moringa_ip_week4 (3).ipynb

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  "metadata": {
    "colab": {
      "name": "moringa ip week4",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpzDDxD3K4Yb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#lets import the important libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfylXCRwL7hq",
        "colab_type": "code",
        "outputId": "4a2bb9cd-7b5d-4872-81d8-c02e40fff3aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "source": [
        "#reading our data set from our csv file\n",
        "url='http://bit.ly/Autolibdataset'\n",
        "df = pd.read_csv(url)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Address</th>\n",
              "      <th>Cars</th>\n",
              "      <th>Bluecar counter</th>\n",
              "      <th>Utilib counter</th>\n",
              "      <th>Utilib 1.4 counter</th>\n",
              "      <th>Charge Slots</th>\n",
              "      <th>Charging Status</th>\n",
              "      <th>City</th>\n",
              "      <th>Displayed comment</th>\n",
              "      <th>ID</th>\n",
              "      <th>Kind</th>\n",
              "      <th>Geo point</th>\n",
              "      <th>Postal code</th>\n",
              "      <th>Public name</th>\n",
              "      <th>Rental status</th>\n",
              "      <th>Scheduled at</th>\n",
              "      <th>Slots</th>\n",
              "      <th>Station type</th>\n",
              "      <th>Status</th>\n",
              "      <th>Subscription status</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>minute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>24 Rue Vulpian</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>operational</td>\n",
              "      <td>Paris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>paris-vulpian-24</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8310493, 2.3460584</td>\n",
              "      <td>75013</td>\n",
              "      <td>Paris/Vulpian/24</td>\n",
              "      <td>operational</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>station</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>8 Boulevard Richard Lenoir</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>Paris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>paris-richardlenoir-8</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8545026, 2.370183</td>\n",
              "      <td>75011</td>\n",
              "      <td>Paris/Richard Lenoir/8</td>\n",
              "      <td>operational</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>station</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4 Rue Charles Bossut</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>operational</td>\n",
              "      <td>Paris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>paris-charlesbossut-4</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8431847, 2.3837959</td>\n",
              "      <td>75012</td>\n",
              "      <td>Paris/Charles Bossut/4</td>\n",
              "      <td>operational</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>station</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3 avenue Claude Vellefaux</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>operational</td>\n",
              "      <td>Paris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>paris-claudevellefaux-3</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8740623, 2.3703034</td>\n",
              "      <td>75010</td>\n",
              "      <td>Paris/Claude Vellefaux/3</td>\n",
              "      <td>operational</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>station</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>236 Boulevard Voltaire</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>operational</td>\n",
              "      <td>Paris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>paris-voltaire-236</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.852023, 2.389719</td>\n",
              "      <td>75011</td>\n",
              "      <td>Paris/Voltaire/236</td>\n",
              "      <td>operational</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>station</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>2018</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   Unnamed: 0                     Address  Cars  ...  day  hour  minute\n",
              "0           0              24 Rue Vulpian     3  ...    1     0       0\n",
              "1           1  8 Boulevard Richard Lenoir     0  ...    1     0       0\n",
              "2           2        4 Rue Charles Bossut     3  ...    1     0       0\n",
              "3           3   3 avenue Claude Vellefaux     4  ...    1     0       0\n",
              "4           4      236 Boulevard Voltaire     1  ...    1     0       0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKFsOQxZMcOn",
        "colab_type": "code",
        "outputId": "0d9e71a1-5d24-4d8f-cc35-d56eb706a608",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "#lets check the size our data\n",
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14421829, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ff_hLdYWMjKo",
        "colab_type": "code",
        "outputId": "1cd91a57-7e05-4736-f07a-a3036e0b0110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "#lets get information about our data\n",
        "df.info()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14421829 entries, 0 to 14421828\n",
            "Data columns (total 26 columns):\n",
            "Unnamed: 0             int64\n",
            "Address                object\n",
            "Cars                   int64\n",
            "Bluecar counter        int64\n",
            "Utilib counter         int64\n",
            "Utilib 1.4 counter     int64\n",
            "Charge Slots           int64\n",
            "Charging Status        object\n",
            "City                   object\n",
            "Displayed comment      object\n",
            "ID                     object\n",
            "Kind                   object\n",
