Cars45_Prediction_of_Best_Price.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cars45 - Prediction of Best Price.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN9hd6B9gyG6HK5YTXt6rC3",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Seghelicious/Cars45/blob/main/Cars45_Prediction_of_Best_Price.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CY3Jjy0AUM58"
      },
      "source": [
        "# **Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGTH-CTgAI-J"
      },
      "source": [
        "The goal of this assessment is to predict the best prices to buy cars from customers based on the data provided."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ENvasCH2T5_J"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMvLFzI0CpBR"
      },
      "source": [
        "# **Importing Required Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TK9qIibAUU8h"
      },
      "source": [
        "# Data Manipulation libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime, timedelta, date\n",
        "\n",
        "# Visualisation libraries\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot\n",
        "from matplotlib import style\n",
        "plt.style.use('ggplot')\n",
        "import itertools\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Missing Values Imputation libraries\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "# Preprocessing libraries\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Model Development libraries\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split \n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "# Model Evaluation libraries\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "pd.options.display.float_format = '{:.2f}'.format\n",
        "pd.options.display.max_columns = None\n",
        "pd.options.display.max_rows = None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Q3meOhMTVpVg",
        "outputId": "d3837de0-4882-48d3-c35f-045f56d35471"
      },
      "source": [
        "cars45 = pd.read_csv('/content/drive/My Drive/Cars45/Cars_Data_CSV.csv')\n",
        "cars45.head()"
      ],
      "execution_count": null,
      "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>make</th>\n",
              "      <th>model</th>\n",
              "      <th>mileage</th>\n",
              "      <th>manufacture_year</th>\n",
              "      <th>engine_displacement</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>body_type</th>\n",
              "      <th>color_slug</th>\n",
              "      <th>transmission</th>\n",
              "      <th>door_count</th>\n",
              "      <th>seat_count</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>date_created</th>\n",
              "      <th>price_NGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>audi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>330000.00</td>\n",
              "      <td>1995</td>\n",
              "      <td>2600.00</td>\n",
              "      <td>110.00</td>\n",
              "      <td>compact</td>\n",
              "      <td>nan</td>\n",
              "      <td>auto</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>2015-11-14 20:10:48.033192+00</td>\n",
              "      <td>873428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>audi</td>\n",
              "      <td>200</td>\n",
              "      <td>nan</td>\n",
              "      <td>1995</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>compact</td>\n",
              "      <td>nan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>2015-11-14 20:11:19.607788+00</td>\n",
              "      <td>66616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>audi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>nan</td>\n",
              "      <td>1995</td>\n",
              "      <td>11199.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>compact</td>\n",
              "      <td>nan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>2015-11-14 20:11:20.320579+00</td>\n",
              "      <td>66616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>audi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>nan</td>\n",
              "      <td>1995</td>\n",
              "      <td>nan</td>\n",
              "      <td>nan</td>\n",
              "      <td>compact</td>\n",
              "      <td>nan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>2015-11-14 20:11:21.801285+00</td>\n",
              "      <td>66604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>audi</td>\n",
              "      <td>a6</td>\n",
              "      <td>347000.00</td>\n",
              "      <td>1995</td>\n",
              "      <td>nan</td>\n",
              "      <td>85.00</td>\n",
              "      <td>compact</td>\n",
              "      <td>nan</td>\n",
              "      <td>auto</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>gasoline</td>\n",
              "      <td>2015-11-14 20:12:41.082787+00</td>\n",
              "      <td>843820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   make model   mileage  manufacture_year  engine_displacement  engine_power  \\\n",
              "0  audi   NaN 330000.00              1995              2600.00        110.00   \n",
              "1  audi   200       nan              1995                  nan           nan   \n",
              "2  audi   NaN       nan              1995             11199.00           nan   \n",
              "3  audi   NaN       nan              1995                  nan           nan   \n",
              "4  audi    a6 347000.00              1995                  nan         85.00   \n",
              "\n",
              "  body_type  color_slug transmission door_count seat_count fuel_type  \\\n",
              "0   compact         nan         auto       None       None  gasoline   \n",
              "1   compact         nan          NaN       None       None  gasoline   \n",
              "2   compact         nan          NaN       None       None  gasoline   \n",
              "3   compact         nan          NaN       None       None  gasoline   \n",
              "4   compact         nan         auto       None       None  gasoline   \n",
              "\n",
              "                    date_created  price_NGN  \n",
              "0  2015-11-14 20:10:48.033192+00     873428  \n",
              "1  2015-11-14 20:11:19.607788+00      66616  \n",
              "2  2015-11-14 20:11:20.320579+00      66616  \n",
              "3  2015-11-14 20:11:21.801285+00      66604  \n",
              "4  2015-11-14 20:12:41.082787+00     843820  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em1Lr9qtWuh8",
        "outputId": "5bf68262-957e-4ed5-aeec-ee148553ca74"
      },
      "source": [
        "cars45.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 638562 entries, 0 to 638561\n",
            "Data columns (total 14 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   make                 638562 non-null  object \n",
            " 1   model                454990 non-null  object \n",
            " 2   mileage              603981 non-null  float64\n",
            " 3   manufacture_year     638562 non-null  int64  \n",
            " 4   engine_displacement  489134 non-null  float64\n",
            " 5   engine_power         537375 non-null  float64\n",
            " 6   body_type            72636 non-null   object \n",
            " 7   color_slug           0 non-null       float64\n",
            " 8   transmission         541441 non-null  object \n",
            " 9   door_count           638562 non-null  object \n",
            " 10  seat_count           638562 non-null  object \n",
            " 11  fuel_type            638562 non-null  object \n",
            " 12  date_created         638562 non-null  object \n",
            " 13  price_NGN            638562 non-null  int64  \n",
            "dtypes: float64(4), int64(2), object(8)\n",
            "memory usage: 68.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3lfiXhURCt_",
        "outputId": "18348568-e5ed-493f-bee1-492dcf02b9b2"
      },
      "source": [
        "cars45.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(638562, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d71oDqugaxVT"
      },
      "source": [
        "# **Data Cleaning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aR8J1ZCbKeT"
      },
      "source": [
        "I have split the date_created column into a date and time column. I have also changed the data types appropriately.\n",
        "\n",
        "The data types of the manafacture_year and price_NGN columns have also been changed from float to integer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJBReQ5K7TAI"
      },
      "source": [
        "cars45['date_created'] = pd.to_datetime(cars45['date_created'])\n",
        "cars45['date'] = cars45['date_created'].dt.strftime('%Y-%m-%d')\n",
        "cars45['time'] = cars45['date_created'].dt.strftime('%H:%M')\n",
        "cars45 = cars45.drop(columns = ['date_created'])\n",
        "cars45 = cars45.drop(columns = ['color_slug'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yqw1-MuNT_EX"
      },
      "source": [
        "cars45['manufacture_year'] = cars45['manufacture_year'].astype(int)\n",
        "cars45['price_NGN'] = cars45['price_NGN'].astype(int)\n",
        "cars45['door_count'] = cars45['door_count'].replace('None', '100')\n",
        "cars45['door_count'] = cars45['door_count'].astype(int)\n",
        "cars45['door_count'] = cars45['door_count'].replace(100, np.nan)\n",
        "cars45['seat_count'] = cars45['seat_count'].replace('None', '100')\n",
        "cars45['seat_count'] = cars45['seat_count'].astype(int)\n",
        "cars45['seat_count'] = cars45['seat_count'].replace(100, np.nan)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3C_GI8xCkIDb"
      },
      "source": [
        "# **Handling Extreme Values - Outliers**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "rsBVhd_eRdU7",
        "outputId": "af158e0b-5665-4873-e13e-a7a6bff8835f"
      },
      "source": [
        "cars45.describe().transpose()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mileage</th>\n",
              "      <td>603981.00</td>\n",
              "      <td>115283.86</td>\n",
              "      <td>365012.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>22000.00</td>\n",
              "      <td>82500.00</td>\n",
              "      <td>150000.00</td>\n",
              "      <td>9999999.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacture_year</th>\n",
              "      <td>638562.00</td>\n",
              "      <td>2008.72</td>\n",
              "      <td>5.50</td>\n",
              "      <td>1995.00</td>\n",
              "      <td>2005.00</td>\n",
              "      <td>2010.00</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>2016.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_displacement</th>\n",
              "      <td>489134.00</td>\n",
              "      <td>2100.23</td>\n",
              "      <td>1931.07</td>\n",
              "      <td>10.00</td>\n",
              "      <td>1461.00</td>\n",
              "      <td>1898.00</td>\n",
              "      <td>2143.00</td>\n",
              "      <td>32000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_power</th>\n",
              "      <td>537375.00</td>\n",
              "      <td>105.05</td>\n",
              "      <td>53.30</td>\n",
              "      <td>3.00</td>\n",
              "      <td>73.00</td>\n",
              "      <td>94.00</td>\n",
              "      <td>125.00</td>\n",
              "      <td>999.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>door_count</th>\n",
              "      <td>451206.00</td>\n",
              "      <td>3.68</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seat_count</th>\n",
              "      <td>409865.00</td>\n",
              "      <td>4.87</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>74.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price_NGN</th>\n",
              "      <td>638562.00</td>\n",
              "      <td>6171796.44</td>\n",
              "      <td>179858592.43</td>\n",
              "      <td>16.00</td>\n",
              "      <td>1600592.00</td>\n",
              "      <td>3980296.00</td>\n",
              "      <td>7377492.00</td>\n",
              "      <td>82310297260.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count       mean          std     min        25%  \\\n",
              "mileage             603981.00  115283.86    365012.42    0.00   22000.00   \n",
              "manufacture_year    638562.00    2008.72         5.50 1995.00    2005.00   \n",
              "engine_displacement 489134.00    2100.23      1931.07   10.00    1461.00   \n",
              "engine_power        537375.00     105.05        53.30    3.00      73.00   \n",
              "door_count          451206.00       3.68         0.84    1.00       4.00   \n",
              "seat_count          409865.00       4.87         0.86    1.00       5.00   \n",
              "price_NGN           638562.00 6171796.44 179858592.43   16.00 1600592.00   \n",
              "\n",
              "                           50%        75%            max  \n",
              "mileage               82500.00  150000.00     9999999.00  \n",
              "manufacture_year       2010.00    2014.00        2016.00  \n",
              "engine_displacement    1898.00    2143.00       32000.00  \n",
              "engine_power             94.00     125.00         999.00  \n",
              "door_count                4.00       4.00           6.00  \n",
              "seat_count                5.00       5.00          74.00  \n",
              "price_NGN           3980296.00 7377492.00 82310297260.00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IyQz6sF6lzf-"
      },
      "source": [
        "Observations. \n",
        "\n",
        "- There is a noticeably large difference between the mean with the 50% and 75% percentiles for the mileage variable.\n",
        "- There is a noticeable large difference between the 75th percentile and the maximum values of the mileage, engine_displacement, engine_power and price_NGN.\n",
        "- There is also a noticeably large difference between the minimum values and the 25% percentile of the price_NGN variable.\n",
        "- The standard deviation between the mileage, engine_displacement and price_NGN variables is noticeably high.\n",
        "\n",
        "These observations suggest that there are outliers in our data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1E-KuDa4aja"
      },
      "source": [
        "### Fixing Extreme Values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wv428ZB5_wMl"
      },
      "source": [
        "#### Seat Count"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmQSHG-t_ui4",
        "outputId": "2849df9b-48f7-4be6-e7c9-aafe51533c2c"
      },
      "source": [
        "cars45['seat_count'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   409865.00\n",
              "mean         4.87\n",
              "std          0.86\n",
              "min          1.00\n",
              "25%          5.00\n",
              "50%          5.00\n",
              "75%          5.00\n",
              "max         74.00\n",
              "Name: seat_count, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3oCub6C_6GY"
      },
      "source": [
        "cars45.loc[cars45['seat_count'] > 15, 'seat_count'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAKCo5DwAHdT",
        "outputId": "db4b80b9-3a33-4d7a-ee9d-8f866049610c"
      },
      "source": [
        "cars45['seat_count'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   409847.00\n",
              "mean         4.87\n",
              "std          0.84\n",
              "min          1.00\n",
              "25%          5.00\n",
              "50%          5.00\n",
              "75%          5.00\n",
              "max         15.00\n",
              "Name: seat_count, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igqBujz9_rbU"
      },
      "source": [
        "\n",
        "#### Mileage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "BFWhjvHm-Rs3",
        "outputId": "c31cf2b0-a952-44b5-c677-f0eac15bd7fa"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Mileage')\n",
        "sns.distplot(cars45['mileage'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fc0015f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96meUk68csHy"
      },
      "source": [
        "The minimum value for mileage is 0. Brand new cars typically have a few hundred miles on their odometers. This is because the new cars are vigorously test-driven or moved from the assembly plant to storage or from the dealership to the client's location.\n",
        "\n",
        "For this reason, I have pegged the floor and ceiling values at 100 and 200000 miles. Mileage values not in this range will be replaced with np.nan. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRUInVgwXiRD"
      },
      "source": [
        "cars45.loc[cars45['mileage'] < 100, 'mileage'] = np.nan\n",
        "cars45.loc[cars45['mileage'] > 200000, 'mileage'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98q3gB9De03D",
        "outputId": "d2082c41-e1f6-4b10-e03e-86ed4b85eb32"
      },
      "source": [
        "cars45['mileage'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   482767.00\n",
              "mean     84086.05\n",
              "std      58825.21\n",
              "min        100.00\n",
              "25%      27580.50\n",
              "50%      79850.00\n",
              "75%     133000.00\n",
              "max     200000.00\n",
              "Name: mileage, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "XTBJy0SNXjeV",
        "outputId": "71852bfe-78cc-4b60-c4a5-51f49b340494"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Mileage of Cars')\n",
        "sns.distplot(cars45.query('mileage <= 200000')['mileage'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fbc7510f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GrLtPIOdDvy"
      },
      "source": [
        "We can see that the histogram for mileage is right-skewed, with 75% of cars having a mileage less than 130000 miles."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDzrsL3SgB9d"
      },
      "source": [
        "#### Engine Displacement"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoUeY3cqX5o-",
        "outputId": "61770e9b-eaad-4f57-836d-5de598bccd0c"
      },
      "source": [
        "cars45['engine_displacement'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   489134.00\n",
              "mean      2100.23\n",
              "std       1931.07\n",
              "min         10.00\n",
              "25%       1461.00\n",
              "50%       1898.00\n",
              "75%       2143.00\n",
              "max      32000.00\n",
              "Name: engine_displacement, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "qBmwYSFrqXG3",
        "outputId": "53962fbf-df75-4716-e73d-a8d7966041aa"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Engine Capacity')\n",
        "sns.distplot(cars45.query('engine_displacement <= 5000')['engine_displacement'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fbc6bf550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqwm2fB0hPD_"
      },
      "source": [
        "It can be observed that the minimum engine displacement value is about 10 cc which is about 10 cc (0.01 liters) and the maximum is 32000 cc (32 liters). This values are grossly unrealistic. According to Drive Spark (https://www.drivespark.com/four-wheelers/2016/five-smallest-displacement-engines-production-cars/articlecontent-pf50711-019057.html), the smallest car engine is the smart fortwo with 898 cc. \n",
        "\n",
        "The strategy for handling extreme values for engine_displacement shall be similar to that of mileage. The floor and ceiling values will be pegged between 1000 and 5000 cc.\n",
        "\n",
        "So, like the mileage values, besides the smart fortwo, the range of values for the engine_displacement is between 1000 and 5000 cc, extreme values outside this range will be replaced with np.nan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwzEFLYLpM4g"
      },
      "source": [
        "cars45.loc[cars45['engine_displacement'] < 1000, 'engine_displacement'] = np.nan\n",
        "cars45.loc[cars45['engine_displacement'] > 5000, 'engine_displacement'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "N-BRwZvVsFPJ",
        "outputId": "26c4eecd-dd57-4f78-dcd9-e8743f916d34"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Engine Capacity')\n",
        "sns.distplot(cars45['engine_displacement'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fc4a41518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wk4S1ttTva1G"
      },
      "source": [
        "#### Engine Power"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MM-Jb0fQvkhO",
        "outputId": "0e36a5b2-fc18-4266-a6e3-819ad0c2f9bd"
      },
      "source": [
        "cars45['engine_power'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   537375.00\n",
              "mean       105.05\n",
              "std         53.30\n",
              "min          3.00\n",
              "25%         73.00\n",
              "50%         94.00\n",
              "75%        125.00\n",
              "max        999.00\n",
              "Name: engine_power, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "RsH0Wh6owlyr",
        "outputId": "d530d250-182c-4d19-e5e7-28d1df308ab6"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Engine Power')\n",
        "sns.distplot(cars45['engine_power'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fbc49c908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdXnT01aIiWO"
      },
      "source": [
        "An engine_power of 3 horsepower (hp) is both unrealistic and highly unlikely. According to Autoguide (https://www.autoguide.com/auto-news/2017/07/top-10-cars-with-the-least-horsepower-in-2017.html#:~:text=The%20least%20powerful%20car%20available,74%20lb%2Dft%20of%20torque.) puts the least engine power at 78hp.\n",
        "\n",
        "As per the strategy for extreme values, I shall peg the floor - ceiling range between 100 and 400 hp."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6-anphMN3pg"
      },
      "source": [
        "cars45.loc[cars45['engine_power'] < 100, 'engine_power'] = np.nan\n",
        "cars45.loc[cars45['engine_power'] > 400, 'engine_power'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "CNxvtPRqscIV",
        "outputId": "d36f3f90-df89-48f2-9ce8-166365076ed6"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Engine Power')\n",
        "sns.distplot(cars45.query('engine_power < 400')['engine_power'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fbc39c940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFP8bb61OPVK",
        "outputId": "6ce91d94-6afe-4201-f7a1-7709b085e774"
      },
      "source": [
        "cars45['engine_power'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   253173.00\n",
              "mean       141.32\n",
              "std         48.36\n",
              "min        100.00\n",
              "25%        110.00\n",
              "50%        125.00\n",
              "75%        155.00\n",
              "max        400.00\n",
              "Name: engine_power, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgUl13tJR4Pb"
      },
      "source": [
        "#### Price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMJl_1nYSn0o",
        "outputId": "68752a1a-f936-4928-f131-47fd8194b0dd"
      },
      "source": [
        "cars45['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        638562.00\n",
              "mean        6171796.44\n",
              "std       179858592.43\n",
              "min              16.00\n",
              "25%         1600592.00\n",
              "50%         3980296.00\n",
              "75%         7377492.00\n",
              "max     82310297260.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "qeqw7m2AzG_Q",
        "outputId": "ddf4f268-d63c-46dc-8d8e-4b3e04763595"
      },
      "source": [
        "plt.figure(figsize = (8, 5))\n",
        "plt.title('Distribution of Price (NGN)')\n",
        "sns.distplot(cars45['price_NGN'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fbc3f6a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHnaf-bHxcoI"
      },
      "source": [
        "The price_NGN variable is riddled with extreme values, mostly unrealistic!\n",
        "- The minimum price is 16 NGN.\n",
        "- The standard deviation is 179 858 600 NGN.\n",
        "- The 25th - 75th percentile values look fine (between 1,600,592 - 7,377,492 NGN)\n",
        "- The maximum price is 82,310,300,000 NGN.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kijAZMl_zUlU"
      },
      "source": [
        "The price variable is the trickiest and also the most important variable in our data. My strategy for cleaning the price data is different from the rest. \n",
        "\n",
        "I have grouped the data into seperate dataframes by the make of the car, i.e. all audis in one dataframe, all jaguars in another, etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drDPGzZ4vIr9"
      },
      "source": [
        "So I'll be looking at the price data by make."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VlsbwD2BTV8"
      },
      "source": [
        "audi = cars45.loc[cars45['make'] == 'audi'].reset_index(drop = True)\n",
        "bentley = cars45.loc[cars45['make'] == 'bentley'].reset_index(drop = True)\n",
        "bmw = cars45.loc[cars45['make'] == 'bmw'].reset_index(drop = True)\n",
        "chevrolet = cars45.loc[cars45['make'] == 'chevrolet'].reset_index(drop = True)\n",
        "chrysler = cars45.loc[cars45['make'] == 'chrysler'].reset_index(drop = True)\n",
        "citroen = cars45.loc[cars45['make'] == 'citroen'].reset_index(drop = True)\n",
        "dodge = cars45.loc[cars45['make'] == 'dodge'].reset_index(drop = True)\n",
        "fiat = cars45.loc[cars45['make'] == 'fiat'].reset_index(drop = True)\n",
        "ford = cars45.loc[cars45['make'] == 'ford'].reset_index(drop = True)\n",
        "honda = cars45.loc[cars45['make'] == 'honda'].reset_index(drop = True)\n",
        "hyundai = cars45.loc[cars45['make'] == 'hyundai'].reset_index(drop = True)\n",
        "isuzu = cars45.loc[cars45['make'] == 'isuzu'].reset_index(drop = True)\n",
        "jaguar = cars45.loc[cars45['make'] == 'jaguar'].reset_index(drop = True)\n",
        "jeep = cars45.loc[cars45['make'] == 'jeep'].reset_index(drop = True)\n",
        "kia = cars45.loc[cars45['make'] == 'kia'].reset_index(drop = True)\n",
        "lamborghini = cars45.loc[cars45['make'] == 'lamborghini'].reset_index(drop = True)\n",
        "lancia = cars45.loc[cars45['make'] == 'lancia'].reset_index(drop = True)\n",
        "maserati = cars45.loc[cars45['make'] == 'maserati'].reset_index(drop = True)\n",
        "mazda = cars45.loc[cars45['make'] == 'mazda'].reset_index(drop = True)\n",
        "mercedes = cars45.loc[cars45['make'] == 'mercedes-benz'].reset_index(drop = True)\n",
        "mini = cars45.loc[cars45['make'] == 'mini'].reset_index(drop = True)\n",
        "mitsubishi = cars45.loc[cars45['make'] == 'mitsubishi'].reset_index(drop = True)\n",
        "nissan = cars45.loc[cars45['make'] == 'nissan'].reset_index(drop = True)\n",
        "opel = cars45.loc[cars45['make'] == 'opel'].reset_index(drop = True)\n",
        "porsche = cars45.loc[cars45['make'] == 'porsche'].reset_index(drop = True)\n",
        "rover = cars45.loc[cars45['make'] == 'rover'].reset_index(drop = True)\n",
        "seat = cars45.loc[cars45['make'] == 'seat'].reset_index(drop = True)\n",
        "skoda = cars45.loc[cars45['make'] == 'skoda'].reset_index(drop = True)\n",
        "subaru = cars45.loc[cars45['make'] == 'subaru'].reset_index(drop = True)\n",
        "suzuki = cars45.loc[cars45['make'] == 'suzuki'].reset_index(drop = True)\n",
        "toyota = cars45.loc[cars45['make'] == 'toyota'].reset_index(drop = True)\n",
        "volvo = cars45.loc[cars45['make'] == 'volvo'].reset_index(drop = True)\n",
        "hummer = cars45.loc[cars45['make'] == 'hummer'].reset_index(drop = True)\n",
        "lexus = cars45.loc[cars45['make'] == 'lexus'].reset_index(drop = True)\n",
        "tesla = cars45.loc[cars45['make'] == 'tesla'].reset_index(drop = True)\n",
        "lotus = cars45.loc[cars45['make'] == 'lotus'].reset_index(drop = True)\n",
        "smart = cars45.loc[cars45['make'] == 'smart'].reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O16OFaiwvPaN"
      },
      "source": [
        "##### Audi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftZn962kvEsX",
        "outputId": "d6d9949c-f69b-4b11-89e8-b83af2aa2235"
      },
      "source": [
        "audi['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         72529.00\n",
              "mean        8329650.78\n",
              "std       148648557.41\n",
              "min              16.00\n",
              "25%         3300488.00\n",
              "50%         6891412.00\n",
              "75%        10460356.00\n",
              "max     40004440756.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StKyybNNxFv-"
      },
      "source": [
        "There are some extreme values in the audi prices.\n",
        "The minimum price is 16 NGN and the maximum price is over 40 billion NGN.\n",
        "There is a noticeably large difference between the prices in the 75% percentile and the maximum price. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qG1_9pYJtlaH",
        "outputId": "c6e41cef-094b-488f-ae9c-fca761fb4904"
      },
      "source": [
        "audi.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>22204</td>\n",
              "      <td>33304928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>16</td>\n",
              "      <td>36861584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>88</td>\n",
              "      <td>60176904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>281272</td>\n",
              "      <td>43672052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a1</th>\n",
              "      <td>2761020</td>\n",
              "      <td>48055516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a2</th>\n",
              "      <td>340192</td>\n",
              "      <td>10000712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a3</th>\n",
              "      <td>16</td>\n",
              "      <td>22002444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a4</th>\n",
              "      <td>16</td>\n",
              "      <td>164487208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a4-allroad</th>\n",
              "      <td>503332</td>\n",
              "      <td>19924100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a5</th>\n",
              "      <td>14788</td>\n",
              "      <td>25183732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a6</th>\n",
              "      <td>16</td>\n",
              "      <td>444608884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a6-allroad</th>\n",
              "      <td>16</td>\n",
              "      <td>69563288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a7</th>\n",
              "      <td>120</td>\n",
              "      <td>35799972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a8</th>\n",
              "      <td>16</td>\n",
              "      <td>70548440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coupe</th>\n",
              "      <td>16</td>\n",
              "      <td>56016580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>q3</th>\n",
              "      <td>44012</td>\n",
              "      <td>24185700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>q5</th>\n",
              "      <td>29608</td>\n",
              "      <td>39605864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>q7</th>\n",
              "      <td>22204</td>\n",
              "      <td>36994820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>r8</th>\n",
              "      <td>3762088</td>\n",
              "      <td>78008660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs2</th>\n",
              "      <td>4762648</td>\n",
              "      <td>20002560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs3</th>\n",
              "      <td>6203448</td>\n",
              "      <td>28637988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs4</th>\n",
              "      <td>839912</td>\n",
              "      <td>43612908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs5</th>\n",
              "      <td>460132</td>\n",
              "      <td>29170792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs6</th>\n",
              "      <td>1644</td>\n",
              "      <td>56310732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rs7</th>\n",
              "      <td>26495500</td>\n",
              "      <td>48672804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s3</th>\n",
              "      <td>10364</td>\n",
              "      <td>25047416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s4</th>\n",
              "      <td>207252</td>\n",
              "      <td>22833736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s5</th>\n",
              "      <td>5960888</td>\n",
              "      <td>27054404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s6</th>\n",
              "      <td>1000076</td>\n",
              "      <td>31211548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s7</th>\n",
              "      <td>15091428</td>\n",
              "      <td>29176432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s8</th>\n",
              "      <td>962252</td>\n",
              "      <td>52306336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sq5</th>\n",
              "      <td>8396936</td>\n",
              "      <td>31163464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tt</th>\n",
              "      <td>148</td>\n",
              "      <td>47042604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v8</th>\n",
              "      <td>1320148</td>\n",
              "      <td>13201376</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           price_NGN           \n",
              "                 min        max\n",
              "model                          \n",
              "100            22204   33304928\n",
              "200               16   36861584\n",
              "80                88   60176904\n",
              "90            281272   43672052\n",
              "a1           2761020   48055516\n",
              "a2            340192   10000712\n",
              "a3                16   22002444\n",
              "a4                16  164487208\n",
              "a4-allroad    503332   19924100\n",
              "a5             14788   25183732\n",
              "a6                16  444608884\n",
              "a6-allroad        16   69563288\n",
              "a7               120   35799972\n",
              "a8                16   70548440\n",
