DF_pandas_3_amaliyot.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/AbdullahTheAnalyst/Data-Science-and-AI-mohir_dev-/blob/NumPy-and-Pandas/DF_pandas_3_amaliyot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNLOb9yhxI0b"
      },
      "source": [
        "### **1-mashq**. `Pandas` kutubxonasini `pd`, `numpy` kutubxonasini `np` deb chaqirib oling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrbBfqRpxIFr"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4SPeoLHh4tv"
      },
      "source": [
        "### Mashg'ulot uchun DF yuklab olamiz.\n",
        "Quyidagi kodni bajaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 547
        },
        "outputId": "e1cdfad3-964f-4174-baad-ab0ab481cd2c"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/anvarnarz/praktikum_datasets/main/usa_cars.csv\", index_col=0)\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      price      brand    model  year   title_status  mileage   color  \\\n",
              "0      6300     toyota  cruiser  2008  clean vehicle   274117   black   \n",
              "1      2899       ford       se  2011  clean vehicle   190552  silver   \n",
              "2      5350      dodge      mpv  2018  clean vehicle    39590  silver   \n",
              "3     25000       ford     door  2014  clean vehicle    64146    blue   \n",
              "4     27700  chevrolet     1500  2018  clean vehicle     6654     red   \n",
              "...     ...        ...      ...   ...            ...      ...     ...   \n",
              "2494   7800     nissan    versa  2019  clean vehicle    23609     red   \n",
              "2495   9200     nissan    versa  2018  clean vehicle    34553  silver   \n",
              "2496   9200     nissan    versa  2018  clean vehicle    31594  silver   \n",
              "2497   9200     nissan    versa  2018  clean vehicle    32557   black   \n",
              "2498   9200     nissan    versa  2018  clean vehicle    31371  silver   \n",
              "\n",
              "                      vin        lot       state country      condition  \n",
              "0       jtezu11f88k007763  159348797  new jersey     usa   10 days left  \n",
              "1       2fmdk3gc4bbb02217  166951262   tennessee     usa    6 days left  \n",
              "2       3c4pdcgg5jt346413  167655728     georgia     usa    2 days left  \n",
              "3       1ftfw1et4efc23745  167753855    virginia     usa  22 hours left  \n",
              "4       3gcpcrec2jg473991  167763266     florida     usa  22 hours left  \n",
              "...                   ...        ...         ...     ...            ...  \n",
              "2494    3n1cn7ap9kl880319  167722715  california     usa    1 days left  \n",
              "2495    3n1cn7ap5jl884088  167762225     florida     usa  21 hours left  \n",
              "2496    3n1cn7ap9jl884191  167762226     florida     usa  21 hours left  \n",
              "2497    3n1cn7ap3jl883263  167762227     florida     usa    2 days left  \n",
              "2498    3n1cn7ap4jl884311  167762228     florida     usa  21 hours left  \n",
              "\n",
              "[2499 rows x 12 columns]"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2499,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12116,\n        \"min\": 0,\n        \"max\": 84900,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          12995,\n          16200,\n          9280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"mercedes-benz\",\n          \"maserati\",\n          \"infiniti\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 127,\n        \"samples\": [\n          \"challenger\",\n          \"altima\",\n          \"cx-3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1984,\n          2000,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"salvage insurance\",\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59705,\n        \"min\": 0,\n        \"max\": 1017936,\n        \"num_unique_values\": 2439,\n        \"samples\": [\n          20103,\n          31233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"green\",\n          \"cayenne red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          \"  1n4aa5ap4cc822996\",\n          \"  1fmjk2at0kea45301\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 203877,\n        \"min\": 159348797,\n        \"max\": 167805500,\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          167553965,\n          167615142\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"louisiana\",\n          \"indiana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" canada\",\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"16 minutes\",\n          \"29 minutes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QXG45J6jAa7"
      },
      "source": [
        "### **2-mashq.** DF hajmini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OlZRDM8jFbF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea897c3e-1fa0-4d88-ef8d-acc1b30c8747"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2499, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yzPsYM6qiE8D"
      },
      "source": [
        "### **3-mashq**. DF boshidan 6 ta qator chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dz8VMR3h0uv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "f7e32e13-0c38-4810-81f3-b0d51b56ca07"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df.head(6)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price      brand    model  year   title_status  mileage   color  \\\n",
              "0   6300     toyota  cruiser  2008  clean vehicle   274117   black   \n",
              "1   2899       ford       se  2011  clean vehicle   190552  silver   \n",
              "2   5350      dodge      mpv  2018  clean vehicle    39590  silver   \n",
              "3  25000       ford     door  2014  clean vehicle    64146    blue   \n",
              "4  27700  chevrolet     1500  2018  clean vehicle     6654     red   \n",
              "5   5700      dodge      mpv  2018  clean vehicle    45561   white   \n",
              "\n",
              "                   vin        lot       state country      condition  \n",
              "0    jtezu11f88k007763  159348797  new jersey     usa   10 days left  \n",
              "1    2fmdk3gc4bbb02217  166951262   tennessee     usa    6 days left  \n",
              "2    3c4pdcgg5jt346413  167655728     georgia     usa    2 days left  \n",
              "3    1ftfw1et4efc23745  167753855    virginia     usa  22 hours left  \n",
              "4    3gcpcrec2jg473991  167763266     florida     usa  22 hours left  \n",
              "5    2c4rdgeg9jr237989  167655771       texas     usa    2 days left  "
            ],
            "text/html": [
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              "      <td>6300</td>\n",
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              "      <td>2011</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>190552</td>\n",
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              "      <td>166951262</td>\n",
              "      <td>tennessee</td>\n",
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              "      <td>39590</td>\n",
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              "      <td>167655728</td>\n",
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              "      <td>usa</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25000</td>\n",
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              "      <td>blue</td>\n",
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              "      <td>167753855</td>\n",
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              "    <tr>\n",
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              "      <td>clean vehicle</td>\n",
              "      <td>6654</td>\n",
              "      <td>red</td>\n",
              "      <td>3gcpcrec2jg473991</td>\n",
              "      <td>167763266</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5700</td>\n",
              "      <td>dodge</td>\n",
              "      <td>mpv</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>45561</td>\n",
              "      <td>white</td>\n",
              "      <td>2c4rdgeg9jr237989</td>\n",
              "      <td>167655771</td>\n",
              "      <td>texas</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "        document.querySelector('#df-c05c73df-ee25-4338-8c9f-46ca7150b40c button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-c05c73df-ee25-4338-8c9f-46ca7150b40c');\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",
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              "        element.appendChild(docLink);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\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",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2499,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12116,\n        \"min\": 0,\n        \"max\": 84900,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          12995,\n          16200,\n          9280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"mercedes-benz\",\n          \"maserati\",\n          \"infiniti\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 127,\n        \"samples\": [\n          \"challenger\",\n          \"altima\",\n          \"cx-3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1984,\n          2000,\n          1994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"salvage insurance\",\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59705,\n        \"min\": 0,\n        \"max\": 1017936,\n        \"num_unique_values\": 2439,\n        \"samples\": [\n          20103,\n          31233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"green\",\n          \"cayenne red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          \"  1n4aa5ap4cc822996\",\n          \"  1fmjk2at0kea45301\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 203877,\n        \"min\": 159348797,\n        \"max\": 167805500,\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          167553965,\n          167615142\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"louisiana\",\n          \"indiana\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" canada\",\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"16 minutes\",\n          \"29 minutes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6SCYPfqisQI"
      },
      "source": [
        "### **4-mashq.** DF oxiridan 5 ta qator chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbY-WoLriSU-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "a7478d34-365f-48bd-d797-91045c2920ff"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df.tail()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price   brand  model  year   title_status  mileage   color  \\\n",
              "2494   7800  nissan  versa  2019  clean vehicle    23609     red   \n",
              "2495   9200  nissan  versa  2018  clean vehicle    34553  silver   \n",
              "2496   9200  nissan  versa  2018  clean vehicle    31594  silver   \n",
              "2497   9200  nissan  versa  2018  clean vehicle    32557   black   \n",
              "2498   9200  nissan  versa  2018  clean vehicle    31371  silver   \n",
              "\n",
              "                      vin        lot       state country      condition  \n",
              "2494    3n1cn7ap9kl880319  167722715  california     usa    1 days left  \n",
              "2495    3n1cn7ap5jl884088  167762225     florida     usa  21 hours left  \n",
              "2496    3n1cn7ap9jl884191  167762226     florida     usa  21 hours left  \n",
              "2497    3n1cn7ap3jl883263  167762227     florida     usa    2 days left  \n",
              "2498    3n1cn7ap4jl884311  167762228     florida     usa  21 hours left  "
            ],
            "text/html": [
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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>price</th>\n",
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              "      <td>nissan</td>\n",
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              "      <td>clean vehicle</td>\n",
              "      <td>23609</td>\n",
              "      <td>red</td>\n",
              "      <td>3n1cn7ap9kl880319</td>\n",
              "      <td>167722715</td>\n",
              "      <td>california</td>\n",
              "      <td>usa</td>\n",
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              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>9200</td>\n",
              "      <td>nissan</td>\n",
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              "      <td>clean vehicle</td>\n",
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              "      <td>silver</td>\n",
              "      <td>3n1cn7ap5jl884088</td>\n",
              "      <td>167762225</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
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              "      <td>9200</td>\n",
              "      <td>nissan</td>\n",
              "      <td>versa</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>31594</td>\n",
              "      <td>silver</td>\n",
              "      <td>3n1cn7ap9jl884191</td>\n",
              "      <td>167762226</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2497</th>\n",
              "      <td>9200</td>\n",
              "      <td>nissan</td>\n",
              "      <td>versa</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>32557</td>\n",
              "      <td>black</td>\n",
              "      <td>3n1cn7ap3jl883263</td>\n",
              "      <td>167762227</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2498</th>\n",
              "      <td>9200</td>\n",
              "      <td>nissan</td>\n",
              "      <td>versa</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>31371</td>\n",
              "      <td>silver</td>\n",
              "      <td>3n1cn7ap4jl884311</td>\n",
              "      <td>167762228</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: 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",
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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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4e5111a7-40f1-4b0a-8f3b-5ce2172d405c 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-4e5111a7-40f1-4b0a-8f3b-5ce2172d405c');\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",
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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",
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              "      --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",
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              "    20% {\n",
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              "      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-c9ffcaa5-d6be-44c2-b6d8-b779dcd3f0cc 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 626,\n        \"min\": 7800,\n        \"max\": 9200,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9200,\n          7800\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"nissan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"versa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2018,\n        \"max\": 2019,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4177,\n        \"min\": 23609,\n        \"max\": 34553,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34553\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"  3n1cn7ap5jl884088\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17670,\n        \"min\": 167722715,\n        \"max\": 167762228,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          167762225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"1 days left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZdvX0JlkXPs"