            "Geo point              object\n",
            "Postal code            int64\n",
            "Public name            object\n",
            "Rental status          object\n",
            "Scheduled at           object\n",
            "Slots                  int64\n",
            "Station type           object\n",
            "Status                 object\n",
            "Subscription status    object\n",
            "year                   int64\n",
            "month                  int64\n",
            "day                    int64\n",
            "hour                   int64\n",
            "minute                 int64\n",
            "dtypes: int64(13), object(13)\n",
            "memory usage: 2.8+ GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNGEd-U8Mn6H",
        "colab_type": "code",
        "outputId": "ea13653d-d49d-4450-c509-47d01e964ced",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "source": [
        "#lets check for any missing values\n",
        "df.isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                    0\n",
              "Address                       0\n",
              "Cars                          0\n",
              "Bluecar counter               0\n",
              "Utilib counter                0\n",
              "Utilib 1.4 counter            0\n",
              "Charge Slots                  0\n",
              "Charging Status               0\n",
              "City                          0\n",
              "Displayed comment      14099610\n",
              "ID                            0\n",
              "Kind                          0\n",
              "Geo point                     0\n",
              "Postal code                   0\n",
              "Public name                   0\n",
              "Rental status                 0\n",
              "Scheduled at           14310213\n",
              "Slots                         0\n",
              "Station type                  0\n",
              "Status                        0\n",
              "Subscription status           0\n",
              "year                          0\n",
              "month                         0\n",
              "day                           0\n",
              "hour                          0\n",
              "minute                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ob9cc8zRdnt",
        "colab_type": "code",
        "outputId": "56e760d2-90c2-4b33-c991-4d5ae28d3635",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "source": [
        "#we now begin to clean our data\n",
        "#lets start by dropping the columns that have missing values and those of year, minute,city,month, Public name, Cars, station type,slots, \n",
        "#explanation: we drop the year, city, month because we have detailed information about them in the research question so from the on set we already know\n",
        "#what we are talking about.we will not need minute column as our research question needs it not. as for public name and columns we have other columns\n",
        "#that give the same info they hold.station type and slots add no info for the nature of research question we are having so we drop them\n",
        "\n",
        "df1 =  df.drop(['Scheduled at','Displayed comment','Public name','year','month','minute','Slots','Station type','Cars','City','Address'], axis=1)\n",
        "df1.head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Bluecar counter</th>\n",
              "      <th>Utilib counter</th>\n",
              "      <th>Utilib 1.4 counter</th>\n",
              "      <th>Charge Slots</th>\n",
              "      <th>Charging Status</th>\n",
              "      <th>ID</th>\n",
              "      <th>Kind</th>\n",
              "      <th>Geo point</th>\n",
              "      <th>Postal code</th>\n",
              "      <th>Rental status</th>\n",
              "      <th>Status</th>\n",
              "      <th>Subscription status</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
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              "  </thead>\n",
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              "      <td>paris-vulpian-24</td>\n",
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              "      <td>48.8310493, 2.3460584</td>\n",
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              "      <td>operational</td>\n",
              "      <td>paris-richardlenoir-8</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8545026, 2.370183</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>operational</td>\n",
              "      <td>paris-charlesbossut-4</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8431847, 2.3837959</td>\n",
              "      <td>75012</td>\n",
              "      <td>operational</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>operational</td>\n",
              "      <td>paris-claudevellefaux-3</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.8740623, 2.3703034</td>\n",
              "      <td>75010</td>\n",
              "      <td>operational</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>operational</td>\n",
              "      <td>paris-voltaire-236</td>\n",
              "      <td>STATION</td>\n",
              "      <td>48.852023, 2.389719</td>\n",
              "      <td>75011</td>\n",
              "      <td>operational</td>\n",
              "      <td>ok</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0  Bluecar counter  Utilib counter  ...  Subscription status  day hour\n",
              "0           0                3               1  ...          nonexistent    1    0\n",
              "1           1                0               0  ...          nonexistent    1    0\n",
              "2           2                3               0  ...          nonexistent    1    0\n",
              "3           3                4               0  ...          nonexistent    1    0\n",
              "4           4                1               0  ...          nonexistent    1    0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFQ4MsLcOgva",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1.to_csv('clean.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyNjGKWzWbr7",
