Basic_Of_Data_visualization_using_Matplotlib.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN9TW7MZ8vUSsmBYVD66vhh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kishankumar1328/-DATA-ANALYSICS/blob/main/Basic_Of_Data_visualization_using_Matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "E37eLhoFUatf"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "eNgd8HUKSLV3"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "9GctXUCzUpm-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/engine size and its price2.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6dM2Fw7mUsFl",
        "outputId": "e66825f3-127f-46bc-ef07-7ca0756921a4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   engine size   price vehicle  horsepower  mpg  top\n",
              "0          100  200000      4w         110   22    1\n",
              "1          150  234000      2w         120   70    4\n",
              "2          200  345000      4w         175   34    2\n",
              "3          300  500000      4w         210   34    3\n",
              "4          450  700000      2w         300   65    8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d785c7d9-0f82-47ac-9d8a-2cf08f8e3842\" class=\"colab-df-container\">\n",
              "    <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>engine size</th>\n",
              "      <th>price</th>\n",
              "      <th>vehicle</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>mpg</th>\n",
              "      <th>top</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>200000</td>\n",
              "      <td>4w</td>\n",
              "      <td>110</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150</td>\n",
              "      <td>234000</td>\n",
              "      <td>2w</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>345000</td>\n",
              "      <td>4w</td>\n",
              "      <td>175</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300</td>\n",
              "      <td>500000</td>\n",
              "      <td>4w</td>\n",
              "      <td>210</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>450</td>\n",
              "      <td>700000</td>\n",
              "      <td>2w</td>\n",
              "      <td>300</td>\n",
              "      <td>65</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d785c7d9-0f82-47ac-9d8a-2cf08f8e3842')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d785c7d9-0f82-47ac-9d8a-2cf08f8e3842 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d785c7d9-0f82-47ac-9d8a-2cf08f8e3842');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e2e7e8a4-2f6c-4410-9334-d16f5aef29ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2e7e8a4-2f6c-4410-9334-d16f5aef29ba')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e2e7e8a4-2f6c-4410-9334-d16f5aef29ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"vehicle\"],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8oD0IgsyVFVn",
        "outputId": "4b4ba211-2644-4df7-d341-987861a4a668"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   engine size   price  horsepower  mpg  top\n",
              "0          100  200000         110   22    1\n",
              "1          150  234000         120   70    4\n",
              "2          200  345000         175   34    2\n",
              "3          300  500000         210   34    3\n",
              "4          450  700000         300   65    8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e8e9292-5f47-4682-99e6-262d8d4a9285\" class=\"colab-df-container\">\n",
              "    <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>engine size</th>\n",
              "      <th>price</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>mpg</th>\n",
              "      <th>top</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>200000</td>\n",
              "      <td>110</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150</td>\n",
              "      <td>234000</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>345000</td>\n",
              "      <td>175</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300</td>\n",
              "      <td>500000</td>\n",
              "      <td>210</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>450</td>\n",
              "      <td>700000</td>\n",
              "      <td>300</td>\n",
              "      <td>65</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e8e9292-5f47-4682-99e6-262d8d4a9285')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7e8e9292-5f47-4682-99e6-262d8d4a9285 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7e8e9292-5f47-4682-99e6-262d8d4a9285');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-297e364e-2b29-4dd9-b1f6-829907a3c7eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-297e364e-2b29-4dd9-b1f6-829907a3c7eb')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-297e364e-2b29-4dd9-b1f6-829907a3c7eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([\"top\"],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1eKhmxXoVOIo",
        "outputId": "9f7c7dcb-2481-4cdf-c4a8-27a336dd05cb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   engine size   price vehicle  horsepower  mpg\n",
              "0          100  200000      4w         110   22\n",
              "1          150  234000      2w         120   70\n",
