Dataset_statistikasi_Korrelyasiya.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNVwXfK82mclMwL2KNX0Fq6",
      "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/AbdullahTheAnalyst/Data-Science-and-AI-mohir_dev-/blob/NumPy-and-Pandas/Dataset_statistikasi_Korrelyasiya.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": "X9yF6gWXgeoY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "SKOaxnYHhPFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'age':[10,20,30,40,50,60],\n",
        "    'year':[2010,2000,1990,1980,1970,1960]\n",
        "    }\n",
        "\n",
        "obj = pd.DataFrame(data)\n",
        "obj"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "A0DY0sfLhSPZ",
        "outputId": "62b64553-5a65-494d-936c-7092708ca3c7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year\n",
              "0   10  2010\n",
              "1   20  2000\n",
              "2   30  1990\n",
              "3   40  1980\n",
              "4   50  1970\n",
              "5   60  1960"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>1990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>1980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>60</td>\n",
              "      <td>1960</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4ab776f-97dc-4460-86ac-d77a2f4f136a')\"\n",
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              "\n",
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              "    .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",
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              "      display: none;\n",
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              "\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4ab776f-97dc-4460-86ac-d77a2f4f136a 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-c4ab776f-97dc-4460-86ac-d77a2f4f136a');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75270ca8-5a56-47cc-aa5e-e4cbaf70d460')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      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-75270ca8-5a56-47cc-aa5e-e4cbaf70d460 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "obj",
              "summary": "{\n  \"name\": \"obj\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 10,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10,\n          20,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 1960,\n        \"max\": 2010,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2010,\n          2000,\n          1960\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Korrelyatsiya degani ikki data'ning bir-biriga bog'liqligi.\n",
        "# -1 va 1 oralig'ida bo'ladi.\n",
        "# -1 va 1 ya yaqin bo'lsa bog'liqlik shuncha yaxshi degani\n",
        "# manfiy (negativ) chiqsa, bittasi o'sib, bittasi kamayishini bilishimiz mumkin\n",
        "# musbat (activ) chiqsa, bittasi o'sib, ikkinchi ham o'sishini bilamiz\n",
        "obj[\"age\"].corr(obj[\"year\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xy4q2R1mhGWK",
        "outputId": "b641948c-e8ae-427a-f5bc-d62038a5bba3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.9999999999999998"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/anvarnarz/praktikum_datasets/main/merc.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "d4I-DVoXiUBl",
        "outputId": "1a329988-a675-4d4b-90aa-c8575e929d1f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
              "0             SLK  2005   5200    Automatic    63000   Petrol  325  32.1   \n",
              "1         S Class  2017  34948    Automatic    27000   Hybrid   20  61.4   \n",
              "2        SL CLASS  2016  49948    Automatic     6200   Petrol  555  28.0   \n",
              "3         G Class  2016  61948    Automatic    16000   Petrol  325  30.4   \n",
              "4         G Class  2016  73948    Automatic     4000   Petrol  325  30.1   \n",
              "...           ...   ...    ...          ...      ...      ...  ...   ...   \n",
              "13114     C Class  2020  35999    Automatic      500   Diesel  145  55.4   \n",
              "13115     B Class  2020  24699    Automatic     2500   Diesel  145  55.4   \n",
              "13116   GLC Class  2019  30999    Automatic    11612   Diesel  145  41.5   \n",
              "13117   CLS Class  2019  37990    Automatic     2426   Diesel  145  45.6   \n",
              "13118     S Class  2019  54999    Automatic     2075   Diesel  145  52.3   \n",
              "\n",
              "       engineSize  \n",
              "0             1.8  \n",
              "1             2.1  \n",
              "2             5.5  \n",
              "3             4.0  \n",
              "4             4.0  \n",
              "...           ...  \n",
              "13114         2.0  \n",
              "13115         2.0  \n",
              "13116         2.1  \n",
              "13117         2.0  \n",
              "13118         2.9  \n",
              "\n",
              "[13119 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>year</th>\n",
              "      <th>price</th>\n",
              "      <th>transmission</th>\n",
              "      <th>mileage</th>\n",
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              "      <th>engineSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SLK</td>\n",
              "      <td>2005</td>\n",
              "      <td>5200</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>63000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>32.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S Class</td>\n",
              "      <td>2017</td>\n",
              "      <td>34948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>27000</td>\n",
              "      <td>Hybrid</td>\n",
              "      <td>20</td>\n",
              "      <td>61.4</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SL CLASS</td>\n",
              "      <td>2016</td>\n",
              "      <td>49948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>6200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>555</td>\n",
              "      <td>28.0</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>G Class</td>\n",
              "      <td>2016</td>\n",
              "      <td>61948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>16000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>G Class</td>\n",