      },
      "source": [
        "### **5-mashq**. DF ni yili `year` bo'yicha tartiblab chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iirb0FVldRe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "b7291840-5601-46e3-ce15-8ecb42bd0104"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df.sort_values(by = \"year\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price      brand     model  year       title_status  mileage  \\\n",
              "32    29800  chevrolet    camaro  1973      clean vehicle    46226   \n",
              "405      25       ford      door  1984  salvage insurance    41577   \n",
              "545       0        gmc      door  1993  salvage insurance        0   \n",
              "362      25       ford    pickup  1994  salvage insurance   206162   \n",
              "322       0       ford   chassis  1994  salvage insurance        0   \n",
              "...     ...        ...       ...   ...                ...      ...   \n",
              "426   55600      lexus        gx  2020      clean vehicle     8186   \n",
              "806   28300  chevrolet  colorado  2020      clean vehicle    13886   \n",
              "2322  15000     nissan    altima  2020      clean vehicle    14320   \n",
              "2344  20100     nissan     rogue  2020      clean vehicle    10875   \n",
              "1388  17000       ford    fusion  2020      clean vehicle     1117   \n",
              "\n",
              "           color                  vin        lot         state country  \\\n",
              "32           red        1q87t3n166389  167763370  pennsylvania     usa   \n",
              "405        white    2ftcf15y9eca14589  167611661      arkansas     usa   \n",
              "545   light blue    1gkfk16k5pj701631  167358601      colorado     usa   \n",
              "362        white    1ftdf15y2rnb12612  167361489       georgia     usa   \n",
              "322        green    1fdee14n7rha47894  167359174    california     usa   \n",
              "...          ...                  ...        ...           ...     ...   \n",
              "426       silver    jtjam7bx4l5251250  167605747       florida     usa   \n",
              "806       silver    1gcgtcen7l1101496  167805497       indiana     usa   \n",
              "2322        blue    1n4bl4cv4lc134145  167656212          ohio     usa   \n",
              "2344        blue    jn8at2mvxlw103235  167614842     minnesota     usa   \n",
              "1388        gray    3fa6p0d98lr194138  167742945  pennsylvania     usa   \n",
              "\n",
              "          condition  \n",
              "32    22 hours left  \n",
              "405   17 hours left  \n",
              "545   18 hours left  \n",
              "362     2 days left  \n",
              "322   19 hours left  \n",
              "...             ...  \n",
              "426     2 days left  \n",
              "806     2 days left  \n",
              "2322    2 days left  \n",
              "2344  14 hours left  \n",
              "1388    2 days left  \n",
              "\n",
              "[2499 rows x 12 columns]"
            ],
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              "      <td>18 hours left</td>\n",
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              "      <td>1ftdf15y2rnb12612</td>\n",
              "      <td>167361489</td>\n",
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              "      <td>green</td>\n",
              "      <td>1fdee14n7rha47894</td>\n",
              "      <td>167359174</td>\n",
              "      <td>california</td>\n",
              "      <td>usa</td>\n",
              "      <td>19 hours left</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>55600</td>\n",
              "      <td>lexus</td>\n",
              "      <td>gx</td>\n",
              "      <td>2020</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>8186</td>\n",
              "      <td>silver</td>\n",
              "      <td>jtjam7bx4l5251250</td>\n",
              "      <td>167605747</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>806</th>\n",
              "      <td>28300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>colorado</td>\n",
              "      <td>2020</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>13886</td>\n",
              "      <td>silver</td>\n",
              "      <td>1gcgtcen7l1101496</td>\n",
              "      <td>167805497</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2322</th>\n",
              "      <td>15000</td>\n",
              "      <td>nissan</td>\n",
              "      <td>altima</td>\n",
              "      <td>2020</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>14320</td>\n",
              "      <td>blue</td>\n",
              "      <td>1n4bl4cv4lc134145</td>\n",
              "      <td>167656212</td>\n",
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              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
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              "      <td>10875</td>\n",
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              "      <td>167614842</td>\n",
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              "      <td>1117</td>\n",
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              "      <td>167742945</td>\n",
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              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2499 rows × 12 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2499,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12116,\n        \"min\": 0,\n        \"max\": 84900,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          40514,\n          6940,\n          30200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"lincoln\",\n          \"ram\",\n          \"honda\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 127,\n        \"samples\": [\n          \"f-150\",\n          \"acadia\",\n          \"sl-class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          2018,\n          2006,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"salvage insurance\",\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59705,\n        \"min\": 0,\n        \"max\": 1017936,\n        \"num_unique_values\": 2439,\n        \"samples\": [\n          26292,\n          56703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"yellow\",\n          \"lightning blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          \"  1c4sdjct3kc632985\",\n          \"  3n1ab7ap4ky222670\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 203877,\n        \"min\": 159348797,\n        \"max\": 167805500,\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          167615133,\n          167457191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"idaho\",\n          \"new jersey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" canada\",\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"9 minutes\",\n          \"48 minutes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdApCJ1zlhEg"
      },
      "source": [
        "### **6-mashq**. DF qatorlarini narxi bo'yicha reytinglang va konsolga narxi bo'yicha tartiblangan holatda chiqaring."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEZkgxoclhEh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "719aaf63-aff1-4a66-b79d-da9dde880ef5"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df['Rank'] = df['price'].rank()\n",
        "df.sort_values('price')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price          brand       model  year       title_status  mileage  \\\n",
              "410       0      chevrolet        door  1995  salvage insurance   274706   \n",
              "330       0           ford        door  1996  salvage insurance   296860   \n",
              "331       0           ford        door  2006  salvage insurance   203158   \n",
              "339       0           ford        door  2002  salvage insurance   214800   \n",
              "496       0           ford      pickup  1996  salvage insurance   252588   \n",
              "...     ...            ...         ...   ...                ...      ...   \n",
              "1215  65500           ford         srw  2019      clean vehicle     6500   \n",
              "277   67000          dodge  challenger  2019      clean vehicle    10944   \n",
              "1336  70000           ford         drw  2019      clean vehicle     9643   \n",
              "1340  74000           ford         drw  2019      clean vehicle    10536   \n",
              "502   84900  mercedes-benz    sl-class  2017      clean vehicle    25302   \n",
              "\n",
              "         color                  vin        lot           state country  \\\n",
              "410      green    2gcec19h8s1195266  167425634         arizona     usa   \n",
              "330      green    1falp62w5th144314  167359712      california     usa   \n",
              "331        red    1fmzk04136ga07119  167610991        illinois     usa   \n",
              "339      black    3fafp37372r151014  167360232  south carolina     usa   \n",
              "496        red    1ftef15n0tlc14455  167357804        oklahoma     usa   \n",
              "...        ...                  ...        ...             ...     ...   \n",
              "1215     black    1ft7w2bt0kec44818  167718954         indiana     usa   \n",
              "277       blue    2c3cdzl97kh518237  167759490            ohio     usa   \n",
              "1336  no_color    1ft8w3dt3kee48276  167780680        illinois     usa   \n",
              "1340  no_color    1ft8w4dt6ked32656  167780682        illinois     usa   \n",
              "502     silver    wddjk7ea3hf044968  167607883         florida     usa   \n",
              "\n",
              "          condition    rank    Rank  \n",
              "410     2 days left    22.0    22.0  \n",
              "330   19 hours left    22.0    22.0  \n",
              "331   17 hours left    22.0    22.0  \n",
              "339     2 days left    22.0    22.0  \n",
              "496   17 hours left    22.0    22.0  \n",
              "...             ...     ...     ...  \n",
              "1215  21 hours left  2495.0  2495.0  \n",
              "277   21 hours left  2496.0  2496.0  \n",
              "1336    2 days left  2497.0  2497.0  \n",
              "1340    2 days left  2498.0  2498.0  \n",
              "502     2 days left  2499.0  2499.0  \n",
              "\n",
              "[2499 rows x 14 columns]"
            ],
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              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>brand</th>\n",
              "      <th>model</th>\n",
              "      <th>year</th>\n",
              "      <th>title_status</th>\n",
              "      <th>mileage</th>\n",
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              "      <th>vin</th>\n",
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              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>condition</th>\n",
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              "      <th>Rank</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>0</td>\n",
              "      <td>chevrolet</td>\n",
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              "      <td>1995</td>\n",
              "      <td>salvage insurance</td>\n",
              "      <td>274706</td>\n",
              "      <td>green</td>\n",
              "      <td>2gcec19h8s1195266</td>\n",
              "      <td>167425634</td>\n",
              "      <td>arizona</td>\n",
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              "      <td>2 days left</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
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              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>0</td>\n",
              "      <td>ford</td>\n",
              "      <td>door</td>\n",
              "      <td>1996</td>\n",
              "      <td>salvage insurance</td>\n",
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              "      <td>green</td>\n",
              "      <td>1falp62w5th144314</td>\n",
              "      <td>167359712</td>\n",
              "      <td>california</td>\n",
              "      <td>usa</td>\n",
              "      <td>19 hours left</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
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              "    <tr>\n",
              "      <th>331</th>\n",
              "      <td>0</td>\n",
              "      <td>ford</td>\n",
              "      <td>door</td>\n",
              "      <td>2006</td>\n",
              "      <td>salvage insurance</td>\n",
              "      <td>203158</td>\n",
              "      <td>red</td>\n",
              "      <td>1fmzk04136ga07119</td>\n",
              "      <td>167610991</td>\n",
              "      <td>illinois</td>\n",
              "      <td>usa</td>\n",
              "      <td>17 hours left</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>0</td>\n",
              "      <td>ford</td>\n",
              "      <td>door</td>\n",
              "      <td>2002</td>\n",
              "      <td>salvage insurance</td>\n",
              "      <td>214800</td>\n",
              "      <td>black</td>\n",
              "      <td>3fafp37372r151014</td>\n",
              "      <td>167360232</td>\n",
              "      <td>south carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0</td>\n",
              "      <td>ford</td>\n",
              "      <td>pickup</td>\n",
              "      <td>1996</td>\n",
              "      <td>salvage insurance</td>\n",
              "      <td>252588</td>\n",
              "      <td>red</td>\n",
              "      <td>1ftef15n0tlc14455</td>\n",
              "      <td>167357804</td>\n",
              "      <td>oklahoma</td>\n",
              "      <td>usa</td>\n",
              "      <td>17 hours left</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1215</th>\n",
              "      <td>65500</td>\n",
              "      <td>ford</td>\n",
              "      <td>srw</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>6500</td>\n",
              "      <td>black</td>\n",
              "      <td>1ft7w2bt0kec44818</td>\n",
              "      <td>167718954</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>2495.0</td>\n",
              "      <td>2495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>67000</td>\n",
              "      <td>dodge</td>\n",
              "      <td>challenger</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>10944</td>\n",
              "      <td>blue</td>\n",
              "      <td>2c3cdzl97kh518237</td>\n",
              "      <td>167759490</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>2496.0</td>\n",
              "      <td>2496.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1336</th>\n",
              "      <td>70000</td>\n",
              "      <td>ford</td>\n",
              "      <td>drw</td>\n",
              "      <td>2019</td>\n",
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              "      <td>9643</td>\n",
              "      <td>no_color</td>\n",
              "      <td>1ft8w3dt3kee48276</td>\n",
              "      <td>167780680</td>\n",
              "      <td>illinois</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>2497.0</td>\n",
              "      <td>2497.0</td>\n",
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              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>74000</td>\n",
