=1.1, label = ['Annual', 'Casual'])\n",
    "plt.title(\"Frequency of time of the day for all users\")\n",
    "plt.xlabel(\"Time of the day\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/daytime_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac801f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Want to confirm the starting hours? Let's see:\n",
    "\n",
    "plt.hist([df_member[\"started_hour\"], df_casual[\"started_hour\"]], edgecolor='w', linewidth=0.5, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(df_member[\"started_hour\"].min()+1, df_member[\"started_hour\"].max() + 1, 1))\n",
    "plt.title(\"Frequency of the starting hours by members\")\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/startedhour_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "46f75bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ending hours:\n",
    "plt.hist([df_member[\"ended_hour\"], df_casual[\"ended_hour\"]], edgecolor='w', linewidth=0.5, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(df_member[\"ended_hour\"].min()+1, df_member[\"ended_hour\"].max() + 1, 1))\n",
    "plt.title(\"Frequency of the ending hours by members\")\n",
    "plt.xlabel(\"Hours\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/endedhour_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8056946a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the day of the week feature:\n",
    "\n",
    "plt.hist([df_member[\"day_of_week\"], df_casual[\"day_of_week\"]], bins=df_casual[\"day_of_week\"].nunique(), edgecolor='w', linewidth=1.1, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(df_member[\"day_of_week\"].min(), df_member[\"day_of_week\"].max() + 1, 1))\n",
    "plt.title(\"Frequency of day of the week for all users\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/dayweek_freq_bothusers.png\")\n",
    "plt.show()\n",
    "# 0: Monday, 6: Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3966d72a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_casual[\"day_of_week\"], bins=df_casual[\"day_of_week\"].nunique(), edgecolor='w', linewidth=1.1, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(df_casual[\"day_of_week\"].min(), df_casual[\"day_of_week\"].max() + 1, 1))\n",
    "plt.title(\"Frequency of day of the week for casual users\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()\n",
    "# 0: Monday, 6: Sunday"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "add92468",
   "metadata": {},
   "source": [
    "By taking a closer look to the values from the histogram above we can see that **Casual users' highest frequency peak is by the middle of the week with a value of around 11000 and even tends to mantain the use of the service in the weekend with a frequency value of ~7000 to ~8000, so compared to that, there is no big gap as there is in the annual members' graph where the peak at nearly 40000 drops to nearly 17000, diminishing by a little over 50%.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "403c3a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Day of the week feature:\n",
    "\n",
    "plt.hist([df_member[\"day_of_month\"], df_casual[\"day_of_month\"]], edgecolor='w', linewidth=1.1, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(df_member[\"day_of_month\"].min(), df_member[\"day_of_month\"].max() + 1, 5))\n",
    "plt.title(\"Frequency of day of the month for all users\")\n",
    "plt.xlabel(\"Day of the month\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig(\"images/daymonth_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "be7047b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bb7/9NnJycvDHH39g6tSp8PHxwfnz56XeTJKPoYlqJA8PD9SrVw9nzpypsJfF0NAQL730EjZs2IDZs2dLwSgrKwtbt27VamtrawsjIyOtO4wAYPPmzVqvTUxM0KNHD/z1119o06YNDA0NH3t7unTpApVKhcWLF+P111+v8Mve1dUVXbp0wcyZM5GYmIh3330XpqamFS7f1NQUbm5u2LBhA+bMmSPdalxUVIRVq1ahUaNGaN68eaXr7tGjB2bNmoUTJ05onaJbs2ZNpZajVCrh6emJevXqYefOnfjrr7/g7u5eqjeomEKhgBBCml7shx9+0Lq4vjK8vLygp6eHRYsWlRvY5B535SlveypSfCyU3NYlS5aU2f6NN97ApEmTEBUVhUuXLqFhw4bw8vKSpjs5OcHR0REnTpwoFRAepTLz9ujRA1u2bMHNmzelIFlYWKh16rCy3N3dYWxsjFWrVuE///mPNP7atWv47bff8Nprr1V52QqFotQ+3r59O65fv45mzZpVebkPs7e3R1pamtY+ycvLw86dO8udp7zPho+PD8LCwmBlZVXp/6SVxdTUFP369UNeXh5eeeUVnD59mqGpChiaqEaqW7cuFixYgOHDh+POnTt47bXXYGNjg1u3buHEiRO4desWFi1aBAD46quv0LdvX/Tp0wfBwcEoLCzEzJkzYWpqqtUroVAo8NZbb+HHH3/Eiy++iLZt2+LIkSNlBoBvvvkGXbt2xcsvv4z3338f9vb2yMrKwj///IOtW7fit99+q/T2zJ07F++88w569+6N0aNHw9bWFv/88w9OnDiByMhIrfYTJ07E0KFDoVAopNMijxIeHo4+ffqgR48eCAkJgaGhIRYuXIjExESsXbu2Uv8rLxYUFIQff/wRAwYMwPTp02Fra4vVq1fj77//fuS8X3zxBa5du4ZevXqhUaNGuHv3Lr755hsYGBjA09MTwINnOxkbG2P16tVo2bIl6tatCzs7O9jZ2aFbt26YPXs2rK2tYW9vj7i4OCxbtqzKvQL29vb45JNP8NVXXyE3NxdvvPEGVCoVzpw5g3///RfTpk2r1HFXloq2pzwtWrTAiy++iI8//hhCCFhaWmLr1q3lnkKpV68eBg0ahKioKNy9exchISGoU0f7SoslS5agX79+8Pb2xogRI9CwYUPcuXMHZ8+exfHjx/HLL7+UW4/ceT/77DNs2bIFPXv2xBdffAETExN89913j3XNV7169fD555/jk08+wbBhw/DGG2/g9u3bmDZtGoyMjDB16tQqL9vHxwdRUVFo0aIF2rRpg/j4eMyePfuRp8wrY+jQofjiiy/w+uuv48MPP8T9+/fx7bfflgr6cj4bQUFBWL9+Pbp164YPPvgAbdq0QVFREZKTkxEbG4vg4GC4ublVWM/o0aNhbGwMDw8PNGjQAKmpqQgPD4dKpZKuq6JK0uVV6PTsK+sOmYcV373zyy+/lDk9Li5ODBgwQFhaWgoDAwPRsGFDMWDAgFLtt2zZItq0aSMMDQ1FkyZNxIwZM0rdsSKEEBkZGeKdd94Rtra2wtTUVPj6+oorV66UuntOiAd30YwcOVI0bNhQGBgYiPr164suXbqI6dOnP7L+8u7U+/XXX4Wnp6cwNTUVJiYmolWrVmLmzJmltluj0QilUin69u1b5n4pz759+0TPnj2FqampMDY2Fp07dxZbt24tszY5d88J8eDurD59+ggjIyNhaWkpRo0aJTZv3vzIu+e2bdsm+vXrJxo2bCgMDQ2FjY2N6N+/v9i3b5/W8teuXStatGghDAwMtN6Ha9euiVdffVVYWFgIMzMz0bdvX5GYmCiaNm0qhg8fLs1f3jFW1p1hQgjx008/iU6dOgkjIyNRt25d0b59+1Lvk9zjrizlbc/w4cOFqalphfvYzMxMWFhYiP/85z8iOTm5zONSCCFiY2Olu1DPnz9f5jJPnDghhgwZImxsbISBgYFQq9WiZ8+eYvHixY/cR3LmFUKIP//8U3Tu3FkolUqhVqvFhx9+KJYuXVrlu+eK/fDDD9LnWaVSiYEDB4rTp09rtalof5YlPT1djBo1StjY2AgTExPRtWtXsW/fPuHp6Sk8PT2ldo9z95wQDz7j7dq1E8bGxuKFF14QkZGRpb6L5H42srOzxWeffSacnJykfeHi4iI++OADrbsWAYixY8eWqmXFihWiR48ewtbWVhgaGgo7OzsxZMgQcfLkSdn7jbQphCjxsAeiZ0RoaCimTZtW6nkmtcHWrVvh5+eH7du3SxefExGRbvH0HFENcubMGSQlJSE4OBjt2rVDv379dF0SERH9Pz5ygKgGCQwMhJ+fHywsLKp8HRIRET0ZPD1HREREJAN7moiIiIhkYGgiIiIikkGnocne3h4KhaLUUPwUZiEEQkNDYWdnB2NjY3Tv3h2nT5/WWoZGo8H48eNhbW0NU1NT+Pn5lXrqcnp6OgICAqBSqaBSqRAQEIC7d+9qtUlOToavry9MTU1hbW2NCRMmIC8v74luPxEREdUeOr177ujRo1oP/UpMTESfPn2kJ8HOmjULERERiIqKQvPmzTF9+nT06dMH586dg5mZGYAHDwDbunUroqOjYWVlheDgYPj4+CA+Ph56enoAHvyG1bVr1xATEwMAePfddxEQECA9MbqwsBADBgxA/fr1sX//fty+fRvDhw+HEAILFiyQvT1FRUW4ceMGzMzMeAEvERFRLSGEQFZWFuzs7Eo9LLZkwxpj4sSJ4sUXXxRFRUWiqKhIqNVqMWPGDGn6/fv3hUqlkh6wdvfuXWFgYCCio6OlNtevXxd16tQRMTExQogHD40DIA4dOiS1OXjwoAAg/v77byHEg4eR1alTR1y/fl1qs3btWqFUKkVGRobs+q9evSo9cI4DBw4cOHDgULuGq1evVvh3vsY8pykvLw+rVq3CpEmToFAocOnSJaSmpmr9plLxb/QcOHAAY8aMQXx8PPLz87Xa2NnZwdnZGQcOHIC3tzcOHjwIlUql9bj5zp07Q6VS4cCBA3BycsLBgwfh7Oys9VMH3t7e0Gg0iI+PR48ePcqsWaPRaP04o/j/GxGvXr0q+5e3iYiISLcyMzPRuHFj6SxWeWpMaNq0aRPu3r2LESNGAPjfL2WX/EVxW1tbJCUlSW0MDQ1hYWFRqk3x/KmpqbCxsSm1PhsbG602JddjYWEBQ0PDCn+xOzw8HNOmTSs13tzcnKGJiIiolnnUpTU15u65ZcuWoV+/fqV+2LLkBgghHrlRJduU1b4qbUqaMmUKMjIypOHq1asV1kVERES1V40ITUlJSdi9ezfeeecdaZxarQaAUj09aWlpUq+QWq1GXl4e0tPTK2xz8+bNUuu8deuWVpuS60lPT0d+fn6pHqiHKZVKqVeJvUtERETPthoRmpYvXw4bGxsMGDBAGufg4AC1Wo1du3ZJ4/Ly8hAXF4cuXboAAFxdXWFgYKDVJiUlBYmJiVIbd3d3ZGRk4MiRI1Kbw4cPIyMjQ6tNYmIiUlJSpDaxsbFQKpVwdXV9MhtNREREtYrOr2kqKirC8uXLMXz4cOjr/68chUKBoKAghIWFwdHREY6OjggLC4OJiQn8/f0BACqVCqNGjUJwcDCsrKxgaWmJkJAQuLi4oHfv3gCAli1bom/fvhg9ejSWLFkC4MEjB3x8fODk5AQA8PLyQqtWrRAQEIDZs2fjzp07CAkJwejRo9l7RERE1aKwsBD5+fm6LuO5ZGBgID2G6HHoPDTt3r0bycnJGDlyZKlpkydPRm5uLgIDA5Geng43NzfExsZqXd0+b9486OvrY8iQIcjNzUWvXr0QFRWltXNWr16NCRMmSHfZ+fn5ITIyUpqup6eH7du3IzAwEB4eHjA2Noa/vz/mzJnzBLeciIieB0IIpKamlnqoMj1d9erVg1qtfqznKPIHe6tRZmYmVCoVMjIy2ENFREQAHlw2cvfuXdjY2MDExIQPP37KhBC4d+8e0tLSUK9ePTRo0KBUG7l/v3Xe00RERPSsKiwslAKTlZWVrst5bhkbGwN4cKOYjY1NlU/V1YgLwYmIiJ5FxdcwmZiY6LgSKn4PHue6MoYmIiKiJ4yn5HSvOt4DhiYiIiIiGRiaiIiIqFaLiopCvXr1nvh6eCE4ERGRDtzK0iDz/tN7bpO5kQHqmykrPd+BAwfw8ssvo0+fPoiJiXkCldUeDE1EREQ6kHk/H73mxj219e0J9qxSaPrxxx8xfvx4/PDDD0hOTkaTJk2eQHW1A0/PERERUZlycnKwbt06vP/++/Dx8UFUVJQ07ffff4dCocCePXvQsWNHmJiYoEuXLjh37pzUJjQ0FO3atcPKlSthb28PlUqF119/HVlZWVIbe3t7zJ8/X2u97dq1Q2hoqPQ6IiICLi4uMDU1RePGjREYGIjs7OwntdnlYmgiIiKiMv38889wcnKCk5MT3nrrLSxfvhwln4n96aefYu7cuTh27Bj09fVL/cLHxYsXsWnTJmzbtg3btm1DXFwcZsyYUak66tSpg2+//RaJiYlYsWIFfvvtN0yePPmxt6+yeHruOfSo8+hVPe9NRETPlmXLluGtt94CAPTt2xfZ2dnYs2eP9PuuAPD111/D09MTAPDxxx9jwIABuH//PoyMjAA8+I3ZqKgo6SfQAgICsGfPHnz99dey6wgKCpL+7eDggK+++grvv/8+Fi5c+LibWCkMTbVEdQadR51Hr+p5byIienacO3cOR44cwYYNGwAA+vr6GDp0KH788Uet0NSmTRvp38U/UZKWliZd+2Rvb6/1m7ENGjRAWlpapWrZu3cvwsLCcObMGWRmZqKgoAD3799HTk4OTE1Nq7yNlcXQVEsw6BAR0dO0bNkyFBQUoGHDhtI4IQQMDAyQnp4ujTMwMJD+XfwAyaKiojKnF7d5eHqdOnVKnfJ7+KndSUlJ6N+/P9577z189dVXsLS0xP79+zFq1KjHerp3VTA0ERERkZaCggL89NNPmDt3Lry8vLSmvfrqq1i9ejWcnZ2rZV3169dHSkqK9DozMxOXL1+WXh87dgwFBQWYO3cu6tR5cCn2unXrqmXdlcXQRERERFq2bduG9PR0jBo1CiqVSmvaa6+9hmXLlmHevHnVsq6ePXsiKioKvr6+sLCwwOeff671g7ovvvgiCgoKsGDBAvj6+uLPP//E4sWLq2XdlcW754iIiEjLsmXL0Lt371KBCXjQ05SQkIDjx49Xy7qmTJmCbt26wcfHB/3798crr7yCF198UZrerl07REREYObMmXB2dsbq1asRHh5eLeuuLIUoeSKRqiwzMxMqlQoZGRkwNzev1mVfvJX9yGuaXqxf96kvi4iIynf//n1cvnwZDg4O0t1kxWrLE8GfFRW9F3L/fvP0HBERkQ7UN1M+1yGmNuLpOSIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiKq1ezt7TF//vwnvh4+EZyIiEgXsm8C9zOf3vqMzIG6tpWeLTU1FV9//TW2b9+O69evw8bGBu3atUNQUBB69er1BAqtuRiaiIiIdOF+JhDZ8emtb9yxSoemK1euwMPDA/Xq1cOsWbPQpk0b5OfnY+fOnRg7diz+/vvvJ1RszcTTc0RERFSmwMBAKBQKHDlyBK+99hqaN2+O1q1bY9KkSTh06BAAICIiAi4uLjA1NUXjxo0RGBiI7OxsaRlJSUnw9fWFhYUFTE1N0bp1a/z6668AgKioKNSrV09rnZs2bYJCoZBeX7x4EQMHDoStrS3q1q2LTp06Yffu3U9+48vA0ERERESl3LlzBzExMRg7dixMTU1LTS8OO3Xq1MG3336LxMRErFixAr/99hsmT54stRs7diw0Gg3++OMPnDp1CjNnzkTdunVl15GdnY3+/ftj9+7d+Ouvv+Dt7Q1fX18kJyc/9jZWFk/PERERUSn//PMPhBBo0aJFhe2CgoKkfzs4OOCrr77C+++/j4ULFwIAkpOT8eqrr8LFxQUA8MILL1SqjrZt26Jt27bS6+nTp2Pjxo3YsmULxo0bV6llPS72NBEREVEpQggA0DpVVpa9e/eiT58+aNiwIczMzDBs2DDcvn0bOTk5AIAJEyZg+vTp8PDwwNSpU3Hy5MlK1ZGTk4PJkyejVatWqFevHurWrYu///5bJz1NDE1ERERUiqOjIxQKBc6ePVtum6SkJPTv3x/Ozs5Yv3494uPj8d133wEA8vPzAQDvvPMOLl26hICAAJw6dQodO3bEggULADw4tVcczooVz1fsww8/xPr16/H1119j3759SEhIgIuLC/Ly8qpzc2VhaCIiIqJSLC0t4e3tje+++07qNXrY3bt3cezYMRQUFGDu3Lno3Lkzmjdvjhs3bpRq27hxY7z33nvYsGEDgoOD8f333wMA6tevj6ysLK3lJyQkaM27b98+jBgxAoMGDYKLiwvUajWuXLlSrdsqF0MTERERlWnhwoUoLCzESy+9hPXr1+PChQs4e/Ysvv32W7i7u+PFF19EQUEBFixYgEuXLmHlypVYvHix1jKCgoKwc+dOXL58GcePH8dvv/2Gli1bAgDc3NxgYmKCTz75BP/88w/WrFmDqKgorfmbNWuGDRs2ICEhASdOnIC/vz+Kioqe1i7QwtBEREREZXJwcMDx48fRo0cPBAcHw9nZGX369MGePXuwaNEitGvXDhEREZg5cyacnZ2xevVqhIeHay2jsLAQY8eORcuWLdG3b184OTlJF4lbWlpi1apV+PXXX+Hi4oK1a9ciNDRUa/558+bBwsICXbp0ga+vL7y9vdGhQ4entQu0KETJk4lUZZmZmVCpVMjIyIC5uXm1LvvirWz0mhtX7vQ9wZ54sb68Wzirc1lERFS++/fv4/Lly3BwcICRkZH2xFryRPBnRUXvhdy/33zkABERkS7UtX2uQ0xtxNNzRERERDIwNBERERHJwNBEREREJANDExER0RPGe650rzreA52HpuvXr+Ott96ClZUVTExM0K5dO8THx0vThRAIDQ2FnZ0djI2N0b17d5w+fVprGRqNBuPHj4e1tTVMTU3h5+eHa9euabVJT09HQEAAVCoVVCoVAgICcPfuXa02ycnJ8PX1hampKaytrTFhwgSdPHGUiIieDQYGBgCAe/fu6bgSKn4Pit+TqtDp3XPp6enw8PBAjx49sGPHDtjY2ODixYvSLycDwKxZsxAREYGoqCg0b94c06dPR58+fXDu3DmYmZkBePDgrK1btyI6OhpWVlYIDg6Gj48P4uPjoaenBwDw9/fHtWvXEBMTAwB49913ERAQgK1btwJ48ByJAQMGoH79+ti/fz9u376N4cOHQwghPe6diIioMvT09FCvXj2kpaUBAExMTB75W25UvYQQuHfvHtLS0lCvXj0pF1SFTkPTzJkz0bhxYyxfvlwaZ29vL/1bCIH58+fj008/xeDBgwEAK1asgK2tLdasWYMxY8YgIyMDy5Ytw8qVK9G7d28AwKpVq9C4cWPs3r0b3t7eOHv2LGJiYnDo0CG4ubkBAL7//nu4u7vj3LlzcHJyQmxsLM6cOYOrV6/Czs4OADB37lyMGDECX3/9dbU/d4mIiJ4ParUaAKTgRLpRr1496b2oKp2Gpi1btsDb2xv/+c9/EBcXh4YNGyIwMBCjR48GAFy+fBmpqanw8vKS5lEqlfD09MSBAwcwZswYxMfHIz8/X6uNnZ0dnJ2dceDAAXh7e+PgwYNQqVRSYAKAzp07Q6VS4cCBA3BycsLBgwfh7OwsBSYA8Pb2hkajQXx8PHr06FGqfo1GA41GI73OzHyKDykjIqJaQaFQoEGDBrCxsSn1Y7T0dBgYGDxWD1MxnYamS5cuYdGiRZg0aRI++eQTHDlyBBMmTIBSqcSwYcOQmpoKALC11X74l62tLZKSkgAAqampMDQ0hIWFRak2xfOnpqbCxsam1PptbGy02pRcj4WFBQwNDaU2JYWHh2PatGlV2HIiInre6OnpVcsfbtIdnV4IXlRUhA4dOiAsLAzt27fHmDFjMHr0aCxatEirXcnzv0KIR54TLtmmrPZVafOwKVOmICMjQxquXr1aYU1ERERUe+k0NDVo0ACtWrXSGteyZUskJycD+N954JI9PWlpaVKvkFqtRl5eHtLT0ytsc/PmzVLrv3XrllabkutJT09Hfn5+qR6oYkqlEubm5loDERERPZt0Gpo8PDxw7tw5rXHnz59H06ZNATz4dWW1Wo1du3ZJ0/Py8hAXF4cuXboAAFxdXWFgYKDVJiUlBYmJiVIbd3d3ZGRk4MiRI1Kbw4cPIyMjQ6tNYmIiUlJSpDaxsbFQKpVwdXWt5i0nIiKi2kan1zR98MEH6NKlC8LCwjBkyBAcOXIES5cuxdKlSwE8OF0WFBSEsLAwODo6wtHREWFhYTAxMYG/vz8AQKVSYdSoUQgODoaVlRUsLS0REhICFxcX6W66li1bom/fvhg9ejSWLFkC4MEjB3x8fODk5AQA8PLyQqtWrRAQEIDZs2fjzp07CAkJwejRo9mDRERERLoNTZ06dcLGjRsxZcoUfPnll3BwcMD8+fPx5ptvSm0mT56M3NxcBAYGIj09HW5uboiNjZWe0QQA8+bNg76+PoYMGYLc3Fz06tULUVFRWhfcrV69GhMmTJDusvPz80NkZKQ0XU9PD9u3b0dgYCA8PDxgbGwMf39/zJkz5ynsCSIiIqrpFILPdq82mZmZUKlUyMjIqPbeqYu3stFrbly50/cEe+LF+nWf+rKIiIhqO7l/v3X+MypEREREtQFDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkg05DU2hoKBQKhdagVqul6UIIhIaGws7ODsbGxujevTtOnz6ttQyNRoPx48fD2toapqam8PPzw7Vr17TapKenIyAgACqVCiqVCgEBAbh7965Wm+TkZPj6+sLU1BTW1taYMGEC8vLynti2ExERUe2i856m1q1bIyUlRRpOnTolTZs1axYiIiIQGRmJo0ePQq1Wo0+fPsjKypLaBAUFYePGjYiOjsb+/fuRnZ0NHx8fFBYWSm38/f2RkJCAmJgYxMTEICEhAQEBAdL0wsJCDBgwADk5Odi/fz+io6Oxfv16BAcHP52dQERERDWevs4L0NfX6l0qJoTA/Pnz8emnn2Lw4MEAgBUrVsDW1hZr1qzBmDFjkJGRgWXLlmHlypXo3bs3AGDVqlVo3Lgxdu/eDW9vb5w9exYxMTE4dOgQ3NzcAADff/893N3dce7cOTg5OSE2NhZnzpzB1atXYWdnBwCYO3cuRowYga+//hrm5uZPaW8QERFRTaXznqYLFy7Azs4ODg4OeP3113Hp0iUAwOXLl5GamgovLy+prVKphKenJw4cOAAAiI+PR35+vlYbOzs7ODs7S20OHjwIlUolBSYA6Ny5M1QqlVYbZ2dnKTABgLe3NzQaDeLj48utXaPRIDMzU2sgIiKiZ5NOQ5Obmxt++ukn7Ny5E99//z1SU1PRpUsX3L59G6mpqQAAW1tbrXlsbW2laampqTA0NISFhUWFbWxsbEqt28bGRqtNyfVYWFjA0NBQalOW8PBw6ToplUqFxo0bV3IPEBERUW2h09DUr18/vPrqq3BxcUHv3r2xfft2AA9OwxVTKBRa8wghSo0rqWSbstpXpU1JU6ZMQUZGhjRcvXq1wrqIiIio9tL56bmHmZqawsXFBRcuXJCucyrZ05OWlib1CqnVauTl5SE9Pb3CNjdv3iy1rlu3bmm1Kbme9PR05Ofnl+qBephSqYS5ubnWQERERM+mGhWaNBoNzp49iwYNGsDBwQFqtRq7du2Spufl5SEuLg5dunQBALi6usLAwECrTUpKChITE6U27u7uyMjIwJEjR6Q2hw8fRkZGhlabxMREpKSkSG1iY2OhVCrh6ur6RLeZiIiIaged3j0XEhICX19fNGnSBGlpaZg+fToyMzMxfPhwKBQKBAUFISwsDI6OjnB0dERYWBhMTEzg7+8PAFCpVBg1ahSCg4NhZWUFS0tLhISESKf7AKBly5bo27cvRo8ejSVLlgAA3n33Xfj4+MDJyQkA4OXlhVatWiEgIACzZ8/GnTt3EBISgtGjR7P3iIiIiADoODRdu3YNb7zxBv7991/Ur18fnTt3xqFDh9C0aVMAwOTJk5Gbm4vAwECkp6fDzc0NsbGxMDMzk5Yxb9486OvrY8iQIcjNzUWvXr0QFRUFPT09qc3q1asxYcIE6S47Pz8/REZGStP19PSwfft2BAYGwsPDA8bGxvD398ecOXOe0p4gIiKimk4hhBC6LuJZkZmZCZVKhYyMjGrvobp4Kxu95saVO31PsCderF/3qS+LiIiotpP797tGXdNEREREVFMxNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJUKXQdPny5equg4iIiKhGq1JoatasGXr06IFVq1bh/v371V0TERERUY1TpdB04sQJtG/fHsHBwVCr1RgzZgyOHDlS3bURERER1RhVCk3Ozs6IiIjA9evXsXz5cqSmpqJr165o3bo1IiIicOvWrUovMzw8HAqFAkFBQdI4IQRCQ0NhZ2cHY2NjdO/eHadPn9aaT6PRYPz48bC2toapqSn8/Pxw7do1rTbp6ekICAiASqWCSqVCQEAA7t69q9UmOTkZvr6+MDU1hbW1NSZMmIC8vLxKbwcRERE9mx7rQnB9fX0MGjQI69atw8yZM3Hx4kWEhISgUaNGGDZsGFJSUmQt5+jRo1i6dCnatGmjNX7WrFmIiIhAZGQkjh49CrVajT59+iArK0tqExQUhI0bNyI6Ohr79+9HdnY2fHx8UFhYKLXx9/dHQkICYmJiEBMTg4SEBAQEBEjTCwsLMWDAAOTk5GD//v2Ijo7G+vXrERwc/Di7h4iIiJ4hjxWajh07hsDAQDRo0AAREREICQnBxYsX8dtvv+H69esYOHDgI5eRnZ2NN998E99//z0sLCyk8UIIzJ8/H59++ikGDx4MZ2dnrFixAvfu3cOaNWsAABkZGVi2bBnmzp2L3r17o3379li1ahVOnTqF3bt3AwDOnj2LmJgY/PDDD3B3d4e7uzu+//57bNu2DefOnQMAxMbG4syZM1i1ahXat2+P3r17Y+7cufj++++RmZn5OLuIiIiInhFVCk0RERFwcXFBly5dcOPGDfz0009ISkrC9OnT4eDgAA8PDyxZsgTHjx9/5LLGjh2LAQMGoHfv3lrjL1++jNTUVHh5eUnjlEolPD09ceDAAQBAfHw88vPztdrY2dnB2dlZanPw4EGoVCq4ublJbTp37gyVSqXVxtnZGXZ2dlIbb29vaDQaxMfHV2EPERER0bNGvyozLVq0CCNHjsTbb78NtVpdZpsmTZpg2bJlFS4nOjoa8fHxOHbsWKlpqampAABbW1ut8ba2tkhKSpLaGBoaavVQFbcpnj81NRU2Njallm9jY6PVpuR6LCwsYGhoKLUpi0ajgUajkV6zV4qIiOjZVaXQdOHChUe2MTQ0xPDhw8udfvXqVUycOBGxsbEwMjIqt51CodB6LYQoNa6kkm3Kal+VNiWFh4dj2rRpFdZCREREz4YqnZ5bvnw5fvnll1Ljf/nlF6xYsULWMuLj45GWlgZXV1fo6+tDX18fcXFx+Pbbb6Gvry/1/JTs6UlLS5OmqdVq5OXlIT09vcI2N2/eLLX+W7duabUpuZ709HTk5+eX6oF62JQpU5CRkSENV69elbXtREREVPtUKTTNmDED1tbWpcbb2NggLCxM1jJ69eqFU6dOISEhQRo6duyIN998EwkJCXjhhRegVquxa9cuaZ68vDzExcWhS5cuAABXV1cYGBhotUlJSUFiYqLUxt3dHRkZGVrPkTp8+DAyMjK02iQmJmrd7RcbGwulUglXV9dyt0GpVMLc3FxrICIiomdTlU7PJSUlwcHBodT4pk2bIjk5WdYyzMzM4OzsrDXO1NQUVlZW0vigoCCEhYXB0dERjo6OCAsLg4mJCfz9/QEAKpUKo0aNQnBwMKysrGBpaYmQkBC4uLhIF5a3bNkSffv2xejRo7FkyRIAwLvvvgsfHx84OTkBALy8vNCqVSsEBARg9uzZuHPnDkJCQjB69GgGISIiIgJQxdBkY2ODkydPwt7eXmv8iRMnYGVlVR11AQAmT56M3NxcBAYGIj09HW5uboiNjYWZmZnUZt68edDX18eQIUOQm5uLXr16ISoqCnp6elKb1atXY8KECdJddn5+foiMjJSm6+npYfv27QgMDISHhweMjY3h7++POXPmVNu2EBERUe2mEEKIys40efJkrFu3DsuXL0e3bt0AAHFxcRg5ciRee+215zZsZGZmQqVSISMjo9p7qC7eykavuXHlTt8T7IkX69d96ssiIiKq7eT+/a5ST9P06dORlJSEXr16QV//wSKKioowbNgw2dc0EREREdUmVQpNhoaG+Pnnn/HVV1/hxIkTMDY2houLC5o2bVrd9RERERHVCFUKTcWaN2+O5s2bV1ctRERERDVWlUJTYWEhoqKisGfPHqSlpaGoqEhr+m+//VYtxRERERHVFFUKTRMnTkRUVBQGDBgAZ2fnRz6hm4iIiKi2q1Joio6Oxrp169C/f//qroeIiIioRqrSE8ENDQ3RrFmz6q6FiIiIqMaqUmgKDg7GN998gyo84omIiIioVqrS6bn9+/dj79692LFjB1q3bg0DAwOt6Rs2bKiW4oiIiIhqiiqFpnr16mHQoEHVXQsRERFRjVWl0LR8+fLqroOIiIioRqvSNU0AUFBQgN27d2PJkiXIysoCANy4cQPZ2dnVVhwRERFRTVGlnqakpCT07dsXycnJ0Gg06NOnD8zMzDBr1izcv38fixcvru46iYiIiHSqSj1NEydORMeOHZGeng5jY2Np/KBBg7Bnz55qK46IiIiopqjy3XN//vknDA0NtcY3bdoU169fr5bCiIiIiGqSKvU0FRUVobCwsNT4a9euwczM7LGLIiIiIqppqhSa+vTpg/nz50uvFQoFsrOzMXXqVP60ChERET2TqnR6bt68eejRowdatWqF+/fvw9/fHxcuXIC1tTXWrl1b3TUSERER6VyVQpOdnR0SEhKwdu1aHD9+HEVFRRg1ahTefPNNrQvDiYiIiJ4VVQpNAGBsbIyRI0di5MiR1VkPERERUY1UpdD0008/VTh92LBhVSqGiIiIqKaqUmiaOHGi1uv8/Hzcu3cPhoaGMDExYWgiIiKiZ06V7p5LT0/XGrKzs3Hu3Dl07dqVF4ITERHRM6nKvz1XkqOjI2bMmFGqF4qIiIjoWVBtoQkA9PT0cOPGjepcJBEREVGNUKVrmrZs2aL1WgiBlJQUREZGwsPDo1oKIyIiIqpJqhSaXnnlFa3XCoUC9evXR8+ePTF37tzqqIuIiIioRqlSaCoqKqruOoiIiIhqtGq9pomIiIjoWVWlnqZJkybJbhsREVGVVRARERHVKFUKTX/99ReOHz+OgoICODk5AQDOnz8PPT09dOjQQWqnUCiqp0oiIiIiHatSaPL19YWZmRlWrFgBCwsLAA8eePn222/j5ZdfRnBwcLUWSURERKRrVbqmae7cuQgPD5cCEwBYWFhg+vTpvHuOiIiInklVCk2ZmZm4efNmqfFpaWnIysp67KKIiIiIapoqhaZBgwbh7bffxn//+19cu3YN165dw3//+1+MGjUKgwcPru4aiYiIiHSuStc0LV68GCEhIXjrrbeQn5//YEH6+hg1ahRmz55drQUSERER1QRVCk0mJiZYuHAhZs+ejYsXL0IIgWbNmsHU1LS66yMiIiKqER7r4ZYpKSlISUlB8+bNYWpqCiFEddVFREREVKNUKTTdvn0bvXr1QvPmzdG/f3+kpKQAAN555x0+boCIiIieSVUKTR988AEMDAyQnJwMExMTafzQoUMRExNTbcURERER1RRVuqYpNjYWO3fuRKNGjbTGOzo6IikpqVoKIyIiIqpJqtTTlJOTo9XDVOzff/+FUql87KKIiIiIapoqhaZu3brhp59+kl4rFAoUFRVh9uzZ6NGjh+zlLFq0CG3atIG5uTnMzc3h7u6OHTt2SNOFEAgNDYWdnR2MjY3RvXt3nD59WmsZGo0G48ePh7W1NUxNTeHn54dr165ptUlPT0dAQABUKhVUKhUCAgJw9+5drTbJycnw9fWFqakprK2tMWHCBOTl5VVirxAREdGzrEqhafbs2ViyZAn69euHvLw8TJ48Gc7Ozvjjjz8wc+ZM2ctp1KgRZsyYgWPHjuHYsWPo2bMnBg4cKAWjWbNmISIiApGRkTh69CjUajX69Omj9dTxoKAgbNy4EdHR0di/fz+ys7Ph4+ODwsJCqY2/vz8SEhIQExODmJgYJCQkICAgQJpeWFiIAQMGICcnB/v370d0dDTWr1/Pi9qJiIhIohBVfE5AamoqFi1ahPj4eBQVFaFDhw4YO3YsGjRo8FgFWVpaYvbs2Rg5ciTs7OwQFBSEjz76CMCDXiVbW1vMnDkTY8aMQUZGBurXr4+VK1di6NChAIAbN26gcePG+PXXX+Ht7Y2zZ8+iVatWOHToENzc3AAAhw4dgru7O/7++284OTlhx44d8PHxwdWrV2FnZwcAiI6OxogRI5CWlgZzc3NZtWdmZkKlUiEjI0P2PHJdvJWNXnPjyp2+J9gTL9av+9SXRUREVNvJ/ftd6Z6m/Px89OjRA5mZmZg2bRq2bduGX3/9FdOnT3+swFRYWIjo6Gjk5OTA3d0dly9fRmpqKry8vKQ2SqUSnp6eOHDgAAAgPj4e+fn5Wm3s7Ozg7OwstTl48CBUKpUUmACgc+fOUKlUWm2cnZ2lwAQA3t7e0Gg0iI+PL7dmjUaDzMxMrYGIiIieTZUOTQYGBkhMTIRCoaiWAk6dOoW6detCqVTivffew8aNG9GqVSukpqYCAGxtbbXa29raStNSU1NhaGgICwuLCtvY2NiUWq+NjY1Wm5LrsbCwgKGhodSmLOHh4dJ1UiqVCo0bN67k1hMREVFtUaVrmoYNG4Zly5ZVSwFOTk5ISEjAoUOH8P7772P48OE4c+aMNL1kOBNCPDKwlWxTVvuqtClpypQpyMjIkIarV69WWBcRERHVXlV6TlNeXh5++OEH7Nq1Cx07diz1m3MRERGyl2VoaIhmzZoBADp27IijR4/im2++ka5jSk1N1Trtl5aWJvUKqdVq5OXlIT09Xau3KS0tDV26dJHa3Lx5s9R6b926pbWcw4cPa01PT09Hfn5+qR6ohymVSj5igYiI6DlRqZ6mS5cuoaioCImJiejQoQPMzc1x/vx5/PXXX9KQkJDwWAUJIaDRaODg4AC1Wo1du3ZJ0/Ly8hAXFycFIldXVxgYGGi1SUlJQWJiotTG3d0dGRkZOHLkiNTm8OHDyMjI0GqTmJgo/RwM8OABnkqlEq6uro+1PURERPRsqFRPk6OjI1JSUrB3714AD3425dtvv62wN6Yin3zyCfr164fGjRsjKysL0dHR+P333xETEwOFQoGgoCCEhYXB0dERjo6OCAsLg4mJCfz9/QEAKpUKo0aNQnBwMKysrGBpaYmQkBC4uLigd+/eAICWLVuib9++GD16NJYsWQIAePfdd+Hj4wMnJycAgJeXF1q1aoWAgADMnj0bd+7cQUhICEaPHl3td8ERERFR7VSp0FTy6QQ7duxATk5OlVd+8+ZNBAQEICUlBSqVCm3atEFMTAz69OkDAJg8eTJyc3MRGBiI9PR0uLm5ITY2FmZmZtIy5s2bB319fQwZMgS5ubno1asXoqKioKenJ7VZvXo1JkyYIN1l5+fnh8jISGm6np4etm/fjsDAQHh4eMDY2Bj+/v6YM2dOlbeNiIiIni2Vek5TnTp1tO5GMzMzw4kTJ/DCCy88sQJrEz6niYiIqPaR+/e7Uj1NCoWi1N1k1fXoAaqdbmVpkHk/v9zp5kYGqG/Gi+WJiKj2q/TpuREjRkh3jN2/fx/vvfdeqbvnNmzYUH0VUo2WeT//kb1WDE1ERPQsqFRoGj58uNbrt956q1qLISIiIqqpKhWali9f/qTqICIiIqrRqvREcCIiIqLnDUMTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCSDvq4LoJqngV4m8G9K2RONzIG6tk+3ICIiohqAoYlK0c/PAha9VPbEcccYmoiI6LnE03NEREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERyaDT0BQeHo5OnTrBzMwMNjY2eOWVV3Du3DmtNkIIhIaGws7ODsbGxujevTtOnz6t1Uaj0WD8+PGwtraGqakp/Pz8cO3aNa026enpCAgIgEqlgkqlQkBAAO7evavVJjk5Gb6+vjA1NYW1tTUmTJiAvLy8J7LtREREVLvoNDTFxcVh7NixOHToEHbt2oWCggJ4eXkhJydHajNr1ixEREQgMjISR48ehVqtRp8+fZCVlSW1CQoKwsaNGxEdHY39+/cjOzsbPj4+KCwslNr4+/sjISEBMTExiImJQUJCAgICAqTphYWFGDBgAHJycrB//35ER0dj/fr1CA4Ofjo7g4iIiGo0nT7cMiYmRuv18uXLYWNjg/j4eHTr1g1CCMyfPx+ffvopBg8eDABYsWIFbG1tsWbNGowZMwYZGRlYtmwZVq5cid69ewMAVq1ahcaNG2P37t3w9vbG2bNnERMTg0OHDsHNzQ0A8P3338Pd3R3nzp2Dk5MTYmNjcebMGVy9ehV2dnYAgLlz52LEiBH4+uuvYW5u/hT3DBEREdU0NeqapoyMDACApaUlAODy5ctITU2Fl5eX1EapVMLT0xMHDhwAAMTHxyM/P1+rjZ2dHZydnaU2Bw8ehEqlkgITAHTu3BkqlUqrjbOzsxSYAMDb2xsajQbx8fFPaIuJiIiotqgxP6MihMCkSZPQtWtXODs7AwBSU1MBALa22j/bYWtri6SkJKmNoaEhLCwsSrUpnj81NRU2Njal1mljY6PVpuR6LCwsYGhoKLUpSaPRQKPRSK8zMzNlby8RERHVLjUmNI0bNw4nT57E/v37S01TKBRar4UQpcaVVLJNWe2r0uZh4eHhmDZtWoV1PC38kV0iIqInq0aEpvHjx2PLli34448/0KhRI2m8Wq0G8KAXqEGDBtL4tLQ0qVdIrVYjLy8P6enpWr1NaWlp6NKli9Tm5s2bpdZ769YtreUcPnxYa3p6ejry8/NL9UAVmzJlCiZNmiS9zszMROPGjSu17dWFP7JLRET0ZOn0miYhBMaNG4cNGzbgt99+g4ODg9Z0BwcHqNVq7Nq1SxqXl5eHuLg4KRC5urrCwMBAq01KSgoSExOlNu7u7sjIyMCRI0ekNocPH0ZGRoZWm8TERKSk/K+3JjY2FkqlEq6urmXWr1QqYW5urjUQERHRs0mnPU1jx47FmjVrsHnzZpiZmUnXDqlUKhgbG0OhUCAoKAhhYWFwdHSEo6MjwsLCYGJiAn9/f6ntqFGjEBwcDCsrK1haWiIkJAQuLi7S3XQtW7ZE3759MXr0aCxZsgQA8O6778LHxwdOTk4AAC8vL7Rq1QoBAQGYPXs27ty5g5CQEIwePZphiIiIiHQbmhYtWgQA6N69u9b45cuXY8SIEQCAyZMnIzc3F4GBgUhPT4ebmxtiY2NhZmYmtZ83bx709fUxZMgQ5ObmolevXoiKioKenp7UZvXq1ZgwYYJ0l52fnx8iIyOl6Xp6eti+fTsCAwPh4eEBY2Nj+Pv7Y86cOU9o64mIiKg20WloEkI8so1CoUBoaChCQ0PLbWNkZIQFCxZgwYIF5baxtLTEqlWrKlxXkyZNsG3btkfWRERERM+fGvWcJiIiIqKaiqGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSQV/XBRAVu5WlQeb9/HKnmxsZoL6Z8ilWRERE9D8MTVRjZN7PR6+5ceVO3xPsydBEREQ6w9NzRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJINOQ9Mff/wBX19f2NnZQaFQYNOmTVrThRAIDQ2FnZ0djI2N0b17d5w+fVqrjUajwfjx42FtbQ1TU1P4+fnh2rVrWm3S09MREBAAlUoFlUqFgIAA3L17V6tNcnIyfH19YWpqCmtra0yYMAF5eXlPYrOJiIioFtJpaMrJyUHbtm0RGRlZ5vRZs2YhIiICkZGROHr0KNRqNfr06YOsrCypTVBQEDZu3Ijo6Gjs378f2dnZ8PHxQWFhodTG398fCQkJiImJQUxMDBISEhAQECBNLywsxIABA5CTk4P9+/cjOjoa69evR3Bw8JPbeCIiIqpV9HW58n79+qFfv35lThNCYP78+fj0008xePBgAMCKFStga2uLNWvWYMyYMcjIyMCyZcuwcuVK9O7dGwCwatUqNG7cGLt374a3tzfOnj2LmJgYHDp0CG5ubgCA77//Hu7u7jh37hycnJwQGxuLM2fO4OrVq7CzswMAzJ07FyNGjMDXX38Nc3Pzp7A3iIiIqCarsdc0Xb58GampqfDy8pLGKZVKeHp64sCBAwCA+Ph45Ofna7Wxs7ODs7Oz1ObgwYNQqVRSYAKAzp07Q6VSabVxdnaWAhMAeHt7Q6PRID4+vtwaNRoNMjMztQbS1kAvE/j3QtlD9k1dl0dERCSbTnuaKpKamgoAsLW11Rpva2uLpKQkqY2hoSEsLCxKtSmePzU1FTY2NqWWb2Njo9Wm5HosLCxgaGgotSlLeHg4pk2bVskte77o52cBi14qe+K4Y0Bd27KnERER1TA1tqepmEKh0HothCg1rqSSbcpqX5U2JU2ZMgUZGRnScPXq1QrrIiIiotqrxoYmtVoNAKV6etLS0qReIbVajby8PKSnp1fY5ubN0qeBbt26pdWm5HrS09ORn59fqgfqYUqlEubm5loDERERPZtqbGhycHCAWq3Grl27pHF5eXmIi4tDly5dAACurq4wMDDQapOSkoLExESpjbu7OzIyMnDkyBGpzeHDh5GRkaHVJjExESkpKVKb2NhYKJVKuLq6PtHtJCIiotpBp9c0ZWdn459//pFeX758GQkJCbC0tESTJk0QFBSEsLAwODo6wtHREWFhYTAxMYG/vz8AQKVSYdSoUQgODoaVlRUsLS0REhICFxcX6W66li1bom/fvhg9ejSWLFkCAHj33Xfh4+MDJycnAICXlxdatWqFgIAAzJ49G3fu3EFISAhGjx7N3iMiIiICoOPQdOzYMfTo0UN6PWnSJADA8OHDERUVhcmTJyM3NxeBgYFIT0+Hm5sbYmNjYWZmJs0zb9486OvrY8iQIcjNzUWvXr0QFRUFPT09qc3q1asxYcIE6S47Pz8/rWdD6enpYfv27QgMDISHhweMjY3h7++POXPmPOldQERERLWETkNT9+7dIYQod7pCoUBoaChCQ0PLbWNkZIQFCxZgwYIF5baxtLTEqlWrKqylSZMm2LZt2yNrJiIioudTjb2miYiIiKgmYWgiIiIikoGhiYiIiEiGGvtEcKLHcStLg8z7+eVONzcyQH0z5VOsiIiIajuGJnomZd7PR6+5ceVO3xPsydBERESVwtNzRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMujrugCimu5WlgaZ9/PLnW5uZID6ZsqnWBEREekCQxPRI2Tez0evuXHlTt8T7MnQRET0HGBoolqjgV4m8G9K2RONzIG6tk+3ICIieq4wNFGtoZ+fBSx6qeyJ444xNBER0RPFC8GJiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhk4N1zRE9RRQ/K5EMyiYhqNoYmoqeoogdl8iGZREQ1G0/PEREREcnAniZ6LvHp4kREVFkMTfRc4tPFiYiosnh6joiIiEgG9jQR1VK8E4+I6OliaCKqpXgnHhHR08XQRPSYeFE5EdHzgaGJ6DE9CxeV81QfEdGjMTSVsHDhQsyePRspKSlo3bo15s+fj5dfflnXZRE9UTzVR0T0aAxND/n5558RFBSEhQsXwsPDA0uWLEG/fv1w5swZNGnSRNfl0TOuwtN8QK051cdeKyJ6VjE0PSQiIgKjRo3CO++8AwCYP38+du7ciUWLFiE8PFzH1dGzrsLTfECtOdXHXisielYxNP2/vLw8xMfH4+OPP9Ya7+XlhQMHDuioKqKqYa8VEVH1Y2j6f//++y8KCwtha6v9h8TW1hapqallzqPRaKDRaKTXGRkZAIDMzMxqry8rKxtFmnvlTs/MyoahRpQzczZg+L+auKyauawKl1PJZd27k4KCZT3KX9a7cUCRsaxlmeZcR2Z2+dsPpRlQ1+aJLOv6v9nwW/BnmU2PTGyDzH/lL+vfLA0yNWUHsEb6OTAszK6WZZkrDWCtgzBXUU2A7uoiqg2K/24LUcF38P83ICHE9evXBQBx4MABrfHTp08XTk5OZc4zdepUAYADBw4cOHDg8AwMV69erTArsKfp/1lbW0NPT69Ur1JaWlqp3qdiU6ZMwaRJk6TXRUVFuHPnDqysrKBQKB67pszMTDRu3BhXr16Fubn5Yy+vOtXU2lhX5dXU2lhX5dXU2lhX5dXU2mpqXY9LCIGsrCzY2dlV2I6h6f8ZGhrC1dUVu3btwqBBg6Txu3btwsCBA8ucR6lUQqnU7u6uV69etddmbm5eYw/Omlob66q8mlob66q8mlob66q8mlpbTa3rcahUqke2YWh6yKRJkxAQEICOHTvC3d0dS5cuRXJyMt577z1dl0ZEREQ6xtD0kKFDh+L27dv48ssvkZKSAmdnZ/z6669o2rSprksjIiIiHWNoKiEwMBCBgYG6LgPAg9N/U6dOLXUKsCaoqbWxrsqrqbWxrsqrqbWxrsqrqbXV1LqeFoUQj7q/joiIiIjq6LoAIiIiotqAoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGiqwRYuXAgHBwcYGRnB1dUV+/bt02k94eHh6NSpE8zMzGBjY4NXXnkF586d02lNZQkPD4dCoUBQUJCuSwEAXL9+HW+99RasrKxgYmKCdu3aIT4+Xqc1FRQU4LPPPoODgwOMjY3xwgsv4Msvv0RRUdFTr+WPP/6Ar68v7OzsoFAosGnTJq3pQgiEhobCzs4OxsbG6N69O06fPq3TuvLz8/HRRx/BxcUFpqamsLOzw7Bhw3Djxg2d1lXSmDFjoFAoMH/+/Cdel9zazp49Cz8/P6hUKpiZmaFz585ITk7WaV3Z2dkYN24cGjVqBGNjY7Rs2RKLFi16ojUB8r5TdXH8P6ouXR7/usbQVEP9/PPPCAoKwqeffoq//voLL7/8Mvr16/fEv1wqEhcXh7Fjx+LQoUPYtWsXCgoK4OXlhZycHJ3VVNLRo0exdOlStGnTRtelAADS09Ph4eEBAwMD7NixA2fOnMHcuXOfyJPjK2PmzJlYvHgxIiMjcfbsWcyaNQuzZ8/GggULnnotOTk5aNu2LSIjI8ucPmvWLERERCAyMhJHjx6FWq1Gnz59kJWVpbO67t27h+PHj+Pzzz/H8ePHsWHDBpw/fx5+fn5PtKZH1fWwTZs24fDhw4/8WYjq9KjaLl68iK5du6JFixb4/fffceLECXz++ecwMjLSaV0ffPABYmJisGrVKpw9exYffPABxo8fj82bNz/RuuR8p+ri+H9UXbo8/nWuOn7slqrfSy+9JN577z2tcS1atBAff/yxjioqLS0tTQAQcXFxui5FCCFEVlaWcHR0FLt27RKenp5i4sSJui5JfPTRR6Jr1666LqOUAQMGiJEjR2qNGzx4sHjrrbd0VNEDAMTGjRul10VFRUKtVosZM2ZI4+7fvy9UKpVYvHixzuoqy5EjRwQAkZSU9HSKEuXXde3aNdGwYUORmJgomjZtKubNm/fUaqqotqFDh9a4Y0wIIVq3bi2+/PJLrXEdOnQQn3322VOsrPR3ak05/uV81+vi+NcF9jTVQHl5eYiPj4eXl5fWeC8vLxw4cEBHVZWWkZEBALC0tNRxJQ+MHTsWAwYMQO/evXVdimTLli3o2LEj/vOf/8DGxgbt27fH999/r+uy0LVrV+zZswfnz58HAJw4cQL79+9H//79dVyZtsuXLyM1NVXrs6BUKuHp6VmjPgvAg8+DQqHQeS9iUVERAgIC8OGHH6J169Y6reVhRUVF2L59O5o3bw5vb2/Y2NjAzc2twtOLT0vXrl2xZcsWXL9+HUII7N27F+fPn4e3t/dTraPkd2pNOf7lfNfXlOP/SWNoqoH+/fdfFBYWwtbWVmu8ra0tUlNTdVSVNiEEJk2ahK5du8LZ2VnX5SA6Ohrx8fEIDw/XdSlaLl26hEWLFsHR0RE7d+7Ee++9hwkTJuCnn37SaV0fffQR3njjDbRo0QIGBgZo3749goKC8MYbb+i0rpKKj/ea/FkAgPv37+Pjjz+Gv7+/zn/EdObMmdDX18eECRN0WkdJaWlpyM7OxowZM9C3b1/ExsZi0KBBGDx4MOLi4nRa27fffotWrVqhUaNGMDQ0RN++fbFw4UJ07dr1qdVQ1ndqTTj+5XzX16Tj/0njz6jUYAqFQuu1EKLUOF0ZN24cTp48if379+u6FFy9ehUTJ05EbGzsE782orKKiorQsWNHhIWFAQDat2+P06dPY9GiRRg2bJjO6vr555+xatUqrFmzBq1bt0ZCQgKCgoJgZ2eH4cOH66yu8tTkz0J+fj5ef/11FBUVYeHChTqtJT4+Ht988w2OHz9eY/ZPseKbDAYOHIgPPvgAANCuXTscOHAAixcvhqenp85q+/bbb3Ho0CFs2bIFTZs2xR9//IHAwEA0aNDgqfVcV/Sdqsvj/1Hf9TXp+H8aGJpqIGtra+jp6ZX6n0RaWlqp/3Howvjx47Flyxb88ccfaNSoka7LQXx8PNLS0uDq6iqNKywsxB9//IHIyEhoNBro6enppLYGDRqgVatWWuNatmyJ9evX66SeYh9++CE+/vhjvP766wAAFxcXJCUlITw8vEaFJrVaDeDB/7gbNGggja8pn4X8/HwMGTIEly9fxm+//abz/2Xv27cPaWlpaNKkiTSusLAQwcHBmD9/Pq5cuaKz2qytraGvr1/m50GX//nKzc3FJ598go0bN2LAgAEAgDZt2iAhIQFz5sx5KqGpvO9UXR//j/qur2nH/9PA03M1kKGhIVxdXbFr1y6t8bt27UKXLl10VNWD/92MGzcOGzZswG+//QYHBwed1fKwXr164dSpU0hISJCGjh074s0330RCQoLOAhMAeHh4lLqF+Pz582jatKmOKnrg3r17qFNH++Ovp6enk0cOVMTBwQFqtVrrs5CXl4e4uDidfhaA//3BuHDhAnbv3g0rKyud1gMAAQEBOHnypNZnwc7ODh9++CF27typ09oMDQ3RqVOnGvd5yM/PR35+vk4+D4/6TtXV8S/nu74mHv9PA3uaaqhJkyYhICAAHTt2hLu7O5YuXYrk5GS89957Oqtp7NixWLNmDTZv3gwzMzOpJ0ylUsHY2FhndZmZmZU6125qagorKyudX2/1wQcfoEuXLggLC8OQIUNw5MgRLF26FEuXLtVpXb6+vvj666/RpEkTtG7dGn/99RciIiIwcuTIp15LdnY2/vnnH+n15cuXkZCQAEtLSzRp0gRBQUEICwuDo6MjHB0dERYWBhMTE/j7++usLjs7O7z22ms4fvw4tm3bhsLCQunzYGlpCUNDQ53U1aRJk1J/vAwMDKBWq+Hk5PTEapJb24cffoihQ4eiW7du6NGjB2JiYrB161b8/vvvOq3L09MTH374IYyNjdG0aVPExcXhp59+QkRExBOt61HfqcXPm3vax/+j6iooKNDZ8a9zurptjx7tu+++E02bNhWGhoaiQ4cOOr+1H0CZw/Lly3VaV1lqyiMHhBBi69atwtnZWSiVStGiRQuxdOlSXZckMjMzxcSJE0WTJk2EkZGReOGFF8Snn34qNBrNU69l7969ZR5Xw4cPF0I8uO166tSpQq1WC6VSKbp16yZOnTql07ouX75c7udh7969OqurLE/zkQNyalu2bJlo1qyZMDIyEm3bthWbNm3SeV0pKSlixIgRws7OThgZGQknJycxd+5cUVRU9ETrkvOdqovj/1F16fL41zWFEEJUYwYjIiIieibxmiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiqXQqHApk2bdF3Gc6E69vWIESPwyiuvPLJdQEAAwsLCpNf29vaYP3/+Y627pFOnTqFRo0bIycmp1uUS6RJDE9FzZsSIEVAoFFAoFDAwMICtrS369OmDH3/8sdQPlKakpKBfv36yllubApbccPEsOnnyJLZv347x48c/0fW4uLjgpZdewrx5857oeoieJoYmoudQ3759kZKSgitXrmDHjh3o0aMHJk6cCB8fHxQUFEjt1Go1lEqlDivVrfz8fF2XUO0iIyPxn//8B2ZmZk98XW+//TYWLVqEwsLCJ74uoqeBoYnoOaRUKqFWq9GwYUN06NABn3zyCTZv3owdO3YgKipKavdw71FeXh7GjRuHBg0awMjICPb29ggPDwfw4PQOAAwaNAgKhUJ6ffHiRQwcOBC2traoW7cuOnXqhN27d2vVYm9vj7CwMIwcORJmZmZo0qQJli