              "      <td>2016</td>\n",
              "      <td>73948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>4000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>30.1</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13114</th>\n",
              "      <td>C Class</td>\n",
              "      <td>2020</td>\n",
              "      <td>35999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>55.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13115</th>\n",
              "      <td>B Class</td>\n",
              "      <td>2020</td>\n",
              "      <td>24699</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>55.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13116</th>\n",
              "      <td>GLC Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>30999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>11612</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>41.5</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13117</th>\n",
              "      <td>CLS Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>37990</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2426</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>45.6</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13118</th>\n",
              "      <td>S Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>54999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2075</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>52.3</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13119 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-367073ff-1cdb-4cc5-95f1-bfe2bbc2f3df 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-367073ff-1cdb-4cc5-95f1-bfe2bbc2f3df');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\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-8d6305ed-7e3b-47aa-a7d4-fa9cd0207494 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13119,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \" GLC Class\",\n          \" CLC Class\",\n          \" C Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1970,\n        \"max\": 2020,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          2014,\n          2009,\n          2005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11842,\n        \"min\": 650,\n        \"max\": 159999,\n        \"num_unique_values\": 4424,\n        \"samples\": [\n          25480,\n          28599,\n          55299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Manual\",\n          \"Other\",\n          \"Automatic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21176,\n        \"min\": 1,\n        \"max\": 259000,\n        \"num_unique_values\": 9716,\n        \"samples\": [\n          39000,\n          32044,\n          30028\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuelType\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Hybrid\",\n          \"Other\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65,\n        \"min\": 0,\n        \"max\": 580,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          315,\n          200,\n          280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.220082137219569,\n        \"min\": 1.1,\n        \"max\": 217.3,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          32.5,\n          46.3,\n          30.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engineSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5724260882180195,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          3.0,\n          6.0,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# yil bilan bosib o'tgan yo'lning nisbatini topamiz (moshina qancha yili eski bo'lsa, shuncha ko'p masofa bosib o'tgan)\n",
        "# korrelyatsiya orqali predictive tasklarga juda muhim\n",
        "df[\"year\"].corr(df[\"mileage\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQQFQPZaiYaU",
        "outputId": "158d5b8b-e134-4ac7-eabb-08aea8f99fa6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.7380268515364232"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"price\"].corr(df[\"mileage\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LcmcwiNi8lh",
        "outputId": "af0828ee-5148-4ab0-a036-12d0b6e686f6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.537214124104646"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2cEdAgeWjnjg",
        "outputId": "ddc63320-9b7a-4ba9-c2a4-b102b0564cf5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model  year  price transmission  mileage fuelType  tax   mpg  \\\n",
              "0             SLK  2005   5200    Automatic    63000   Petrol  325  32.1   \n",
              "1         S Class  2017  34948    Automatic    27000   Hybrid   20  61.4   \n",
              "2        SL CLASS  2016  49948    Automatic     6200   Petrol  555  28.0   \n",
              "3         G Class  2016  61948    Automatic    16000   Petrol  325  30.4   \n",
              "4         G Class  2016  73948    Automatic     4000   Petrol  325  30.1   \n",
              "...           ...   ...    ...          ...      ...      ...  ...   ...   \n",
              "13114     C Class  2020  35999    Automatic      500   Diesel  145  55.4   \n",
              "13115     B Class  2020  24699    Automatic     2500   Diesel  145  55.4   \n",
              "13116   GLC Class  2019  30999    Automatic    11612   Diesel  145  41.5   \n",
              "13117   CLS Class  2019  37990    Automatic     2426   Diesel  145  45.6   \n",
              "13118     S Class  2019  54999    Automatic     2075   Diesel  145  52.3   \n",
              "\n",
              "       engineSize  \n",
              "0             1.8  \n",
              "1             2.1  \n",
              "2             5.5  \n",
              "3             4.0  \n",
              "4             4.0  \n",
              "...           ...  \n",
              "13114         2.0  \n",
              "13115         2.0  \n",
              "13116         2.1  \n",
              "13117         2.0  \n",
              "13118         2.9  \n",
              "\n",
              "[13119 rows x 9 columns]"
            ],
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              "  <div id=\"df-6659c88c-a668-4383-aafa-cda8c15431b0\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