              "      <td>ford</td>\n",
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              "      <td>2019</td>\n",
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              "      <td>10536</td>\n",
              "      <td>no_color</td>\n",
              "      <td>1ft8w4dt6ked32656</td>\n",
              "      <td>167780682</td>\n",
              "      <td>illinois</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>2498.0</td>\n",
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              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>84900</td>\n",
              "      <td>mercedes-benz</td>\n",
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              "      <td>25302</td>\n",
              "      <td>silver</td>\n",
              "      <td>wddjk7ea3hf044968</td>\n",
              "      <td>167607883</td>\n",
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              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>2499.0</td>\n",
              "      <td>2499.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2499 rows × 14 columns</p>\n",
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              "  }\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-5366a71a-feba-4606-83fd-f205e3475463 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2499,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12116,\n        \"min\": 0,\n        \"max\": 84900,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          24502,\n          1825,\n          8000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"audi\",\n          \"land\",\n          \"bmw\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 127,\n        \"samples\": [\n          \"forte\",\n          \"gle\",\n          \"vans\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          2015,\n          2017,\n          1999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"clean vehicle\",\n          \"salvage insurance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59705,\n        \"min\": 0,\n        \"max\": 1017936,\n        \"num_unique_values\": 2439,\n        \"samples\": [\n          92165,\n          35511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"brown\",\n          \"competition orange\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          \"  3gcuksec0hg271643\",\n          \"  1ftew1ep8hfb07225\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 203877,\n        \"min\": 159348797,\n        \"max\": 167805500,\n        \"num_unique_values\": 2495,\n        \"samples\": [\n          167779691,\n          167779857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"nebraska\",\n          \"missouri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" canada\",\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"29 minutes\",\n          \"12 hours left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 721.5370314475898,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          1799.5,\n          116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 721.5370314475898,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 790,\n        \"samples\": [\n          1799.5,\n          116.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aquyXjI0mlbQ"
      },
      "source": [
        "### **7-mashq**. DFdan eng qimmat mashina haqidagi ma'lumotni chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-uYzZ7KmlbR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5bfa824-b506-4cbe-93eb-49c9eda3cdfe"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df['price'].max()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84900"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Umumiy ma'lumotlar\")\n",
        "idx = df['price'].idxmax()\n",
        "df.iloc[idx]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "ASIQXyoZYrEv",
        "outputId": "c1d5781c-5f6a-4615-f7ef-ab1343853c1b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Umumiy ma'lumotlar\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                         84900\n",
              "brand                 mercedes-benz\n",
              "model                      sl-class\n",
              "year                           2017\n",
              "title_status          clean vehicle\n",
              "mileage                       25302\n",
              "color                        silver\n",
              "vin               wddjk7ea3hf044968\n",
              "lot                       167607883\n",
              "state                       florida\n",
              "country                         usa\n",
              "condition               2 days left\n",
              "rank                         2499.0\n",
              "Rank                         2499.0\n",
              "Name: 502, dtype: object"
            ],
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>502</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>price</th>\n",
              "      <td>84900</td>\n",
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              "      <td>mercedes-benz</td>\n",
              "    </tr>\n",
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              "      <td>sl-class</td>\n",
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              "      <td>2017</td>\n",
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              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>clean vehicle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mileage</th>\n",
              "      <td>25302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color</th>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
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              "      <th>vin</th>\n",
              "      <td>wddjk7ea3hf044968</td>\n",
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              "      <td>167607883</td>\n",
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              "      <td>florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>2499.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>2499.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UErA92e9oFBZ"
      },
      "source": [
        "### **8-mashq**. DFdan eng ko'p yurgan (`km_driven`) mashina haqidagi ma'lumotni chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm1ObfbyoFBa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "16d8c9f1-86b8-4ce9-c30b-0cbd1499972f"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "km_driven = df[\"mileage\"].idxmax()\n",
        "df.iloc[km_driven]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                          1025\n",
              "brand                     peterbilt\n",
              "model                         truck\n",
              "year                           2010\n",
              "title_status      salvage insurance\n",
              "mileage                     1017936\n",
              "color                        color:\n",
              "vin               1xp7d49x0ad793710\n",
              "lot                       167529842\n",
              "state                       georgia\n",
              "country                         usa\n",
              "condition             17 hours left\n",
              "rank                           94.5\n",
              "Rank                           94.5\n",
              "Name: 528, dtype: object"
            ],
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "  <tbody>\n",
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              "      <th>price</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>peterbilt</td>\n",
              "    </tr>\n",
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              "      <th>model</th>\n",
              "      <td>truck</td>\n",
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              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>salvage insurance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mileage</th>\n",
              "      <td>1017936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color</th>\n",
              "      <td>color:</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vin</th>\n",
              "      <td>1xp7d49x0ad793710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lot</th>\n",
              "      <td>167529842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>georgia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>17 hours left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>94.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>94.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubHqPsXCvQB4"
      },
      "source": [
        "### **9-mashq**. DF dagi barcha brandlar va ularning sonini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ur-STMLIvQCG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "outputId": "ba853196-5666-4d96-aba2-015cbbf0a07f"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[\"brand\"].value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand\n",
              "ford               1235\n",
              "dodge               432\n",
              "nissan              312\n",
              "chevrolet           297\n",
              "gmc                  42\n",
              "jeep                 30\n",
              "chrysler             18\n",
              "bmw                  17\n",
              "hyundai              15\n",
              "kia                  13\n",
              "buick                13\n",
              "infiniti             12\n",
              "honda                12\n",
              "cadillac             10\n",
              "mercedes-benz        10\n",
              "heartland             5\n",
              "land                  4\n",
              "peterbilt             4\n",
              "audi                  4\n",
              "acura                 3\n",
              "lincoln               2\n",
              "lexus                 2\n",
              "mazda                 2\n",
              "maserati              1\n",
              "toyota                1\n",
              "harley-davidson       1\n",
              "jaguar                1\n",
              "ram                   1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brand</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>ford</th>\n",
              "      <td>1235</td>\n",
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              "    <tr>\n",
              "      <th>dodge</th>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nissan</th>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chevrolet</th>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gmc</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeep</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chrysler</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmw</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hyundai</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kia</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>buick</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>infiniti</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>honda</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cadillac</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mercedes-benz</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heartland</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>land</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>peterbilt</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>audi</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acura</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lincoln</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lexus</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mazda</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maserati</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toyota</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>harley-davidson</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jaguar</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ram</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN5aLizNvQlP"
      },
      "source": [
        "### **10-mashq**. DF dagi raqamli ustunlar haqida umulashtiruvchi statistikani chiqaring (min, max, mean, std va hokazo)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz880iBDvQlQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "6adeefc7-8021-46b7-dbef-2c59fa5d6139"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df.describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price         year       mileage           lot         rank  \\\n",
              "count   2499.000000  2499.000000  2.499000e+03  2.499000e+03  2499.000000   \n",
              "mean   18767.671469  2016.714286  5.229869e+04  1.676914e+08  1250.000000   \n",
              "std    12116.094936     3.442656  5.970552e+04  2.038772e+05   721.537031   \n",
              "min        0.000000  1973.000000  0.000000e+00  1.593488e+08    22.000000   \n",
              "25%    10200.000000  2016.000000  2.146650e+04  1.676253e+08   623.500000   \n",
              "50%    16900.000000  2018.000000  3.536500e+04  1.677451e+08  1251.500000   \n",
              "75%    25555.500000  2019.000000  6.347250e+04  1.677798e+08  1874.500000   \n",
              "max    84900.000000  2020.000000  1.017936e+06  1.678055e+08  2499.000000   \n",
              "\n",
              "              Rank  \n",
              "count  2499.000000  \n",
              "mean   1250.000000  \n",
              "std     721.537031  \n",
              "min      22.000000  \n",
              "25%     623.500000  \n",
              "50%    1251.500000  \n",
              "75%    1874.500000  \n",
              "max    2499.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-275494f0-e1ec-4feb-a992-d8eb3fc67692\" 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>price</th>\n",
              "      <th>year</th>\n",
              "      <th>mileage</th>\n",
              "      <th>lot</th>\n",
              "      <th>rank</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2.499000e+03</td>\n",
              "      <td>2.499000e+03</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>18767.671469</td>\n",
              "      <td>2016.714286</td>\n",
              "      <td>5.229869e+04</td>\n",
              "      <td>1.676914e+08</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12116.094936</td>\n",
              "      <td>3.442656</td>\n",
              "      <td>5.970552e+04</td>\n",
              "      <td>2.038772e+05</td>\n",
              "      <td>721.537031</td>\n",
              "      <td>721.537031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.593488e+08</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10200.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>2.146650e+04</td>\n",
              "      <td>1.676253e+08</td>\n",
              "      <td>623.500000</td>\n",
              "      <td>623.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16900.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>3.536500e+04</td>\n",
              "      <td>1.677451e+08</td>\n",
              "      <td>1251.500000</td>\n",
              "      <td>1251.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25555.500000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>6.347250e+04</td>\n",
              "      <td>1.677798e+08</td>\n",
              "      <td>1874.500000</td>\n",
              "      <td>1874.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>84900.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>1.017936e+06</td>\n",