5dqtXm2rVreP3112FpaQlTU1N07NgRhw8flqZv3boVrq6uMDIywgsvvIBp06Zphb+HhYaGYsWKFdi8ebPU4/b777/jypUrUCgUWLduHbp37w4jIyOsWrUKt2/fxhtvvIFGjRrBxMQELi4uWLt2rbS8JUuWoGHDhqV66fz8/DB8+PAq1QgA169fx9ChQ2FhYQErKysMHDgQV65ckaYXFhZi0qRJqFevHqysrDB58mQ86qdEi4qK8Msvv8DPz6/CdsuXL4dKpcKuXbsAAN27d8f48eMRFBQECwsL2NraYunSpcjJycHbb78NMzMzvPjii9ixY4fWcry9vXH79m3ExcVVuD6iWkPHPxhMRE/Z8OHDxcCBA8uc1rZtW9GvXz/pNQCxceNGIYQQs2fPFo0bNxZ//PGHuHLliti3b59Ys2aNEEKItLQ06VfQU1JSRFpamhBCiISEBLF48WJx8uRJcf78efHpp58KIyMjkZSUJK2jadOmwtLSUnz33XfiwoULIjw8XNSpU0ecPXtWCCFEVlaWeOGFF8TLL78s9u3bJy5cuCB+/vlnceDAASGEEDExMcLc3FxERUWJixcvitjYWGFvby9CQ0PL3MasrCwxZMgQ0bdvX5GSkiJSUlKERqORfrnd3t5erF+/Xly6dElcv35dXLt2TcyePVv89ddf4uLFi+Lbb78Venp64tChQ0IIIW7fvi0MDQ3F7t27pXXcuXNHGBoaip07d8qu8eF9nZOTIxwdHcXIkSPFyZMnxZkzZ4S/v79wcnISGo1GCCHEzJkzhUqlEv/973/FmTNnxKhRo4SZmVm5760QQvz1118CgEhNTdUa37RpUzFv3jzpfba0tBQHDx6Upnt6egozMzPx1VdfifPnz4uvvvpK1KlTR/Tr108sXbpUnD9/Xrz//vvCyspK5OTkaC37pZdeKve9IKptGJqInjMVhaahQ4eKli1bSq8f/kM+fvx40bNnT1FUVFTmvA+3rUirVq3EggULpNdNmzYVb731lvS6qKhI2NjYiEWLFgkhhFiyZIkwMzMTt2/fLnN5L7/8sggLC9Mat3LlStGgQYNyayhrHxSHpvnz5z9yG/r37y+Cg4Ol135+fmLkyJHS6yVLlgi1Wi0KCgpk1/jw/lu2bJlwcnLS2tcajUYYGxtLQaxBgwZixowZ0vT8/HzRqFGjCkPTxo0bhZ6eXqn3sDg0ffzxx6JBgwbi5MmTWtM9PT1F165dpdcFBQXC1NRUBAQESONSUlIEAK2wJYQQgwYNEiNGjCi3JqLaRF9XPVxEVPMIIaBQKMqcNmLECPTp0wdOTk7o27cvfHx84OXlVeHycnJyMG3aNGzbtg03btxAQUEBcnNzkZycrNWuTZs20r8VCgXUajXS0tIAAAkJCWjfvj0sLS3LXEd8fDyOHj2Kr7/+WhpXWFiI+/fv4969ezAxMZG17cU6duyo9bqwsBAzZszAzz//jOvXr0Oj0UCj0cDU1FRq8+abb+Ldd9/FwoULoVQqsXr1arz++uvQ09OrUo3x8fH4559/Sl13dP/+fVy8eBEZGRlISUmBu7u7NE1fXx8dO3as8BRdbm4ulEplme/x3LlzkZOTg2PHjuGFF14oNf3h90hPTw9WVlZwcXGRxtna2gKA9L4VMzY2xr1798qtiag2YWgiIsnZs2fh4OBQ5rQOHTrg8uXL2LFjB3bv3o0hQ4agd+/e+O9//1vu8j788EPs3LkTc+bMQbNmzWBsbIzXXnsNeXl5Wu0MDAy0XisUCukaIWNj4wprLioqwrRp0zB48OBS04yMjCqctywPhyHgQZiYN28e5s+fDxcXF5iamiIoKEhrG3x9fVFUVITt27ejU6dO2LdvHyIiIqpcY1FREVxdXbF69epS0+rXr1/pbSpmbW2Ne/fuIS8vD4aGhlrTXn75ZWzfvh3r1q3Dxx9/XGrest6jh8cVB7GS13bduXMHL774YpVrJqpJGJqICADw22+/4dSpU/jggw/KbWNubo6hQ4di6NCheO2119C3b1/cuXMHlpaWMDAwKHWX1L59+zBixAgMGjQIAJCdna11MbMcbdq0wQ8//CCtp6QOHTrg3LlzaNasmexlGhoayr6ja9++fRg4cCDeeustAA9CwYULF9CyZUupjbGxMQYPHozVq1fjn3/+QfPmzeHq6lrlGjt06ICff/4ZNjY2MDc3L7NNgwYNcOjQIXTr1g0AUFBQgPj4eHTo0KHc5bZr1w4AcObMGenfxV566SWMHz8e3t7e0NPTw4cffiir1kdJTEzEa6+9Vi3LItI13j1H9BzSaDRITU3F9evXcfz4cYSFhWHgwIHw8fHBsGHDypxn3rx5iI6Oxt9//43z58/jl19+gVqtRr169QA8uAtuz549SE1NRXp6OgCgWbNm2LBhAxISEnDixAn4+/uX6ol4lDfeeANqtRqvvPIK/vzzT1y6dAnr16/HwYMHAQBffPEFfvrpJ4SGhuL06dM4e/Ysfv75Z3z22WflLtPe3h4nT57EuXPn8O+//1b4aIFmzZph165dOHDgAM6ePYsxY8YgNTW1VLs333wT27dvx48//igFrGKVrfHNN9+EtbU1Bg4ciH379uHy5cuIi4vDxIkTce3aNQDAxIkTMWPGDGzcuBF///03AgMDcffu3Qr3Zf369dGhQwfs37+/zOnu7u7YsWMHvvzyy2p5vtKVK1dw/fp19O7d+7GXRVQTMDQRPYdiYmLQoEED2Nvbo2/fvti7dy++/fZbbN68WboOp6S6deti5syZ6NixIzp16oQrV67g119/RZ06D75G5s6di127dqFx48Zo3749gAdBy8LCAl26dIGvry+8vb0r7Akpi6GhIWJjY2FjY4P+/fvDxcUFM2bMkOr09vbGtm3bsGvXLnTq1AmdO3dGREQEmjZtWu4yR48eDScnJ3Ts2BH169fHn3/+WW7bzz//HB06dIC3tze6d+8uBbiSevbsCUtLS5w7dw7+/v5a0ypbo4mJCf744w80adIEgwcPRsuWLTFy5Ejk5uZKPU/BwcEYNmwYRowYAXd3d5iZmUk9ehV59913yzztV8zDwwPbt2/H559/jm+//faRy6vI2rVr4eXlVeF7QVSbKERFVw0SEdEz5f79+3ByckJ0dLTWheTVTaPRwNHREWvXroWHh8cTWw/R08SeJiKi54iRkRF++ukn/Pvvv090PUlJSfj0008ZmOiZwp4mIiIiIhnY00REREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJMP/AT8CxRJ8+xq0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing the distance feature for members:\n",
    "\n",
    "plt.hist([df_member[\"distance (km)\"], df_casual[\"distance (km)\"]], bins = 24, edgecolor='w', linewidth=0.5, label = ['Annual', 'Casual'])\n",
    "plt.xticks(np.arange(0, 24, 2))\n",
    "plt.title(\"Frequency of distance traveled for all users\")\n",
    "plt.xlabel(\"Distance traveled (km)\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/distance_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d0859ff7",
   "metadata": {},
   "source": [
    "As stated before, here, we can tell that **both types of users use the service for trips of around 2 km or less.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "47a6fb8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyzing bike preference for members: \n",
    "\n",
    "plt.hist([df_member[\"rideable_type\"], df_casual[\"rideable_type\"]], edgecolor='w', linewidth=0.5, label = ['Annual', 'Casual'])\n",
    "plt.title(\"Frequency of the type of bike chosen by members\")\n",
    "plt.xlabel(\"Type of bike\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig(\"images/bike_freq_bothusers.png\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a438b72b",
   "metadata": {},
   "source": [