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              "      <th>engineSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SLK</td>\n",
              "      <td>2005</td>\n",
              "      <td>5200</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>63000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>32.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S Class</td>\n",
              "      <td>2017</td>\n",
              "      <td>34948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>27000</td>\n",
              "      <td>Hybrid</td>\n",
              "      <td>20</td>\n",
              "      <td>61.4</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SL CLASS</td>\n",
              "      <td>2016</td>\n",
              "      <td>49948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>6200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>555</td>\n",
              "      <td>28.0</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>G Class</td>\n",
              "      <td>2016</td>\n",
              "      <td>61948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>16000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>G Class</td>\n",
              "      <td>2016</td>\n",
              "      <td>73948</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>4000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>325</td>\n",
              "      <td>30.1</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13114</th>\n",
              "      <td>C Class</td>\n",
              "      <td>2020</td>\n",
              "      <td>35999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>55.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13115</th>\n",
              "      <td>B Class</td>\n",
              "      <td>2020</td>\n",
              "      <td>24699</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>55.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13116</th>\n",
              "      <td>GLC Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>30999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>11612</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>41.5</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13117</th>\n",
              "      <td>CLS Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>37990</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2426</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>45.6</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13118</th>\n",
              "      <td>S Class</td>\n",
              "      <td>2019</td>\n",
              "      <td>54999</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>2075</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>145</td>\n",
              "      <td>52.3</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13119 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --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-abb643bc-660a-4cd5-ac95-c6c7395eee23 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13119,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \" GLC Class\",\n          \" CLC Class\",\n          \" C Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1970,\n        \"max\": 2020,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          2014,\n          2009,\n          2005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11842,\n        \"min\": 650,\n        \"max\": 159999,\n        \"num_unique_values\": 4424,\n        \"samples\": [\n          25480,\n          28599,\n          55299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transmission\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Manual\",\n          \"Other\",\n          \"Automatic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21176,\n        \"min\": 1,\n        \"max\": 259000,\n        \"num_unique_values\": 9716,\n        \"samples\": [\n          39000,\n          32044,\n          30028\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fuelType\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Hybrid\",\n          \"Other\",\n          \"Petrol\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65,\n        \"min\": 0,\n        \"max\": 580,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          315,\n          200,\n          280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.220082137219569,\n        \"min\": 1.1,\n        \"max\": 217.3,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          32.5,\n          46.3,\n          30.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engineSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5724260882180195,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          3.0,\n          6.0,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_integer = df.describe()\n",
        "df_integer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "w_oBMsMxkBPn",
        "outputId": "df18eeab-3913-4ac4-92af-4356f26c373b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               year          price        mileage           tax           mpg  \\\n",
              "count  13119.000000   13119.000000   13119.000000  13119.000000  13119.000000   \n",
              "mean    2017.296288   24698.596920   21949.559037    129.972178     55.155843   \n",
              "std        2.224709   11842.675542   21176.512267     65.260286     15.220082   \n",
              "min     1970.000000     650.000000       1.000000      0.000000      1.100000   \n",
              "25%     2016.000000   17450.000000    6097.500000    125.000000     45.600000   \n",
              "50%     2018.000000   22480.000000   15189.000000    145.000000     56.500000   \n",
              "75%     2019.000000   28980.000000   31779.500000    145.000000     64.200000   \n",
              "max     2020.000000  159999.000000  259000.000000    580.000000    217.300000   \n",
              "\n",
              "         engineSize  \n",
              "count  13119.000000  \n",
              "mean       2.071530  \n",
              "std        0.572426  \n",
              "min        0.000000  \n",
              "25%        1.800000  \n",
              "50%        2.000000  \n",
              "75%        2.100000  \n",