              "coupe             16   56016580\n",
              "q3             44012   24185700\n",
              "q5             29608   39605864\n",
              "q7             22204   36994820\n",
              "r8           3762088   78008660\n",
              "rs2          4762648   20002560\n",
              "rs3          6203448   28637988\n",
              "rs4           839912   43612908\n",
              "rs5           460132   29170792\n",
              "rs6             1644   56310732\n",
              "rs7         26495500   48672804\n",
              "s3             10364   25047416\n",
              "s4            207252   22833736\n",
              "s5           5960888   27054404\n",
              "s6           1000076   31211548\n",
              "s7          15091428   29176432\n",
              "s8            962252   52306336\n",
              "sq5          8396936   31163464\n",
              "tt               148   47042604\n",
              "v8           1320148   13201376"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "215bOz--4g8p"
      },
      "source": [
        "I have grouped the data by minimum and maximum prices in each model category. A lot of the outliers have been exposed. The minimum values by model hold a lot of unrealistic values.\n",
        "\n",
        "I took a look at the prices of Audi cars on the cars45 site (https://buy.cars45.com/cars&filter=2), the cheapest audi car is the 1999 Audi A4 (Mileage: 247,353km) at a price of 304,000 NGN.\n",
        "\n",
        "Based on this I set the floor of the Audi car prices to 300,000 NGN.\n",
        "The 75% percentile price is at 10,460,356 NGN. So based on this I set the floor of the Audi cars to 15,000,000 NGN. Any price that falls outside this floor - ceiling range is replaced with np.nan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MG7KsFEj8zoZ"
      },
      "source": [
        "audi.loc[audi['price_NGN'] < 300000, 'price_NGN'] = np.nan\n",
        "audi.loc[audi['price_NGN'] > 15000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DT9R18y9Nht",
        "outputId": "c4a1c5ff-4320-461a-91ec-ae5ae4ecb65c"
      },
      "source": [
        "audi['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      64827.00\n",
              "mean     6480795.98\n",
              "std      3864013.22\n",
              "min       300016.00\n",
              "25%      3080460.00\n",
              "50%      6380948.00\n",
              "75%      9409654.00\n",
              "max     14999852.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAyNdGBf9byo"
      },
      "source": [
        "##### Bentley"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6DA8uyg9fFf",
        "outputId": "272e50e7-b4e9-4067-e23c-58db04ac843b"
      },
      "source": [
        "bentley['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         273.00\n",
              "mean     47132871.43\n",
              "std      24747424.98\n",
              "min       3800564.00\n",
              "25%      25122784.00\n",
              "50%      47649132.00\n",
              "75%      63967104.00\n",
              "max     127126412.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "NEiUyuXf91IY",
        "outputId": "2d2ee9b4-5a74-46c1-acfd-eb119bc6b0be"
      },
      "source": [
        "bentley.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arnage</th>\n",
              "      <td>6602444</td>\n",
              "      <td>32013768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brooklands</th>\n",
              "      <td>3800564</td>\n",
              "      <td>7901168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>continental-flying-spur</th>\n",
              "      <td>13206928</td>\n",
              "      <td>69217692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>continental-gt</th>\n",
              "      <td>13021924</td>\n",
              "      <td>81550568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>continental-gtc</th>\n",
              "      <td>22863568</td>\n",
              "      <td>86031548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>continental-r</th>\n",
              "      <td>15234256</td>\n",
              "      <td>36802724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>continental-t</th>\n",
              "      <td>50027756</td>\n",
              "      <td>50027756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>turbo-r</th>\n",
              "      <td>32404796</td>\n",
              "      <td>32404796</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        price_NGN          \n",
              "                              min       max\n",
              "model                                      \n",
              "arnage                    6602444  32013768\n",
              "brooklands                3800564   7901168\n",
              "continental-flying-spur  13206928  69217692\n",
              "continental-gt           13021924  81550568\n",
              "continental-gtc          22863568  86031548\n",
              "continental-r            15234256  36802724\n",
              "continental-t            50027756  50027756\n",
              "turbo-r                  32404796  32404796"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpB_a37H-X5H"
      },
      "source": [
        "It is observed that the Bentley is a luxury car. The minimum Bentley pricing across all models ranges from 3.8 million NGN to 50 million NGN and the maximum pricing ranges from 7.9 million NGN to 86 million NGN. \n",
        "\n",
        "Leading with this, the floor - ceiling prices for the bentley will be pegged from 3.5 million NGN to 90 million NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryCthTA9_k0r"
      },
      "source": [
        "bentley.loc[bentley['price_NGN'] < 3500000, 'price_NGN'] = np.nan\n",
        "bentley.loc[bentley['price_NGN'] > 90000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL6x2zkX_zCV",
        "outputId": "afdae9f3-c56f-4c80-fd99-c8256dccca95"
      },
      "source": [
        "bentley['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        262.00\n",
              "mean    44558340.53\n",
              "std     21606597.22\n",
              "min      3800564.00\n",
              "25%     24401808.00\n",
              "50%     45941294.00\n",
              "75%     62121447.00\n",
              "max     86050376.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mh670iM_-yO"
      },
      "source": [
        "##### BMW"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olq48v0mAAuN",
        "outputId": "05b9d9a5-739e-45ba-c5bc-879e60ccdd6b"
      },
      "source": [
        "bmw['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         78587.00\n",
              "mean        7706573.99\n",
              "std        45545072.06\n",
              "min              16.00\n",
              "25%         3101716.00\n",
              "50%         6382592.00\n",
              "75%        10316234.00\n",
              "max     12001332348.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fUoCjl6AqRK"
      },
      "source": [
        "There are certainly some extremely priced BMWs. \n",
        "The standard deviation is more than the mean and more than the 75th percentile price. And the minimum price for BMWs is 16 NGN and the maximum price is 12 billion NGN!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "VDKUdjnKASDY",
        "outputId": "ca0435d6-e16f-45d1-a70d-ca52d21e6422"
      },
      "source": [
        "bmw.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alpina</th>\n",
              "      <td>799628</td>\n",
              "      <td>148021908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i3</th>\n",
              "      <td>5003080</td>\n",
              "      <td>20208408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i8</th>\n",
              "      <td>36640076</td>\n",
              "      <td>62027536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m3</th>\n",
              "      <td>16</td>\n",
              "      <td>67967548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m4</th>\n",
              "      <td>16</td>\n",
              "      <td>38813236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m5</th>\n",
              "      <td>1464</td>\n",
              "      <td>40869652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m6</th>\n",
              "      <td>840472</td>\n",
              "      <td>50293044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x1</th>\n",
              "      <td>7400</td>\n",
              "      <td>83986308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x3</th>\n",
              "      <td>488</td>\n",
              "      <td>117977468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x4</th>\n",
              "      <td>614360</td>\n",
              "      <td>30092244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x5</th>\n",
              "      <td>16</td>\n",
              "      <td>49007252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x6</th>\n",
              "      <td>3048</td>\n",
              "      <td>47997780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z3</th>\n",
              "      <td>44412</td>\n",
              "      <td>15982368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z4</th>\n",
              "      <td>29608</td>\n",
              "      <td>27651992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z8</th>\n",
              "      <td>55988292</td>\n",
              "      <td>146021612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       price_NGN           \n",
              "             min        max\n",
              "model                      \n",
              "alpina    799628  148021908\n",
              "i3       5003080   20208408\n",
              "i8      36640076   62027536\n",
              "m3            16   67967548\n",
              "m4            16   38813236\n",
              "m5          1464   40869652\n",
              "m6        840472   50293044\n",
              "x1          7400   83986308\n",
              "x3           488  117977468\n",
              "x4        614360   30092244\n",
              "x5            16   49007252\n",
              "x6          3048   47997780\n",
              "z3         44412   15982368\n",
              "z4         29608   27651992\n",
              "z8      55988292  146021612"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-SNMguBB3ct"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 600,000 NGN and 147,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZVMmrSWCjXa"
      },
      "source": [
        "bmw.loc[bmw['price_NGN'] < 600000, 'price_NGN'] = np.nan\n",
        "bmw.loc[bmw['price_NGN'] > 147000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmTVzfCWCjUI",
        "outputId": "163e1480-8fb5-4832-d3c2-30e09325d25c"
      },
      "source": [
        "bmw['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       75846.00\n",
              "mean      7747747.70\n",
              "std       5980615.84\n",
              "min        600028.00\n",
              "25%       3401260.00\n",
              "50%       6672896.00\n",
              "75%      10418352.00\n",
              "max     146021612.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOz8B46NDMxl"
      },
      "source": [
        "##### Chevrolet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH-tN3jYDkMW",
        "outputId": "a4382037-8b5b-4ef5-bd1d-9a0119389209"
      },
      "source": [
        "chevrolet['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       4179.00\n",
              "mean     3490246.78\n",
              "std      4181831.72\n",
              "min           16.00\n",
              "25%      1200532.00\n",
              "50%      2305936.00\n",
              "75%      4240326.00\n",
              "max     39974788.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fcvjTtxlD0ua",
        "outputId": "c5111a34-5a5e-4c98-f4ca-6226e0f65113"
      },
      "source": [
        "chevrolet.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alero</th>\n",
              "      <td>399704</td>\n",
              "      <td>814212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>astro-van</th>\n",
              "      <td>3241436</td>\n",
              "      <td>5180384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avalanche</th>\n",
              "      <td>1400208</td>\n",
              "      <td>11803244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aveo</th>\n",
              "      <td>51812</td>\n",
              "      <td>5196580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blazer</th>\n",
              "      <td>64</td>\n",
              "      <td>5000564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>camaro</th>\n",
              "      <td>185048</td>\n",
              "      <td>39974788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caprice</th>\n",
              "      <td>3601200</td>\n",
              "      <td>3601200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>captiva</th>\n",
              "      <td>16</td>\n",
              "      <td>10761200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cobalt</th>\n",
              "      <td>3390080</td>\n",
              "      <td>3390080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colorado</th>\n",
              "      <td>3440504</td>\n",
              "      <td>18208720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>corvette</th>\n",
              "      <td>147892</td>\n",
              "      <td>35482620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cruze</th>\n",
              "      <td>29652</td>\n",
              "      <td>7198136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>epica</th>\n",
              "      <td>962252</td>\n",
              "      <td>3140120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>evanda</th>\n",
              "      <td>325684</td>\n",
              "      <td>1760784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hhr</th>\n",
              "      <td>1699792</td>\n",
              "      <td>3441524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>impala</th>\n",
              "      <td>3026144</td>\n",
              "      <td>17966648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lacetti</th>\n",
              "      <td>429312</td>\n",
              "      <td>3120460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lumina</th>\n",
              "      <td>399628</td>\n",
              "      <td>399628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malibu</th>\n",
              "      <td>2900104</td>\n",
              "      <td>9765420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>matiz</th>\n",
              "      <td>147892</td>\n",
              "      <td>2372348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>niva</th>\n",
              "      <td>1400208</td>\n",
              "      <td>1400208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nubira</th>\n",
              "      <td>16</td>\n",
              "      <td>2980224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orlando</th>\n",
              "      <td>2463716</td>\n",
              "      <td>6998592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s10</th>\n",
              "      <td>500280</td>\n",
              "      <td>889060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>silverado</th>\n",
              "      <td>2800828</td>\n",
              "      <td>31971828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spark</th>\n",
              "      <td>16</td>\n",
              "      <td>4280312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>suburban</th>\n",
              "      <td>1332348</td>\n",
              "      <td>34048112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tacuma</th>\n",
              "      <td>504</td>\n",
              "      <td>1560756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tahoe</th>\n",
              "      <td>1560872</td>\n",
              "      <td>32772124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tracker</th>\n",
              "      <td>556076</td>\n",
              "      <td>556076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trailblazer</th>\n",
              "      <td>16</td>\n",
              "      <td>5996892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trans-sport</th>\n",
              "      <td>200120</td>\n",
              "      <td>1761660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trax</th>\n",
              "      <td>4402604</td>\n",
              "      <td>10280756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>venture</th>\n",
              "      <td>888232</td>\n",
              "      <td>888232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price_NGN          \n",
              "                  min       max\n",
              "model                          \n",
              "alero          399704    814212\n",
              "astro-van     3241436   5180384\n",
              "avalanche     1400208  11803244\n",
              "aveo            51812   5196580\n",
              "blazer             64   5000564\n",
              "camaro         185048  39974788\n",
              "caprice       3601200   3601200\n",
              "captiva            16  10761200\n",
              "cobalt        3390080   3390080\n",
              "colorado      3440504  18208720\n",
              "corvette       147892  35482620\n",
              "cruze           29652   7198136\n",
              "epica          962252   3140120\n",
              "evanda         325684   1760784\n",
              "hhr           1699792   3441524\n",
              "impala        3026144  17966648\n",
              "lacetti        429312   3120460\n",
              "lumina         399628    399628\n",
              "malibu        2900104   9765420\n",
              "matiz          147892   2372348\n",
              "niva          1400208   1400208\n",
              "nubira             16   2980224\n",
              "orlando       2463716   6998592\n",
              "s10            500280    889060\n",
              "silverado     2800828  31971828\n",
              "spark              16   4280312\n",
              "suburban      1332348  34048112\n",
              "tacuma            504   1560756\n",
              "tahoe         1560872  32772124\n",
              "tracker        556076    556076\n",
              "trailblazer        16   5996892\n",
              "trans-sport    200120   1761660\n",
              "trax          4402604  10280756\n",
              "venture        888232    888232"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "St9H0uPIFeM1"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 325,000 NGN and 12,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meXfjNfTGB64"
      },
      "source": [
        "chevrolet.loc[chevrolet['price_NGN'] < 325000, 'price_NGN'] = np.nan\n",
        "chevrolet.loc[chevrolet['price_NGN'] > 40000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjY9U83IGTlG",
        "outputId": "bd5d657e-9754-4be8-bc21-a1c7fb11a751"
      },
      "source": [
        "chevrolet['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       4147.00\n",
              "mean     3516294.95\n",
              "std      4187357.40\n",
              "min       325684.00\n",
              "25%      1280088.00\n",
              "50%      2306276.00\n",
              "75%      4286854.00\n",
              "max     39974788.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ugoj_TUJHug5"
      },
      "source": [
        "##### Chrysler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcsZ07BkHx9n",
        "outputId": "3574baa7-4d9f-401e-d6b8-8f18fdcba347"
      },
      "source": [
        "chrysler['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       2145.00\n",
              "mean     2061694.88\n",
              "std      2167667.23\n",
              "min           16.00\n",
              "25%       760060.00\n",
              "50%      1316064.00\n",
              "75%      2600296.00\n",
              "max     27533680.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "lcYPAtjFH8Pw",
        "outputId": "83f75560-7a99-43d3-fb2e-739df7ca2fe1"
      },
      "source": [
        "chrysler.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>962252</td>\n",
              "      <td>17098432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300c</th>\n",
              "      <td>16</td>\n",
              "      <td>11056004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300m</th>\n",
              "      <td>16</td>\n",
              "      <td>1100120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concorde</th>\n",
              "      <td>518120</td>\n",
              "      <td>518120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crossfire</th>\n",
              "      <td>1702444</td>\n",
              "      <td>11158608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>es</th>\n",
              "      <td>220016</td>\n",
              "      <td>7963836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand-voyager</th>\n",
              "      <td>140636</td>\n",
              "      <td>13413088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gts</th>\n",
              "      <td>15800340</td>\n",
              "      <td>15800340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neon</th>\n",
              "      <td>29680</td>\n",
              "      <td>1100120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new-yorker</th>\n",
              "      <td>580280</td>\n",
              "      <td>2220576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pacifica</th>\n",
              "      <td>1472540</td>\n",
              "      <td>3894300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pt-cruiser</th>\n",
              "      <td>196064</td>\n",
              "      <td>3961168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sebring</th>\n",
              "      <td>28</td>\n",
              "      <td>5961760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stratus</th>\n",
              "      <td>72540</td>\n",
              "      <td>2361140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>town-country</th>\n",
              "      <td>740192</td>\n",
              "      <td>11464100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vision</th>\n",
              "      <td>240028</td>\n",
              "      <td>1980224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>voyager</th>\n",
              "      <td>16</td>\n",
              "      <td>11077248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price_NGN          \n",
              "                    min       max\n",
              "model                            \n",
              "200              962252  17098432\n",
              "300c                 16  11056004\n",
              "300m                 16   1100120\n",
              "concorde         518120    518120\n",
              "crossfire       1702444  11158608\n",
              "es               220016   7963836\n",
              "grand-voyager    140636  13413088\n",
              "gts            15800340  15800340\n",
              "neon              29680   1100120\n",
              "new-yorker       580280   2220576\n",
              "pacifica        1472540   3894300\n",
              "pt-cruiser       196064   3961168\n",
              "sebring              28   5961760\n",
              "stratus           72540   2361140\n",
              "town-country     740192  11464100\n",
              "vision           240028   1980224\n",
              "voyager              16  11077248"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvGx8RedIiGC"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onsNbZm7IAsm"
      },
      "source": [
        "chrysler.loc[chrysler['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "chrysler.loc[chrysler['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYBdois9IA0q",
        "outputId": "ce58dfe1-abe6-4087-b72b-b714040306d8"
      },
      "source": [
        "chrysler['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       2116.00\n",
              "mean     2064694.30\n",
              "std      2044972.51\n",
              "min       200028.00\n",
              "25%       780120.00\n",
              "50%      1320436.00\n",
              "75%      2620480.00\n",
              "max     17098432.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mq2-mc9sJhbc"
      },
      "source": [
        "##### Citroen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMLFAxMzIAxI",
        "outputId": "fcba8b62-c47f-412d-f8f4-821f37aa2f50"
      },
      "source": [
        "citroen['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        27839.00\n",
              "mean       4239340.94\n",
              "std       65340203.39\n",
              "min             16.00\n",
              "25%        1280148.00\n",
              "50%        3346380.00\n",
              "75%        5601976.00\n",
              "max     7698179244.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "SsCICSFTJw-I",
        "outputId": "791417a8-0b7e-4eee-d094-2d2fbc835bca"
      },
      "source": [
        "citroen.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ax</th>\n",
              "      <td>14804</td>\n",
              "      <td>2669592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>berlingo</th>\n",
              "      <td>16</td>\n",
              "      <td>295514464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-crosser</th>\n",
              "      <td>1924500</td>\n",
              "      <td>8800888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-elysee</th>\n",
              "      <td>2649888</td>\n",
              "      <td>4825168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c1</th>\n",
              "      <td>54776</td>\n",
              "      <td>5600428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c2</th>\n",
              "      <td>200016</td>\n",
              "      <td>10066616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c3</th>\n",
              "      <td>16</td>\n",
              "      <td>6402680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c3-picasso</th>\n",
              "      <td>81420</td>\n",
              "      <td>7200668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c4</th>\n",
              "      <td>16</td>\n",
              "      <td>14801792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c4-aircross</th>\n",
              "      <td>4706544</td>\n",
              "      <td>11970288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c4-cactus</th>\n",
              "      <td>5091044</td>\n",
              "      <td>5847520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c4-picasso</th>\n",
              "      <td>29608</td>\n",
              "      <td>11599436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c5</th>\n",
              "      <td>16</td>\n",
              "      <td>14803836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c6</th>\n",
              "      <td>1240768</td>\n",
              "      <td>12602992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c8</th>\n",
              "      <td>16</td>\n",
              "      <td>8404664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds3</th>\n",
              "      <td>11844</td>\n",
              "      <td>46393784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds4</th>\n",
              "      <td>2442636</td>\n",
              "      <td>12270156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds5</th>\n",
              "      <td>2361124</td>\n",
              "      <td>14957660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>evasion</th>\n",
              "      <td>16</td>\n",
              "      <td>1520904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jumper</th>\n",
              "      <td>16</td>\n",
              "      <td>12337364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jumpy</th>\n",
              "      <td>16</td>\n",
              "      <td>17483360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nemo</th>\n",
              "      <td>207252</td>\n",
              "      <td>4397304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>saxo</th>\n",
              "      <td>16</td>\n",
              "      <td>2599880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xantia</th>\n",
              "      <td>16</td>\n",
              "      <td>2000592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xm</th>\n",
              "      <td>44</td>\n",
              "      <td>2080236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xsara</th>\n",
              "      <td>16</td>\n",
              "      <td>2779540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xsara-picasso</th>\n",
              "      <td>16</td>\n",
              "      <td>7698179244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zx</th>\n",
              "      <td>20</td>\n",
              "      <td>460268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price_NGN            \n",
              "                    min         max\n",
              "model                              \n",
              "ax                14804     2669592\n",
              "berlingo             16   295514464\n",
              "c-crosser       1924500     8800888\n",
              "c-elysee        2649888     4825168\n",
              "c1                54776     5600428\n",
              "c2               200016    10066616\n",
              "c3                   16     6402680\n",
              "c3-picasso        81420     7200668\n",
              "c4                   16    14801792\n",
              "c4-aircross     4706544    11970288\n",
              "c4-cactus       5091044     5847520\n",
              "c4-picasso        29608    11599436\n",
              "c5                   16    14803836\n",
              "c6              1240768    12602992\n",
              "c8                   16     8404664\n",
              "ds3               11844    46393784\n",
              "ds4             2442636    12270156\n",
              "ds5             2361124    14957660\n",
              "evasion              16     1520904\n",
              "jumper               16    12337364\n",
              "jumpy                16    17483360\n",
              "nemo             207252     4397304\n",
              "saxo                 16     2599880\n",
              "xantia               16     2000592\n",
              "xm                   44     2080236\n",
              "xsara                16     2779540\n",
              "xsara-picasso        16  7698179244\n",
              "zx                   20      460268"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCQbH6gDfWRz"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 350,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4bXVpqEgQqB"
      },
      "source": [
        "citroen.loc[citroen['price_NGN'] < 350000, 'price_NGN'] = np.nan\n",
        "citroen.loc[citroen['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3O0VYnBekh_",
        "outputId": "802a1f25-d985-4f17-e4eb-34c1ee1d5418"
      },
      "source": [
        "citroen['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      26557.00\n",
              "mean     3818325.86\n",
              "std      2554991.64\n",
              "min       350112.00\n",
              "25%      1465580.00\n",
              "50%      3560132.00\n",
              "75%      5683152.00\n",
              "max     17483360.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "233N-sw1gfqn"
      },
      "source": [
        "##### Dodge"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79EG5A-oekfI",
        "outputId": "49a39ce6-a111-459f-cc9d-49574a08b316"
      },
      "source": [
        "dodge['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       1094.00\n",
              "mean    10225288.37\n",
              "std      9967345.15\n",
              "min         1480.00\n",
              "25%      3003438.00\n",
              "50%      5562264.00\n",
              "75%     14791399.00\n",
              "max     55620576.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "IZIsiI8ngnlD",
        "outputId": "115e6f0d-cbad-4a41-97f2-9f41acedcd5e"
      },
      "source": [
        "dodge.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>avenger</th>\n",
              "      <td>1613620</td>\n",
              "      <td>5203080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caliber</th>\n",
              "      <td>1160340</td>\n",
              "      <td>4396656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>caravan</th>\n",
              "      <td>14804</td>\n",
              "      <td>5043492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charger</th>\n",
              "      <td>2339008</td>\n",
              "      <td>41975528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dakota</th>\n",
              "      <td>1340800</td>\n",
              "      <td>5101896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>durango</th>\n",
              "      <td>1200668</td>\n",
              "      <td>27970348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand-caravan</th>\n",
              "      <td>518136</td>\n",
              "      <td>9815960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>intrepid</th>\n",
              "      <td>1480</td>\n",
              "      <td>1021464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>journey</th>\n",
              "      <td>2000488</td>\n",
              "      <td>6758992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>magnum</th>\n",
              "      <td>2000192</td>\n",
              "      <td>10205668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nitro</th>\n",
              "      <td>2664692</td>\n",
              "      <td>7962944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ram</th>\n",
              "      <td>73872</td>\n",
              "      <td>29974420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ram-1500</th>\n",
              "      <td>370080</td>\n",
              "      <td>29574228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sx</th>\n",
              "      <td>162696</td>\n",
              "      <td>10681968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>viper</th>\n",
              "      <td>9602844</td>\n",
              "      <td>55620576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price_NGN          \n",
              "                    min       max\n",
              "model                            \n",
              "avenger         1613620   5203080\n",
              "caliber         1160340   4396656\n",
              "caravan           14804   5043492\n",