              "      <td>1.678055e+08</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-275494f0-e1ec-4feb-a992-d8eb3fc67692 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-275494f0-e1ec-4feb-a992-d8eb3fc67692');\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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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     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-ace94759-0cfb-497d-8a6a-9c06054227bc 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27001.395373815525,\n        \"min\": 0.0,\n        \"max\": 84900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18767.671468587436,\n          16900.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 754.0599416653441,\n        \"min\": 3.442656170052959,\n        \"max\": 2499.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2016.7142857142858,\n          2018.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348889.9554648,\n        \"min\": 0.0,\n        \"max\": 1017936.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52298.685474189675,\n          35365.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77003633.14574681,\n        \"min\": 2499.0,\n        \"max\": 167805500.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          167691389.06002402,\n          167745058.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 896.1123872343678,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2499.0,\n          1250.0,\n          1251.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 896.1123872343678,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2499.0,\n          1250.0,\n          1251.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ0UG6y6vSZt"
      },
      "source": [
        "### **11-mashq**. DF dan ford kompaniyasining barcha mashinalarnini ajratib chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6OcdG2JvSZv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "e65b2c3d-bae0-467e-c912-420702c87ba3"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[ df[\"brand\"] == \"ford\"]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price brand   model  year   title_status  mileage     color  \\\n",
              "1      2899  ford      se  2011  clean vehicle   190552    silver   \n",
              "3     25000  ford    door  2014  clean vehicle    64146      blue   \n",
              "9      5250  ford     mpv  2017  clean vehicle    63418     black   \n",
              "14    20700  ford    door  2013  clean vehicle   100757     black   \n",
              "18     5210  ford     mpv  2017  clean vehicle    35714     white   \n",
              "...     ...   ...     ...   ...            ...      ...       ...   \n",
              "2199  13800  ford  fusion  2019  clean vehicle    29345     white   \n",
              "2200  56000  ford     srw  2018  clean vehicle    25371     black   \n",
              "2201  13800  ford  fusion  2019  clean vehicle    30054    silver   \n",
              "2202  45800  ford     srw  2018  clean vehicle    11173     white   \n",
              "2203  13800  ford  fusion  2019  clean vehicle    30902  no_color   \n",
              "\n",
              "                      vin        lot           state country      condition  \\\n",
              "1       2fmdk3gc4bbb02217  166951262       tennessee     usa    6 days left   \n",
              "3       1ftfw1et4efc23745  167753855        virginia     usa  22 hours left   \n",
              "9       2fmpk3j92hbc12542  167656121           texas     usa    2 days left   \n",
              "14      1ftfw1et7dfa47790  167753916        virginia     usa  22 hours left   \n",
              "18      2fmpk3j95hbb73607  167656124           texas     usa    2 days left   \n",
              "...                   ...        ...             ...     ...            ...   \n",
              "2199    3fa6p0lu1kr222799  167802379  north carolina     usa    2 days left   \n",
              "2200    1ft7w2bt4jec50359  167749689    pennsylvania     usa    3 days left   \n",
              "2201    3fa6p0lu3kr210749  167802380  north carolina     usa    2 days left   \n",
              "2202    1ft7w2bt3jeb02932  167749690    pennsylvania     usa    2 days left   \n",
              "2203    3fa6p0lu8kr225585  167802381  north carolina     usa    2 days left   \n",
              "\n",
              "        rank    Rank  \n",
              "1      156.0   156.0  \n",
              "3     1832.5  1832.5  \n",
              "9      323.0   323.0  \n",
              "14    1525.0  1525.0  \n",
              "18     322.0   322.0  \n",
              "...      ...     ...  \n",
              "2199   911.5   911.5  \n",
              "2200  2476.0  2476.0  \n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>1525.0</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
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              "      <td>mpv</td>\n",
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              "      <td>322.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <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",
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              "      <th>2199</th>\n",
              "      <td>13800</td>\n",
              "      <td>ford</td>\n",
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              "      <td>clean vehicle</td>\n",
              "      <td>29345</td>\n",
              "      <td>white</td>\n",
              "      <td>3fa6p0lu1kr222799</td>\n",
              "      <td>167802379</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>911.5</td>\n",
              "      <td>911.5</td>\n",
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              "    <tr>\n",
              "      <th>2200</th>\n",
              "      <td>56000</td>\n",
              "      <td>ford</td>\n",
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              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>25371</td>\n",
              "      <td>black</td>\n",
              "      <td>1ft7w2bt4jec50359</td>\n",
              "      <td>167749689</td>\n",
              "      <td>pennsylvania</td>\n",
              "      <td>usa</td>\n",
              "      <td>3 days left</td>\n",
              "      <td>2476.0</td>\n",
              "      <td>2476.0</td>\n",
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              "    <tr>\n",
              "      <th>2201</th>\n",
              "      <td>13800</td>\n",
              "      <td>ford</td>\n",
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              "      <td>911.5</td>\n",
              "      <td>911.5</td>\n",
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              "      <td>2405.0</td>\n",
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              "      <td>911.5</td>\n",
              "      <td>911.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1235 rows × 14 columns</p>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[ df[\\\"brand\\\"] == \\\"ford\\\"]\",\n  \"rows\": 1235,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12945,\n        \"min\": 0,\n        \"max\": 74000,\n        \"num_unique_values\": 560,\n        \"samples\": [\n          42000,\n          24900,\n          9000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ford\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"cargo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1984,\n        \"max\": 2020,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"salvage insurance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54948,\n        \"min\": 0,\n        \"max\": 999999,\n        \"num_unique_values\": 1224,\n        \"samples\": [\n          15656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"ingot silver\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1235,\n        \"samples\": [\n          \"  1fmcu9hdxkub65946\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98290,\n        \"min\": 166951262,\n        \"max\": 167802381,\n        \"num_unique_values\": 1235,\n        \"samples\": [\n          167796758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"west virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"15 days left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 733.5346980724686,\n        \"min\": 22.0,\n        \"max\": 2498.0,\n        \"num_unique_values\": 560,\n        \"samples\": [\n          2374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 733.5346980724686,\n        \"min\": 22.0,\n        \"max\": 2498.0,\n        \"num_unique_values\": 560,\n        \"samples\": [\n          2374.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4PToFlKxvTDx"
      },
      "source": [
        "### **12-mashq**. Nissan kompaniyasining eng qimmat va eng arzon mashinalarini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzJsC6LcvTDy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "c71b21ab-931d-4187-b262-9eb4a872009c"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df_nissan = df[df.brand =='nissan']\n",
        "df_nissan.loc[df_nissan['price'].idxmin()]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                           375\n",
              "brand                        nissan\n",
              "model                          door\n",
              "year                           2017\n",
              "title_status      salvage insurance\n",
              "mileage                           1\n",
              "color                           red\n",
              "vin               3n1ce2cp0hl360793\n",
              "lot                       167619908\n",
              "state                north carolina\n",
              "country                         usa\n",
              "condition               2 days left\n",
              "rank                           74.0\n",
              "Rank                           74.0\n",
              "Name: 2417, dtype: object"
            ],
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              "      <td>nissan</td>\n",
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              "      <th>model</th>\n",
              "      <td>door</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>salvage insurance</td>\n",
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              "      <th>mileage</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color</th>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vin</th>\n",
              "      <td>3n1ce2cp0hl360793</td>\n",
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              "      <th>lot</th>\n",
              "      <td>167619908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>north carolina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>2 days left</td>\n",
              "    </tr>\n",
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              "      <th>rank</th>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nissan = df[df.brand =='nissan']\n",
        "df_nissan.loc[df_nissan['price'].idxmax()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "zNcmWCTsex23",
        "outputId": "c717d3ce-db0f-4a41-c9a2-b2070be698eb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                         36300\n",
              "brand                        nissan\n",
              "model                            xd\n",
              "year                           2017\n",
              "title_status          clean vehicle\n",
              "mileage                       16224\n",
              "color                         white\n",
              "vin               1n6ba1f46hn535299\n",
              "lot                       167753308\n",
              "state                      virginia\n",
              "country                         usa\n",
              "condition             21 hours left\n",
              "rank                         2305.5\n",
              "Rank                         2305.5\n",
              "Name: 2375, dtype: object"
            ],
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              "      <th>2375</th>\n",
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              "      <td>36300</td>\n",
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              "      <th>mileage</th>\n",
              "      <td>16224</td>\n",
              "    </tr>\n",
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              "      <th>color</th>\n",
              "      <td>white</td>\n",
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              "      <td>1n6ba1f46hn535299</td>\n",
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              "      <th>lot</th>\n",
              "      <td>167753308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>virginia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>21 hours left</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>2305.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>2305.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPDinYlcvXGi"
      },
      "source": [
        "### **13-mashq**. Chevrolet kompaniyasining barcha modellarini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSwaAhWCvXGi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "19ac2278-9891-401b-9a4f-f12eec2c2df7"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[df[\"brand\"] == \"chevrolet\"]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price      brand     model  year   title_status  mileage   color  \\\n",
              "4    27700  chevrolet      1500  2018  clean vehicle     6654     red   \n",
              "6     7300  chevrolet        pk  2010  clean vehicle   149050   black   \n",
              "8    14600  chevrolet    malibu  2018  clean vehicle     9371  silver   \n",
              "12   31900  chevrolet      1500  2018  clean vehicle    22909   black   \n",
              "22   15000  chevrolet      door  2015  clean vehicle    61578   black   \n",
              "..     ...        ...       ...   ...            ...      ...     ...   \n",
              "803  28300  chevrolet        ld  2019  clean vehicle    27665     red   \n",
              "804  27300  chevrolet        ld  2019  clean vehicle    25427   black   \n",
              "805   3500  chevrolet      door  2013  clean vehicle    78198    gray   \n",
              "806  28300  chevrolet  colorado  2020  clean vehicle    13886  silver   \n",
              "807  18300  chevrolet    impala  2019  clean vehicle    34538  silver   \n",
              "\n",
              "                     vin        lot       state country      condition  \\\n",
              "4      3gcpcrec2jg473991  167763266     florida     usa  22 hours left   \n",
              "6      1gcsksea1az121133  167753872     georgia     usa  22 hours left   \n",
              "8      1g1zd5st5jf191860  167763267     florida     usa  22 hours left   \n",
              "12     3gcukrec0jg176059  167763273   tennessee     usa  22 hours left   \n",
              "22     2gnflhe33f6214023  167763280        ohio     usa    2 days left   \n",
              "..                   ...        ...         ...     ...            ...   \n",
              "803    2gcvkpec1k1124569  167805479     indiana     usa    2 days left   \n",
              "804    2gcvkpec4k1105269  167805483     indiana     usa    2 days left   \n",
              "805    1g11c5saxdf350889  167617243  new jersey     usa    7 days left   \n",
              "806    1gcgtcen7l1101496  167805497     indiana     usa    2 days left   \n",
              "807    2g11z5s37k9134455  167805500     indiana     usa    2 days left   \n",
              "\n",
              "       rank    Rank  \n",
              "4    2005.0  2005.0  \n",
              "6     440.0   440.0  \n",
              "8     994.5   994.5  \n",
              "12   2193.5  2193.5  \n",