              "2          200  345000      4w         175   34\n",
              "3          300  500000      4w         210   34\n",
              "4          450  700000      2w         300   65"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d884a4d0-bbad-4f84-9243-1079aa5962da\" class=\"colab-df-container\">\n",
              "    <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>engine size</th>\n",
              "      <th>price</th>\n",
              "      <th>vehicle</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>mpg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100</td>\n",
              "      <td>200000</td>\n",
              "      <td>4w</td>\n",
              "      <td>110</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150</td>\n",
              "      <td>234000</td>\n",
              "      <td>2w</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200</td>\n",
              "      <td>345000</td>\n",
              "      <td>4w</td>\n",
              "      <td>175</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300</td>\n",
              "      <td>500000</td>\n",
              "      <td>4w</td>\n",
              "      <td>210</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>450</td>\n",
              "      <td>700000</td>\n",
              "      <td>2w</td>\n",
              "      <td>300</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d884a4d0-bbad-4f84-9243-1079aa5962da')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d884a4d0-bbad-4f84-9243-1079aa5962da button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d884a4d0-bbad-4f84-9243-1079aa5962da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0a6ad565-1778-4cb4-99a8-b2782dcecfa1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a6ad565-1778-4cb4-99a8-b2782dcecfa1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a6ad565-1778-4cb4-99a8-b2782dcecfa1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bar plot\n",
        "x=df[\"engine size\"].head(3)\n",
        "y=df[\"price\"].head(3)\n",
        "plt.xlabel(\"Engine size\",fontsize=13,color='blue')\n",
        "plt.ylabel(\"Price\",fontsize=13,color='red')\n",
        "plt.xticks(color='red',fontsize=16)\n",
        "plt.yticks(color=\"blue\",fontsize=16)\n",
        "plt.title(\"Car price\",fontsize=29,color=\"red\",alpha=1,)\n",
        "plt.tight_layout()\n",
        "plt.legend(['price'])\n",
        "plt.bar(x,y,label=\"Price\",color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "raI9t2RPVWxx",
        "outputId": "8712544a-1c4a-429c-9b55-29dd60414243"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 3 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#line plot\n",
        "x=df[\"engine size\"]\n",
        "y=df[\"price\"]\n",
        "plt.xlabel(\"Engine size\",fontsize=16,color='deeppink')\n",
        "plt.ylabel(\"Price\",fontsize=16,color='deeppink')\n",
        "plt.xticks(color='yellowgreen',fontsize=16)\n",
        "plt.yticks(color=\"yellowgreen\",fontsize=16)\n",
        "plt.title(\"Car price\",fontsize=29,color=\"yellowgreen\",alpha=1,)\n",
        "plt.tight_layout()\n",
        "plt.plot(x,y,label=\"Price\",color='deeppink',alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "7HIlldfaVW8u",
        "outputId": "8abfa4a3-12c6-4c66-8efc-64dbbdf61a4f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7c1b2a612e60>]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scatter plot\n",
        "x=df[\"horsepower\"]\n",
        "y=df[\"price\"]\n",
        "plt.xlabel(\"Horse Power\",fontsize=16,color='grey')\n",
        "plt.ylabel(\"Price\",fontsize=16,color='grey')\n",
        "plt.xticks(color='grey',fontsize=16)\n",
        "plt.yticks(color=\"grey\",fontsize=16)\n",
        "plt.title(\"Car price\",fontsize=29,color=\"grey\",alpha=1,)\n",
        "plt.tight_layout()\n",
        "plt.scatter(x,y,label=\"Price\",color='yellow',alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "Naveba_DZl8_",
        "outputId": "cae28bc9-3b91-4166-8180-7cd20064df1c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7c1b2a2a5420>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram\n",
        "y=df[\"price\"]\n",
        "plt.hist(y,color='green',alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "KiDyxDCvbhms",
        "outputId": "a560a6f3-91a3-4286-d127-704a60655c99"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2., 0., 1., 0., 0., 0., 1., 0., 0., 1.]),\n",
              " array([200000., 250000., 300000., 350000., 400000., 450000., 500000.,\n",
              "        550000., 600000., 650000., 700000.]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Area plot\n",
        "x = df[\"engine size\"]\n",
        "y = df[\"price\"]\n",
        "plt.xlabel(\"Engine size\", fontsize=16, color='purple')\n",
        "plt.ylabel(\"Price\", fontsize=16, color='purple')\n",
        "plt.xticks( fontsize=16)\n",
        "plt.yticks( fontsize=16)\n",
        "plt.title(\"Car Price vs. Engine Size\", fontsize=20,color='purple' )\n",
        "plt.tight_layout()\n",
        "plt.fill_between(x, y, color='violet', alpha=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "MS3jQrPvcUlH",
        "outputId": "9c844548-736e-4841-e819-d94b65a71c1b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7c1b2a1da860>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}