              "charger         2339008  41975528\n",
              "dakota          1340800   5101896\n",
              "durango         1200668  27970348\n",
              "grand-caravan    518136   9815960\n",
              "intrepid           1480   1021464\n",
              "journey         2000488   6758992\n",
              "magnum          2000192  10205668\n",
              "nitro           2664692   7962944\n",
              "ram               73872  29974420\n",
              "ram-1500         370080  29574228\n",
              "sx               162696  10681968\n",
              "viper           9602844  55620576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rsvtVGyQhOCg"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 900,000 NGN and 60,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlalaV-4ekZv"
      },
      "source": [
        "dodge.loc[dodge['price_NGN'] < 900000, 'price_NGN'] = np.nan\n",
        "dodge.loc[dodge['price_NGN'] > 60000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pikAo10ekWb",
        "outputId": "42efebee-7133-4dae-a203-34b316f92ed6"
      },
      "source": [
        "dodge['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       1070.00\n",
              "mean    10445922.37\n",
              "std      9967752.34\n",
              "min       900268.00\n",
              "25%      3156240.00\n",
              "50%      5798534.00\n",
              "75%     15001806.00\n",
              "max     55620576.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxbEDqvNhqFd"
      },
      "source": [
        "##### Fiat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSOY-Wpghsdf",
        "outputId": "7d8422a9-d8e3-4bbf-dfb9-e8f6b6765f4f"
      },
      "source": [
        "fiat['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      33038.00\n",
              "mean     2943399.47\n",
              "std      2248672.48\n",
              "min           16.00\n",
              "25%      1076312.00\n",
              "50%      2662102.00\n",
              "75%      4199024.00\n",
              "max     57130332.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Qniai2Oahsmy",
        "outputId": "3aef68e3-7ef7-41ac-aeab-6907914e399f"
      },
      "source": [
        "fiat.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>74020</td>\n",
              "      <td>1997188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>5180</td>\n",
              "      <td>31915160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500l</th>\n",
              "      <td>2796108</td>\n",
              "      <td>8764872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500x</th>\n",
              "      <td>1200088</td>\n",
              "      <td>12804736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>279616</td>\n",
              "      <td>2761020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>barchetta</th>\n",
              "      <td>29608</td>\n",
              "      <td>3402012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brava</th>\n",
              "      <td>16</td>\n",
              "      <td>1672836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bravo</th>\n",
              "      <td>16</td>\n",
              "      <td>17024428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cinquecento</th>\n",
              "      <td>28008</td>\n",
              "      <td>3739808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coupe</th>\n",
              "      <td>7400</td>\n",
              "      <td>4361612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>croma</th>\n",
              "      <td>16</td>\n",
              "      <td>4796536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>doblo</th>\n",
              "      <td>16</td>\n",
              "      <td>14803836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freemont</th>\n",
              "      <td>2404084</td>\n",
              "      <td>57130332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grande-punto</th>\n",
              "      <td>2960</td>\n",
              "      <td>8660252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>idea</th>\n",
              "      <td>276152</td>\n",
              "      <td>4198668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>linea</th>\n",
              "      <td>29608</td>\n",
              "      <td>2360872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marea</th>\n",
              "      <td>16</td>\n",
              "      <td>1400428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>multipla</th>\n",
              "      <td>16</td>\n",
              "      <td>4760712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>palio</th>\n",
              "      <td>16</td>\n",
              "      <td>2956476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>panda</th>\n",
              "      <td>60</td>\n",
              "      <td>40002560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>punto</th>\n",
              "      <td>16</td>\n",
              "      <td>5402000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>punto-evo</th>\n",
              "      <td>380148</td>\n",
              "      <td>7197068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qubo</th>\n",
              "      <td>1400104</td>\n",
              "      <td>7920592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>regata</th>\n",
              "      <td>59200</td>\n",
              "      <td>59200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ritmo</th>\n",
              "      <td>296064</td>\n",
              "      <td>488528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sedici</th>\n",
              "      <td>962252</td>\n",
              "      <td>6603908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seicento</th>\n",
              "      <td>16</td>\n",
              "      <td>1800208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stilo</th>\n",
              "      <td>16</td>\n",
              "      <td>2596964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tempra</th>\n",
              "      <td>222044</td>\n",
              "      <td>889192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tipo</th>\n",
              "      <td>4098860</td>\n",
              "      <td>6410716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ulysse</th>\n",
              "      <td>14804</td>\n",
              "      <td>4321272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>uno</th>\n",
              "      <td>240132</td>\n",
              "      <td>240132</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             price_NGN          \n",
              "                   min       max\n",
              "model                           \n",
              "126              74020   1997188\n",
              "500               5180  31915160\n",
              "500l           2796108   8764872\n",
              "500x           1200088  12804736\n",
              "600             279616   2761020\n",
              "barchetta        29608   3402012\n",
              "brava               16   1672836\n",
              "bravo               16  17024428\n",
              "cinquecento      28008   3739808\n",
              "coupe             7400   4361612\n",
              "croma               16   4796536\n",
              "doblo               16  14803836\n",
              "freemont       2404084  57130332\n",
              "grande-punto      2960   8660252\n",
              "idea            276152   4198668\n",
              "linea            29608   2360872\n",
              "marea               16   1400428\n",
              "multipla            16   4760712\n",
              "palio               16   2956476\n",
              "panda               60  40002560\n",
              "punto               16   5402000\n",
              "punto-evo       380148   7197068\n",
              "qubo           1400104   7920592\n",
              "regata           59200     59200\n",
              "ritmo           296064    488528\n",
              "sedici          962252   6603908\n",
              "seicento            16   1800208\n",
              "stilo               16   2596964\n",
              "tempra          222044    889192\n",
              "tipo           4098860   6410716\n",
              "ulysse           14804   4321272\n",
              "uno             240132    240132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlFSl6XAiHr5"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 900,000 NGN and 60,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2dBTpbHhsio"
      },
      "source": [
        "fiat.loc[fiat['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "fiat.loc[fiat['price_NGN'] > 60000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi2cUzDeekJ3",
        "outputId": "a8b0818c-b991-4511-ed40-a96d3bb9a9c0"
      },
      "source": [
        "fiat['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      30068.00\n",
              "mean     3209396.31\n",
              "std      2183490.34\n",
              "min       400016.00\n",
              "25%      1465580.00\n",
              "50%      2916416.00\n",
              "75%      4380488.00\n",
              "max     57130332.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDsMGWsSioQt"
      },
      "source": [
        "##### Ford"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHgIkHH6iq4k",
        "outputId": "b30e98f2-abbc-4f4d-e675-072f303fa172"
      },
      "source": [
        "ford['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         56844.00\n",
              "mean        4690804.04\n",
              "std        75425709.97\n",
              "min              16.00\n",
              "25%          980104.00\n",
              "50%         2600384.00\n",
              "75%         5082444.00\n",
              "max     10917493588.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KGUfoDDbirZh",
        "outputId": "668fc764-bf49-47cc-b1c9-4eba9400a3ec"
      },
      "source": [
        "ford.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>b-max</th>\n",
              "      <td>2622592</td>\n",
              "      <td>32802176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c-max</th>\n",
              "      <td>16</td>\n",
              "      <td>14803836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>capri</th>\n",
              "      <td>140016</td>\n",
              "      <td>540164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>connect</th>\n",
              "      <td>7388</td>\n",
              "      <td>8237216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cougar</th>\n",
              "      <td>16</td>\n",
              "      <td>2760416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crown-victoria</th>\n",
              "      <td>22204</td>\n",
              "      <td>9963688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>edge</th>\n",
              "      <td>29732</td>\n",
              "      <td>19665464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>escape</th>\n",
              "      <td>1139896</td>\n",
              "      <td>5356196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>escort</th>\n",
              "      <td>16</td>\n",
              "      <td>295366396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>excursion</th>\n",
              "      <td>5181540</td>\n",
              "      <td>13207328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>expedition</th>\n",
              "      <td>760088</td>\n",
              "      <td>4202340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>explorer</th>\n",
              "      <td>44</td>\n",
              "      <td>24369016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f-150</th>\n",
              "      <td>10747580</td>\n",
              "      <td>25973352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiesta</th>\n",
              "      <td>16</td>\n",
              "      <td>316053544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>focus</th>\n",
              "      <td>16</td>\n",
              "      <td>10917493588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freestar</th>\n",
              "      <td>5955424</td>\n",
              "      <td>5955424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fusion</th>\n",
              "      <td>20</td>\n",
              "      <td>4380488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>galaxy</th>\n",
              "      <td>16</td>\n",
              "      <td>16346868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand-c-max</th>\n",
              "      <td>2220576</td>\n",
              "      <td>10802400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gt</th>\n",
              "      <td>240016</td>\n",
              "      <td>147610924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ka</th>\n",
              "      <td>16</td>\n",
              "      <td>4938636728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kuga</th>\n",
              "      <td>23688</td>\n",
              "      <td>400236460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maverick</th>\n",
              "      <td>64</td>\n",
              "      <td>3801420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mondeo</th>\n",
              "      <td>16</td>\n",
              "      <td>4737409432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mustang</th>\n",
              "      <td>16</td>\n",
              "      <td>400177248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>probe</th>\n",
              "      <td>14804</td>\n",
              "      <td>2201228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>puma</th>\n",
              "      <td>1480</td>\n",
              "      <td>1599836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s-max</th>\n",
              "      <td>16</td>\n",
              "      <td>16766824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scorpio</th>\n",
              "      <td>1480</td>\n",
              "      <td>1596656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sierra</th>\n",
              "      <td>800060</td>\n",
              "      <td>800060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>streetka</th>\n",
              "      <td>320044</td>\n",
              "      <td>2300680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>taunus</th>\n",
              "      <td>399704</td>\n",
              "      <td>399704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>taurus</th>\n",
              "      <td>74020</td>\n",
              "      <td>1596240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thunderbird</th>\n",
              "      <td>1184308</td>\n",
              "      <td>5041600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tourneo-connect</th>\n",
              "      <td>480016</td>\n",
              "      <td>41499140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tourneo-courier</th>\n",
              "      <td>3688704</td>\n",
              "      <td>6740992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tourneo-custom</th>\n",
              "      <td>5602280</td>\n",
              "      <td>16998284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transit</th>\n",
              "      <td>16</td>\n",
              "      <td>296254804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>windstar</th>\n",
              "      <td>16</td>\n",
              "      <td>2760312</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                price_NGN             \n",
              "                      min          max\n",
              "model                                 \n",
              "b-max             2622592     32802176\n",
              "c-max                  16     14803836\n",
              "capri              140016       540164\n",
              "connect              7388      8237216\n",
              "cougar                 16      2760416\n",
              "crown-victoria      22204      9963688\n",
              "edge                29732     19665464\n",
              "escape            1139896      5356196\n",
              "escort                 16    295366396\n",
              "excursion         5181540     13207328\n",
              "expedition         760088      4202340\n",
              "explorer               44     24369016\n",
              "f-150            10747580     25973352\n",
              "fiesta                 16    316053544\n",
              "focus                  16  10917493588\n",
              "freestar          5955424      5955424\n",
              "fusion                 20      4380488\n",
              "galaxy                 16     16346868\n",
              "grand-c-max       2220576     10802400\n",
              "gt                 240016    147610924\n",
              "ka                     16   4938636728\n",
              "kuga                23688    400236460\n",
              "maverick               64      3801420\n",
              "mondeo                 16   4737409432\n",
              "mustang                16    400177248\n",
              "probe               14804      2201228\n",
              "puma                 1480      1599836\n",
              "s-max                  16     16766824\n",
              "scorpio              1480      1596656\n",
              "sierra             800060       800060\n",
              "streetka           320044      2300680\n",
              "taunus             399704       399704\n",
              "taurus              74020      1596240\n",
              "thunderbird       1184308      5041600\n",
              "tourneo-connect    480016     41499140\n",
              "tourneo-courier   3688704      6740992\n",
              "tourneo-custom    5602280     16998284\n",
              "transit                16    296254804\n",
              "windstar               16      2760312"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1SMXs6Ki9I4"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 400,000 NGN and 40,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFwO_Y0birdF"
      },
      "source": [
        "ford.loc[ford['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "ford.loc[ford['price_NGN'] > 40000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVwXxIadirg6",
        "outputId": "48aa89a9-e4c2-4848-e352-7d7c25bacdb1"
      },
      "source": [
        "ford['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      51290.00\n",
              "mean     3743853.25\n",
              "std      3030411.91\n",
              "min       400016.00\n",
              "25%      1316728.00\n",
              "50%      3000444.00\n",
              "75%      5377780.00\n",
              "max     37974048.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBx2qSGLkjMh"
      },
      "source": [
        "##### Honda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLPBsjsbklUB",
        "outputId": "da7c0cc3-fa2c-4452-f14e-ff9c488fc2e6"
      },
      "source": [
        "honda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count          5613.00\n",
              "mean       24737262.24\n",
              "std       924783019.56\n",
              "min              16.00\n",
              "25%         1140340.00\n",
              "50%         2720208.00\n",
              "75%         5025492.00\n",
              "max     40023685760.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "id": "2PR6pUWnklbG",
        "outputId": "a5ce1692-a7eb-4603-87eb-70c6f9b03f97"
      },
      "source": [
        "honda.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>accord</th>\n",
              "      <td>28</td>\n",
              "      <td>14362132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>29712</td>\n",
              "      <td>7661244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>civic</th>\n",
              "      <td>16</td>\n",
              "      <td>40023685760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cr-v</th>\n",
              "      <td>29608</td>\n",
              "      <td>15166736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cr-z</th>\n",
              "      <td>2797928</td>\n",
              "      <td>5562680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>element</th>\n",
              "      <td>36</td>\n",
              "      <td>738712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fr-v</th>\n",
              "      <td>1120164</td>\n",
              "      <td>6196920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hr-v</th>\n",
              "      <td>2960</td>\n",
              "      <td>10122872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>insight</th>\n",
              "      <td>2119912</td>\n",
              "      <td>5511060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>integra</th>\n",
              "      <td>520044</td>\n",
              "      <td>4001080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jazz</th>\n",
              "      <td>29728</td>\n",
              "      <td>7389488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>legend</th>\n",
              "      <td>132</td>\n",
              "      <td>8000784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nsx</th>\n",
              "      <td>16809328</td>\n",
              "      <td>39964440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odyssey</th>\n",
              "      <td>399704</td>\n",
              "      <td>12583272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pilot</th>\n",
              "      <td>2515172</td>\n",
              "      <td>17098432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prelude</th>\n",
              "      <td>200016</td>\n",
              "      <td>3801124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridgeline</th>\n",
              "      <td>6362352</td>\n",
              "      <td>7604500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s2000</th>\n",
              "      <td>3801124</td>\n",
              "      <td>7989180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shuttle</th>\n",
              "      <td>320120</td>\n",
              "      <td>2796832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stream</th>\n",
              "      <td>28</td>\n",
              "      <td>2596772</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          price_NGN             \n",
              "                min          max\n",
              "model                           \n",
              "accord           28     14362132\n",
              "city          29712      7661244\n",
              "civic            16  40023685760\n",
              "cr-v          29608     15166736\n",
              "cr-z        2797928      5562680\n",
              "element          36       738712\n",
              "fr-v        1120164      6196920\n",
              "hr-v           2960     10122872\n",
              "insight     2119912      5511060\n",
              "integra      520044      4001080\n",
              "jazz          29728      7389488\n",
              "legend          132      8000784\n",
              "nsx        16809328     39964440\n",
              "odyssey      399704     12583272\n",
              "pilot       2515172     17098432\n",
              "prelude      200016      3801124\n",
              "ridgeline   6362352      7604500\n",
              "s2000       3801124      7989180\n",
              "shuttle      320120      2796832\n",
              "stream           28      2596772"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWS6Lf4Xlh2M"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 400,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIgHW2QhklYL"
      },
      "source": [
        "honda.loc[honda['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "honda.loc[honda['price_NGN'] > 40000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4o0GZrprl4Pr",
        "outputId": "7a090f92-e216-40fc-98c2-6a80bee46415"
      },
      "source": [
        "honda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       5161.00\n",
              "mean     3625795.84\n",
              "std      2750999.63\n",
              "min       400016.00\n",
              "25%      1400208.00\n",
              "50%      2980324.00\n",
              "75%      5200768.00\n",
              "max     39964440.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHpwnDeJmaA4"
      },
      "source": [
        "##### Hyundai"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfziBeC3miD7",
        "outputId": "eca6c645-ed57-44e1-c9ca-cfe537a1d3d9"
      },
      "source": [
        "hyundai['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      14202.00\n",
              "mean     4028466.82\n",
              "std      3023462.86\n",
              "min           16.00\n",
              "25%      1599836.00\n",
              "50%      3523774.00\n",
              "75%      5708336.00\n",
              "max     88026056.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "wpWpkRNWmh_h",
        "outputId": "8a1e5b67-a283-4a58-cbb6-a258f724fd26"
      },
      "source": [
        "hyundai.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>accent</th>\n",
              "      <td>20</td>\n",
              "      <td>2120312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coupe</th>\n",
              "      <td>4440</td>\n",
              "      <td>7180532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>elantra</th>\n",
              "      <td>28</td>\n",
              "      <td>4441156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>galloper</th>\n",
              "      <td>444116</td>\n",
              "      <td>2800844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>genesis</th>\n",
              "      <td>28</td>\n",
              "      <td>24801836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>getz</th>\n",
              "      <td>16</td>\n",
              "      <td>6201836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grandeur</th>\n",
              "      <td>1560060</td>\n",
              "      <td>4201244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i10</th>\n",
              "      <td>148</td>\n",
              "      <td>5580828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i20</th>\n",
              "      <td>444116</td>\n",
              "      <td>7240532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i30</th>\n",
              "      <td>2960</td>\n",
              "      <td>10397544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i40</th>\n",
              "      <td>1480384</td>\n",
              "      <td>14461064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ix20</th>\n",
              "      <td>26648</td>\n",
              "      <td>8220608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ix35</th>\n",
              "      <td>29592</td>\n",
              "      <td>12660932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ix55</th>\n",
              "      <td>3403404</td>\n",
              "      <td>8580312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lantra</th>\n",
              "      <td>16</td>\n",
              "      <td>780088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>matrix</th>\n",
              "      <td>24</td>\n",
              "      <td>88026056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pony</th>\n",
              "      <td>200016</td>\n",
              "      <td>340044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santa-fe</th>\n",
              "      <td>16</td>\n",
              "      <td>19318312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>santamo</th>\n",
              "      <td>239688</td>\n",
              "      <td>880268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sonata</th>\n",
              "      <td>14804</td>\n",
              "      <td>14803848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>terracan</th>\n",
              "      <td>16</td>\n",
              "      <td>4596508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trajet</th>\n",
              "      <td>17764</td>\n",
              "      <td>3798104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>veloster</th>\n",
              "      <td>2396712</td>\n",
              "      <td>8964528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xg</th>\n",
              "      <td>133236</td>\n",
              "      <td>1799868</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN          \n",
              "               min       max\n",
              "model                       \n",
              "accent          20   2120312\n",
              "coupe         4440   7180532\n",
              "elantra         28   4441156\n",
              "galloper    444116   2800844\n",
              "genesis         28  24801836\n",
              "getz            16   6201836\n",
              "grandeur   1560060   4201244\n",
              "i10            148   5580828\n",
              "i20         444116   7240532\n",
              "i30           2960  10397544\n",
              "i40        1480384  14461064\n",
              "ix20         26648   8220608\n",
              "ix35         29592  12660932\n",
              "ix55       3403404   8580312\n",
              "lantra          16    780088\n",
              "matrix          24  88026056\n",
              "pony        200016    340044\n",
              "santa-fe        16  19318312\n",
              "santamo     239688    880268\n",
              "sonata       14804  14803848\n",
              "terracan        16   4596508\n",
              "trajet       17764   3798104\n",
              "veloster   2396712   8964528\n",
              "xg          133236   1799868"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe3gQ-Y2nTDK"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 400,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvkMA1pTmh8C"
      },
      "source": [
        "hyundai.loc[hyundai['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "hyundai.loc[hyundai['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXgaTZ-ml4Zl",
        "outputId": "e0f2d5c2-3440-45f5-8194-c70efe407861"
      },
      "source": [
        "hyundai['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      13582.00\n",
              "mean     4191286.87\n",
              "std      2876402.83\n",
              "min       400028.00\n",
              "25%      1909696.00\n",
              "50%      3682844.00\n",
              "75%      5811210.00\n",
              "max     19493840.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-2CpoYxsOhP"
      },
      "source": [
        "##### Isuzu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THvlKiHPl4WL",
        "outputId": "73a86f0d-6f3b-4a23-ca1b-d469b007d591"
      },
      "source": [
        "isuzu['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        388.00\n",
              "mean     8326945.18\n",
              "std      3090141.12\n",
              "min        29532.00\n",
              "25%      6993387.00\n",
              "50%      8655366.00\n",
              "75%      9983936.00\n",
              "max     25002784.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "5iV4xkX2l4Tm",
        "outputId": "f6d23cf9-0f45-4735-f79b-e52afae1fbad"
      },
      "source": [
        "isuzu.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d-max</th>\n",
              "      <td>3082916</td>\n",
              "      <td>25002784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trooper</th>\n",
              "      <td>500044</td>\n",
              "      <td>2761020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price_NGN          \n",
              "              min       max\n",
              "model                      \n",
              "d-max     3082916  25002784\n",
              "trooper    500044   2761020"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwXUg3w2tWQJ"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 500,000 NGN and 25,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3vDZOgvul08"
      },
      "source": [
        "isuzu.loc[isuzu['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "isuzu.loc[isuzu['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhoX1TCmumRU",
        "outputId": "5ca7be0e-32f9-4b9d-d164-cf5b6daae693"
      },
      "source": [
        "isuzu['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        382.00\n",
              "mean     8329632.93\n",
              "std      2769086.88\n",
              "min       500044.00\n",
              "25%      7268594.00\n",
              "50%      8694810.00\n",
              "75%      9983659.00\n",
              "max     18724768.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FH6vjyXlvKEo"
      },
      "source": [
        "##### Jaguar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIIq5C80ulwQ",
        "outputId": "ecd9331b-532d-4ca0-eb74-6745ba890b54"
      },
      "source": [
        "jaguar['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        2014.00\n",
              "mean     10328074.57\n",
              "std      10176385.28\n",
              "min            16.00\n",
              "25%       2801178.00\n",
              "50%       7997632.00\n",
              "75%      14169886.00\n",
              "max     180013324.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "mCVZ0Jr5ulsG",
        "outputId": "a3661f9b-3031-4f93-d4fd-37c10ac6c119"
      },
      "source": [
        "jaguar.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>daimler</th>\n",
              "      <td>2720904</td>\n",
              "      <td>17961332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f-type</th>\n",
              "      <td>17415160</td>\n",
              "      <td>66135172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s-type</th>\n",
              "      <td>40000</td>\n",
              "      <td>7953176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x-type</th>\n",
              "      <td>429312</td>\n",
              "      <td>7563360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xf</th>\n",
              "      <td>2220576</td>\n",
              "      <td>36021316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xj</th>\n",
              "      <td>16</td>\n",
              "      <td>180013324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xj6</th>\n",
              "      <td>725388</td>\n",
              "      <td>15961776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xj8</th>\n",
              "      <td>480164</td>\n",
              "      <td>8373236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xjr</th>\n",
              "      <td>1560576</td>\n",
              "      <td>11406336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xjs</th>\n",
              "      <td>7922340</td>\n",