              "22   1031.0  1031.0  \n",
              "..      ...     ...  \n",
              "803  2036.5  2036.5  \n",
              "804  1988.0  1988.0  \n",
              "805   201.0   201.0  \n",
              "806  2036.5  2036.5  \n",
              "807  1362.0  1362.0  \n",
              "\n",
              "[297 rows x 14 columns]"
            ],
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              "      <th>brand</th>\n",
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              "  <tbody>\n",
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              "      <th>4</th>\n",
              "      <td>27700</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>1500</td>\n",
              "      <td>2018</td>\n",
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              "      <td>red</td>\n",
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              "      <td>2005.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>pk</td>\n",
              "      <td>2010</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>149050</td>\n",
              "      <td>black</td>\n",
              "      <td>1gcsksea1az121133</td>\n",
              "      <td>167753872</td>\n",
              "      <td>georgia</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>440.0</td>\n",
              "      <td>440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14600</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>9371</td>\n",
              "      <td>silver</td>\n",
              "      <td>1g1zd5st5jf191860</td>\n",
              "      <td>167763267</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>994.5</td>\n",
              "      <td>994.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>31900</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>1500</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>22909</td>\n",
              "      <td>black</td>\n",
              "      <td>3gcukrec0jg176059</td>\n",
              "      <td>167763273</td>\n",
              "      <td>tennessee</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>2193.5</td>\n",
              "      <td>2193.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>15000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>door</td>\n",
              "      <td>2015</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>61578</td>\n",
              "      <td>black</td>\n",
              "      <td>2gnflhe33f6214023</td>\n",
              "      <td>167763280</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1031.0</td>\n",
              "      <td>1031.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>28300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>ld</td>\n",
              "      <td>2019</td>\n",
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              "      <td>27665</td>\n",
              "      <td>red</td>\n",
              "      <td>2gcvkpec1k1124569</td>\n",
              "      <td>167805479</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>2036.5</td>\n",
              "      <td>2036.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>804</th>\n",
              "      <td>27300</td>\n",
              "      <td>chevrolet</td>\n",
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              "      <td>25427</td>\n",
              "      <td>black</td>\n",
              "      <td>2gcvkpec4k1105269</td>\n",
              "      <td>167805483</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>1988.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>805</th>\n",
              "      <td>3500</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>door</td>\n",
              "      <td>2013</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>78198</td>\n",
              "      <td>gray</td>\n",
              "      <td>1g11c5saxdf350889</td>\n",
              "      <td>167617243</td>\n",
              "      <td>new jersey</td>\n",
              "      <td>usa</td>\n",
              "      <td>7 days left</td>\n",
              "      <td>201.0</td>\n",
              "      <td>201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>806</th>\n",
              "      <td>28300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>colorado</td>\n",
              "      <td>2020</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>13886</td>\n",
              "      <td>silver</td>\n",
              "      <td>1gcgtcen7l1101496</td>\n",
              "      <td>167805497</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>2036.5</td>\n",
              "      <td>2036.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>807</th>\n",
              "      <td>18300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>34538</td>\n",
              "      <td>silver</td>\n",
              "      <td>2g11z5s37k9134455</td>\n",
              "      <td>167805500</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1362.0</td>\n",
              "      <td>1362.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>297 rows × 14 columns</p>\n",
              "</div>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "        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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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    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",
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              "    20% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\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-ab4bf898-7947-4c32-bce9-b59374423914 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",
              "summary": "{\n  \"name\": \"df[df[\\\"brand\\\"] == \\\"chevrolet\\\"]\",\n  \"rows\": 297,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12195,\n        \"min\": 0,\n        \"max\": 63200,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          26300,\n          16100,\n          35000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"chevrolet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 28,\n        \"samples\": [\n          \"van\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1973,\n        \"max\": 2020,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"salvage insurance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60906,\n        \"min\": 0,\n        \"max\": 507985,\n        \"num_unique_values\": 293,\n        \"samples\": [\n          268040\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 293,\n        \"samples\": [\n          \"  1g1zt53f46f254868\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60743,\n        \"min\": 167417659,\n        \"max\": 167805500,\n        \"num_unique_values\": 293,\n        \"samples\": [\n          167650486\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"west virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" canada\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"3 days left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 759.2086282336136,\n        \"min\": 22.0,\n        \"max\": 2493.0,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          1918.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 759.2086282336136,\n        \"min\": 22.0,\n        \"max\": 2493.0,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          1918.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijQt0c434-sZ"
      },
      "source": [
        "### **14-mashq**. Mashina narxi va yili o'rtasidagi korrelyasiya qiymatini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8QGeweY4-sa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29c60878-1971-49b6-df8a-5a6068f6597b"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[\"price\"].corr(df.year)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4182735684917942"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ay59JNT5UCV"
      },
      "source": [
        "### **14-mashq**. Mashina narxi va boshqa ustunlar o'rtasidagi korrelyasiyani chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bw9SECvR5UCV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d9cd715f-de1a-4963-8afc-2e5857475043"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df_for_corr = df.describe()\n",
        "df_for_corr"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price         year       mileage           lot         rank  \\\n",
              "count   2499.000000  2499.000000  2.499000e+03  2.499000e+03  2499.000000   \n",
              "mean   18767.671469  2016.714286  5.229869e+04  1.676914e+08  1250.000000   \n",
              "std    12116.094936     3.442656  5.970552e+04  2.038772e+05   721.537031   \n",
              "min        0.000000  1973.000000  0.000000e+00  1.593488e+08    22.000000   \n",
              "25%    10200.000000  2016.000000  2.146650e+04  1.676253e+08   623.500000   \n",
              "50%    16900.000000  2018.000000  3.536500e+04  1.677451e+08  1251.500000   \n",
              "75%    25555.500000  2019.000000  6.347250e+04  1.677798e+08  1874.500000   \n",
              "max    84900.000000  2020.000000  1.017936e+06  1.678055e+08  2499.000000   \n",
              "\n",
              "              Rank  \n",
              "count  2499.000000  \n",
              "mean   1250.000000  \n",
              "std     721.537031  \n",
              "min      22.000000  \n",
              "25%     623.500000  \n",
              "50%    1251.500000  \n",
              "75%    1874.500000  \n",
              "max    2499.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72dea823-3151-41e3-8a40-decfbcb24523\" 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>price</th>\n",
              "      <th>year</th>\n",
              "      <th>mileage</th>\n",
              "      <th>lot</th>\n",
              "      <th>rank</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2.499000e+03</td>\n",
              "      <td>2.499000e+03</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>18767.671469</td>\n",
              "      <td>2016.714286</td>\n",
              "      <td>5.229869e+04</td>\n",
              "      <td>1.676914e+08</td>\n",
              "      <td>1250.000000</td>\n",
              "      <td>1250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12116.094936</td>\n",
              "      <td>3.442656</td>\n",
              "      <td>5.970552e+04</td>\n",
              "      <td>2.038772e+05</td>\n",
              "      <td>721.537031</td>\n",
              "      <td>721.537031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.593488e+08</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10200.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>2.146650e+04</td>\n",
              "      <td>1.676253e+08</td>\n",
              "      <td>623.500000</td>\n",
              "      <td>623.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16900.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>3.536500e+04</td>\n",
              "      <td>1.677451e+08</td>\n",
              "      <td>1251.500000</td>\n",
              "      <td>1251.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25555.500000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>6.347250e+04</td>\n",
              "      <td>1.677798e+08</td>\n",
              "      <td>1874.500000</td>\n",
              "      <td>1874.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>84900.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>1.017936e+06</td>\n",
              "      <td>1.678055e+08</td>\n",
              "      <td>2499.000000</td>\n",
              "      <td>2499.000000</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72dea823-3151-41e3-8a40-decfbcb24523');\n",
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              "                                                    [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",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
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              "\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    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",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-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",
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              "    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",
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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",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_for_corr",
              "summary": "{\n  \"name\": \"df_for_corr\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27001.395373815525,\n        \"min\": 0.0,\n        \"max\": 84900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18767.671468587436,\n          16900.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 754.0599416653441,\n        \"min\": 3.442656170052959,\n        \"max\": 2499.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2016.7142857142858,\n          2018.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 348889.9554648,\n        \"min\": 0.0,\n        \"max\": 1017936.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52298.685474189675,\n          35365.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77003633.14574681,\n        \"min\": 2499.0,\n        \"max\": 167805500.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          167691389.06002402,\n          167745058.0,\n          2499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 896.1123872343678,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2499.0,\n          1250.0,\n          1251.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 896.1123872343678,\n        \"min\": 22.0,\n        \"max\": 2499.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2499.0,\n          1250.0,\n          1251.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRvlU1A05w7M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "1595e772-58de-46ec-f204-9dde1eedc91f"
      },
      "source": [
        "df_for_corr.corr()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            price      year   mileage       lot      rank      Rank\n",
              "price    1.000000  0.074933  0.967568  0.336656  0.569857  0.569857\n",
              "year     0.074933  1.000000  0.070671  0.465911  0.395613  0.395613\n",
              "mileage  0.967568  0.070671  1.000000  0.231128  0.526921  0.526921\n",
              "lot      0.336656  0.465911  0.231128  1.000000 -0.162819 -0.162819\n",
              "rank     0.569857  0.395613  0.526921 -0.162819  1.000000  1.000000\n",
              "Rank     0.569857  0.395613  0.526921 -0.162819  1.000000  1.000000"
            ],
            "text/html": [
              "\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.074933</td>\n",
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              "      <th>mileage</th>\n",
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              "      <td>0.526921</td>\n",
              "      <td>0.526921</td>\n",
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              "      <td>0.569857</td>\n",
              "      <td>0.395613</td>\n",
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              "      <td>-0.162819</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
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              "      <td>0.395613</td>\n",
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              "\n",
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              "        document.querySelector('#df-0416c052-e98f-420b-9b6b-73722ceef768 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-0416c052-e98f-420b-9b6b-73722ceef768');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "  }\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-1b429383-8fbe-4a91-8602-2bbd78c7ac1c 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",