        "colab_type": "code",
        "outputId": "4aa52eef-5426-416b-e266-b919b641dcdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "#check whether we dropped the columns suuccessfuly\n",
        "df1.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14421829, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26tigRtEiwof",
        "colab_type": "code",
        "outputId": "840d4296-4b78-4d8b-a3db-a6ffe5bc5441",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "#What is the most popular hour for returning cars?\n",
        "df1.groupby(['hour','Bluecar counter'])['Bluecar counter'].sum().sort_values(ascending=False).head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hour  Bluecar counter\n",
              "5     5                  466250\n",
              "4     5                  458750\n",
              "6     5                  457870\n",
              "3     5                  452940\n",
              "2     5                  444015\n",
              "Name: Bluecar counter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzkXlEWRkAhM",
        "colab_type": "code",
        "outputId": "e4fc786d-81bf-4819-b5a6-214c65472ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "#What station is the most popular\n",
        "df.groupby(['Bluecar counter', 'ID'])['Bluecar counter'].count().sort_values(ascending=False).head()\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bluecar counter  ID                           \n",
              "0                paris-pierrechausson-1           12921\n",
              "                 gennevilliers-gabrielperi-169    12912\n",
              "                 colombes-renouillers-183         12911\n",
              "                 paris-boucry-35                  12907\n",
              "                 bievres-gare-12                  12904\n",
              "Name: Bluecar counter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vm-PBTOqwRLC",
        "colab_type": "code",
        "outputId": "be1411c0-a789-4846-e9fb-4396b3f96f30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "#what station is most popular at the most popular picking hour?\n",
        "df1.groupby(['hour', 'ID','Bluecar counter'])[['Bluecar counter']].count().sort_values(by='hour', ascending=False).head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th></th>\n",
              "      <th>Bluecar counter</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hour</th>\n",
              "      <th>ID</th>\n",
              "      <th>Bluecar counter</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">23</th>\n",
              "      <th>yerres-raymondpoincare-200</th>\n",
              "      <th>7</th>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pantin-cartierbresson-84</th>\n",
              "      <th>2</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">pantin-anatolefrance-1</th>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 Bluecar counter\n",
              "hour ID                         Bluecar counter                 \n",
              "23   yerres-raymondpoincare-200 7                            104\n",
              "     pantin-cartierbresson-84   2                             29\n",
              "     pantin-anatolefrance-1     0                             60\n",
              "                                1                            114\n",
              "                                2                             64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2HgG5UYwPsX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "dfc08950-93ed-4aff-d11b-3e222555c657"
      },
      "source": [
        "#What postal code is the most popular for picking up Bluecars?\n",
        "df1.groupby(['Postal code','Bluecar counter','hour'])['Bluecar counter'].count().sort_values(ascending=False).head()\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Postal code  Bluecar counter  hour\n",
              "75008        0                3       14419\n",
              "                              2       14362\n",
              "                              4       13643\n",
              "                              5       13067\n",
              "                              6       12158\n",
              "Name: Bluecar counter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqRnVEnDzhpG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "1345318f-22a3-4fd0-b499-6d7a9c986d1c"
      },
      "source": [
        "#Do the results change if you consider Utilib and Utilib 1.4 instead of Bluecars?\n",
        "df1.groupby(['Postal code','Utilib counter','hour'])['Utilib counter'].count().sort_values(ascending=False).head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Postal code  Utilib counter  hour\n",
              "75015        0               17      31325\n",
              "                             18      31226\n",
              "                             21      31115\n",
              "                             19      31091\n",
              "                             15      31036\n",
              "Name: Utilib counter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wvvaa5QfDmUp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "ea7d171c-da91-47a4-a079-fed612d958c4"
      },
      "source": [
        "#Do the results change if you consider Utilib and Utilib 1.4 instead of Bluecars?\n",
        "df1.groupby(['Postal code','Utilib 1.4 counter','hour'])['Utilib 1.4 counter'].count().sort_values(ascending=False).head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Postal code  Utilib 1.4 counter  hour\n",
              "75015        0                   15      29427\n",
              "                                 13      29275\n",
              "                                 16      29164\n",
              "                                 17      29066\n",
              "                                 14      29058\n",
              "Name: Utilib 1.4 counter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    }
  ]
}