              "      <td>11400844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xk</th>\n",
              "      <td>2601452</td>\n",
              "      <td>25018504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xk8</th>\n",
              "      <td>2599792</td>\n",
              "      <td>159623628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xkr</th>\n",
              "      <td>3397260</td>\n",
              "      <td>33613736</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price_NGN           \n",
              "              min        max\n",
              "model                       \n",
              "daimler   2720904   17961332\n",
              "f-type   17415160   66135172\n",
              "s-type      40000    7953176\n",
              "x-type     429312    7563360\n",
              "xf        2220576   36021316\n",
              "xj             16  180013324\n",
              "xj6        725388   15961776\n",
              "xj8        480164    8373236\n",
              "xjr       1560576   11406336\n",
              "xjs       7922340   11400844\n",
              "xk        2601452   25018504\n",
              "xk8       2599792  159623628\n",
              "xkr       3397260   33613736"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e1hR6Q8wYMr"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 2,700,000 NGN and 180,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6QiyfoMwi5c"
      },
      "source": [
        "jaguar.loc[jaguar['price_NGN'] < 2700000, 'price_NGN'] = np.nan\n",
        "jaguar.loc[jaguar['price_NGN'] > 180000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlE6ieKHwjDx",
        "outputId": "ec215f09-1e66-4a4d-b5f6-257a23199b66"
      },
      "source": [
        "jaguar['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        1545.00\n",
              "mean     12881209.03\n",
              "std       9290874.59\n",
              "min       2716804.00\n",
              "25%       6222696.00\n",
              "50%      11564280.00\n",
              "75%      15961776.00\n",
              "max     159623628.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJiCDOCDw_vb"
      },
      "source": [
        "##### Jeep"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lITCwI6zwjBC",
        "outputId": "c6f6e87c-8fa1-4491-b32f-e547bc632dba"
      },
      "source": [
        "jeep['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       3943.00\n",
              "mean     8653191.77\n",
              "std      5261599.76\n",
              "min         1480.00\n",
              "25%      4399348.00\n",
              "50%      8034788.00\n",
              "75%     11970748.00\n",
              "max     35202604.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "3TPbEQ6Jwi-C",
        "outputId": "108fd37d-fe70-4f06-b3f3-4535b684e372"
      },
      "source": [
        "jeep.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cherokee</th>\n",
              "      <td>3212</td>\n",
              "      <td>23805372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>commander</th>\n",
              "      <td>3397260</td>\n",
              "      <td>9161360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compass</th>\n",
              "      <td>2205772</td>\n",
              "      <td>9841464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand-cherokee</th>\n",
              "      <td>1480</td>\n",
              "      <td>33050984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liberty</th>\n",
              "      <td>1628424</td>\n",
              "      <td>9201020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patriot</th>\n",
              "      <td>1720104</td>\n",
              "      <td>6793012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>renegade</th>\n",
              "      <td>98684</td>\n",
              "      <td>14205256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrangler</th>\n",
              "      <td>51812</td>\n",
              "      <td>35202604</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               price_NGN          \n",
              "                     min       max\n",
              "model                             \n",
              "cherokee            3212  23805372\n",
              "commander        3397260   9161360\n",
              "compass          2205772   9841464\n",
              "grand-cherokee      1480  33050984\n",
              "liberty          1628424   9201020\n",
              "patriot          1720104   6793012\n",
              "renegade           98684  14205256\n",
              "wrangler           51812  35202604"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXT4PmFixfdp"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 1,600,000 NGN and 36,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6At5tIzTxox2"
      },
      "source": [
        "jeep.loc[jeep['price_NGN'] < 1600000, 'price_NGN'] = np.nan\n",
        "jeep.loc[jeep['price_NGN'] > 36000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X-bK2fxwi2G",
        "outputId": "b350cf9c-0647-4654-e38d-8c4de0e03a4a"
      },
      "source": [
        "jeep['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       3687.00\n",
              "mean     9179201.28\n",
              "std      5033451.97\n",
              "min      1600120.00\n",
              "25%      5689964.00\n",
              "50%      8402796.00\n",
              "75%     12361606.00\n",
              "max     35202604.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XX5UFF7dx-3S"
      },
      "source": [
        "##### Kia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ApVmOJCyBWv",
        "outputId": "ac700d69-4814-49b1-f336-1ea177106726"
      },
      "source": [
        "kia['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      10740.00\n",
              "mean     4254347.97\n",
              "std      2971441.17\n",
              "min           16.00\n",
              "25%      1900208.00\n",
              "50%      3700964.00\n",
              "75%      6216120.00\n",
              "max     19960740.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "th7YEX1xyBTk",
        "outputId": "432d15b0-1e2c-4f95-f91d-1858c295e580"
      },
      "source": [
        "kia.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>carens</th>\n",
              "      <td>74020</td>\n",
              "      <td>10956416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carnival</th>\n",
              "      <td>16</td>\n",
              "      <td>18702768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cerato</th>\n",
              "      <td>16</td>\n",
              "      <td>2360356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>clarus</th>\n",
              "      <td>148040</td>\n",
              "      <td>1080164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>joice</th>\n",
              "      <td>14804</td>\n",
              "      <td>720076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>magentis</th>\n",
              "      <td>1480</td>\n",
              "      <td>3921600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>opirus</th>\n",
              "      <td>738712</td>\n",
              "      <td>5181924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>optima</th>\n",
              "      <td>2480372</td>\n",
              "      <td>14342784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>picanto</th>\n",
              "      <td>16</td>\n",
              "      <td>5997292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pride</th>\n",
              "      <td>60028</td>\n",
              "      <td>718340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>retona</th>\n",
              "      <td>296076</td>\n",
              "      <td>1280712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rio</th>\n",
              "      <td>16</td>\n",
              "      <td>7848348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sephia</th>\n",
              "      <td>11844</td>\n",
              "      <td>759688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shuma</th>\n",
              "      <td>29608</td>\n",
              "      <td>1596240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sorento</th>\n",
              "      <td>36</td>\n",
              "      <td>19960740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>soul</th>\n",
              "      <td>1440060</td>\n",
              "      <td>11842768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sportage</th>\n",
              "      <td>16</td>\n",
              "      <td>15161688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>venga</th>\n",
              "      <td>1450776</td>\n",
              "      <td>8600964</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN          \n",
              "               min       max\n",
              "model                       \n",
              "carens       74020  10956416\n",
              "carnival        16  18702768\n",
              "cerato          16   2360356\n",
              "clarus      148040   1080164\n",
              "joice        14804    720076\n",
              "magentis      1480   3921600\n",
              "opirus      738712   5181924\n",
              "optima     2480372  14342784\n",
              "picanto         16   5997292\n",
              "pride        60028    718340\n",
              "retona      296076   1280712\n",
              "rio             16   7848348\n",
              "sephia       11844    759688\n",
              "shuma        29608   1596240\n",
              "sorento         36  19960740\n",
              "soul       1440060  11842768\n",
              "sportage        16  15161688\n",
              "venga      1450776   8600964"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DonvaoERy4Qw"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 296,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo4vhhJ1yBQc"
      },
      "source": [
        "kia.loc[kia['price_NGN'] < 296000, 'price_NGN'] = np.nan\n",
        "kia.loc[kia['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85KjjQJqzMZU",
        "outputId": "6f3d8d87-563f-417a-c717-863e1687328d"
      },
      "source": [
        "kia['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      10512.00\n",
              "mean     4343839.65\n",
              "std      2939977.66\n",
              "min       296076.00\n",
              "25%      1992584.00\n",
              "50%      3839806.00\n",
              "75%      6299199.00\n",
              "max     19960740.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVxDoSrlzUva"
      },
      "source": [
        "##### Lamborghini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXGKh7qPzMoz",
        "outputId": "16bb81d0-f57e-4dcd-9cfe-0e89e02ab8cf"
      },
      "source": [
        "lamborghini['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count          175.00\n",
              "mean      88947573.42\n",
              "std      302258101.42\n",
              "min       10005552.00\n",
              "25%       32017764.00\n",
              "50%       47547832.00\n",
              "75%       83690600.00\n",
              "max     3992443228.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "xzT4ZLaFzMlf",
        "outputId": "a0e16b1f-6ace-4448-ec71-8c2216f0b7ea"
      },
      "source": [
        "lamborghini.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>aventador</th>\n",
              "      <td>88003256</td>\n",
              "      <td>275661008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gallardo</th>\n",
              "      <td>22523300</td>\n",
              "      <td>100055516</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          price_NGN           \n",
              "                min        max\n",
              "model                         \n",
              "aventador  88003256  275661008\n",
              "gallardo   22523300  100055516"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZD2WHBnzs4W"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 25,000,000 NGN and 280,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tu0RPyWzMhn"
      },
      "source": [
        "lamborghini.loc[lamborghini['price_NGN'] < 25000000, 'price_NGN'] = np.nan\n",
        "lamborghini.loc[lamborghini['price_NGN'] > 280000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzA8Bl0izMdx",
        "outputId": "600a7424-8787-42e8-da3f-2598774352c0"
      },
      "source": [
        "lamborghini['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         167.00\n",
              "mean     63244075.90\n",
              "std      39127351.95\n",
              "min      25807640.00\n",
              "25%      32895788.00\n",
              "50%      48013812.00\n",
              "75%      82603094.00\n",
              "max     275661008.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwhjeSGv0LAA"
      },
      "source": [
        "##### Lancia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKY5b_TC0MCT",
        "outputId": "ae056af2-7eb9-4b0a-af4c-9f18cdb0f556"
      },
      "source": [
        "lancia['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       3623.00\n",
              "mean     2947524.60\n",
              "std      1738779.60\n",
              "min           16.00\n",
              "25%      1729436.00\n",
              "50%      3149460.00\n",
              "75%      3739770.00\n",
              "max     15588644.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "etlQVNrh0MHh",
        "outputId": "43f1807b-b492-4a7b-9fbf-621bd64a52f8"
      },
      "source": [
        "lancia.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>delta</th>\n",
              "      <td>14804</td>\n",
              "      <td>7160532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lybra</th>\n",
              "      <td>148040</td>\n",
              "      <td>1340800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>musa</th>\n",
              "      <td>920520</td>\n",
              "      <td>4722104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>phedra</th>\n",
              "      <td>28128</td>\n",
              "      <td>5001852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thema</th>\n",
              "      <td>4960372</td>\n",
              "      <td>15588644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thesis</th>\n",
              "      <td>162844</td>\n",
              "      <td>5001480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>voyager</th>\n",
              "      <td>5597868</td>\n",
              "      <td>14385328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>16</td>\n",
              "      <td>11514108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z</th>\n",
              "      <td>260028</td>\n",
              "      <td>1996300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price_NGN          \n",
              "              min       max\n",
              "model                      \n",
              "delta       14804   7160532\n",
              "lybra      148040   1340800\n",
              "musa       920520   4722104\n",
              "phedra      28128   5001852\n",
              "thema     4960372  15588644\n",
              "thesis     162844   5001480\n",
              "voyager   5597868  14385328\n",
              "y              16  11514108\n",
              "z          260028   1996300"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqqK7wy30nq-"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 170,000 NGN and 16,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HurpoM7T0L-_"
      },
      "source": [
        "lancia.loc[lancia['price_NGN'] < 170000, 'price_NGN'] = np.nan\n",
        "lancia.loc[lancia['price_NGN'] > 16000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDhQaRnS0L8C",
        "outputId": "71a5d9ca-ec82-41d3-db17-6af93b1b3f33"
      },
      "source": [
        "lancia['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       3601.00\n",
              "mean     2964984.00\n",
              "std      1729624.17\n",
              "min       177632.00\n",
              "25%      1796136.00\n",
              "50%      3149460.00\n",
              "75%      3741392.00\n",
              "max     15588644.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckUf94Uo1BaA"
      },
      "source": [
        "##### Maserati"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-0rRCLO0L4N",
        "outputId": "c18351ea-96ef-4255-a83b-42adadc0a1f0"
      },
      "source": [
        "maserati['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        587.00\n",
              "mean    21001280.52\n",
              "std     10101736.58\n",
              "min      2400176.00\n",
              "25%     11406336.00\n",
              "50%     21211368.00\n",
              "75%     27199024.00\n",
              "max     57316684.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "taiAa36F1LiD",
        "outputId": "bd479079-f146-4dc4-cc95-772aaa72f00f"
      },
      "source": [
        "maserati.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3200</th>\n",
              "      <td>5200504</td>\n",
              "      <td>17188260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>coupe</th>\n",
              "      <td>6400948</td>\n",
              "      <td>22002444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ghibli</th>\n",
              "      <td>6399556</td>\n",
              "      <td>38364264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grancabrio</th>\n",
              "      <td>23170112</td>\n",
              "      <td>55998076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>granturismo</th>\n",
              "      <td>8763568</td>\n",
              "      <td>57316684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quattroporte</th>\n",
              "      <td>3001776</td>\n",
              "      <td>55282056</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             price_NGN          \n",
              "                   min       max\n",
              "model                           \n",
              "3200           5200504  17188260\n",
              "coupe          6400948  22002444\n",
              "ghibli         6399556  38364264\n",
              "grancabrio    23170112  55998076\n",
              "granturismo    8763568  57316684\n",
              "quattroporte   3001776  55282056"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgofXELN1V7E"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 3,000,000 NGN and 56,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yITkSBuY1L1G"
      },
      "source": [
        "maserati.loc[maserati['price_NGN'] < 3000000, 'price_NGN'] = np.nan\n",
        "maserati.loc[maserati['price_NGN'] > 56000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mp2RP0nb1LyT",
        "outputId": "6c860723-aac4-4223-b156-22f751861c94"
      },
      "source": [
        "maserati['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        584.00\n",
              "mean    20911014.70\n",
              "std      9879530.29\n",
              "min      3001776.00\n",
              "25%     11406336.00\n",
              "50%     21183396.00\n",
              "75%     27198268.00\n",
              "max     55998076.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCfiflcY1wml"
      },
      "source": [
        "##### Mazda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lPMVQUe1Lvj",
        "outputId": "d2d16554-bae6-434d-fbf3-e14c9662e67f"
      },
      "source": [
        "mazda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       10772.00\n",
              "mean      3712344.00\n",
              "std       7228945.36\n",
              "min            16.00\n",
              "25%       1000148.00\n",
              "50%       2353812.00\n",
              "75%       5778505.00\n",
              "max     540299776.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "O8U2E7R31Lss",
        "outputId": "313c9fef-6184-40e1-c98e-7a2b4bf8712d"
      },
      "source": [
        "mazda.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>29608</td>\n",
              "      <td>10798388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16</td>\n",
              "      <td>16720948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>472</td>\n",
              "      <td>40013768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323</th>\n",
              "      <td>16</td>\n",
              "      <td>540299776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6660</td>\n",
              "      <td>15204440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>88</td>\n",
              "      <td>14045892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>1480</td>\n",
              "      <td>1400428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bt</th>\n",
              "      <td>460252</td>\n",
              "      <td>6396712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cx</th>\n",
              "      <td>1169504</td>\n",
              "      <td>13562012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cx-3</th>\n",
              "      <td>5276920</td>\n",
              "      <td>11800444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cx-5</th>\n",
              "      <td>5200192</td>\n",
              "      <td>17254552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cx-7</th>\n",
              "      <td>16</td>\n",
              "      <td>9145076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cx-9</th>\n",
              "      <td>3640268</td>\n",
              "      <td>17024428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>demio</th>\n",
              "      <td>148040</td>\n",
              "      <td>1199380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mpv</th>\n",
              "      <td>16</td>\n",
              "      <td>5822220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mx</th>\n",
              "      <td>29548</td>\n",
              "      <td>10280756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mx-3</th>\n",
              "      <td>99644</td>\n",
              "      <td>47370524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mx-5</th>\n",
              "      <td>11844</td>\n",
              "      <td>400029608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mx-6</th>\n",
              "      <td>740324</td>\n",
              "      <td>740324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>premacy</th>\n",
              "      <td>16</td>\n",
              "      <td>2799912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-8</th>\n",
              "      <td>740192</td>\n",
              "      <td>5599112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tribute</th>\n",
              "      <td>599776</td>\n",
              "      <td>3160356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xedos</th>\n",
              "      <td>177648</td>\n",
              "      <td>2361316</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price_NGN           \n",
              "              min        max\n",
              "model                       \n",
              "121         29608   10798388\n",
              "2              16   16720948\n",
              "3             472   40013768\n",
              "323            16  540299776\n",
              "5            6660   15204440\n",
              "6              88   14045892\n",
              "626          1480    1400428\n",
              "bt         460252    6396712\n",
              "cx        1169504   13562012\n",
              "cx-3      5276920   11800444\n",
              "cx-5      5200192   17254552\n",
              "cx-7           16    9145076\n",
              "cx-9      3640268   17024428\n",
              "demio      148040    1199380\n",
              "mpv            16    5822220\n",
              "mx          29548   10280756\n",
              "mx-3        99644   47370524\n",
              "mx-5        11844  400029608\n",
              "mx-6       740324     740324\n",
              "premacy        16    2799912\n",
              "rx-8       740192    5599112\n",
              "tribute    599776    3160356\n",
              "xedos      177648    2361316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmij4PHF2wDD"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 500,000 NGN and 20,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWBFHuoi1LqF"
      },
      "source": [
        "mazda.loc[mazda['price_NGN'] < 500000, 'price_NGN'] = np.nan\n",
        "mazda.loc[mazda['price_NGN'] > 20000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9t-hLBHS1LnH",
        "outputId": "d068c61e-abcc-47f4-dca3-a82d9153ec26"
      },
      "source": [
        "mazda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       9547.00\n",
              "mean     4037964.39\n",
              "std      3206167.77\n",
              "min       500044.00\n",
              "25%      1360096.00\n",
              "50%      2799140.00\n",
              "75%      6300472.00\n",
              "max     17254552.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTAGSl1W3QOg"
      },
      "source": [
        "##### Mercedes-Benz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpXBOawE3JzD",
        "outputId": "d75d427b-9015-481a-e0ea-36ae12fd8af8"
      },
      "source": [
        "mercedes['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        78624.00\n",
              "mean       7945477.07\n",
              "std       23520039.91\n",
              "min             16.00\n",
              "25%        2800828.00\n",
              "50%        6679792.00\n",
              "75%       10381156.00\n",
              "max     4400325684.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "XKOU8jgR3J6e",
        "outputId": "353274d5-925e-4f63-ac62-1213a4299959"
      },
      "source": [
        "mercedes.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>1160088</td>\n",
              "      <td>41966216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>560044</td>\n",
              "      <td>15610124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>480148</td>\n",
              "      <td>6196460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>359628</td>\n",
              "      <td>9650320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>799720</td>\n",
              "      <td>12404116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>16</td>\n",
              "      <td>23963552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>200076</td>\n",
              "      <td>53402708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>viano</th>\n",
              "      <td>10364</td>\n",
              "      <td>55824780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vito</th>\n",
              "      <td>16</td>\n",
              "      <td>63602352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      price_NGN          \n",
              "            min       max\n",
              "model                    \n",
              "115     1160088  41966216\n",
              "116      560044  15610124\n",
              "123      480148   6196460\n",
              "124      359628   9650320\n",
              "126      799720  12404116\n",
              "140          16  23963552\n",
              "190      200076  53402708\n",
              "viano     10364  55824780\n",
              "vito         16  63602352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyCgFYkH3oeN"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 56,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXkyglri3J3m"
      },
      "source": [
        "mercedes.loc[mercedes['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "mercedes.loc[mercedes['price_NGN'] > 56000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zlfe3_ct3Jvl",
        "outputId": "84669558-2544-42cf-81c6-629f064f9a5a"
      },
      "source": [
        "mercedes['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      78261.00\n",
              "mean     7578809.87\n",
              "std      6291927.64\n",
              "min       200016.00\n",
              "25%      2801036.00\n",
              "50%      6665848.00\n",
              "75%     10361156.00\n",
              "max     55936284.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Exon9Q2V4AqT"
      },
      "source": [
        "##### Mini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBd11_Rj3JsM",
        "outputId": "03a51a92-0511-4aa3-dd0a-892c24bd8aa6"
      },
      "source": [
        "mini['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      10732.00\n",
              "mean     6008908.90\n",
              "std      2774425.12\n",
              "min        28128.00\n",
              "25%      3796416.00\n",
              "50%      5982434.00\n",
              "75%      7661108.00\n",
              "max     18367476.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "YcsiI8UE3JoN",
        "outputId": "1f82f550-824d-4f39-e3d2-05a1119bd478"
      },
      "source": [
        "mini.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>clubman</th>\n",
              "      <td>1000564</td>\n",
              "      <td>15562308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cooper</th>\n",
              "      <td>28128</td>\n",
              "      <td>17169532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cooper-s</th>\n",
              "      <td>135620</td>\n",
              "      <td>18367476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>countryman</th>\n",
              "      <td>6365656</td>\n",
              "      <td>10732792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>one</th>\n",
              "      <td>400148</td>\n",
              "      <td>10000740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           price_NGN          \n",
              "                 min       max\n",
              "model                         \n",
              "clubman      1000564  15562308\n",
              "cooper         28128  17169532\n",
              "cooper-s      135620  18367476\n",
              "countryman   6365656  10732792\n",
              "one           400148  10000740"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTiMBTE74kMw"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 135,000 NGN and 19,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CAXGoVN4TzN"
      },
      "source": [
        "mini.loc[mini['price_NGN'] < 135000, 'price_NGN'] = np.nan\n",
        "mini.loc[mini['price_NGN'] > 19000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUul7var4Uu1",
        "outputId": "e5b454f4-2df1-4e3e-f649-c6afe8a88b14"
      },
      "source": [
        "mini['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      10727.00\n",
              "mean     6011689.09\n",
              "std      2772080.58\n",
              "min       135620.00\n",
              "25%      3796846.00\n",
              "50%      5985816.00\n",
              "75%      7662102.00\n",
              "max     18367476.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uJAK3g84-VI"
      },
      "source": [
        "##### Mitsubishi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoT4j97M4Uld",
        "outputId": "6de27e49-b9cf-4328-a45a-d7b3e967890c"
      },
      "source": [
        "mitsubishi['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count          7096.00\n",
              "mean       27273190.64\n",
              "std      1381701481.03\n",
              "min              16.00\n",
              "25%         1021464.00\n",
              "50%         2908676.00\n",
              "75%         6551680.00\n",
              "max     82310297260.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "ggWviWND4UdA",
        "outputId": "46e4237d-0919-481b-cc85-9e4a51e58c7f"
      },
      "source": [
        "mitsubishi.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3000-gt</th>\n",
              "      <td>1200088</td>\n",
              "      <td>7964708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>asx</th>\n",
              "      <td>4872</td>\n",
              "      <td>10795188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colt</th>\n",
              "      <td>1480</td>\n",
              "      <td>49386056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>eclipse</th>\n",
              "      <td>400148</td>\n",
              "      <td>6682472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>galant</th>\n",
              "      <td>7400</td>\n",
              "      <td>3040904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grandis</th>\n",
              "      <td>840088</td>\n",
              "      <td>5192776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l200</th>\n",
              "      <td>2220</td>\n",
              "      <td>13323464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lancer</th>\n",
              "      <td>1480</td>\n",
              "      <td>82310297260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outlander</th>\n",
              "      <td>32</td>\n",
              "      <td>17044632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pajero</th>\n",
              "      <td>52</td>\n",
              "      <td>400146440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pajero-pinin</th>\n",
              "      <td>460224</td>\n",
              "      <td>3580400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pajero-sport</th>\n",
              "      <td>29608</td>\n",
              "      <td>4196624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sigma</th>\n",
              "      <td>3561984</td>\n",
              "      <td>3561984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>space</th>\n",
              "      <td>20</td>\n",
              "      <td>5880428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>space-star</th>\n",
              "      <td>74020</td>\n",
              "      <td>1176076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spacewagon</th>\n",
              "      <td>873428</td>\n",
              "      <td>873428</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             price_NGN             \n",
              "                   min          max\n",
              "model                              \n",
              "3000-gt        1200088      7964708\n",
              "asx               4872     10795188\n",
              "colt              1480     49386056\n",
              "eclipse         400148      6682472\n",
              "galant            7400      3040904\n",
              "grandis         840088      5192776\n",
              "l200              2220     13323464\n",
              "lancer            1480  82310297260\n",
              "outlander           32     17044632\n",
              "pajero              52    400146440\n",
              "pajero-pinin    460224      3580400\n",
              "pajero-sport     29608      4196624\n",