              "summary": "{\n  \"name\": \"df_for_corr\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35801434972703866,\n        \"min\": 0.07493342514834295,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07493342514834295,\n          0.5698569936671884,\n          0.967567799283869\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34014920665236714,\n        \"min\": 0.0706708809530999,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          0.3956127863164281,\n          0.0706708809530999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.376636137141825,\n        \"min\": 0.0706708809530999,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0706708809530999,\n          0.5269209118638194,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43602929264380147,\n        \"min\": -0.1628192627543094,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.46591072540853307,\n          -0.1628192627543094,\n          0.23112837324039337\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43306533287857824,\n        \"min\": -0.1628192627543094,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3956127863164281,\n          1.0,\n          0.5269209118638194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43306533287857824,\n        \"min\": -0.1628192627543094,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3956127863164281,\n          1.0,\n          0.5269209118638194\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iFvLxMW5xUr"
      },
      "source": [
        "### **15-mashq**. DF dan kia va hyundai mashinalarini ajratib alohida DF ga saqlang. Yangi DFni chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taP9pojY5xUr"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "brands = [\"kia\", \"hyundai\"]\n",
        "df2 = df[df[\"brand\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          0
        ],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        },
        "id": "6_A9WrdL5xUr",
        "outputId": "8ea3c7e3-f2c3-4f96-bfa8-0ca4578fa7b5"
      },
      "source": [
        "#@title **To'g'ri javob**\n",
        "brands = ['kia','hyundai']\n",
        "df2 = df[df['brand'].isin(brands)]\n",
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .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>price</th>\n",
              "      <th>brand</th>\n",
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              "  </thead>\n",
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              "      <th>16</th>\n",
              "      <td>5200</td>\n",
              "      <td>kia</td>\n",
              "      <td>forte</td>\n",
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              "      <td>2 days left</td>\n",
              "      <td>319.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>7300</td>\n",
              "      <td>kia</td>\n",
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              "      <td>2 days left</td>\n",
              "      <td>440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>21500</td>\n",
              "      <td>kia</td>\n",
              "      <td>sorento</td>\n",
              "      <td>2020</td>\n",
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              "      <td>2 days left</td>\n",
              "      <td>1566.5</td>\n",
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              "      <th>287</th>\n",
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              "      <td>506.5</td>\n",
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              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>3000</td>\n",
              "      <td>kia</td>\n",
              "      <td>door</td>\n",
              "      <td>2013</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>97142</td>\n",
              "      <td>silver</td>\n",
              "      <td>knagm4a75d5378293</td>\n",
              "      <td>167552545</td>\n",
              "      <td>missouri</td>\n",
              "      <td>usa</td>\n",
              "      <td>9 days left</td>\n",
              "      <td>168.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>12700</td>\n",
              "      <td>kia</td>\n",
              "      <td>soul</td>\n",
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              "      <td>167679072</td>\n",
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              "      <td>10 days left</td>\n",
              "      <td>806.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>13800</td>\n",
              "      <td>kia</td>\n",
              "      <td>sorento</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>86959</td>\n",
              "      <td>gray</td>\n",
              "      <td>5xyphda50hg232046</td>\n",
              "      <td>167803771</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>3 days left</td>\n",
              "      <td>911.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>9800</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>elantra</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
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              "      <td>5 hours left</td>\n",
              "      <td>605.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>5350</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>mpv</td>\n",
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              "      <td>22 hours left</td>\n",
              "      <td>326.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389</th>\n",
              "      <td>3760</td>\n",
              "      <td>hyundai</td>\n",
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              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>30382</td>\n",
              "      <td>silver</td>\n",
              "      <td>5npd84lf6kh456317</td>\n",
              "      <td>167698685</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>5998</td>\n",
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              "      <td>doors</td>\n",
              "      <td>2014</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>122975</td>\n",
              "      <td>phantom black</td>\n",
              "      <td>5npdh4ae0eh480157</td>\n",
              "      <td>167279690</td>\n",
              "      <td>new york</td>\n",
              "      <td>usa</td>\n",
              "      <td>5 days left</td>\n",
              "      <td>358.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>0</td>\n",
              "      <td>kia</td>\n",
              "      <td>wagon</td>\n",
              "      <td>2008</td>\n",
              "      <td>salvage insurance</td>\n",
              "      <td>148463</td>\n",
              "      <td>silver</td>\n",
              "      <td>knafg525787197602</td>\n",
              "      <td>167247592</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>16 hours left</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>3960</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>doors</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>35623</td>\n",
              "      <td>red</td>\n",
              "      <td>5npd84lf5kh403222</td>\n",
              "      <td>167697946</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>232.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438</th>\n",
              "      <td>4010</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>doors</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>31233</td>\n",
              "      <td>silver</td>\n",
              "      <td>kmhd84lf4ku746279</td>\n",
              "      <td>167697947</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>440</th>\n",
              "      <td>13000</td>\n",
              "      <td>kia</td>\n",
              "      <td>sportage</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>71991</td>\n",
              "      <td>silver</td>\n",
              "      <td>kndpn3ac7h7113313</td>\n",
              "      <td>167606881</td>\n",
              "      <td>new jersey</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 hours left</td>\n",
              "      <td>841.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>4690</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>mpv</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>33785</td>\n",
              "      <td>silver</td>\n",
              "      <td>km8k12aa0ku242609</td>\n",
              "      <td>167697948</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>290.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>8000</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>door</td>\n",
              "      <td>2015</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>88274</td>\n",
              "      <td>red</td>\n",
              "      <td>km8jucag9fu984199</td>\n",
              "      <td>167607388</td>\n",
              "      <td>pennsylvania</td>\n",
              "      <td>usa</td>\n",
              "      <td>7 days left</td>\n",
              "      <td>493.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>4730</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>mpv</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>30121</td>\n",
              "      <td>red</td>\n",
              "      <td>km8j23a45ku893842</td>\n",
              "      <td>167697951</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>513</th>\n",
              "      <td>4650</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>mpv</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>27492</td>\n",
              "      <td>white</td>\n",
              "      <td>km8j23a44ku882752</td>\n",
              "      <td>167697953</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>289.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>3440</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>doors</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>34895</td>\n",
              "      <td>gray</td>\n",
              "      <td>3kpc24a34ke050725</td>\n",
              "      <td>167697954</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>192.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>3810</td>\n",
              "      <td>kia</td>\n",
              "      <td>door</td>\n",
              "      <td>2016</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>39408</td>\n",
              "      <td>black</td>\n",
              "      <td>kndjx3ae3g7012477</td>\n",
              "      <td>167692796</td>\n",
              "      <td>california</td>\n",
              "      <td>usa</td>\n",
              "      <td>20 hours left</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>4750</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>mpv</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>25847</td>\n",
              "      <td>gray</td>\n",
              "      <td>km8j23a4xku846404</td>\n",
              "      <td>167697955</td>\n",
              "      <td>ohio</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>296.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>3460</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>door</td>\n",
              "      <td>2012</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>142106</td>\n",
              "      <td>white</td>\n",
              "      <td>5npeb4ac2ch437716</td>\n",
              "      <td>167697983</td>\n",
              "      <td>mississippi</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>197.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>3250</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>door</td>\n",
              "      <td>2015</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>111612</td>\n",
              "      <td>gray</td>\n",
              "      <td>5npdh4ae3fh644096</td>\n",
              "      <td>167692885</td>\n",
              "      <td>california</td>\n",
              "      <td>usa</td>\n",
              "      <td>20 hours left</td>\n",
              "      <td>185.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>14000</td>\n",
              "      <td>kia</td>\n",
              "      <td>sorento</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>53424</td>\n",
              "      <td>black</td>\n",
              "      <td>5xypgda51hg221253</td>\n",
              "      <td>167119104</td>\n",
              "      <td>connecticut</td>\n",
              "      <td>usa</td>\n",
              "      <td>5 hours left</td>\n",
              "      <td>949.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>626</th>\n",
              "      <td>13300</td>\n",
              "      <td>kia</td>\n",
              "      <td>door</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>37125</td>\n",
              "      <td>no_color</td>\n",
              "      <td>5xypgda39hg262132</td>\n",
              "      <td>167119105</td>\n",
              "      <td>connecticut</td>\n",
              "      <td>usa</td>\n",
              "      <td>5 hours left</td>\n",
              "      <td>862.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>15500</td>\n",
              "      <td>kia</td>\n",
              "      <td>sorento</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>37668</td>\n",
              "      <td>red</td>\n",
              "      <td>5xypgda59hg292944</td>\n",
              "      <td>167119106</td>\n",
              "      <td>connecticut</td>\n",
              "      <td>usa</td>\n",
              "      <td>5 hours left</td>\n",
              "      <td>1091.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>631</th>\n",
              "      <td>21500</td>\n",
              "      <td>kia</td>\n",
              "      <td>sorento</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>43761</td>\n",
              "      <td>gray</td>\n",
              "      <td>5xypkda50hg299327</td>\n",
              "      <td>167119107</td>\n",
              "      <td>connecticut</td>\n",
              "      <td>usa</td>\n",
              "      <td>5 hours left</td>\n",
              "      <td>1566.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     price    brand     model  ...  country      condition    rank\n",
              "16    5200      kia     forte  ...      usa    2 days left   319.0\n",
              "21    7300      kia     forte  ...      usa    2 days left   440.0\n",
              "26   21500      kia   sorento  ...      usa    2 days left  1566.5\n",
              "287   8200  hyundai      door  ...      usa   5 hours left   506.5\n",
              "288   3000      kia      door  ...      usa    9 days left   168.0\n",
              "291  12700      kia      soul  ...      usa   10 days left   806.5\n",
              "292  13800      kia   sorento  ...      usa    3 days left   911.5\n",
              "296   9800  hyundai   elantra  ...      usa   5 hours left   605.0\n",
              "366   5350  hyundai       mpv  ...      usa  22 hours left   326.5\n",
              "389   3760  hyundai     doors  ...      usa  22 hours left   216.0\n",
              "417   5998  hyundai     doors  ...      usa    5 days left   358.0\n",
              "419      0      kia     wagon  ...      usa  16 hours left    22.0\n",
              "424   3960  hyundai     doors  ...      usa    2 days left   232.5\n",
              "438   4010  hyundai     doors  ...      usa    2 days left   240.0\n",
              "440  13000      kia  sportage  ...      usa   2 hours left   841.5\n",
              "450   4690  hyundai       mpv  ...      usa    2 days left   290.0\n",
              "463   8000  hyundai      door  ...      usa    7 days left   493.5\n",