              "max        6.200000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47bc2148-2556-472a-a326-90d762f1e174\" 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>year</th>\n",
              "      <th>price</th>\n",
              "      <th>mileage</th>\n",
              "      <th>tax</th>\n",
              "      <th>mpg</th>\n",
              "      <th>engineSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13119.000000</td>\n",
              "      <td>13119.000000</td>\n",
              "      <td>13119.000000</td>\n",
              "      <td>13119.000000</td>\n",
              "      <td>13119.000000</td>\n",
              "      <td>13119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2017.296288</td>\n",
              "      <td>24698.596920</td>\n",
              "      <td>21949.559037</td>\n",
              "      <td>129.972178</td>\n",
              "      <td>55.155843</td>\n",
              "      <td>2.071530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.224709</td>\n",
              "      <td>11842.675542</td>\n",
              "      <td>21176.512267</td>\n",
              "      <td>65.260286</td>\n",
              "      <td>15.220082</td>\n",
              "      <td>0.572426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1970.000000</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>17450.000000</td>\n",
              "      <td>6097.500000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>45.600000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>22480.000000</td>\n",
              "      <td>15189.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>56.500000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>28980.000000</td>\n",
              "      <td>31779.500000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>64.200000</td>\n",
              "      <td>2.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2020.000000</td>\n",
              "      <td>159999.000000</td>\n",
              "      <td>259000.000000</td>\n",
              "      <td>580.000000</td>\n",
              "      <td>217.300000</td>\n",
              "      <td>6.200000</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-47bc2148-2556-472a-a326-90d762f1e174')\"\n",
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              "\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",
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              "      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",
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              "    [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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47bc2148-2556-472a-a326-90d762f1e174 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-47bc2148-2556-472a-a326-90d762f1e174');\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",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "  }\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",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-bbb13a00-c803-4e7b-91b8-190c1bf08516 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# umumiy korrelyatsiyani analiz qilish uchun\n",
        "# o ga yaqinlari bo'g'liqligi yo'q\n",
        "# -1 va 1 ga yaqin bo'lsa bog'liqligi juda yuqori bo'ladi\n",
        "df_integer.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "qt025m9XkcAu",
        "outputId": "1abd1a9a-451c-46df-a33f-9b33dbbcae3b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                year     price   mileage       tax       mpg  engineSize\n",
              "year        1.000000 -0.131669 -0.126080  0.986091  0.985811    0.985149\n",
              "price      -0.131669  1.000000  0.994025 -0.134247 -0.157698   -0.171176\n",
              "mileage    -0.126080  0.994025  1.000000 -0.116885 -0.140029   -0.153224\n",
              "tax         0.986091 -0.134247 -0.116885  1.000000  0.999714    0.999290\n",
              "mpg         0.985811 -0.157698 -0.140029  0.999714  1.000000    0.999904\n",
              "engineSize  0.985149 -0.171176 -0.153224  0.999290  0.999904    1.000000"
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_integer\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5774331484479432,\n        \"min\": -0.13166920466009183,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          -0.13166920466009183,\n          0.9851493972765717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5918286940222804,\n        \"min\": -0.17117639058989426,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.13166920466009183,\n          1.0,\n          -0.17117639058989426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5842141299179944,\n        \"min\": -0.15322422345924322,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.12608037036171904,\n          0.9940254160972105,\n          -0.15322422345924322\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5793655860647318,\n        \"min\": -0.13424681445152709,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9860909429341458,\n          -0.13424681445152709,\n          0.9992902322212414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5914410183325348,\n        \"min\": -0.15769832250243612,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9858106569210774,\n          -0.15769832250243612,\n          0.9999039389434602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"engineSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5981900709191221,\n        \"min\": -0.17117639058989426,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9851493972765717,\n          -0.17117639058989426,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# soliq bilan qolgan variable'ning bog'liqligini alohida olish kodi\n",
        "df_integer.corrwith(df_integer[\"tax\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "MkyXG9P5k72n",
        "outputId": "f3e018c1-d818-4478-974c-327d2a3cf5b9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year          0.986091\n",
              "price        -0.134247\n",
              "mileage      -0.116885\n",
              "tax           1.000000\n",
              "mpg           0.999714\n",
              "engineSize    0.999290\n",
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          "metadata": {},
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