              "sigma          3561984      3561984\n",
              "space               20      5880428\n",
              "space-star       74020      1176076\n",
              "spacewagon      873428       873428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVeB-f-u5d_Q"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 400,000 NGN and 50,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIQYDeE64UTs"
      },
      "source": [
        "mitsubishi.loc[mitsubishi['price_NGN'] < 400000, 'price_NGN'] = np.nan\n",
        "mitsubishi.loc[mitsubishi['price_NGN'] > 50000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsj-g37H5vh8",
        "outputId": "05bb19f8-8289-4f7a-a6f6-c7f78b091be0"
      },
      "source": [
        "mitsubishi['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       6438.00\n",
              "mean     4401751.19\n",
              "std      3536335.57\n",
              "min       400016.00\n",
              "25%      1427381.00\n",
              "50%      3247196.00\n",
              "75%      7121270.00\n",
              "max     49386056.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-BkR1UX6BLC"
      },
      "source": [
        "##### Nissan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfwLWv6I5vu3",
        "outputId": "829178bd-1dcc-481b-ee32-34725414f3f2"
      },
      "source": [
        "nissan['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      16493.00\n",
              "mean     4662241.39\n",
              "std      3269849.91\n",
              "min           16.00\n",
              "25%      1796268.00\n",
              "50%      4624456.00\n",
              "75%      6641508.00\n",
              "max     59608824.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DxjIZy5l5vsG",
        "outputId": "a0f75f45-0a07-4888-e9d7-110cfdb59a1d"
      },
      "source": [
        "nissan.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>200-sx</th>\n",
              "      <td>2601452</td>\n",
              "      <td>5300784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350-z</th>\n",
              "      <td>2664692</td>\n",
              "      <td>13201464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370-z</th>\n",
              "      <td>6600088</td>\n",
              "      <td>15405700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>almera</th>\n",
              "      <td>16</td>\n",
              "      <td>4001924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>almera-tino</th>\n",
              "      <td>32</td>\n",
              "      <td>2796108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>altima</th>\n",
              "      <td>799408</td>\n",
              "      <td>799408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>armada</th>\n",
              "      <td>4628748</td>\n",
              "      <td>8000784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cube</th>\n",
              "      <td>2595440</td>\n",
              "      <td>3960148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>double-cab</th>\n",
              "      <td>29532</td>\n",
              "      <td>13561508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>evalia</th>\n",
              "      <td>2680800</td>\n",
              "      <td>10760784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frontier</th>\n",
              "      <td>16</td>\n",
              "      <td>1465580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gt-r</th>\n",
              "      <td>7983540</td>\n",
              "      <td>59608824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>juke</th>\n",
              "      <td>1960800</td>\n",
              "      <td>11023628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>king-cab</th>\n",
              "      <td>479616</td>\n",
              "      <td>13378992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>leaf</th>\n",
              "      <td>5540016</td>\n",
              "      <td>12801896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxima</th>\n",
              "      <td>356032</td>\n",
              "      <td>2300860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>micra</th>\n",
              "      <td>16</td>\n",
              "      <td>5396804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>murano</th>\n",
              "      <td>1302740</td>\n",
              "      <td>17453736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>navara</th>\n",
              "      <td>36</td>\n",
              "      <td>14924856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>note</th>\n",
              "      <td>760028</td>\n",
              "      <td>6798016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nv200</th>\n",
              "      <td>29608</td>\n",
              "      <td>10083788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pathfinder</th>\n",
              "      <td>756</td>\n",
              "      <td>17166352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patrol</th>\n",
              "      <td>64</td>\n",
              "      <td>28004144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primera</th>\n",
              "      <td>16</td>\n",
              "      <td>5200576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pulsar</th>\n",
              "      <td>4501464</td>\n",
              "      <td>9196684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qashqai</th>\n",
              "      <td>340</td>\n",
              "      <td>12397840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>serena</th>\n",
              "      <td>320088</td>\n",
              "      <td>1959496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sunny</th>\n",
              "      <td>16</td>\n",
              "      <td>1140164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>terrano</th>\n",
              "      <td>14804</td>\n",
              "      <td>4361452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>terrano-ii</th>\n",
              "      <td>29580</td>\n",
              "      <td>4001184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tiida</th>\n",
              "      <td>1320148</td>\n",
              "      <td>3989180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>titan</th>\n",
              "      <td>5600428</td>\n",
              "      <td>8002560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x-trail</th>\n",
              "      <td>3228</td>\n",
              "      <td>14286336</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price_NGN          \n",
              "                  min       max\n",
              "model                          \n",
              "200-sx        2601452   5300784\n",
              "350-z         2664692  13201464\n",
              "370-z         6600088  15405700\n",
              "almera             16   4001924\n",
              "almera-tino        32   2796108\n",
              "altima         799408    799408\n",
              "armada        4628748   8000784\n",
              "cube          2595440   3960148\n",
              "double-cab      29532  13561508\n",
              "evalia        2680800  10760784\n",
              "frontier           16   1465580\n",
              "gt-r          7983540  59608824\n",
              "juke          1960800  11023628\n",
              "king-cab       479616  13378992\n",
              "leaf          5540016  12801896\n",
              "maxima         356032   2300860\n",
              "micra              16   5396804\n",
              "murano        1302740  17453736\n",
              "navara             36  14924856\n",
              "note           760028   6798016\n",
              "nv200           29608  10083788\n",
              "pathfinder        756  17166352\n",
              "patrol             64  28004144\n",
              "primera            16   5200576\n",
              "pulsar        4501464   9196684\n",
              "qashqai           340  12397840\n",
              "serena         320088   1959496\n",
              "sunny              16   1140164\n",
              "terrano         14804   4361452\n",
              "terrano-ii      29580   4001184\n",
              "tiida         1320148   3989180\n",
              "titan         5600428   8002560\n",
              "x-trail          3228  14286336"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0sQ0ahg6c6g"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 300,000 NGN and 60,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zEhQA205vo3"
      },
      "source": [
        "nissan.loc[nissan['price_NGN'] < 300000, 'price_NGN'] = np.nan\n",
        "nissan.loc[nissan['price_NGN'] > 60000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MDMQOv75vey",
        "outputId": "1c2c09ec-069f-410d-902b-5302a88eb3cb"
      },
      "source": [
        "nissan['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      16005.00\n",
              "mean     4798771.80\n",
              "std      3222985.58\n",
              "min       300016.00\n",
              "25%      1999896.00\n",
              "50%      4760712.00\n",
              "75%      6716492.00\n",
              "max     59608824.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4UpK2Bf64W2"
      },
      "source": [
        "##### Opel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4b7fpvu5vbY",
        "outputId": "bdb65f90-7ff2-4b71-c61b-1b8a9f91d81b"
      },
      "source": [
        "opel['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        65464.00\n",
              "mean       3256492.12\n",
              "std       12127377.56\n",
              "min             16.00\n",
              "25%         940132.00\n",
              "50%        2596772.00\n",
              "75%        4871116.00\n",
              "max     2971430096.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "id": "B7800DLv5vYT",
        "outputId": "44345984-0b47-4f43-c029-3031874bd693"
      },
      "source": [
        "opel.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>adam</th>\n",
              "      <td>16</td>\n",
              "      <td>8719868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agila</th>\n",
              "      <td>29624</td>\n",
              "      <td>6201836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ampera</th>\n",
              "      <td>4133932</td>\n",
              "      <td>14458148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>antara</th>\n",
              "      <td>148040</td>\n",
              "      <td>11063300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>astra</th>\n",
              "      <td>16</td>\n",
              "      <td>373538092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>calibra</th>\n",
              "      <td>7400</td>\n",
              "      <td>2796832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cascada</th>\n",
              "      <td>5879452</td>\n",
              "      <td>14613308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>combo</th>\n",
              "      <td>16</td>\n",
              "      <td>9549652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>corsa</th>\n",
              "      <td>16</td>\n",
              "      <td>2971430096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frontera</th>\n",
              "      <td>16</td>\n",
              "      <td>3561052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gt</th>\n",
              "      <td>280104</td>\n",
              "      <td>10001080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>insignia</th>\n",
              "      <td>16</td>\n",
              "      <td>63981732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meriva</th>\n",
              "      <td>17616</td>\n",
              "      <td>11523836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mokka</th>\n",
              "      <td>2400176</td>\n",
              "      <td>12000444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>monterey</th>\n",
              "      <td>29608</td>\n",
              "      <td>1554404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omega</th>\n",
              "      <td>16</td>\n",
              "      <td>3400252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sintra</th>\n",
              "      <td>16</td>\n",
              "      <td>2241244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>speedster</th>\n",
              "      <td>3580532</td>\n",
              "      <td>14402132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tigra</th>\n",
              "      <td>16</td>\n",
              "      <td>3880428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vectra</th>\n",
              "      <td>16</td>\n",
              "      <td>6002888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vivaro</th>\n",
              "      <td>1480</td>\n",
              "      <td>12098492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zafira</th>\n",
              "      <td>16</td>\n",
              "      <td>63702368</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          price_NGN            \n",
              "                min         max\n",
              "model                          \n",
              "adam             16     8719868\n",
              "agila         29624     6201836\n",
              "ampera      4133932    14458148\n",
              "antara       148040    11063300\n",
              "astra            16   373538092\n",
              "calibra        7400     2796832\n",
              "cascada     5879452    14613308\n",
              "combo            16     9549652\n",
              "corsa            16  2971430096\n",
              "frontera         16     3561052\n",
              "gt           280104    10001080\n",
              "insignia         16    63981732\n",
              "meriva        17616    11523836\n",
              "mokka       2400176    12000444\n",
              "monterey      29608     1554404\n",
              "omega            16     3400252\n",
              "sintra           16     2241244\n",
              "speedster   3580532    14402132\n",
              "tigra            16     3880428\n",
              "vectra           16     6002888\n",
              "vivaro         1480    12098492\n",
              "zafira           16    63702368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0DtB2vYF7OYM"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 65,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bg6niRNe5vVK"
      },
      "source": [
        "opel.loc[opel['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "opel.loc[opel['price_NGN'] > 65000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJQQYWKC4ULy",
        "outputId": "4ea48cbc-8327-41dc-aad4-f18b780f6210"
      },
      "source": [
        "opel['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      64233.00\n",
              "mean     3252522.41\n",
              "std      2610961.19\n",
              "min       200016.00\n",
              "25%      1000120.00\n",
              "50%      2676788.00\n",
              "75%      4940372.00\n",
              "max     63981732.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4vRmCAP7oHB"
      },
      "source": [
        "##### Porsche"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x38tKYTo3Jj0",
        "outputId": "6004292a-7078-446b-fb82-ea88cd8f9a07"
      },
      "source": [
        "porsche['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         6536.00\n",
              "mean      25021346.73\n",
              "std       47787996.71\n",
              "min          14760.00\n",
              "25%       13710994.00\n",
              "50%       20676988.00\n",
              "75%       29162431.00\n",
              "max     3361842012.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "qPeMvN8o7yhW",
        "outputId": "4634f1c1-0a7c-4440-9392-47e54a1dcf04"
      },
      "source": [
        "porsche.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>911</th>\n",
              "      <td>4032184</td>\n",
              "      <td>199622160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>987-boxster</th>\n",
              "      <td>4426352</td>\n",
              "      <td>8001184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carrera-gt</th>\n",
              "      <td>6658772</td>\n",
              "      <td>400059216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cayenne</th>\n",
              "      <td>1960724</td>\n",
              "      <td>3361842012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cayman</th>\n",
              "      <td>14804</td>\n",
              "      <td>52431028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macan</th>\n",
              "      <td>20468720</td>\n",
              "      <td>41603080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>panamera</th>\n",
              "      <td>29740</td>\n",
              "      <td>87194448</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price_NGN            \n",
              "                  min         max\n",
              "model                            \n",
              "911           4032184   199622160\n",
              "987-boxster   4426352     8001184\n",
              "carrera-gt    6658772   400059216\n",
              "cayenne       1960724  3361842012\n",
              "cayman          14804    52431028\n",
              "macan        20468720    41603080\n",
              "panamera        29740    87194448"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nLYM7Fq8GhL"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 2,000,000 NGN and 400,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ub-RXm77zC-"
      },
      "source": [
        "porsche.loc[porsche['price_NGN'] < 2000000, 'price_NGN'] = np.nan\n",
        "porsche.loc[porsche['price_NGN'] > 400000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_A4nywy7y_3",
        "outputId": "5a8f5707-7d1d-4f51-d516-6c145a96fd9e"
      },
      "source": [
        "porsche['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        6521.00\n",
              "mean     24080329.88\n",
              "std      18558596.53\n",
              "min       2220576.00\n",
              "25%      13797528.00\n",
              "50%      20678520.00\n",
              "75%      29131856.00\n",
              "max     312745124.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BhlMZ1e8rZS"
      },
      "source": [
        "##### Rover"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hW9fg_hX7y8a",
        "outputId": "374037a8-8287-4b62-e4a8-f2a64fbd3685"
      },
      "source": [
        "rover['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       5204.00\n",
              "mean    10711760.53\n",
              "std      9762613.69\n",
              "min           16.00\n",
              "25%      3242044.00\n",
              "50%      8761304.00\n",
              "75%     14767378.00\n",
              "max     76042192.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "dgEif_Pq7y5E",
        "outputId": "f50de971-72e5-40c1-d29d-e5c60a903707"
      },
      "source": [
        "rover.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>6144</td>\n",
              "      <td>23671356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2960</td>\n",
              "      <td>15604220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>16</td>\n",
              "      <td>29241124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400</th>\n",
              "      <td>5004</td>\n",
              "      <td>15800592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>7860</td>\n",
              "      <td>14782192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>222056</td>\n",
              "      <td>10766364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>296076</td>\n",
              "      <td>10362680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800</th>\n",
              "      <td>666172</td>\n",
              "      <td>29404352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mini</th>\n",
              "      <td>1213736</td>\n",
              "      <td>15004440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>streetwise</th>\n",
              "      <td>780120</td>\n",
              "      <td>1316152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           price_NGN          \n",
              "                 min       max\n",
              "model                         \n",
              "100             6144  23671356\n",
              "200             2960  15604220\n",
              "25                16  29241124\n",
              "400             5004  15800592\n",
              "45              7860  14782192\n",
              "600           222056  10766364\n",
              "75            296076  10362680\n",
              "800           666172  29404352\n",
              "mini         1213736  15004440\n",
              "streetwise    780120   1316152"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENIF-9F-88qp"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 30,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQq1MiBi7y16"
      },
      "source": [
        "rover.loc[rover['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "rover.loc[rover['price_NGN'] > 30000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7YB2Vsi7yyZ",
        "outputId": "0e17aab5-89f7-4265-d77a-2aca838a1c11"
      },
      "source": [
        "rover['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       4845.00\n",
              "mean     9406822.52\n",
              "std      6888720.49\n",
              "min       200016.00\n",
              "25%      3356372.00\n",
              "50%      8490912.00\n",
              "75%     14337956.00\n",
              "max     29944428.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpNttaZp9Wy-"
      },
      "source": [
        "##### Seat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ru_HFF0v9auB",
        "outputId": "86f7d63f-9a79-40c6-ebc2-6b58f60e9d36"
      },
      "source": [
        "seat['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        17488.00\n",
              "mean       3827443.88\n",
              "std       14137556.97\n",
              "min             16.00\n",
              "25%        1221209.00\n",
              "50%        3183020.00\n",
              "75%        5498724.00\n",
              "max     1811351428.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "OwKHQbXX9Z9I",
        "outputId": "6aa9c801-c6e6-4af0-bf03-98a8f5a36835"
      },
      "source": [
        "seat.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alhambra</th>\n",
              "      <td>28</td>\n",
              "      <td>18162696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>altea</th>\n",
              "      <td>16</td>\n",
              "      <td>9241760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arosa</th>\n",
              "      <td>29624</td>\n",
              "      <td>3401008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cordoba</th>\n",
              "      <td>16</td>\n",
              "      <td>2360268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exeo</th>\n",
              "      <td>1000044</td>\n",
              "      <td>9682576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ibiza</th>\n",
              "      <td>16</td>\n",
              "      <td>8803908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inca</th>\n",
              "      <td>103628</td>\n",
              "      <td>1420416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>leon</th>\n",
              "      <td>16</td>\n",
              "      <td>33322472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marbella</th>\n",
              "      <td>59216</td>\n",
              "      <td>600224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mii</th>\n",
              "      <td>1547252</td>\n",
              "      <td>5321568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toledo</th>\n",
              "      <td>16</td>\n",
              "      <td>1811351428</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN            \n",
              "               min         max\n",
              "model                         \n",
              "alhambra        28    18162696\n",
              "altea           16     9241760\n",
              "arosa        29624     3401008\n",
              "cordoba         16     2360268\n",
              "exeo       1000044     9682576\n",
              "ibiza           16     8803908\n",
              "inca        103628     1420416\n",
              "leon            16    33322472\n",
              "marbella     59216      600224\n",
              "mii        1547252     5321568\n",
              "toledo          16  1811351428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkDbl2AA9tof"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 35,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ku6CFF159Z6O"
      },
      "source": [
        "seat.loc[seat['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "seat.loc[seat['price_NGN'] > 35000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-axafhq9Z2n",
        "outputId": "0ce98135-c49d-4cc6-d79e-64e7a183c0b8"
      },
      "source": [
        "seat['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      17170.00\n",
              "mean     3771323.24\n",
              "std      2857152.37\n",
              "min       200016.00\n",
              "25%      1320192.00\n",
              "50%      3223716.00\n",
              "75%      5544393.00\n",
              "max     33322472.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVnCKUFe-F30"
      },
      "source": [
        "##### Skoda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLhR0o5q9Zzo",
        "outputId": "aaa297f4-68fd-4393-cf0f-e661170bc636"
      },
      "source": [
        "skoda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       51915.00\n",
              "mean      3042011.90\n",
              "std       4019227.33\n",
              "min            16.00\n",
              "25%       1110288.00\n",
              "50%       2309400.00\n",
              "75%       4302598.00\n",
              "max     296846912.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "Jn6jl_MB9Zwn",
        "outputId": "9f0cfe49-dfb2-486a-9acc-dec8d93d4418"
      },
      "source": [
        "skoda.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>16</td>\n",
              "      <td>5634864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>29608</td>\n",
              "      <td>4145076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>185048</td>\n",
              "      <td>5314580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>177648</td>\n",
              "      <td>9918580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>1006660</td>\n",
              "      <td>1006660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citigo</th>\n",
              "      <td>1000120</td>\n",
              "      <td>6370228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fabia</th>\n",
              "      <td>16</td>\n",
              "      <td>296106692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>favorit</th>\n",
              "      <td>1036</td>\n",
              "      <td>1110288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>felicia</th>\n",
              "      <td>16</td>\n",
              "      <td>295514448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>octavia</th>\n",
              "      <td>16</td>\n",
              "      <td>296846912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rapid</th>\n",
              "      <td>1924</td>\n",
              "      <td>9108676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roomster</th>\n",
              "      <td>16</td>\n",
              "      <td>40023288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>superb</th>\n",
              "      <td>16</td>\n",
              "      <td>18800888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yeti</th>\n",
              "      <td>16</td>\n",
              "      <td>13105448</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN           \n",
              "               min        max\n",
              "model                        \n",
              "105             16    5634864\n",
              "120          29608    4145076\n",
              "125         185048    5314580\n",
              "130         177648    9918580\n",
              "860        1006660    1006660\n",
              "citigo     1000120    6370228\n",
              "fabia           16  296106692\n",
              "favorit       1036    1110288\n",
              "felicia         16  295514448\n",
              "octavia         16  296846912\n",
              "rapid         1924    9108676\n",
              "roomster        16   40023288\n",
              "superb          16   18800888\n",
              "yeti            16   13105448"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1A6m8tH-Zub"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 50,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJl2KPp09Ztk"
      },
      "source": [
        "skoda.loc[skoda['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "skoda.loc[skoda['price_NGN'] > 50000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGH8qnnB9Zqb",
        "outputId": "eda449c9-c292-4e63-ae40-934e82a15743"
      },
      "source": [
        "skoda['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      50501.00\n",
              "mean     3088156.31\n",
              "std      2466531.47\n",
              "min       200016.00\n",
              "25%      1184292.00\n",
              "50%      2367136.00\n",
              "75%      4366720.00\n",
              "max     40023288.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbUY5Z5V-zyv"
      },
      "source": [
        "##### Subaru"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olk0iJxD9Zmh",
        "outputId": "c386c54f-53b1-481a-ed6b-7589d4501f3d"
      },
      "source": [
        "subaru['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       2296.00\n",
              "mean     4557980.72\n",
              "std      4000520.90\n",
              "min           28.00\n",
              "25%      1479837.00\n",
              "50%      3256848.00\n",
              "75%      6996106.00\n",
              "max     60004440.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "QidO39mI9ZiY",
        "outputId": "290107d1-bc03-481b-faa1-4718f50cadae"
      },
      "source": [
        "subaru.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>baja</th>\n",
              "      <td>3140932</td>\n",
              "      <td>3520636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brz</th>\n",
              "      <td>3699480</td>\n",
              "      <td>11780872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>forester</th>\n",
              "      <td>28</td>\n",
              "      <td>15181688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>impreza</th>\n",
              "      <td>22204</td>\n",
              "      <td>60004440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>justy</th>\n",
              "      <td>14804</td>\n",
              "      <td>3196360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>legacy</th>\n",
              "      <td>36</td>\n",
              "      <td>9748008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>levorg</th>\n",
              "      <td>7840356</td>\n",
              "      <td>15001672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outback</th>\n",
              "      <td>7400</td>\n",
              "      <td>43671356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svx</th>\n",
              "      <td>1399808</td>\n",
              "      <td>3160356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trezia</th>\n",
              "      <td>2800016</td>\n",
              "      <td>6353500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tribeca</th>\n",
              "      <td>1998520</td>\n",
              "      <td>5284976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vivio</th>\n",
              "      <td>220060</td>\n",
              "      <td>473724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrx</th>\n",
              "      <td>42932</td>\n",
              "      <td>18808292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xv</th>\n",
              "      <td>4891164</td>\n",
              "      <td>11960888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN          \n",
              "               min       max\n",
              "model                       \n",
              "baja       3140932   3520636\n",
              "brz        3699480  11780872\n",
              "forester        28  15181688\n",
              "impreza      22204  60004440\n",
              "justy        14804   3196360\n",
              "legacy          36   9748008\n",
              "levorg     7840356  15001672\n",
              "outback       7400  43671356\n",
              "svx        1399808   3160356\n",
              "trezia     2800016   6353500\n",
              "tribeca    1998520   5284976\n",
              "vivio       220060    473724\n",
              "wrx          42932  18808292\n",
              "xv         4891164  11960888"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WeUoyOF_DSm"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 65,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRKuFNpf9Zek"
      },
      "source": [
        "subaru.loc[subaru['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "subaru.loc[subaru['price_NGN'] > 65000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqDS8QdC7yvH",
        "outputId": "974c9c04-8eb2-4447-9201-5867cf54ae22"
      },
      "source": [
        "subaru['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       2253.00\n",
              "mean     4643265.89\n",
              "std      3990126.07\n",
              "min       200016.00\n",
              "25%      1580472.00\n",
              "50%      3375276.00\n",
              "75%      7056120.00\n",
              "max     60004440.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEy3Ec47_ift"
      },
      "source": [
        "##### Suzuki"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPKPKlAp7ysk",
        "outputId": "f5788b11-4c1d-4432-dd08-bff3ba89fe79"
      },
      "source": [
        "suzuki['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       6887.00\n",
              "mean     3118577.79\n",
              "std      2211752.46\n",
              "min           16.00\n",
              "25%      1398090.00\n",
              "50%      2600384.00\n",
              "75%      4702176.00\n",
              "max     10568764.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "qPsbBX-Z_mT_",
        "outputId": "d535a873-e415-46d0-d99c-9d51fb739680"