              "486   4730  hyundai       mpv  ...      usa    2 days left   295.0\n",
              "513   4650  hyundai       mpv  ...      usa    2 days left   289.0\n",
              "525   3440  hyundai     doors  ...      usa    2 days left   192.5\n",
              "527   3810      kia      door  ...      usa  20 hours left   222.0\n",
              "537   4750  hyundai       mpv  ...      usa    2 days left   296.0\n",
              "549   3460  hyundai      door  ...      usa    2 days left   197.0\n",
              "551   3250  hyundai      door  ...      usa  20 hours left   185.5\n",
              "623  14000      kia   sorento  ...      usa   5 hours left   949.0\n",
              "626  13300      kia      door  ...      usa   5 hours left   862.5\n",
              "629  15500      kia   sorento  ...      usa   5 hours left  1091.0\n",
              "631  21500      kia   sorento  ...      usa   5 hours left  1566.5\n",
              "\n",
              "[28 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jz0yKgdi6MuQ"
      },
      "source": [
        "### **16-mashq**. Chevrolet kompaniyasining malibu va impala mashinalarini alohida DF ko'rinishida chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj8JYqGG6MuQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cf6f23a5-9029-4fe9-f325-7b4f62130b21"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "brands2 = [\"malibu\", \"impala\"]\n",
        "df3 = df[df[\"model\"].isin(brands2)]\n",
        "df3"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price      brand   model  year   title_status  mileage     color  \\\n",
              "8    14600  chevrolet  malibu  2018  clean vehicle     9371    silver   \n",
              "47   19000  chevrolet  malibu  2017  clean vehicle    18414     white   \n",
              "107  18003  chevrolet  malibu  2017  clean vehicle    19117  no_color   \n",
              "204   6000  chevrolet  malibu  2018  clean vehicle    42090      gray   \n",
              "266  17100  chevrolet  malibu  2019  clean vehicle    15780     white   \n",
              "278  18900  chevrolet  impala  2019  clean vehicle    27601     white   \n",
              "281  18300  chevrolet  impala  2019  clean vehicle    25809     black   \n",
              "569  13500  chevrolet  impala  2018  clean vehicle    53194     white   \n",
              "647   9000  chevrolet  malibu  2016  clean vehicle    67527      gray   \n",
              "677  13700  chevrolet  malibu  2019  clean vehicle    47282    silver   \n",
              "686  12300  chevrolet  malibu  2018  clean vehicle    53599     black   \n",
              "688  13600  chevrolet  malibu  2018  clean vehicle    36047     white   \n",
              "691  13000  chevrolet  malibu  2018  clean vehicle    41751      gray   \n",
              "731  25000  chevrolet  impala  2020  clean vehicle    18715    silver   \n",
              "740  13700  chevrolet  impala  2019  clean vehicle    14939      blue   \n",
              "741  15900  chevrolet  malibu  2019  clean vehicle    22026     black   \n",
              "768  10200  chevrolet  malibu  2018  clean vehicle    45836     white   \n",
              "791  15200  chevrolet  impala  2019  clean vehicle    32248       red   \n",
              "792  15000  chevrolet  impala  2019  clean vehicle    36165    silver   \n",
              "793  15500  chevrolet  impala  2019  clean vehicle    29922  charcoal   \n",
              "794  15700  chevrolet  impala  2019  clean vehicle    26848    silver   \n",
              "795  14800  chevrolet  impala  2019  clean vehicle    41163  charcoal   \n",
              "797   8200  chevrolet  impala  2017  clean vehicle   100315     black   \n",
              "807  18300  chevrolet  impala  2019  clean vehicle    34538    silver   \n",
              "\n",
              "                     vin        lot           state country      condition  \\\n",
              "8      1g1zd5st5jf191860  167763267         florida     usa  22 hours left   \n",
              "47     1g1zh5sx9hf118136  167763675       tennessee     usa  22 hours left   \n",
              "107    1g1zh5sx7hf141060  167765112        michigan     usa  17 hours left   \n",
              "204    1g1zb5st8jf226848  167765944           texas     usa  21 hours left   \n",
              "266    1g1zd5st5kf112138  167767034      washington     usa    2 days left   \n",
              "278    1g11z5s37ku129047  167767312           texas     usa  21 hours left   \n",
              "281    2g11z5s33k9132248  167767315           texas     usa  21 hours left   \n",
              "569    2g1105s3xj9121918  167773137      new jersey     usa   7 hours left   \n",
              "647    1g1zj5su8gf337274  167780142       wisconsin     usa  21 hours left   \n",
              "677    1g1zd5st4kf126371  167787112        oklahoma     usa  21 hours left   \n",
              "686    1g1zd5stxjf140841  167787955        oklahoma     usa  21 hours left   \n",
              "688    1g1zd5st5jf249837  167787964        oklahoma     usa  21 hours left   \n",
              "691    1g1zd5st8jf242655  167787968        oklahoma     usa  21 hours left   \n",
              "731    1g1105s31lu100901  167796081          nevada     usa   6 hours left   \n",
              "740    2g11z5s30k9137729  167796203          nevada     usa      9 minutes   \n",
              "741    1g1zd5st0kf187281  167796205          nevada     usa   1 hours left   \n",
              "768    1g1zd5stxjf175198  167800168           texas     usa    2 days left   \n",
              "791    1g11z5sa4ku113760  167801930  north carolina     usa    2 days left   \n",
              "792    1g11z5sa7ku117222  167801932  north carolina     usa    2 days left   \n",
              "793    2g11z5saxk9120208  167801934  north carolina     usa    2 days left   \n",
              "794    2g11z5sa5k9140950  167801935  north carolina     usa    2 days left   \n",
              "795    2g11z5sa8k9139310  167801936  north carolina     usa    2 days left   \n",
              "797    1g1105sa9hu160835  167802211  north carolina     usa    2 days left   \n",
              "807    2g11z5s37k9134455  167805500         indiana     usa    2 days left   \n",
              "\n",
              "       rank    Rank  \n",
              "8     994.5   994.5  \n",
              "47   1419.5  1419.5  \n",
              "107  1342.0  1342.0  \n",
              "204   362.5   362.5  \n",
              "266  1274.0  1274.0  \n",
              "278  1410.5  1410.5  \n",
              "281  1362.0  1362.0  \n",
              "569   878.5   878.5  \n",
              "647   554.5   554.5  \n",
              "677   897.5   897.5  \n",
              "686   782.0   782.0  \n",
              "688   889.0   889.0  \n",
              "691   841.5   841.5  \n",
              "731  1832.5  1832.5  \n",
              "740   897.5   897.5  \n",
              "741  1145.0  1145.0  \n",
              "768   623.5   623.5  \n",
              "791  1054.0  1054.0  \n",
              "792  1031.0  1031.0  \n",
              "793  1091.0  1091.0  \n",
              "794  1118.0  1118.0  \n",
              "795  1011.5  1011.5  \n",
              "797   506.5   506.5  \n",
              "807  1362.0  1362.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f68d29d-c434-4d05-b88e-657590f261de\" 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>price</th>\n",
              "      <th>brand</th>\n",
              "      <th>model</th>\n",
              "      <th>year</th>\n",
              "      <th>title_status</th>\n",
              "      <th>mileage</th>\n",
              "      <th>color</th>\n",
              "      <th>vin</th>\n",
              "      <th>lot</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>condition</th>\n",
              "      <th>rank</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14600</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>9371</td>\n",
              "      <td>silver</td>\n",
              "      <td>1g1zd5st5jf191860</td>\n",
              "      <td>167763267</td>\n",
              "      <td>florida</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>994.5</td>\n",
              "      <td>994.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>19000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>18414</td>\n",
              "      <td>white</td>\n",
              "      <td>1g1zh5sx9hf118136</td>\n",
              "      <td>167763675</td>\n",
              "      <td>tennessee</td>\n",
              "      <td>usa</td>\n",
              "      <td>22 hours left</td>\n",
              "      <td>1419.5</td>\n",
              "      <td>1419.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>18003</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>19117</td>\n",
              "      <td>no_color</td>\n",
              "      <td>1g1zh5sx7hf141060</td>\n",
              "      <td>167765112</td>\n",
              "      <td>michigan</td>\n",
              "      <td>usa</td>\n",
              "      <td>17 hours left</td>\n",
              "      <td>1342.0</td>\n",
              "      <td>1342.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>6000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>42090</td>\n",
              "      <td>gray</td>\n",
              "      <td>1g1zb5st8jf226848</td>\n",
              "      <td>167765944</td>\n",
              "      <td>texas</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>362.5</td>\n",
              "      <td>362.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>17100</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>15780</td>\n",
              "      <td>white</td>\n",
              "      <td>1g1zd5st5kf112138</td>\n",
              "      <td>167767034</td>\n",
              "      <td>washington</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>1274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>18900</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>27601</td>\n",
              "      <td>white</td>\n",
              "      <td>1g11z5s37ku129047</td>\n",
              "      <td>167767312</td>\n",
              "      <td>texas</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>1410.5</td>\n",
              "      <td>1410.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>18300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>25809</td>\n",
              "      <td>black</td>\n",
              "      <td>2g11z5s33k9132248</td>\n",
              "      <td>167767315</td>\n",
              "      <td>texas</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>1362.0</td>\n",
              "      <td>1362.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>13500</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>53194</td>\n",
              "      <td>white</td>\n",
              "      <td>2g1105s3xj9121918</td>\n",
              "      <td>167773137</td>\n",
              "      <td>new jersey</td>\n",
              "      <td>usa</td>\n",
              "      <td>7 hours left</td>\n",
              "      <td>878.5</td>\n",
              "      <td>878.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>647</th>\n",
              "      <td>9000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2016</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>67527</td>\n",
              "      <td>gray</td>\n",
              "      <td>1g1zj5su8gf337274</td>\n",
              "      <td>167780142</td>\n",
              "      <td>wisconsin</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>554.5</td>\n",
              "      <td>554.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>13700</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>47282</td>\n",
              "      <td>silver</td>\n",
              "      <td>1g1zd5st4kf126371</td>\n",
              "      <td>167787112</td>\n",
              "      <td>oklahoma</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>897.5</td>\n",
              "      <td>897.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>686</th>\n",
              "      <td>12300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>53599</td>\n",
              "      <td>black</td>\n",
              "      <td>1g1zd5stxjf140841</td>\n",
              "      <td>167787955</td>\n",
              "      <td>oklahoma</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>782.0</td>\n",
              "      <td>782.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>688</th>\n",
              "      <td>13600</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>36047</td>\n",
              "      <td>white</td>\n",
              "      <td>1g1zd5st5jf249837</td>\n",
              "      <td>167787964</td>\n",
              "      <td>oklahoma</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>889.0</td>\n",
              "      <td>889.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>691</th>\n",
              "      <td>13000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>41751</td>\n",
              "      <td>gray</td>\n",
              "      <td>1g1zd5st8jf242655</td>\n",
              "      <td>167787968</td>\n",
              "      <td>oklahoma</td>\n",
              "      <td>usa</td>\n",
              "      <td>21 hours left</td>\n",
              "      <td>841.5</td>\n",
              "      <td>841.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>731</th>\n",
              "      <td>25000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2020</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>18715</td>\n",
              "      <td>silver</td>\n",
              "      <td>1g1105s31lu100901</td>\n",
              "      <td>167796081</td>\n",
              "      <td>nevada</td>\n",
              "      <td>usa</td>\n",
              "      <td>6 hours left</td>\n",
              "      <td>1832.5</td>\n",
              "      <td>1832.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>13700</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>14939</td>\n",
              "      <td>blue</td>\n",
              "      <td>2g11z5s30k9137729</td>\n",
              "      <td>167796203</td>\n",
              "      <td>nevada</td>\n",
              "      <td>usa</td>\n",
              "      <td>9 minutes</td>\n",
              "      <td>897.5</td>\n",
              "      <td>897.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>15900</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>22026</td>\n",
              "      <td>black</td>\n",
              "      <td>1g1zd5st0kf187281</td>\n",
              "      <td>167796205</td>\n",
              "      <td>nevada</td>\n",
              "      <td>usa</td>\n",
              "      <td>1 hours left</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>768</th>\n",
              "      <td>10200</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>2018</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>45836</td>\n",
              "      <td>white</td>\n",
              "      <td>1g1zd5stxjf175198</td>\n",
              "      <td>167800168</td>\n",
              "      <td>texas</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>623.5</td>\n",
              "      <td>623.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>791</th>\n",
              "      <td>15200</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>32248</td>\n",
              "      <td>red</td>\n",
              "      <td>1g11z5sa4ku113760</td>\n",
              "      <td>167801930</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>1054.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>792</th>\n",
              "      <td>15000</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>36165</td>\n",
              "      <td>silver</td>\n",