    "In this case, **none of the annual members use docked bike, those few come from casual users. And for both, they prefer electric bike as well. Classic bike is the next preferred, especially by the members.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a5c7bc97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "electric_bike    57.436272\n",
       "classic_bike     41.396253\n",
       "docked_bike       1.167475\n",
       "Name: rideable_type, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_bikes[\"rideable_type\"].value_counts()/df_bikes[\"rideable_type\"].value_counts().sum())*100"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "07496aaf",
   "metadata": {},
   "source": [
    "The majority of time spent (or length) in Cyclistic (~57%) comes from electric bikes, hence, we could infere that **users may prefer electric bikes over the classic bike and docked bike.**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "70ac5f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Annual member's trip time by bike:\n",
    "plt.bar(x = df_member[\"rideable_type\"], height=df_member[\"time_length (min)\"])\n",
    "plt.title(\"Annual Members: Type of Bike versus Time Length\")\n",
    "plt.xlabel(\"Type of bikes\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "80e84d19",
   "metadata": {},
   "source": [
    "From the previous plot we can notice that, **in total, for the annual members the electric bikes are, of course, the fastest option as these take around 3 times less than classic bikes in their trips.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f07b6403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Casual user's trip time:\n",
    "plt.bar(x = df_casual[\"rideable_type\"], height=df_casual[\"time_length (min)\"])\n",
    "plt.title(\"Casual Users: Type of Bike versus Time Length\")\n",
    "plt.xlabel(\"Type of bikes\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "821bad84",
   "metadata": {},
   "source": [
    "Now considering the previous observation, we can say that **the docked bike is the slowest among all of the 3 types available.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8b88761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distance traveled for each bike\n",
    "\n",
    "plt.bar(x = df_bikes[\"rideable_type\"], height=df_bikes[\"distance (km)\"])\n",
    "plt.title(\"Type of Bike versus Distance Traveled\")\n",
    "plt.xlabel(\"Type of bikes\")\n",
    "plt.ylabel(\"Distance traveled (km)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f172a00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluating the distance traveled for users\n",
    "\n",
    "plt.bar(x=df_bikes[\"member_casual\"], height=df_bikes[\"distance (km)\"])\n",
    "plt.title(\"Cyclistic's Users versus Distance\")\n",
    "plt.xlabel(\"Type of users\")\n",
    "plt.ylabel(\"Distance traveled (km)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6a635c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's take a look at the time in total all users spend in their trips:\n",
    "\n",
    "plt.bar(x = df_bikes[\"day_of_week\"], height = df_bikes[\"time_length (min)\"])\n",
    "plt.title(\"Day of the Week versus Time Length\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7dee2127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Considering the same graph now for annual members only:\n",
    "\n",
    "plt.bar(x = df_member[\"day_of_week\"], height = df_member[\"time_length (min)\"])\n",
    "plt.title(\"Annual Members: Day of the Week versus Time Length\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bc84fe6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking now the time length for casual members:\n",
    "\n",
    "plt.bar(x = df_casual[\"day_of_week\"], height = df_casual[\"time_length (min)\"])\n",
    "plt.title(\"Casual Users: Day of the Week versus Time Length\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Time Length (min)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "be9f7ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x = df_bikes[\"day_of_week\"], height=df_bikes[\"distance (km)\"])\n",
    "plt.title(\"Day of the Week versus Distance\")\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.ylabel(\"Distance (km)\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "38a23a95",
   "metadata": {},
   "source": [
    "At this point for this dataset, we can see that casual users have traveled a slightly longer distance; the time length during the week is greater for days 0 (Monday) and 2 (Wednesday), then 5 (Saturday) and 6 (Sunday); and, lastly, for the distance traveled throughout the week we can say it pretty much mantains a sustained behaviour!🤗"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6c192b30",
   "metadata": {},
   "source": [
    "🎈🎈📊🎈🎈"
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