      },
      "source": [
        "suzuki.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alto</th>\n",
              "      <td>76016</td>\n",
              "      <td>3582132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baleno</th>\n",
              "      <td>16</td>\n",
              "      <td>999704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carry</th>\n",
              "      <td>880132</td>\n",
              "      <td>1596064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>celerio</th>\n",
              "      <td>2689504</td>\n",
              "      <td>5081880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand-vitara</th>\n",
              "      <td>28</td>\n",
              "      <td>9596712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ignis</th>\n",
              "      <td>7400</td>\n",
              "      <td>2792420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jimny</th>\n",
              "      <td>60</td>\n",
              "      <td>7361096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kizashi</th>\n",
              "      <td>4300636</td>\n",
              "      <td>7796580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liana</th>\n",
              "      <td>7400</td>\n",
              "      <td>2399868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>samurai</th>\n",
              "      <td>192</td>\n",
              "      <td>3200948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>splash</th>\n",
              "      <td>32</td>\n",
              "      <td>4996552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>swift</th>\n",
              "      <td>16</td>\n",
              "      <td>6673976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sx4</th>\n",
              "      <td>17764</td>\n",
              "      <td>7997188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sx4-s-cross</th>\n",
              "      <td>3841704</td>\n",
              "      <td>10568764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vitara</th>\n",
              "      <td>14804</td>\n",
              "      <td>10521556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wagon-r</th>\n",
              "      <td>16</td>\n",
              "      <td>1740252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             price_NGN          \n",
              "                   min       max\n",
              "model                           \n",
              "alto             76016   3582132\n",
              "baleno              16    999704\n",
              "carry           880132   1596064\n",
              "celerio        2689504   5081880\n",
              "grand-vitara        28   9596712\n",
              "ignis             7400   2792420\n",
              "jimny               60   7361096\n",
              "kizashi        4300636   7796580\n",
              "liana             7400   2399868\n",
              "samurai            192   3200948\n",
              "splash              32   4996552\n",
              "swift               16   6673976\n",
              "sx4              17764   7997188\n",
              "sx4-s-cross    3841704  10568764\n",
              "vitara           14804  10521556\n",
              "wagon-r             16   1740252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhQeKBk7iRMB"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 880,000 NGN and 11,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5sdxgOP_mRE"
      },
      "source": [
        "suzuki.loc[suzuki['price_NGN'] < 880000, 'price_NGN'] = np.nan\n",
        "suzuki.loc[suzuki['price_NGN'] > 11000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1FumB3p_mOE",
        "outputId": "d4d386fb-7743-4884-9da3-4a56c6893123"
      },
      "source": [
        "suzuki['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       5690.00\n",
              "mean     3671839.66\n",
              "std      2036818.56\n",
              "min       880028.00\n",
              "25%      2000192.00\n",
              "50%      3196952.00\n",
              "75%      4994684.00\n",
              "max     10568764.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyAYCvNBi8-j"
      },
      "source": [
        "##### Toyota"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRHj0yUG_mKi",
        "outputId": "e3d443b7-774c-4965-9f09-7ac3006b97b8"
      },
      "source": [
        "toyota['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       17997.00\n",
              "mean      3953368.91\n",
              "std       5031177.54\n",
              "min            16.00\n",
              "25%       1821080.00\n",
              "50%       3263848.00\n",
              "75%       5270172.00\n",
              "max     296698876.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ERi5ASvl_mHj",
        "outputId": "e17c56c9-0ad9-4041-83c8-209edc2316c5"
      },
      "source": [
        "toyota.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>auris</th>\n",
              "      <td>20</td>\n",
              "      <td>10701184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avensis</th>\n",
              "      <td>16</td>\n",
              "      <td>24003552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avensis-verso</th>\n",
              "      <td>600176</td>\n",
              "      <td>2701600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>aygo</th>\n",
              "      <td>16</td>\n",
              "      <td>5458608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>camry</th>\n",
              "      <td>29608</td>\n",
              "      <td>16136180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carina</th>\n",
              "      <td>200076</td>\n",
              "      <td>6796508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>celica</th>\n",
              "      <td>7400</td>\n",
              "      <td>6761008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>corolla</th>\n",
              "      <td>3700</td>\n",
              "      <td>296402752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>corolla-verso</th>\n",
              "      <td>7400</td>\n",
              "      <td>296698876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fj-cruiser</th>\n",
              "      <td>29608</td>\n",
              "      <td>23212880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gt86</th>\n",
              "      <td>6942648</td>\n",
              "      <td>14807120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highlander</th>\n",
              "      <td>8002368</td>\n",
              "      <td>8734272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hilux</th>\n",
              "      <td>66616</td>\n",
              "      <td>15598312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iq</th>\n",
              "      <td>1460060</td>\n",
              "      <td>6785404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>land-cruiser</th>\n",
              "      <td>16</td>\n",
              "      <td>35005180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mr2</th>\n",
              "      <td>799408</td>\n",
              "      <td>1998520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paseo</th>\n",
              "      <td>1480</td>\n",
              "      <td>1980296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>picnic</th>\n",
              "      <td>16</td>\n",
              "      <td>5201924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previa</th>\n",
              "      <td>516064</td>\n",
              "      <td>6202812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prius</th>\n",
              "      <td>880324</td>\n",
              "      <td>11964872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proace</th>\n",
              "      <td>4689888</td>\n",
              "      <td>8624560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rav4</th>\n",
              "      <td>22204</td>\n",
              "      <td>13729088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>runner</th>\n",
              "      <td>1169504</td>\n",
              "      <td>8480548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sequoia</th>\n",
              "      <td>74020</td>\n",
              "      <td>26014432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sienna</th>\n",
              "      <td>2487048</td>\n",
              "      <td>22057720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>solara</th>\n",
              "      <td>11201244</td>\n",
              "      <td>11201244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>starlet</th>\n",
              "      <td>29608</td>\n",
              "      <td>1276092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>supra</th>\n",
              "      <td>1700252</td>\n",
              "      <td>10605892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tacoma</th>\n",
              "      <td>1599720</td>\n",
              "      <td>1600176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tundra</th>\n",
              "      <td>4737232</td>\n",
              "      <td>29170792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verso</th>\n",
              "      <td>13324</td>\n",
              "      <td>9959688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yaris</th>\n",
              "      <td>60</td>\n",
              "      <td>40007848</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price_NGN           \n",
              "                    min        max\n",
              "model                             \n",
              "auris                20   10701184\n",
              "avensis              16   24003552\n",
              "avensis-verso    600176    2701600\n",
              "aygo                 16    5458608\n",
              "camry             29608   16136180\n",
              "carina           200076    6796508\n",
              "celica             7400    6761008\n",
              "corolla            3700  296402752\n",
              "corolla-verso      7400  296698876\n",
              "fj-cruiser        29608   23212880\n",
              "gt86            6942648   14807120\n",
              "highlander      8002368    8734272\n",
              "hilux             66616   15598312\n",
              "iq              1460060    6785404\n",
              "land-cruiser         16   35005180\n",
              "mr2              799408    1998520\n",
              "paseo              1480    1980296\n",
              "picnic               16    5201924\n",
              "previa           516064    6202812\n",
              "prius            880324   11964872\n",
              "proace          4689888    8624560\n",
              "rav4              22204   13729088\n",
              "runner          1169504    8480548\n",
              "sequoia           74020   26014432\n",
              "sienna          2487048   22057720\n",
              "solara         11201244   11201244\n",
              "starlet           29608    1276092\n",
              "supra           1700252   10605892\n",
              "tacoma          1599720    1600176\n",
              "tundra          4737232   29170792\n",
              "verso             13324    9959688\n",
              "yaris                60   40007848"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Voe6SswGjYrs"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 500,000 NGN and 40,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WH-KSVst_mEb"
      },
      "source": [
        "toyota.loc[toyota['price_NGN'] < 500000, 'price_NGN'] = np.nan\n",
        "toyota.loc[toyota['price_NGN'] > 40000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUjfi4DQ_mBo",
        "outputId": "b0ac1b1a-443a-4c4f-8189-00291ddcc197"
      },
      "source": [
        "toyota['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      17519.00\n",
              "mean     3987724.12\n",
              "std      2951785.72\n",
              "min       500016.00\n",
              "25%      1924500.00\n",
              "50%      3377572.00\n",
              "75%      5376388.00\n",
              "max     35005180.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wArXO7iskZDk"
      },
      "source": [
        "##### Volvo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQEsWrk6_l9s",
        "outputId": "0a62e4aa-469d-44c0-9b91-fc9523708cf1"
      },
      "source": [
        "volvo['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       10913.00\n",
              "mean      5847652.44\n",
              "std       5763652.50\n",
              "min            16.00\n",
              "25%       1980224.00\n",
              "50%       4988188.00\n",
              "75%       8361244.00\n",
              "max     336171324.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "yutqhgfp_l6m",
        "outputId": "7bcaed0e-cdc4-48a6-e23c-4a1cb830a359"
      },
      "source": [
        "volvo.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>900400</td>\n",
              "      <td>12404132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>2360964</td>\n",
              "      <td>2360964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>444116</td>\n",
              "      <td>444116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>440</th>\n",
              "      <td>1644</td>\n",
              "      <td>26646928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>60000</td>\n",
              "      <td>20581644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>319628</td>\n",
              "      <td>5160384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>156016</td>\n",
              "      <td>7564472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>940</th>\n",
              "      <td>376048</td>\n",
              "      <td>6761008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>960</th>\n",
              "      <td>222056</td>\n",
              "      <td>8387640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c30</th>\n",
              "      <td>29712</td>\n",
              "      <td>6673144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c70</th>\n",
              "      <td>720060</td>\n",
              "      <td>21361584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s40</th>\n",
              "      <td>29608</td>\n",
              "      <td>5196772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s60</th>\n",
              "      <td>888</td>\n",
              "      <td>19661452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s70</th>\n",
              "      <td>222056</td>\n",
              "      <td>1140340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s80</th>\n",
              "      <td>25168</td>\n",
              "      <td>17501304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s90</th>\n",
              "      <td>1036256</td>\n",
              "      <td>1036256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v40</th>\n",
              "      <td>16</td>\n",
              "      <td>14118640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v50</th>\n",
              "      <td>744</td>\n",
              "      <td>40001080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v60</th>\n",
              "      <td>1184</td>\n",
              "      <td>21818432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v70</th>\n",
              "      <td>16</td>\n",
              "      <td>19281420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v90</th>\n",
              "      <td>960324</td>\n",
              "      <td>960324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xc60</th>\n",
              "      <td>4440</td>\n",
              "      <td>336171324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xc70</th>\n",
              "      <td>1480</td>\n",
              "      <td>21361956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xc90</th>\n",
              "      <td>16</td>\n",
              "      <td>34553664</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      price_NGN           \n",
              "            min        max\n",
              "model                     \n",
              "240      900400   12404132\n",
              "242     2360964    2360964\n",
              "360      444116     444116\n",
              "440        1644   26646928\n",
              "460       60000   20581644\n",
              "480      319628    5160384\n",
              "850      156016    7564472\n",
              "940      376048    6761008\n",
              "960      222056    8387640\n",
              "c30       29712    6673144\n",
              "c70      720060   21361584\n",
              "s40       29608    5196772\n",
              "s60         888   19661452\n",
              "s70      222056    1140340\n",
              "s80       25168   17501304\n",
              "s90     1036256    1036256\n",
              "v40          16   14118640\n",
              "v50         744   40001080\n",
              "v60        1184   21818432\n",
              "v70          16   19281420\n",
              "v90      960324     960324\n",
              "xc60       4440  336171324\n",
              "xc70       1480   21361956\n",
              "xc90         16   34553664"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yr_hfGKl-is1"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 200,000 NGN and 40,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bbDKfbw-htY"
      },
      "source": [
        "volvo.loc[volvo['price_NGN'] < 200000, 'price_NGN'] = np.nan\n",
        "volvo.loc[volvo['price_NGN'] > 40000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpjcSItJ-hqq",
        "outputId": "eeb93974-c9a9-4eb1-cecf-40c044fb57d8"
      },
      "source": [
        "volvo['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      10788.00\n",
              "mean     5871701.84\n",
              "std      4728937.70\n",
              "min       200028.00\n",
              "25%      2010610.00\n",
              "50%      5013752.00\n",
              "75%      8369344.00\n",
              "max     35565272.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uS0mebNE_Ahq"
      },
      "source": [
        "##### Hummer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vTnqo1V-hnT",
        "outputId": "4204a09a-56a4-436f-f681-b21d31c8f0a8"
      },
      "source": [
        "hummer['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        211.00\n",
              "mean    12435796.93\n",
              "std      9540711.58\n",
              "min           28.00\n",
              "25%      6789504.00\n",
              "50%      8761304.00\n",
              "75%     16098654.00\n",
              "max     72010660.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "Q7vb9Lbf-hj1",
        "outputId": "cedf7616-9549-4ed6-e92e-ed75320ef56a"
      },
      "source": [
        "hummer.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>h1</th>\n",
              "      <td>15575900</td>\n",
              "      <td>68390124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h2</th>\n",
              "      <td>28</td>\n",
              "      <td>39962960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h3</th>\n",
              "      <td>3403404</td>\n",
              "      <td>19965908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      price_NGN          \n",
              "            min       max\n",
              "model                    \n",
              "h1     15575900  68390124\n",
              "h2           28  39962960\n",
              "h3      3403404  19965908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFFG3E3S_kAs"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 3,400,000 NGN and 70,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOEhXFmC-hgs"
      },
      "source": [
        "hummer.loc[hummer['price_NGN'] < 3400000, 'price_NGN'] = np.nan\n",
        "hummer.loc[hummer['price_NGN'] > 70000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1pzkbn5-ha-",
        "outputId": "3fd53035-bf21-4342-9598-673a0446312d"
      },
      "source": [
        "hummer['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        208.00\n",
              "mean    12267822.52\n",
              "std      8585111.16\n",
              "min      3403404.00\n",
              "25%      6799656.00\n",
              "50%      8762274.00\n",
              "75%     16032281.00\n",
              "max     68390124.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebVHjXtXAHzV"
      },
      "source": [
        "##### Lexus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jR3Qjalm-hW3",
        "outputId": "fb858d56-1300-44a2-b667-ee878c078c44"
      },
      "source": [
        "lexus['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         879.00\n",
              "mean      8459541.01\n",
              "std      10859505.68\n",
              "min         14804.00\n",
              "25%       3598136.00\n",
              "50%       6402604.00\n",
              "75%      10761200.00\n",
              "max     268611356.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "wkrdmXdNALIG",
        "outputId": "e2ae8cd5-2d97-4b78-c760-d41da7dab409"
      },
      "source": [
        "lexus.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>1034788</td>\n",
              "      <td>19601452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>1169504</td>\n",
              "      <td>19851740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400</th>\n",
              "      <td>1000564</td>\n",
              "      <td>9202724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ct-200h</th>\n",
              "      <td>3080520</td>\n",
              "      <td>12707476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>es</th>\n",
              "      <td>1800800</td>\n",
              "      <td>16001776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gs</th>\n",
              "      <td>1280712</td>\n",
              "      <td>16469032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gs-300</th>\n",
              "      <td>600340</td>\n",
              "      <td>16809328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gs-450h</th>\n",
              "      <td>3600132</td>\n",
              "      <td>24379924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gx-470</th>\n",
              "      <td>5200888</td>\n",
              "      <td>5200888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is</th>\n",
              "      <td>2501540</td>\n",
              "      <td>14606484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is-200</th>\n",
              "      <td>800060</td>\n",
              "      <td>15329876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is-220d</th>\n",
              "      <td>2319852</td>\n",
              "      <td>18241348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is-250</th>\n",
              "      <td>2957808</td>\n",
              "      <td>13004812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is-f</th>\n",
              "      <td>10880800</td>\n",
              "      <td>13922056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ls</th>\n",
              "      <td>370096</td>\n",
              "      <td>11502592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ls-430</th>\n",
              "      <td>1400416</td>\n",
              "      <td>5906736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ls-460</th>\n",
              "      <td>5474464</td>\n",
              "      <td>20141852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ls-600h</th>\n",
              "      <td>6880252</td>\n",
              "      <td>14688380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ls-600h-l</th>\n",
              "      <td>8725388</td>\n",
              "      <td>14360532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lx-570</th>\n",
              "      <td>15002220</td>\n",
              "      <td>90006660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx</th>\n",
              "      <td>3700964</td>\n",
              "      <td>19027652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-300</th>\n",
              "      <td>14804</td>\n",
              "      <td>5980444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-330</th>\n",
              "      <td>1776460</td>\n",
              "      <td>1776460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-350</th>\n",
              "      <td>4800176</td>\n",
              "      <td>17988528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-400</th>\n",
              "      <td>3330868</td>\n",
              "      <td>7581124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-400h</th>\n",
              "      <td>2600384</td>\n",
              "      <td>7602412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-450h</th>\n",
              "      <td>2660296</td>\n",
              "      <td>24171576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rx-hybrid</th>\n",
              "      <td>10823596</td>\n",
              "      <td>21973708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sc</th>\n",
              "      <td>3141748</td>\n",
              "      <td>11202220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          price_NGN          \n",
              "                min       max\n",
              "model                        \n",
              "200         1034788  19601452\n",
              "300         1169504  19851740\n",
              "400         1000564   9202724\n",
              "ct-200h     3080520  12707476\n",
              "es          1800800  16001776\n",
              "gs          1280712  16469032\n",
              "gs-300       600340  16809328\n",
              "gs-450h     3600132  24379924\n",
              "gx-470      5200888   5200888\n",
              "is          2501540  14606484\n",
              "is-200       800060  15329876\n",
              "is-220d     2319852  18241348\n",
              "is-250      2957808  13004812\n",
              "is-f       10880800  13922056\n",
              "ls           370096  11502592\n",
              "ls-430      1400416   5906736\n",
              "ls-460      5474464  20141852\n",
              "ls-600h     6880252  14688380\n",
              "ls-600h-l   8725388  14360532\n",
              "lx-570     15002220  90006660\n",
              "rx          3700964  19027652\n",
              "rx-300        14804   5980444\n",
              "rx-330      1776460   1776460\n",
              "rx-350      4800176  17988528\n",
              "rx-400      3330868   7581124\n",
              "rx-400h     2600384   7602412\n",
              "rx-450h     2660296  24171576\n",
              "rx-hybrid  10823596  21973708\n",
              "sc          3141748  11202220"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjB2GQLXBdrs"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 370,000 NGN and 45,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-9mS5-hB6qK"
      },
      "source": [
        "lexus.loc[lexus['price_NGN'] < 370000, 'price_NGN'] = np.nan\n",
        "lexus.loc[lexus['price_NGN'] > 45000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqhYCekjALEM",
        "outputId": "f2f3e4c5-8632-49cd-e939-8b1af1a04f0b"
      },
      "source": [
        "lexus['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        876.00\n",
              "mean     8079113.85\n",
              "std      5758427.39\n",
              "min       370096.00\n",
              "25%      3598136.00\n",
              "50%      6401776.00\n",
              "75%     10758691.00\n",
              "max     42454344.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNmVLR8sCirI"
      },
      "source": [
        "##### Tesla"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8m0jJ0tALAf",
        "outputId": "4c3a66f4-1689-4eb3-b921-40251f7633cc"
      },
      "source": [
        "tesla['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         80.00\n",
              "mean    33722711.10\n",
              "std      8503656.15\n",
              "min         7400.00\n",
              "25%     28104621.00\n",
              "50%     31158032.00\n",
              "75%     40806040.00\n",
              "max     51405716.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "sMiRoXm9AK9y",
        "outputId": "004de367-d1f8-4dfb-e430-31e2406c2f19"
      },
      "source": [
        "tesla.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model-s</th>\n",
              "      <td>23142220</td>\n",
              "      <td>51405716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model-x</th>\n",
              "      <td>35531828</td>\n",
              "      <td>35531828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roadster</th>\n",
              "      <td>22002444</td>\n",
              "      <td>22002444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         price_NGN          \n",
              "               min       max\n",
              "model                       \n",
              "model-s   23142220  51405716\n",
              "model-x   35531828  35531828\n",
              "roadster  22002444  22002444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5M0MAG4C7J-"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 23,000,000 NGN and 54,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlReL5dKAK53"
      },
      "source": [
        "tesla.loc[tesla['price_NGN'] < 23000000, 'price_NGN'] = np.nan\n",
        "tesla.loc[tesla['price_NGN'] > 54000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0acy-yN7ypX",
        "outputId": "a92c8fb1-7d8b-4e3d-a546-f7c5d4f3100f"
      },
      "source": [
        "tesla['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         78.00\n",
              "mean    34305218.51\n",
              "std      7569683.08\n",
              "min     23142220.00\n",
              "25%     28105920.00\n",
              "50%     31394072.00\n",
              "75%     40812080.00\n",
              "max     51405716.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFMAxPyMDjX7"
      },
      "source": [
        "##### Lotus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDTe22jEDfIu",
        "outputId": "45307d79-4c83-497e-c723-acd57a35d4fe"
      },
      "source": [
        "lotus['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        110.00\n",
              "mean    15983804.69\n",
              "std      7102119.80\n",
              "min      2201228.00\n",
              "25%     10730084.00\n",
              "50%     15125240.00\n",
              "75%     19978839.00\n",
              "max     35125404.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "umeuJy3HDXN4",
        "outputId": "7c65c656-4b8f-48c3-c469-d1b81ee6ac5a"
      },
      "source": [
        "lotus.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>elise</th>\n",
              "      <td>2201228</td>\n",
              "      <td>22404292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>esprit</th>\n",
              "      <td>9581420</td>\n",
              "      <td>22006512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       price_NGN          \n",
              "             min       max\n",
              "model                     \n",
              "elise    2201228  22404292\n",
              "esprit   9581420  22006512"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A23v1ZfaD1tj"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 2,200,000 NGN and 23,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnrCDCGxDXmr"
      },
      "source": [
        "lotus.loc[lotus['price_NGN'] < 2200000, 'price_NGN'] = np.nan\n",
        "lotus.loc[lotus['price_NGN'] > 23000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNst8g7iDXyA",
        "outputId": "31076c25-5cb0-4e52-ef63-8d0a766ac2d2"
      },
      "source": [
        "lotus['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count         96.00\n",
              "mean    14143110.79\n",
              "std      5318552.18\n",
              "min      2201228.00\n",
              "25%     10497793.00\n",
              "50%     13677328.00\n",
              "75%     19931828.00\n",
              "max     22826972.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IH3RNvCUET4g"
      },
      "source": [
        "##### Smart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4S3Hbj2DX7j",
        "outputId": "5a4df3c1-2f3d-4586-9d8a-dd0d8280a7de"
      },
      "source": [
        "smart['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       11052.00\n",
              "mean      2668590.56\n",
              "std       3199688.70\n",
              "min          5920.00\n",
              "25%       1520389.00\n",
              "50%       2520280.00\n",
              "75%       3662428.00\n",
              "max     295958624.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "ObGY_l1fDYFW",
        "outputId": "998e414f-f36d-4096-f00d-13fe30467b68"
      },
      "source": [
        "smart.groupby('model').agg({'price_NGN': ['min', 'max']})"
      ],
      "execution_count": null,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">price_NGN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>forfour</th>\n",
              "      <td>29680</td>\n",
              "      <td>7388736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fortwo</th>\n",
              "      <td>7400</td>\n",
              "      <td>80011844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>micro-compact</th>\n",
              "      <td>515172</td>\n",
              "      <td>962252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              price_NGN          \n",
              "                    min       max\n",
              "model                            \n",
              "forfour           29680   7388736\n",
              "fortwo             7400  80011844\n",
              "micro-compact    515172    962252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCAFWY-xElhY"
      },
      "source": [
        "Leading with the pricing aggregates in the table above, the floor - ceiling pricing is pegged between 500,000 NGN and 10,000,000 NGN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvMkulQgDYex"
      },
      "source": [
        "smart.loc[smart['price_NGN'] < 500000, 'price_NGN'] = np.nan\n",
        "smart.loc[smart['price_NGN'] > 10000000, 'price_NGN'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jE1lD4KcDY7c",
        "outputId": "56c0ccc0-5b0d-44ec-8418-78988005e6bb"
      },
      "source": [
        "smart['price_NGN'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     10831.00\n",
              "mean    2677753.05\n",
              "std     1349491.21\n",
              "min      500044.00\n",
              "25%     1580236.00\n",
              "50%     2557572.00\n",
              "75%     3681212.00\n",
              "max     9782280.00\n",
              "Name: price_NGN, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hjxgpt9MSu-"
