Learnpy.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9p5jUpDBMhX"
      },
      "source": [
        "#https://colab.research.google.com/drive/1_Zs5d_ADrtxKKXYIQI-8Lb1lttet2BGS\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFdeimeiJJub"
      },
      "source": [
        "Lab Hello world !\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-ST9NQBJJLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6708f5a5-6555-43f0-e0bf-1bfcf1b8c55f"
      },
      "source": [
        "print(\"Hello world!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hello world!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5Ogjtz5JdIM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e14f0861-56a1-4946-ffb4-94fdd9d9fbb6"
      },
      "source": [
        "print(\"hello\\\n",
        "hello\\\n",
        "hello\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hellohellohello\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4Ck02k0Jsbn"
      },
      "source": [
        "INput LAB!\n",
        "ใช้ input , เปลี่ยน data type และ ตรวจสอบ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87qyyhuKJvSR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "6fe8ca35-c2aa-4808-bd06-3c1bffa1108f"
      },
      "source": [
        "age = int(input(\"input your age \"))\n",
        "\n",
        "print(age)\n",
        "print(type(age))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input your age 6\n",
            "6\n",
            "<class 'int'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N24jMu3nKe2P"
      },
      "source": [
        "LAB \n",
        "Data type\n",
        "int \n",
        "string \n",
        "char \n",
        "boolean \n",
        "list \n",
        "tuple \n",
        "dictionary\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqcBV7ETKlcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "5654a25f-0edb-4965-a1f7-03f8e72fbcae"
      },
      "source": [
        "type_int = 123 #int \n",
        "type_str = \"siwarat \" #string\n",
        "type_chr = 'A' #Char\n",
        "type_boolean = True #boolean true false\n",
        "type_list = list([1,2,3,4]) #list [1,2,3,4]\n",
        "type_tuple = tuple([1,2,3,4])#tuple (1,2,3,4)\n",
        "type_dict = {\"ger\":1,\n",
        "             \"lol\":2 } #dict{}\n",
        "print(type_dict)\n",
        "print(type_tuple)\n",
        "print(type_list)\n",
        "print(type_int)\n",
        "print(type_boolean)\n",
        "print(type_str)\n",
        "print(type_chr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'ger': 1, 'lol': 2}\n",
            "(1, 2, 3, 4)\n",
            "[1, 2, 3, 4]\n",
            "123\n",
            "True\n",
            "siwarat \n",
            "A\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxixJ1BMPFkL"
      },
      "source": [
        "## ***Function  in python***\n",
        "สรุป"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWPFDBt5PDhH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b28d724b-deac-4fbf-8b06-89cb4a277f1d"
      },
      "source": [
        "agrument1 = \"siwa\"\n",
        "agrument2 = \"rat\"\n",
        "def fun_name(parameter1 =\"ger\" , parameter2 = \"lol\"):\n",
        "  print(parameter1,parameter2)\n",
        "fun_name(agrument1,agrument2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "siwa rat\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-H_pUjONuyg"
      },
      "source": [
        "Pandas\n",
        "## ***New Section***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SM38F8BZNuUx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8039b64b-e847-4e92-ac2c-de8befba4ad4"
      },
      "source": [
        "import pandas as pd\n",
        "names = [\"First\",\"ป้อม\",\"พาส\",\"Nan\",\"นายก\"] \n",
        "heights = [180,158,172,170,172]\n",
        "\n",
        "series = pd.Series(heights, names)\n",
        "series[1:4]\n",
        "\n",
        "# series[[0,2,4]]\n",
        "\n",
        "# series[[True,True,True,False,True]]\n",
        "# series[(series > 170)]\n",
        "\n",
        "# series[\"ger\"] = 2\n",
        "\n",
        "# type(series)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ป้อม    158\n",
              "พาส     172\n",
              "Nan     170\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTL-FZF7RufP"
      },
      "source": [
        "Lab Easy series\n",
        "\n",
        "1)สร้างseries weight\n",
        "\n",
        "2)สร้างseries height\n",
        "\n",
        "3)นำมาบวกกัน\n",
        "\n",
        "4) เพิ่มนำ้หนักของเพื่อน 1 คนในข้อ 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUV8MJ4BRt_Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "4b435377-4404-40d2-d1ab-5c521eba9c36"
      },
      "source": [
        "friend_name  = [\"A\",\"B\",\"C\",\"D\",\"F\"]\n",
        "weight_fri = [50,60,70,80,85]\n",
        "height_fri = [170,175,180,185,190]\n",
        "series1 = pd.Series(weight_fri,friend_name)\n",
        "series2 = pd.Series(height_fri,friend_name)\n",
        "series3 = pd.Series(series1+series2,friend_name)\n",
        "print(series3)\n",
        "\n",
        "\n",
        "series2[\"ger\"] = 160\n",
        "print(\"min of height=\",series2.min())\n",
        "print(\"max of height=\",series2.max())\n",
        "print(\"aver of height =\",series2.mean())\n",
        "\n",
        "\n",
        "print(\"min of weight=\",series1.min())\n",
        "print(\"max of weight=\",series1.max())\n",
        "print(\"aver of weight =\",series1.mean())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A    220\n",
            "B    235\n",
            "C    250\n",
            "D    265\n",
            "F    275\n",
            "dtype: int64\n",
            "min of height= 160\n",
            "max of height= 190\n",
            "aver of height = 176.66666666666666\n",
            "min of weight= 50\n",
            "max of weight= 85\n",
            "aver of weight = 69.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQLDyre5c7jx"
      },
      "source": [
        "## ***Dataframe***\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svs-la1YZF7B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a4615e92-7ae7-4329-acea-0650d53a5d71"
      },
      "source": [
        "names = [\"First\",\"ป้อม\",\"พาส\",\"Nan\",\"นายก\"] \n",
        "cols = [\"Height\",\"Weight\"]\n",
        "height_weight = [[180,72],[158,50],[172,55],[170,65],[172,90]]\n",
        "df = pd.DataFrame(height_weight,names,cols)\n",
        "df_tall_person = df[df[\"Height\"]>170]\n",
        "df_tall_person[[\"Height\",\"Weight\"]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Height  Weight\n",
              "First     180      72\n",
              "พาส       172      55\n",
              "นายก      172      90"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>First</th>\n",
              "      <td>180</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>พาส</th>\n",
              "      <td>172</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>นายก</th>\n",