              "      <td>1g11z5sa7ku117222</td>\n",
              "      <td>167801932</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1031.0</td>\n",
              "      <td>1031.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>793</th>\n",
              "      <td>15500</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>29922</td>\n",
              "      <td>charcoal</td>\n",
              "      <td>2g11z5saxk9120208</td>\n",
              "      <td>167801934</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1091.0</td>\n",
              "      <td>1091.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>794</th>\n",
              "      <td>15700</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>26848</td>\n",
              "      <td>silver</td>\n",
              "      <td>2g11z5sa5k9140950</td>\n",
              "      <td>167801935</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>1118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>14800</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>41163</td>\n",
              "      <td>charcoal</td>\n",
              "      <td>2g11z5sa8k9139310</td>\n",
              "      <td>167801936</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1011.5</td>\n",
              "      <td>1011.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>797</th>\n",
              "      <td>8200</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2017</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>100315</td>\n",
              "      <td>black</td>\n",
              "      <td>1g1105sa9hu160835</td>\n",
              "      <td>167802211</td>\n",
              "      <td>north carolina</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>506.5</td>\n",
              "      <td>506.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>807</th>\n",
              "      <td>18300</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>impala</td>\n",
              "      <td>2019</td>\n",
              "      <td>clean vehicle</td>\n",
              "      <td>34538</td>\n",
              "      <td>silver</td>\n",
              "      <td>2g11z5s37k9134455</td>\n",
              "      <td>167805500</td>\n",
              "      <td>indiana</td>\n",
              "      <td>usa</td>\n",
              "      <td>2 days left</td>\n",
              "      <td>1362.0</td>\n",
              "      <td>1362.0</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-2f68d29d-c434-4d05-b88e-657590f261de')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </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",
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              "\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-2f68d29d-c434-4d05-b88e-657590f261de 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-2f68d29d-c434-4d05-b88e-657590f261de');\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",
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              "\n",
              "\n",
              "<div id=\"df-4a715695-ba80-464a-8ce5-3a488216c697\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a715695-ba80-464a-8ce5-3a488216c697')\"\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",
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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",
              "      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-4a715695-ba80-464a-8ce5-3a488216c697 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": "df3",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4022,\n        \"min\": 6000,\n        \"max\": 25000,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          14600,\n          25000,\n          9000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"chevrolet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"impala\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2016,\n        \"max\": 2020,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19800,\n        \"min\": 9371,\n        \"max\": 100315,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          67527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"white\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"  1g1zj5su8gf337274\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15528,\n        \"min\": 167763267,\n        \"max\": 167805500,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          167780142\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"new jersey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"17 hours left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338.1896971911355,\n        \"min\": 362.5,\n        \"max\": 1832.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          994.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338.1896971911355,\n        \"min\": 362.5,\n        \"max\": 1832.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          994.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SZPTK7G65jc"
      },
      "source": [
        "### **17-mashq**. Ford kompaniyasining mustang modelini alohioda DF ga ajrating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSsO-70L65jc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "7bf74914-1b20-439c-e241-41e1c1021469"
      },
      "source": [
        "df_mustang = df[(df['brand']=='ford') & (df['model']=='mustang')]\n",
        "df_mustang.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price brand    model  year   title_status  mileage  color  \\\n",
              "632   25511  ford  mustang  2019  clean vehicle    11342   gray   \n",
              "1224  21900  ford  mustang  2018  clean vehicle    29748    red   \n",
              "1266  22000  ford  mustang  2018  clean vehicle     6248    red   \n",
              "1267  38200  ford  mustang  2019  clean vehicle      878  green   \n",
              "1269  35000  ford  mustang  2018  clean vehicle     2042   blue   \n",
              "\n",
              "                      vin        lot       state country      condition  \\\n",
              "632     1fatp8uh4k5177265  167763745    michigan     usa  19 hours left   \n",
              "1224    1fatp8uh4j5166362  167773235  new jersey     usa    2 days left   \n",
              "1266    1fa6p8th3j5135439  167778967    illinois     usa    2 days left   \n",
              "1267    1fa6p8k02k5503784  167732976     florida     usa    1 days left   \n",
              "1269    1fa6p8cf9j5131958  167778969    illinois     usa  21 hours left   \n",
              "\n",
              "        rank    Rank  \n",
              "632   1873.5  1873.5  \n",
              "1224  1589.0  1589.0  \n",
              "1266  1598.5  1598.5  \n",
              "1267  2333.5  2333.5  \n",
              "1269  2281.5  2281.5  "
            ],
            "text/html": [
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              "      <td>2333.5</td>\n",
              "      <td>2333.5</td>\n",
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              "      <th>1269</th>\n",
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              "      <td>2281.5</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_mustang",
              "summary": "{\n  \"name\": \"df_mustang\",\n  \"rows\": 29,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7781,\n        \"min\": 15500,\n        \"max\": 49000,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          35100,\n          27300,\n          25511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ford\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"mustang\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2016,\n        \"max\": 2020,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"clean vehicle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12153,\n        \"min\": 878,\n        \"max\": 41147,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          25810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29,\n        \"samples\": [\n          \"  1fatp8uhxk5148949\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74645,\n        \"min\": 167555176,\n        \"max\": 167801796,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          167625718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"mississippi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \" usa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"19 hours left\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 390.3961910170118,\n        \"min\": 1091.0,\n        \"max\": 2439.5,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2285.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 390.3961910170118,\n        \"min\": 1091.0,\n        \"max\": 2439.5,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2285.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qwu49AH_bIU"
      },
      "source": [
        "### **18-mashq**. 2018-yildan keyin chiqqan Mustang mashinalirining o'rtacha narxini chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1adGOEuR_bIV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "323b0ae3-e20b-4fcb-a446-c32ee0feca19"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df_mustang[df_mustang['year'] >= 2018]['price'].mean()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24837.444444444445"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktbMxLKAGXjP"
      },
      "source": [
        "### **19-mashq**. Mustang mashinalari haqida umumlashgan statistikani chiqaring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFBQsr5BGXjQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bf169a1-c13e-4b0a-d450-5049b6c11e1a"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[df[\"model\"] == \"mustang\"].info()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 29 entries, 632 to 2128\n",
            "Data columns (total 14 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   price         29 non-null     int64  \n",
            " 1   brand         29 non-null     object \n",
            " 2   model         29 non-null     object \n",
            " 3   year          29 non-null     int64  \n",
            " 4   title_status  29 non-null     object \n",
            " 5   mileage       29 non-null     int64  \n",
            " 6   color         29 non-null     object \n",
            " 7   vin           29 non-null     object \n",
            " 8   lot           29 non-null     int64  \n",
            " 9   state         29 non-null     object \n",
            " 10  country       29 non-null     object \n",
            " 11  condition     29 non-null     object \n",
            " 12  rank          29 non-null     float64\n",
            " 13  Rank          29 non-null     float64\n",
            "dtypes: float64(2), int64(4), object(8)\n",
            "memory usage: 3.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"model\"] == \"mustang\"].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ajr3KhCaor24",
        "outputId": "636ae429-2079-43f0-9512-ae66a0a6aafb"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price         year       mileage           lot         rank  \\\n",
              "count     29.000000    29.000000     29.000000  2.900000e+01    29.000000   \n",
              "mean   25841.758621  2018.517241  21551.931034  1.677386e+08  1791.189655   \n",
              "std     7781.073538     0.784706  12153.315994  7.464566e+04   390.396191   \n",
              "min    15500.000000  2016.000000    878.000000  1.675552e+08  1091.000000   \n",
              "25%    20500.000000  2018.000000  11342.000000  1.677355e+08  1515.500000   \n",
              "50%    22000.000000  2019.000000  25011.000000  1.677637e+08  1598.500000   \n",
              "75%    29800.000000  2019.000000  31561.000000  1.677824e+08  2111.000000   \n",
              "max    49000.000000  2020.000000  41147.000000  1.678018e+08  2439.500000   \n",
              "\n",
              "              Rank  \n",
              "count    29.000000  \n",
              "mean   1791.189655  \n",
              "std     390.396191  \n",
              "min    1091.000000  \n",
              "25%    1515.500000  \n",
              "50%    1598.500000  \n",
              "75%    2111.000000  \n",
              "max    2439.500000  "
            ],
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              "      <th>price</th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25841.758621</td>\n",
              "      <td>2018.517241</td>\n",
              "      <td>21551.931034</td>\n",
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              "    </tr>\n",
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              "      <th>min</th>\n",
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              "      <td>2016.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>2018.000000</td>\n",
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              "      <td>1.677355e+08</td>\n",
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              "      <td>22000.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>25011.000000</td>\n",
              "      <td>1.677637e+08</td>\n",
              "      <td>1598.500000</td>\n",
              "      <td>1598.500000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>29800.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>31561.000000</td>\n",
              "      <td>1.677824e+08</td>\n",
              "      <td>2111.000000</td>\n",
              "      <td>2111.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2020.000000</td>\n",
              "      <td>41147.000000</td>\n",
              "      <td>1.678018e+08</td>\n",
              "      <td>2439.500000</td>\n",
              "      <td>2439.500000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df[\\\"model\\\"] == \\\"mustang\\\"]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14784.905667758783,\n        \"min\": 29.0,\n        \"max\": 49000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          25841.758620689656,\n          22000.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 927.4842353483733,\n        \"min\": 0.7847060257179305,\n        \"max\": 2020.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          29.0,\n          2018.5172413793102,\n          2019.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mileage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14521.28350293095,\n        \"min\": 29.0,\n        \"max\": 41147.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21551.931034482757,\n          25011.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77626439.3742508,\n        \"min\": 29.0,\n        \"max\": 167801796.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          167738578.10344827,\n          167763745.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 826.3029078377419,\n        \"min\": 29.0,\n        \"max\": 2439.5,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1791.1896551724137,\n          1598.5,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 826.3029078377419,\n        \"min\": 29.0,\n        \"max\": 2439.5,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1791.1896551724137,\n          1598.5,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ks1qv8EHBfF"
      },
      "source": [
        "### **20-mashq**. Sotuvdagi ford mashinalarining aksari nechachni yilda ishlab chiqarilganini toping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fk6XSQyCHBfG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adefcdce-a962-4d7f-f8fb-6e09e338e4e0"
      },
      "source": [
        "# Javobni shuyerga yozing\n",
        "df[df[\"brand\"] == \"ford\"][[\"year\"]].value_counts().idxmax()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2019,)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    }
  ]
}