      },
      "source": [
        "# Handling Missing Values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzjPEsycMk4D"
      },
      "source": [
        "Missing values shall be imputed using the KNNImputer which uses a similarity metric for filling in missing values.\n",
        "\n",
        "As KNN does not work on categorical data, the categorical data, except the model variable, will be encoded. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYtmgLJylAfi"
      },
      "source": [
        "imputer = KNNImputer(n_neighbors = 2, weights = 'distance', metric = 'nan_euclidean')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4Ht7p6shnO2"
      },
      "source": [
        "##### Audi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CkUnJ4pZeJ8w"
      },
      "source": [
        "audi_coded = [pd.get_dummies(audi['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(audi['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(audi['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "audi_coded = pd.concat(audi_coded, axis = 1)\n",
        "audi = pd.concat([audi, audi_coded], axis = 1)\n",
        "audi = audi.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "audi_num = audi.select_dtypes(exclude = ['object'])\n",
        "audi_cat = audi.select_dtypes(exclude = ['number'])\n",
        "audi_num = imputer.fit_transform(audi_num)\n",
        "audi_num = pd.DataFrame(data = audi_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "audi = pd.concat([audi_cat, audi_num], axis = 1)\n",
        "audi = audi[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxsitsLlhusZ"
      },
      "source": [
        "##### Bentley"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "F2bkzsVLhxRk"
      },
      "source": [
        "bentley_coded = [pd.get_dummies(bentley['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(bentley['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(bentley['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "bentley_coded = pd.concat(bentley_coded, axis = 1)\n",
        "bentley = pd.concat([bentley, bentley_coded], axis = 1)\n",
        "bentley = bentley.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "bentley_num = bentley.select_dtypes(exclude = ['object'])\n",
        "bentley_cat = bentley.select_dtypes(exclude = ['number'])\n",
        "bentley_num = imputer.fit_transform(bentley_num)\n",
        "bentley_num = pd.DataFrame(data = bentley_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_gasoline'])\n",
        "bentley = pd.concat([bentley_cat, bentley_num], axis = 1)\n",
        "bentley = bentley[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', \n",
        "                   'seat_count', 'body_type_compact', 'transmission_auto', 'transmission_man', \n",
        "                   'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9zbULgHiZ9R"
      },
      "source": [
        "##### BMW"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "G9dwFvEpeJxl"
      },
      "source": [
        "bmw_coded = [pd.get_dummies(bmw['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(bmw['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(bmw['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "bmw_coded = pd.concat(bmw_coded, axis = 1)\n",
        "bmw = pd.concat([bmw, bmw_coded], axis = 1)\n",
        "bmw = bmw.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "bmw_num = bmw.select_dtypes(exclude = ['object'])\n",
        "bmw_cat = bmw.select_dtypes(exclude = ['number'])\n",
        "bmw_num = imputer.fit_transform(bmw_num)\n",
        "bmw_num = pd.DataFrame(data = bmw_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                        'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                        'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "bmw = pd.concat([bmw_cat, bmw_num], axis = 1)\n",
        "bmw = bmw[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "           'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "           'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQJZmilojFnQ"
      },
      "source": [
        "##### Chevrolet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "L1ubFOL-eJtU"
      },
      "source": [
        "chevrolet_coded = [pd.get_dummies(chevrolet['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(chevrolet['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(chevrolet['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "chevrolet_coded = pd.concat(chevrolet_coded, axis = 1)\n",
        "chevrolet = pd.concat([chevrolet, chevrolet_coded], axis = 1)\n",
        "chevrolet = chevrolet.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "chevrolet_num = chevrolet.select_dtypes(exclude = ['object'])\n",
        "chevrolet_cat = chevrolet.select_dtypes(exclude = ['number'])\n",
        "chevrolet_num = imputer.fit_transform(chevrolet_num)\n",
        "chevrolet_num = pd.DataFrame(data = chevrolet_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                    'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                    'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                                    'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "chevrolet = pd.concat([chevrolet_cat, chevrolet_num], axis = 1)\n",
        "chevrolet = chevrolet[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', \n",
        "                       'seat_count', 'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                       'fuel_type_diesel', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ql9c7mSNj3Hu"
      },
      "source": [
        "##### Chrysler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "LNBYxznWkAQ_"
      },
      "source": [
        "chrysler_coded = [pd.get_dummies(chrysler['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(chrysler['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(chrysler['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "chrysler_coded = pd.concat(chrysler_coded, axis = 1)\n",
        "chrysler = pd.concat([chrysler, chrysler_coded], axis = 1)\n",
        "chrysler = chrysler.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "chrysler_num = chrysler.select_dtypes(exclude = ['object'])\n",
        "chrysler_cat = chrysler.select_dtypes(exclude = ['number'])\n",
        "chrysler_num = imputer.fit_transform(chrysler_num)\n",
        "chrysler_num = pd.DataFrame(data = chrysler_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                  'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                  'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                                  'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "chrysler = pd.concat([chrysler_cat, chrysler_num], axis = 1)\n",
        "chrysler = chrysler[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                     'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                     'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uIkPqAUkiJW"
      },
      "source": [
        "##### Citroen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7Z-eFtCKklzT"
      },
      "source": [
        "citroen_coded = [pd.get_dummies(citroen['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(citroen['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(citroen['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "citroen_coded = pd.concat(citroen_coded, axis = 1)\n",
        "citroen = pd.concat([citroen, citroen_coded], axis = 1)\n",
        "citroen = citroen.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "citroen_num = citroen.select_dtypes(exclude = ['object'])\n",
        "citroen_cat = citroen.select_dtypes(exclude = ['number'])\n",
        "citroen_num = imputer.fit_transform(citroen_num)\n",
        "citroen_num = pd.DataFrame(data = citroen_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                                'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "citroen = pd.concat([citroen_cat, citroen_num], axis = 1)\n",
        "citroen = citroen[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', \n",
        "                   'seat_count', 'body_type_compact', 'body_type_van', 'transmission_auto', \n",
        "                   'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtLUj6SelNl4"
      },
      "source": [
        "##### Dodge"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "bGUZwzlblQQj"
      },
      "source": [
        "dodge_coded = [pd.get_dummies(dodge['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(dodge['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(dodge['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "dodge_coded = pd.concat(dodge_coded, axis = 1)\n",
        "dodge = pd.concat([dodge, dodge_coded], axis = 1)\n",
        "dodge = dodge.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "dodge_num = dodge.select_dtypes(exclude = ['object'])\n",
        "dodge_cat = dodge.select_dtypes(exclude = ['number'])\n",
        "dodge_num = imputer.fit_transform(dodge_num)\n",
        "dodge_num = pd.DataFrame(data = dodge_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                            'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "dodge = pd.concat([dodge_cat, dodge_num], axis = 1)\n",
        "dodge = dodge[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "               'fuel_type_diesel', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4u_zly2l7hV"
      },
      "source": [
        "##### Fiat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QU8px_0ol9bj"
      },
      "source": [
        "fiat_coded = [pd.get_dummies(fiat['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(fiat['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(fiat['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "fiat_coded = pd.concat(fiat_coded, axis = 1)\n",
        "fiat = pd.concat([fiat, fiat_coded], axis = 1)\n",
        "fiat = fiat.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "fiat_num = fiat.select_dtypes(exclude = ['object'])\n",
        "fiat_cat = fiat.select_dtypes(exclude = ['number'])\n",
        "fiat_num = imputer.fit_transform(fiat_num)\n",
        "fiat_num = pd.DataFrame(data = fiat_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                          'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "fiat = pd.concat([fiat_cat, fiat_num], axis = 1)\n",
        "fiat = fiat[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAWMijugminX"
      },
      "source": [
        "##### Ford"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "URYmEA2dmkwa"
      },
      "source": [
        "ford_coded = [pd.get_dummies(ford['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(ford['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(ford['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "ford_coded = pd.concat(ford_coded, axis = 1)\n",
        "ford = pd.concat([ford, ford_coded], axis = 1)\n",
        "ford = ford.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "ford_num = ford.select_dtypes(exclude = ['object'])\n",
        "ford_cat = ford.select_dtypes(exclude = ['number'])\n",
        "ford_num = imputer.fit_transform(ford_num)\n",
        "ford_num = pd.DataFrame(data = ford_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                          'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "ford = pd.concat([ford_cat, ford_num], axis = 1)\n",
        "ford = ford[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7agFE2MnHWv"
      },
      "source": [
        "##### Honda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "83FAqLhZkAM-"
      },
      "source": [
        "honda_coded = [pd.get_dummies(honda['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(honda['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(honda['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "honda_coded = pd.concat(honda_coded, axis = 1)\n",
        "honda = pd.concat([honda, honda_coded], axis = 1)\n",
        "honda = honda.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "honda_num = honda.select_dtypes(exclude = ['object'])\n",
        "honda_cat = honda.select_dtypes(exclude = ['number'])\n",
        "honda_num = imputer.fit_transform(honda_num)\n",
        "honda_num = pd.DataFrame(data = honda_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                            'fuel_type_gasoline'])\n",
        "honda = pd.concat([honda_cat, honda_num], axis = 1)\n",
        "honda = honda[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline', \n",
        "               'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QfMuY1eOntbs"
      },
      "source": [
        "##### Hyundai"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "-NQ2sZpInwlE"
      },
      "source": [
        "hyundai_coded = [pd.get_dummies(hyundai['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(hyundai['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(hyundai['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "hyundai_coded = pd.concat(hyundai_coded, axis = 1)\n",
        "hyundai = pd.concat([hyundai, hyundai_coded], axis = 1)\n",
        "hyundai = hyundai.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "hyundai_num = hyundai.select_dtypes(exclude = ['object'])\n",
        "hyundai_cat = hyundai.select_dtypes(exclude = ['number'])\n",
        "hyundai_num = imputer.fit_transform(hyundai_num)\n",
        "hyundai_num = pd.DataFrame(data = hyundai_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                                'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "hyundai = pd.concat([hyundai_cat, hyundai_num], axis = 1)\n",
        "hyundai = hyundai[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                   'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                   'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDueJqVToZZ4"
      },
      "source": [
        "##### Isuzu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Z8Eq-kr-obwE"
      },
      "source": [
        "isuzu_coded = [pd.get_dummies(isuzu['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(isuzu['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(isuzu['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "isuzu_coded = pd.concat(isuzu_coded, axis = 1)\n",
        "isuzu = pd.concat([isuzu, isuzu_coded], axis = 1)\n",
        "isuzu = isuzu.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "isuzu_num = isuzu.select_dtypes(exclude = ['object'])\n",
        "isuzu_cat = isuzu.select_dtypes(exclude = ['number'])\n",
        "isuzu_num = imputer.fit_transform(isuzu_num)\n",
        "isuzu_num = pd.DataFrame(data = isuzu_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                            'fuel_type_gasoline'])\n",
        "isuzu = pd.concat([isuzu_cat, isuzu_num], axis = 1)\n",
        "isuzu = isuzu[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCkARr4rpCPY"
      },
      "source": [
        "##### Jaguar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CjAeJIiVkAJY"
      },
      "source": [
        "jaguar_coded = [pd.get_dummies(jaguar['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(jaguar['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(jaguar['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "jaguar_coded = pd.concat(jaguar_coded, axis = 1)\n",
        "jaguar = pd.concat([jaguar, jaguar_coded], axis = 1)\n",
        "jaguar = jaguar.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "jaguar_num = jaguar.select_dtypes(exclude = ['object'])\n",
        "jaguar_cat = jaguar.select_dtypes(exclude = ['number'])\n",
        "jaguar_num = imputer.fit_transform(jaguar_num)\n",
        "jaguar_num = pd.DataFrame(data = jaguar_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                              'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                              'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                              'fuel_type_gasoline'])\n",
        "jaguar = pd.concat([jaguar_cat, jaguar_num], axis = 1)\n",
        "jaguar = jaguar[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XNawwShptjS"
      },
      "source": [
        "##### Jeep"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "yBjNK7lOpwCP"
      },
      "source": [
        "jeep_coded = [pd.get_dummies(jeep['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(jeep['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(jeep['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "jeep_coded = pd.concat(jeep_coded, axis = 1)\n",
        "jeep = pd.concat([jeep, jeep_coded], axis = 1)\n",
        "jeep = jeep.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "jeep_num = jeep.select_dtypes(exclude = ['object'])\n",
        "jeep_cat = jeep.select_dtypes(exclude = ['number'])\n",
        "jeep_num = imputer.fit_transform(jeep_num)\n",
        "jeep_num = pd.DataFrame(data = jeep_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                          'fuel_type_gasoline'])\n",
        "jeep = pd.concat([jeep_cat, jeep_num], axis = 1)\n",
        "jeep = jeep[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EYDGwUUqhgb"
      },
      "source": [
        "##### Kia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "u9cMe6uEqlAi"
      },
      "source": [
        "kia_coded = [pd.get_dummies(kia['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(kia['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(kia['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "kia_coded = pd.concat(kia_coded, axis = 1)\n",
        "kia = pd.concat([kia, kia_coded], axis = 1)\n",
        "kia = kia.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "kia_num = kia.select_dtypes(exclude = ['object'])\n",
        "kia_cat = kia.select_dtypes(exclude = ['number'])\n",
        "kia_num = imputer.fit_transform(kia_num)\n",
        "kia_num = pd.DataFrame(data = kia_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                        'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                        'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "kia = pd.concat([kia_cat, kia_num], axis = 1)\n",
        "kia = kia[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "           'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "           'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0-pnkNIrIEs"
      },
      "source": [
        "##### Lamborghini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "UCguiysgrKm4"
      },
      "source": [
        "lamborghini_coded = [pd.get_dummies(lamborghini['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(lamborghini['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(lamborghini['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "lamborghini_coded = pd.concat(lamborghini_coded, axis = 1)\n",
        "lamborghini = pd.concat([lamborghini, lamborghini_coded], axis = 1)\n",
        "lamborghini = lamborghini.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "lamborghini_num = lamborghini.select_dtypes(exclude = ['object'])\n",
        "lamborghini_cat = lamborghini.select_dtypes(exclude = ['number'])\n",
        "lamborghini_num = imputer.fit_transform(lamborghini_num)\n",
        "lamborghini_num = pd.DataFrame(data = lamborghini_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', \n",
        "                                                                        'engine_power', 'door_count', 'seat_count', 'price_NGN', \n",
        "                                                                        'transmission_auto', 'transmission_man', 'fuel_type_gasoline'])\n",
        "lamborghini = pd.concat([lamborghini_cat,lamborghini_num], axis = 1)\n",
        "lamborghini = lamborghini[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', \n",
        "                           'seat_count', 'transmission_auto', 'transmission_man', \n",
        "                           'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5u7H07XMruK9"
      },
      "source": [
        "##### Lancia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7zfDX9A5rxKu"
      },
      "source": [
        "lancia_coded = [pd.get_dummies(lancia['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(lancia['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(lancia['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "lancia_coded = pd.concat(lancia_coded, axis = 1)\n",
        "lancia = pd.concat([lancia, lancia_coded], axis = 1)\n",
        "lancia = lancia.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "lancia_num = lancia.select_dtypes(exclude = ['object'])\n",
        "lancia_cat = lancia.select_dtypes(exclude = ['number'])\n",
        "lancia_num = imputer.fit_transform(lancia_num)\n",
        "lancia_num = pd.DataFrame(data = lancia_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                          'fuel_type_gasoline'])\n",
        "lancia = pd.concat([lancia_cat, lancia_num], axis = 1)\n",
        "lancia = lancia[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aE8HtzlUsXSS"
      },
      "source": [
        "##### Maserati"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "1gGGsRxcsarz"
      },
      "source": [
        "maserati_coded = [pd.get_dummies(maserati['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(maserati['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(maserati['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "maserati_coded = pd.concat(maserati_coded, axis = 1)\n",
        "maserati = pd.concat([maserati, maserati_coded], axis = 1)\n",
        "maserati = maserati.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "maserati_num = maserati.select_dtypes(exclude = ['object'])\n",
        "maserati_cat = maserati.select_dtypes(exclude = ['number'])\n",
        "maserati_num = imputer.fit_transform(maserati_num)\n",
        "maserati_num = pd.DataFrame(data = maserati_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                  'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                  'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                                  'fuel_type_gasoline'])\n",
        "maserati = pd.concat([maserati_cat, maserati_num], axis = 1)\n",
        "maserati = maserati[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                     'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                     'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJA77hHNs-Ng"
      },
      "source": [
        "##### Mazda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "J2KNfmUatA8Q"
      },
      "source": [
        "mazda_coded = [pd.get_dummies(mazda['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(mazda['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(mazda['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "mazda_coded = pd.concat(mazda_coded, axis = 1)\n",
        "mazda = pd.concat([mazda, mazda_coded], axis = 1)\n",
        "mazda = mazda.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "mazda_num = mazda.select_dtypes(exclude = ['object'])\n",
        "mazda_cat = mazda.select_dtypes(exclude = ['number'])\n",
        "mazda_num = imputer.fit_transform(mazda_num)\n",
        "mazda_num = pd.DataFrame(data = mazda_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                            'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "mazda = pd.concat([mazda_cat, mazda_num], axis = 1)\n",
        "mazda = mazda[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cyDhnBDtpt7"
      },
      "source": [
        "##### Mercedes-Benz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3PkVOeq_ttSh"
      },
      "source": [
        "mercedes_coded = [pd.get_dummies(mercedes['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "                  pd.get_dummies(mercedes['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "                  pd.get_dummies(mercedes['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "mercedes_coded = pd.concat(mercedes_coded, axis = 1)\n",
        "mercedes = pd.concat([mercedes, mercedes_coded], axis = 1)\n",
        "mercedes = mercedes.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "mercedes_num = mercedes.select_dtypes(exclude = ['object'])\n",
        "mercedes_cat = mercedes.select_dtypes(exclude = ['number'])\n",
        "mercedes_num = imputer.fit_transform(mercedes_num)\n",
        "mercedes_num = pd.DataFrame(data = mercedes_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                  'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                  'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                                  'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "\n",
        "mercedes = pd.concat([mercedes_cat, mercedes_num], axis = 1)\n",
        "mercedes = mercedes[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                     'door_count', 'seat_count', 'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                     'fuel_type_diesel', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-ugZnjfuSK5"
      },
      "source": [
        "##### Mini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "he3yeHunuUam"
      },
      "source": [
        "mini_coded = [pd.get_dummies(mini['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "              pd.get_dummies(mini['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "              pd.get_dummies(mini['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "mini_coded = pd.concat(mini_coded, axis = 1)\n",
        "mini = pd.concat([mini, mini_coded], axis = 1)\n",
        "mini = mini.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "mini_num = mini.select_dtypes(exclude = ['object'])\n",
        "mini_cat = mini.select_dtypes(exclude = ['number'])\n",
        "mini_num = imputer.fit_transform(mini_num)\n",
        "mini_num = pd.DataFrame(data = mini_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "mini = pd.concat([mini_cat, mini_num], axis = 1)\n",
        "mini = mini[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Jiy192hvAm8"
      },
      "source": [
        "##### Mitsubishi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "rH9X1YQ8vECk"
      },
      "source": [
        "mitsubishi_coded = [pd.get_dummies(mitsubishi['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(mitsubishi['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(mitsubishi['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "mitsubishi_coded = pd.concat(mitsubishi_coded, axis = 1)\n",
        "mitsubishi = pd.concat([mitsubishi, mitsubishi_coded], axis = 1)\n",
        "mitsubishi = mitsubishi.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "mitsubishi_num = mitsubishi.select_dtypes(exclude = ['object'])\n",
        "mitsubishi_cat = mitsubishi.select_dtypes(exclude = ['number'])\n",
        "mitsubishi_num = imputer.fit_transform(mitsubishi_num)\n",
        "mitsubishi_num = pd.DataFrame(data = mitsubishi_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', \n",
        "                                                                      'engine_power', 'door_count', 'seat_count', 'price_NGN', \n",
        "                                                                      'body_type_compact', 'transmission_auto', 'transmission_man', \n",
        "                                                                      'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "\n",
        "mitsubishi = pd.concat([mitsubishi_cat, mitsubishi_num], axis = 1)\n",
        "mitsubishi = mitsubishi[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', \n",
        "                         'seat_count', 'body_type_compact', 'transmission_auto', 'transmission_man', \n",
        "                         'fuel_type_diesel', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lfon1i9jvrOr"
      },
      "source": [
        "##### Nissan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "yWrHB9lVvtz7"
      },
      "source": [
        "nissan_coded = [pd.get_dummies(nissan['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(nissan['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(nissan['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "nissan_coded = pd.concat(nissan_coded, axis = 1)\n",
        "nissan = pd.concat([nissan, nissan_coded], axis = 1)\n",
        "nissan = nissan.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "nissan_num = nissan.select_dtypes(exclude = ['object'])\n",
        "nissan_cat = nissan.select_dtypes(exclude = ['number'])\n",
        "nissan_num = imputer.fit_transform(nissan_num)\n",
        "nissan_num = pd.DataFrame(data = nissan_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                              'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                              'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "nissan = pd.concat([nissan_cat, nissan_num], axis = 1)\n",
        "nissan = nissan[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EewTbuCwXST"
      },
      "source": [
        "##### Opel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6ABXhrsWkAGB"
      },
      "source": [
        "opel_coded = [pd.get_dummies(opel['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(opel['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(opel['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "opel_coded = pd.concat(opel_coded, axis = 1)\n",
        "opel = pd.concat([opel, opel_coded], axis = 1)\n",
        "opel = opel.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "opel_num = opel.select_dtypes(exclude = ['object'])\n",
        "opel_cat = opel.select_dtypes(exclude = ['number'])\n",
        "opel_num = imputer.fit_transform(opel_num)\n",
        "opel_num = pd.DataFrame(data = opel_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                          'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "opel = pd.concat([opel_cat, opel_num], axis = 1)\n",
        "opel = opel[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEaJ3y8ExFC4"
      },
      "source": [
        "##### Porsche"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "h_z_cvHXxH_L"
      },
      "source": [
        "porsche_coded = [pd.get_dummies(porsche['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(porsche['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(porsche['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "porsche_coded = pd.concat(porsche_coded, axis = 1)\n",
        "porsche = pd.concat([porsche, porsche_coded], axis = 1)\n",
        "porsche = porsche.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "porsche_num = porsche.select_dtypes(exclude = ['object'])\n",
        "porsche_cat = porsche.select_dtypes(exclude = ['number'])\n",
        "porsche_num = imputer.fit_transform(porsche_num)\n",
        "porsche_num = pd.DataFrame(data = porsche_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                                'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                                'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                                'fuel_type_gasoline'])\n",
        "porsche = pd.concat([porsche_cat, porsche_num], axis = 1)\n",
        "porsche = porsche[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                   'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                   'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kV3DzpZ3xw7P"
      },
      "source": [
        "##### Rover"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "WqXM-i8pxzkz"
      },
      "source": [
        "rover_coded = [pd.get_dummies(rover['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(rover['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(rover['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "rover_coded = pd.concat(rover_coded, axis = 1)\n",
        "rover = pd.concat([rover, rover_coded], axis = 1)\n",
        "rover = rover.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "rover_num = rover.select_dtypes(exclude = ['object'])\n",
        "rover_cat = rover.select_dtypes(exclude = ['number'])\n",
        "rover_num = imputer.fit_transform(rover_num)\n",
        "rover_num = pd.DataFrame(data = rover_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "rover = pd.concat([rover_cat, rover_num], axis = 1)\n",
        "rover = rover[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39DI1HzuyZtr"
      },
      "source": [
        "##### Seat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "urMWgkBJycZi"
      },
      "source": [
        "seat_coded = [pd.get_dummies(seat['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(seat['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(seat['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "seat_coded = pd.concat(seat_coded, axis = 1)\n",
        "seat = pd.concat([seat, seat_coded], axis = 1)\n",
        "seat = seat.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "seat_num = seat.select_dtypes(exclude = ['object'])\n",
        "seat_cat = seat.select_dtypes(exclude = ['number'])\n",
        "seat_num = imputer.fit_transform(seat_num)\n",
        "seat_num = pd.DataFrame(data = seat_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                          'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "seat = pd.concat([seat_cat, seat_num], axis = 1)\n",
        "seat = seat[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "             'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "             'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uClx_XBGzK8C"
      },
      "source": [
        "##### Skoda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "tcqC9ERHzN2s"
      },
      "source": [
        "skoda_coded = [pd.get_dummies(skoda['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(skoda['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(skoda['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "skoda_coded = pd.concat(skoda_coded, axis = 1)\n",
        "skoda = pd.concat([skoda, skoda_coded], axis = 1)\n",
        "skoda = skoda.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "skoda_num = skoda.select_dtypes(exclude = ['object'])\n",
        "skoda_cat = skoda.select_dtypes(exclude = ['number'])\n",
        "skoda_num = imputer.fit_transform(skoda_num)\n",
        "skoda_num = pd.DataFrame(data = skoda_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'body_type_van', 'transmission_auto', 'transmission_man', \n",
        "                                                            'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "skoda = pd.concat([skoda_cat, skoda_num], axis = 1)\n",
        "skoda = skoda[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'body_type_van', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7zWe_N7zzz_"
      },
      "source": [
        "##### Subaru"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XiULoyMxz2lm"
      },
      "source": [
        "subaru_coded = [pd.get_dummies(subaru['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(subaru['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(subaru['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "subaru_coded = pd.concat(subaru_coded, axis = 1)\n",
        "subaru = pd.concat([subaru, subaru_coded], axis = 1)\n",
        "subaru = subaru.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "subaru_num = subaru.select_dtypes(exclude = ['object'])\n",
        "subaru_cat = subaru.select_dtypes(exclude = ['number'])\n",
        "subaru_num = imputer.fit_transform(subaru_num)\n",
        "subaru_num = pd.DataFrame(data = subaru_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                              'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                              'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                              'fuel_type_gasoline'])\n",
        "subaru = pd.concat([subaru_cat, subaru_num], axis = 1)\n",
        "subaru = subaru[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwAbAorC0aop"
      },
      "source": [
        "##### Suzuki"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qiVDkko_0dx8"
      },
      "source": [
        "suzuki_coded = [pd.get_dummies(suzuki['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(suzuki['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(suzuki['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "suzuki_coded = pd.concat(suzuki_coded, axis = 1)\n",
        "suzuki = pd.concat([suzuki, suzuki_coded], axis = 1)\n",
        "suzuki = suzuki.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "suzuki_num = suzuki.select_dtypes(exclude = ['object'])\n",
        "suzuki_cat = suzuki.select_dtypes(exclude = ['number'])\n",
        "suzuki_num = imputer.fit_transform(suzuki_num)\n",
        "suzuki_num = pd.DataFrame(data = suzuki_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                          'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                          'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                          'fuel_type_gasoline'])\n",
        "suzuki = pd.concat([suzuki_cat, suzuki_num], axis = 1)\n",
        "suzuki = subaru[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ax7kJ61N1A8r"
      },
      "source": [
        "##### Toyota"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "duEk8Xgd1EtR"
      },
      "source": [
        "toyota_coded = [pd.get_dummies(toyota['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(toyota['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "           pd.get_dummies(toyota['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "toyota_coded = pd.concat(toyota_coded, axis = 1)\n",
        "toyota = pd.concat([toyota, toyota_coded], axis = 1)\n",
        "toyota = toyota.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "toyota_num = toyota.select_dtypes(exclude = ['object'])\n",
        "toyota_cat = toyota.select_dtypes(exclude = ['number'])\n",
        "toyota_num = imputer.fit_transform(toyota_num)\n",
        "toyota_num = pd.DataFrame(data = toyota_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                              'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                              'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                              'fuel_type_gasoline'])\n",
        "toyota = pd.concat([toyota_cat, toyota_num], axis = 1)\n",
        "toyota = toyota[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLbMKvVQ1rQg"
      },
      "source": [
        "##### Volvo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "n10HkaQN1vkZ"
      },
      "source": [
        "volvo_coded = [pd.get_dummies(volvo['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(volvo['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(volvo['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "volvo_coded = pd.concat(volvo_coded, axis = 1)\n",
        "volvo = pd.concat([volvo, volvo_coded], axis = 1)\n",
        "volvo = volvo.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "volvo_num = volvo.select_dtypes(exclude = ['object'])\n",
        "volvo_cat = volvo.select_dtypes(exclude = ['number'])\n",
        "volvo_num = imputer.fit_transform(volvo_num)\n",
        "volvo_num = pd.DataFrame(data = volvo_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                                                            'fuel_type_gasoline'])\n",
        "volvo = pd.concat([volvo_cat, volvo_num], axis = 1)\n",
        "volvo = volvo[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6yA23X42Woo"
      },
      "source": [
        "##### Hummer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "vGXim95R2aPL"
      },
      "source": [
        "hummer_coded = [pd.get_dummies(hummer['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(hummer['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(hummer['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "hummer_coded = pd.concat(hummer_coded, axis = 1)\n",
        "hummer = pd.concat([hummer, hummer_coded], axis = 1)\n",
        "hummer = hummer.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "hummer_num = hummer.select_dtypes(exclude = ['object'])\n",
        "hummer_cat = hummer.select_dtypes(exclude = ['number'])\n",
        "hummer_num = imputer.fit_transform(hummer_num)\n",
        "hummer_num = pd.DataFrame(data = hummer_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                              'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                              'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "hummer = pd.concat([hummer_cat, hummer_num], axis = 1)\n",
        "hummer = hummer[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "                 'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "                 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA3H4AoD25it"
      },
      "source": [
        "##### Lexus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "P_F5ag4N28o1"
      },
      "source": [
        "lexus_coded = [pd.get_dummies(lexus['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "           pd.get_dummies(lexus['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "           pd.get_dummies(lexus['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "lexus_coded = pd.concat(lexus_coded, axis = 1)\n",
        "lexus = pd.concat([lexus, lexus_coded], axis = 1)\n",
        "lexus = lexus.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "lexus_num = lexus.select_dtypes(exclude = ['object'])\n",
        "lexus_cat = lexus.select_dtypes(exclude = ['number'])\n",
        "lexus_num = imputer.fit_transform(lexus_num)\n",
        "lexus_num = pd.DataFrame(data = lexus_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "lexus = pd.concat([lexus_cat, lexus_num], axis = 1)\n",
        "lexus = lexus[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgLZjA6_4Oa0"
      },
      "source": [
        "##### Lotus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qx6LNYbr4RB9"
      },
      "source": [
        "lotus_coded = [pd.get_dummies(lotus['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "               pd.get_dummies(lotus['transmission'], prefix = 'transmission', prefix_sep = '_'),\n",
        "               pd.get_dummies(lotus['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "lotus_coded = pd.concat(lotus_coded, axis = 1)\n",
        "lotus = pd.concat([lotus, lotus_coded], axis = 1)\n",
        "lotus = lotus.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "lotus_num = lotus.select_dtypes(exclude = ['object'])\n",
        "lotus_cat = lotus.select_dtypes(exclude = ['number'])\n",
        "lotus_num = imputer.fit_transform(lotus_num)\n",
        "lotus_num = pd.DataFrame(data = lotus_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'transmission_auto', \n",
        "                                                            'transmission_man', 'fuel_type_gasoline'])\n",
        "lotus = pd.concat([lotus_cat, lotus_num], axis = 1)\n",
        "lotus = lotus[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'transmission_auto', 'transmission_man', 'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GnTgps1E43NF"
      },
      "source": [
        "##### Smart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "LpXaT4M645kE"
      },
      "source": [
        "smart_coded = [pd.get_dummies(smart['body_type'], prefix = 'body_type', prefix_sep = '_'), \n",
        "               pd.get_dummies(smart['transmission'], prefix = 'transmission', prefix_sep = '_'), \n",
        "               pd.get_dummies(smart['fuel_type'], prefix = 'fuel_type', prefix_sep = '_')]\n",
        "\n",
        "smart_coded = pd.concat(smart_coded, axis = 1)\n",
        "smart = pd.concat([smart, smart_coded], axis = 1)\n",
        "smart = smart.drop(columns = ['body_type', 'transmission', 'fuel_type'])\n",
        "smart_num = smart.select_dtypes(exclude = ['object'])\n",
        "smart_cat = smart.select_dtypes(exclude = ['number'])\n",
        "smart_num = imputer.fit_transform(smart_num)\n",
        "smart_num = pd.DataFrame(data = smart_num[:, :], columns = ['mileage', 'manufacture_year', 'engine_displacement', 'engine_power', \n",
        "                                                            'door_count', 'seat_count', 'price_NGN', 'body_type_compact', \n",
        "                                                            'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline'])\n",
        "smart = pd.concat([smart_cat, smart_num], axis = 1)\n",
        "smart = smart[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', 'engine_power', 'door_count', 'seat_count', \n",
        "               'body_type_compact', 'transmission_auto', 'transmission_man', 'fuel_type_diesel', \n",
        "               'fuel_type_gasoline', 'date', 'time', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYcWx2za5fkp"
      },
      "source": [
        "Merge the car dataframes into one dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "yZphGUFq5rGY"
      },
      "source": [
        "cars45_df = pd.concat([audi, bentley, bmw, chevrolet, chrysler, citroen, \n",
        "                   dodge, fiat, ford, honda, hyundai, isuzu, jaguar, \n",
        "                   jeep, kia, lamborghini, lancia, maserati, mazda, \n",
        "                   mercedes, mini, mitsubishi, nissan, opel, porsche, \n",
        "                   rover, seat, skoda, subaru, suzuki, toyota, volvo, \n",
        "                   hummer, lexus, lotus, smart], axis = 0, ignore_index = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6oSCgQ5j__V",
        "outputId": "eef68c84-7855-4d94-eb40-26048fbd6f3a"
      },
      "source": [
        "cars45_df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "make                        0\n",
              "model                  183373\n",
              "mileage                     0\n",
              "manufacture_year            0\n",
              "engine_displacement         0\n",
              "engine_power                0\n",
              "door_count                  0\n",
              "seat_count                  0\n",
              "body_type_compact       78909\n",
              "transmission_auto           0\n",
              "transmission_man            0\n",
              "fuel_type_diesel          558\n",
              "fuel_type_gasoline          0\n",
              "date                        0\n",
              "time                        0\n",
              "price_NGN                   0\n",
              "body_type_van          270287\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QQd4PPOxj_7n"
      },
      "source": [
        "cars45_df['body_type_compact'] = cars45_df['body_type_compact'].replace(np.nan, 0.0)\n",
        "cars45_df['fuel_type_diesel'] = cars45_df['fuel_type_diesel'].replace(np.nan, 0.0)\n",
        "cars45_df['body_type_van'] = cars45_df['body_type_van'].replace(np.nan, 0.0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "iRaYikZTj_1J"
      },
      "source": [
        "cars45_df['model'] = cars45_df['model'].fillna('unknown')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "V1cKHLZCdZf5"
      },
      "source": [
        "cars45_df_copy = cars45_df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "_ADW6Kp6dz7l"
      },
      "source": [
        "cars45_df1 = cars45_df[['make', 'model', 'mileage', 'manufacture_year', 'engine_displacement', \n",
        "                        'engine_power', 'door_count', 'seat_count', 'body_type_compact',  'body_type_van', \n",
        "                        'transmission_auto', 'transmission_man', 'fuel_type_diesel', 'fuel_type_gasoline', 'price_NGN']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "_NtUM8jAT5FS"
      },
      "source": [
        "cars45_df1 = cars45_df1.astype({'mileage': int, 'manufacture_year': int,'engine_displacement': int, \n",
        "                              'engine_power': int,'door_count': int,'seat_count': int, 'body_type_compact': int, \n",
        "                              'body_type_van': int, 'transmission_auto': int, 'transmission_man': int, \n",
        "                              'fuel_type_diesel': int, 'fuel_type_gasoline': int, 'price_NGN': int})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "Eaw7Ter6VZST",
        "outputId": "68bab3e7-a6d7-4243-a8da-360c0427e4cc"
      },
      "source": [
        "cars45_df1.head()"
      ],
      "execution_count": null,
      "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>make</th>\n",
              "      <th>model</th>\n",
              "      <th>mileage</th>\n",
              "      <th>manufacture_year</th>\n",
              "      <th>engine_displacement</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>door_count</th>\n",
              "      <th>seat_count</th>\n",
              "      <th>body_type_compact</th>\n",
              "      <th>body_type_van</th>\n",
              "      <th>transmission_auto</th>\n",
              "      <th>transmission_man</th>\n",
              "      <th>fuel_type_diesel</th>\n",
              "      <th>fuel_type_gasoline</th>\n",
              "      <th>price_NGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>audi</td>\n",
              "      <td>unknown</td>\n",
              "      <td>164500</td>\n",
              "      <td>1995</td>\n",
              "      <td>2600</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>873428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>audi</td>\n",
              "      <td>200</td>\n",
              "      <td>134751</td>\n",
              "      <td>1995</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>audi</td>\n",
              "      <td>unknown</td>\n",
              "      <td>134751</td>\n",
              "      <td>1995</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>audi</td>\n",
              "      <td>unknown</td>\n",
              "      <td>134751</td>\n",
              "      <td>1995</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>audi</td>\n",
              "      <td>a6</td>\n",
              "      <td>164500</td>\n",
              "      <td>1995</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>843820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   make    model  mileage  manufacture_year  engine_displacement  \\\n",
              "0  audi  unknown   164500              1995                 2600   \n",
              "1  audi      200   134751              1995                 2150   \n",
              "2  audi  unknown   134751              1995                 2150   \n",
              "3  audi  unknown   134751              1995                 2150   \n",
              "4  audi       a6   164500              1995                 2150   \n",
              "\n",
              "   engine_power  door_count  seat_count  body_type_compact  body_type_van  \\\n",
              "0           110           4           5                  1              0   \n",
              "1           103           3           5                  1              0   \n",
              "2           103           3           5                  1              0   \n",
              "3           103           3           5                  1              0   \n",
              "4           103           4           5                  1              0   \n",
              "\n",
              "   transmission_auto  transmission_man  fuel_type_diesel  fuel_type_gasoline  \\\n",
              "0                  1                 0                 0                   1   \n",
              "1                  0                 0                 0                   1   \n",
              "2                  0                 0                 0                   1   \n",
              "3                  0                 0                 0                   1   \n",
              "4                  1                 0                 0                   1   \n",
              "\n",
              "   price_NGN  \n",
              "0     873428  \n",
              "1     666174  \n",
              "2     666174  \n",
              "3     666174  \n",
              "4     843820  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "kql2rh1kWHx3",
        "outputId": "dea11fb9-d6b1-4dda-dc53-bbf76b25b2bf"
      },
      "source": [
        "cars45_df1.describe(include = 'all').transpose()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>633891</td>\n",
              "      <td>35</td>\n",
              "      <td>mercedes-benz</td>\n",
              "      <td>78624</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>633891</td>\n",
              "      <td>569</td>\n",
              "      <td>unknown</td>\n",
              "      <td>183373</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mileage</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>86278.68</td>\n",
              "      <td>59447.62</td>\n",
              "      <td>99.00</td>\n",
              "      <td>29000.00</td>\n",
              "      <td>82500.00</td>\n",
              "      <td>137000.00</td>\n",
              "      <td>200000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacture_year</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008.73</td>\n",
              "      <td>5.50</td>\n",
              "      <td>1995.00</td>\n",
              "      <td>2005.00</td>\n",
              "      <td>2010.00</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>2016.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_displacement</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2016.98</td>\n",
              "      <td>694.15</td>\n",
              "      <td>999.00</td>\n",
              "      <td>1595.00</td>\n",
              "      <td>1950.00</td>\n",
              "      <td>2192.00</td>\n",
              "      <td>5000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>engine_power</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>137.26</td>\n",
              "      <td>53.20</td>\n",
              "      <td>99.00</td>\n",
              "      <td>109.00</td>\n",
              "      <td>118.00</td>\n",
              "      <td>140.00</td>\n",
              "      <td>400.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>door_count</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.58</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seat_count</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.71</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>15.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>body_type_compact</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>body_type_van</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission_auto</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission_man</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_type_diesel</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel_type_gasoline</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price_NGN</th>\n",
              "      <td>633891.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5393640.02</td>\n",
              "      <td>5881817.56</td>\n",
              "      <td>135620.00</td>\n",
              "      <td>1680932.00</td>\n",
              "      <td>3996448.00</td>\n",
              "      <td>7387120.00</td>\n",
              "      <td>312745124.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count unique            top    freq       mean  \\\n",
              "make                   633891     35  mercedes-benz   78624        NaN   \n",
              "model                  633891    569        unknown  183373        NaN   \n",
              "mileage             633891.00    NaN            NaN     NaN   86278.68   \n",
              "manufacture_year    633891.00    NaN            NaN     NaN    2008.73   \n",
              "engine_displacement 633891.00    NaN            NaN     NaN    2016.98   \n",
              "engine_power        633891.00    NaN            NaN     NaN     137.26   \n",
              "door_count          633891.00    NaN            NaN     NaN       3.58   \n",
              "seat_count          633891.00    NaN            NaN     NaN       4.71   \n",
              "body_type_compact   633891.00    NaN            NaN     NaN       0.11   \n",
              "body_type_van       633891.00    NaN            NaN     NaN       0.00   \n",
              "transmission_auto   633891.00    NaN            NaN     NaN       0.29   \n",
              "transmission_man    633891.00    NaN            NaN     NaN       0.56   \n",
              "fuel_type_diesel    633891.00    NaN            NaN     NaN       0.47   \n",
              "fuel_type_gasoline  633891.00    NaN            NaN     NaN       0.53   \n",
              "price_NGN           633891.00    NaN            NaN     NaN 5393640.02   \n",
              "\n",
              "                           std       min        25%        50%        75%  \\\n",
              "make                       NaN       NaN        NaN        NaN        NaN   \n",
              "model                      NaN       NaN        NaN        NaN        NaN   \n",
              "mileage               59447.62     99.00   29000.00   82500.00  137000.00   \n",
              "manufacture_year          5.50   1995.00    2005.00    2010.00    2014.00   \n",
              "engine_displacement     694.15    999.00    1595.00    1950.00    2192.00   \n",
              "engine_power             53.20     99.00     109.00     118.00     140.00   \n",
              "door_count                0.90      1.00       3.00       4.00       4.00   \n",
              "seat_count                0.92      1.00       5.00       5.00       5.00   \n",
              "body_type_compact         0.31      0.00       0.00       0.00       0.00   \n",
              "body_type_van             0.01      0.00       0.00       0.00       0.00   \n",
              "transmission_auto         0.45      0.00       0.00       0.00       1.00   \n",
              "transmission_man          0.50      0.00       0.00       1.00       1.00   \n",
              "fuel_type_diesel          0.50      0.00       0.00       0.00       1.00   \n",
              "fuel_type_gasoline        0.50      0.00       0.00       1.00       1.00   \n",
              "price_NGN           5881817.56 135620.00 1680932.00 3996448.00 7387120.00   \n",
              "\n",
              "                             max  \n",
              "make                         NaN  \n",
              "model                        NaN  \n",
              "mileage                200000.00  \n",
              "manufacture_year         2016.00  \n",
              "engine_displacement      5000.00  \n",
              "engine_power              400.00  \n",
              "door_count                  6.00  \n",
              "seat_count                 15.00  \n",
              "body_type_compact           1.00  \n",
              "body_type_van               1.00  \n",
              "transmission_auto           1.00  \n",
              "transmission_man            1.00  \n",
              "fuel_type_diesel            1.00  \n",
              "fuel_type_gasoline          1.00  \n",
              "price_NGN           312745124.00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Csd_YP6vdCSQ"
      },
      "source": [
        "# **Model Development**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Rf57mJR8DU7K"
      },
      "source": [
        "cars45_num = cars45_df1.select_dtypes(exclude = ['object'])\n",
        "cars45_cat = cars45_df1.select_dtypes(exclude = ['number'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "XsrWyEwQIa-o",
        "outputId": "6c42c2c5-f73e-4174-ca9f-2e3f1043d1e3"
      },
      "source": [
        "corr = np.triu(cars45_df1.corr())\n",
        "plt.figure(figsize = (14, 10))\n",
        "sns.heatmap(cars45_df1.corr(), annot = True, linewidths = 0.2, fmt = '.2f', mask = corr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0ca17fef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JatMOagLC8i"
      },
      "source": [
        "The transmission_auto and transmission_man columns are highly negatively correlated (-0.72). So I'll drop the transmission_man column.\n",
        "\n",
        "There's also the manufacture_year and mileage. I'll drop the manufacture year."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vptsKP2L3JQ"
      },
      "source": [
        "The Price columns holds high values and could rig the model performance. So log transformation shall be applied to normalise the values. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "BP1KsYyQOeFi"
      },
      "source": [
        "cars45_num = cars45_num.drop(columns = ['transmission_man', 'manufacture_year'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "kTF6mdXHcOIo",
        "outputId": "301c3795-c94b-402d-97e1-5d7000a13ad1"
      },
      "source": [
        "cars45_num.head()"
      ],
      "execution_count": null,
      "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>mileage</th>\n",
              "      <th>engine_displacement</th>\n",
              "      <th>engine_power</th>\n",
              "      <th>door_count</th>\n",
              "      <th>seat_count</th>\n",
              "      <th>body_type_compact</th>\n",
              "      <th>body_type_van</th>\n",
              "      <th>transmission_auto</th>\n",
              "      <th>fuel_type_diesel</th>\n",
              "      <th>fuel_type_gasoline</th>\n",
              "      <th>price_NGN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>164500</td>\n",
              "      <td>2600</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>873428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>134751</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>134751</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>134751</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>666174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>164500</td>\n",
              "      <td>2150</td>\n",
              "      <td>103</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>843820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mileage  engine_displacement  engine_power  door_count  seat_count  \\\n",
              "0   164500                 2600           110           4           5   \n",
              "1   134751                 2150           103           3           5   \n",
              "2   134751                 2150           103           3           5   \n",
              "3   134751                 2150           103           3           5   \n",
              "4   164500                 2150           103           4           5   \n",
              "\n",
              "   body_type_compact  body_type_van  transmission_auto  fuel_type_diesel  \\\n",
              "0                  1              0                  1                 0   \n",
              "1                  1              0                  0                 0   \n",
              "2                  1              0                  0                 0   \n",
              "3                  1              0                  0                 0   \n",
              "4                  1              0                  1                 0   \n",
              "\n",
              "   fuel_type_gasoline  price_NGN  \n",
              "0                   1     873428  \n",
              "1                   1     666174  \n",
              "2                   1     666174  \n",
              "3                   1     666174  \n",
              "4                   1     843820  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QqXk9vS9q7P"
      },
      "source": [
        "The values of the price variable are really large and will affect the absolute numbers of the regression model. To manage this, the price data is normalises using log."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0_IAmST_RIKD"
      },
      "source": [
        "Y = np.log(cars45_num['price_NGN']).values\n",
        "X = cars45_num.drop(columns = ['price_NGN']).values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "1ST7EbDwj_gP"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3sg4kEePUzS"
      },
      "source": [
        "The differing scales of the raw data may impact these algorithms. Part of a requirement for a standardised data set is to have each attribute have a mean value of zero and a standard deviation of 1. I shall implement standardisation using pipelines and then use cross-validation to validate performance of all the algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1JtWkkjuqJ3",
        "outputId": "cf579947-b046-416a-e188-797031063390"
      },
      "source": [
        "pipelines = []\n",
        "pipelines.append(('ScaledLR', Pipeline([('Scaler', StandardScaler()), ('LR', LinearRegression())])))\n",
        "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()), ('KNN', KNeighborsRegressor())])))\n",
        "pipelines.append(('ScaledRF', Pipeline([('Scaler', StandardScaler()), ('RF', RandomForestRegressor())])))\n",
        "pipelines.append(('ScaledGB', Pipeline([('Scaler', StandardScaler()), ('GB', GradientBoostingRegressor())])))\n",
        "\n",
        "results = []\n",
        "names = []\n",
        "for name, model in pipelines:\n",
        "    kfold = KFold(n_splits = 3, random_state = 10)\n",
        "    cv_results = cross_val_score(model, X_train, Y_train, cv = kfold, scoring = 'neg_mean_squared_error')\n",
        "    results.append(cv_results)\n",
        "    names.append(name)\n",
        "    notification = '%s: %f (%f)' % (name, cv_results.mean(), cv_results.std())\n",
        "    print(notification)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ScaledLR: -0.438976 (0.001259)\n",
            "ScaledKNN: -0.216174 (0.000748)\n",
            "ScaledRF: -0.142303 (0.000926)\n",
            "ScaledGB: -0.326531 (0.001381)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPzONop4RAoB"
      },
      "source": [
        "From the output above, it looks like the Random Forest Regressor performs the best using a scaled version of the data. From this point onward, I will build the regression algorithm using the Random Forest Regressor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3z0HC-bvJZ_"
      },
      "source": [
        "scaler = preprocessing.StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "param_grid = {'max_depth': np.array([5, 10, 15, 20, 25]), \n",
        "              'n_estimators': np.array([200, 200, 600, 800, 1000])}\n",
        "model = RandomForestRegressor(random_state = 10)\n",
        "kfold = KFold(n_splits = 3, random_state = 10)\n",
        "grid = GridSearchCV(estimator = model, param_grid = param_grid, scoring = 'neg_mean_squared_error', cv = kfold)\n",
        "grid_result = grid.fit(X_train_scaled, Y_train)\n",
        "\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "stds = grid_result.cv_results_['std_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, stdev, param in zip(means, stds, params):\n",
        "    print('%f (%f) with: %r' % (mean, stdev, param))\n",
        "\n",
        "print('Best: %f using %s' % (grid_result.best_score_, grid_result.best_params_))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWWpOdLGmDa9"
      },
      "source": [
        "The best max_depth configuration is 10 with the negative mean square error closest to 0.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmbFk2bcU4H4"
      },
      "source": [
        "#### Finalise and validate model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mKAYlq3Ucnz"
      },
      "source": [
        "model = RandomForestRegressor(random_state = 10, max_depth = 10)\n",
        "model.fit(X_train_scaled, Y_train)\n",
        "\n",
        "X_test_rescaled = scaler.transform(X_test)\n",
        "predictions = model.predict(X_test_rescaled)\n",
        "mse = mean_squared_error(Y_test, predictions, squared = True)\n",
        "rmse = mean_squared_error(Y_test, predictions, squared = False)\n",
        "mse, rmse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZ4PoYm5tSTQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IRgKO-t9XChZ"
      },
      "source": [
        "From the mean square error of 0.27294 between the prediction outputs vs the test data, the Random Forest performed well. Important to note that the mean_square_error is calculated using the scaled data. It does not represent the error between the actual car prices and predicted prices. To better appreciate the outcome of the predictions, I look at the raw predicted values and the corresponding test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Af0lUc1paZYp"
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNrHMJLwXAsj"
      },
      "source": [
        "compare_data = pd.DataFrame({'Prediction': predictions, 'Test Data' : Y_test})\n",
        "compare_data.head(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4XyprN1XWRR"
      },
      "source": [
        "From the data frame output above, the difference between the predicted value and test data is pretty small. Take note that the data is scaled and log normalised. So, I have to inverse transform these data to see the actual values. To do that, I apply the  exp function to the **Prediction** column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlbq6l5khStB"
      },
      "source": [
        "#index = [i for i in range(len(predictions))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzUBd66aXRG8"
      },
      "source": [
        "Y_test_actual = np.exp(Y_test)\n",
        "Y_test_predicted = np.exp(predictions)\n",
        "\n",
        "diff = abs(Y_test_actual - Y_test_predicted)\n",
        "\n",
        "predicted_actual = pd.DataFrame({'Test Data': Y_test_actual, 'Predicted Price': Y_test_predicted, 'Difference': diff})\n",
        "predicted_actual = predicted_actual.astype(int)\n",
        "predicted_actual.head(10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aa1ICGNbgPL"
      },
      "source": [
        "# **The End!**\n",
        "\n",
        "Seghe Momodu"
      ]
    }
  ]
}