              "      <td>172</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      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",
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              "\n",
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              "          document.querySelector('#df-56bbb9a2-2e57-4518-9eae-180c67337332 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-56bbb9a2-2e57-4518-9eae-180c67337332');\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5EdNPTeeexOG"
      },
      "source": [
        "dataframe โจทย์\n",
        "\n",
        "1) เพิ้ม column เข้าไป โดยที column คือ อายุมัวๆ\n",
        "\n",
        "2)find old>30years\n",
        "\n",
        "3)print df_old[[\"Height\",\"age\"]]\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzkH3hVefXMd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "outputId": "257d4d2a-1703-4e80-f9bd-a6b86be0dcca"
      },
      "source": [
        "names = [\"First\",\"ป้อม\",\"พาส\",\"Nan\",\"นายก\"] \n",
        "cols = [\"Height\",\"Weight\",\"age\"]\n",
        "height_weight_age = [[180,72,32],[158,50,11],[172,55,17],[170,65,19],[172,90,22]]\n",
        "df = pd.DataFrame(height_weight_age,names,cols)\n",
        "df_old = df[df[\"age\"]>30]\n",
        "df_old[[\"Height\",\"age\"]]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>Height</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>180</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Height  age\n",
              "First     180   32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlxTLedxmlsd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "708b0617-bb1f-4826-981f-66b0a45fd230"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/st119848/dataset/master/Automobile_data.csv\")\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling normalized-losses         make fuel-type aspiration  \\\n",
              "0            3                 ?  alfa-romero       gas        std   \n",
              "1            3                 ?  alfa-romero       gas        std   \n",
              "2            1                 ?  alfa-romero       gas        std   \n",
              "3            2               164         audi       gas        std   \n",
              "4            2               164         audi       gas        std   \n",
              "..         ...               ...          ...       ...        ...   \n",
              "200         -1                95        volvo       gas        std   \n",
              "201         -1                95        volvo       gas      turbo   \n",
              "202         -1                95        volvo       gas        std   \n",
              "203         -1                95        volvo    diesel      turbo   \n",
              "204         -1                95        volvo       gas      turbo   \n",
              "\n",
              "    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n",
              "0            two  convertible          rwd           front        88.6  ...   \n",
              "1            two  convertible          rwd           front        88.6  ...   \n",
              "2            two    hatchback          rwd           front        94.5  ...   \n",
              "3           four        sedan          fwd           front        99.8  ...   \n",
              "4           four        sedan          4wd           front        99.4  ...   \n",
              "..           ...          ...          ...             ...         ...  ...   \n",
              "200         four        sedan          rwd           front       109.1  ...   \n",
              "201         four        sedan          rwd           front       109.1  ...   \n",
              "202         four        sedan          rwd           front       109.1  ...   \n",
              "203         four        sedan          rwd           front       109.1  ...   \n",
              "204         four        sedan          rwd           front       109.1  ...   \n",
              "\n",
              "     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
              "0            130         mpfi  3.47    2.68               9.0        111   \n",
              "1            130         mpfi  3.47    2.68               9.0        111   \n",
              "2            152         mpfi  2.68    3.47               9.0        154   \n",
              "3            109         mpfi  3.19     3.4              10.0        102   \n",
              "4            136         mpfi  3.19     3.4               8.0        115   \n",
              "..           ...          ...   ...     ...               ...        ...   \n",
              "200          141         mpfi  3.78    3.15               9.5        114   \n",
              "201          141         mpfi  3.78    3.15               8.7        160   \n",
              "202          173         mpfi  3.58    2.87               8.8        134   \n",
              "203          145          idi  3.01     3.4              23.0        106   \n",
              "204          141         mpfi  3.78    3.15               9.5        114   \n",
              "\n",
              "     peak-rpm city-mpg highway-mpg  price  \n",
              "0        5000       21          27  13495  \n",
              "1        5000       21          27  16500  \n",
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              "3        5500       24          30  13950  \n",
              "4        5500       18          22  17450  \n",
              "..        ...      ...         ...    ...  \n",
              "200      5400       23          28  16845  \n",
              "201      5300       19          25  19045  \n",
              "202      5500       18          23  21485  \n",
              "203      4800       26          27  22470  \n",
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              "\n",
              "[205 rows x 26 columns]"
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              "      <td>94.5</td>\n",
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              "      <td>mpfi</td>\n",
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              "      <th>3</th>\n",
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              "      <td>mpfi</td>\n",
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              "      <td>5500</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>5400</td>\n",
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              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
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              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>8.7</td>\n",
              "      <td>160</td>\n",
              "      <td>5300</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>19045</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>-1</td>\n",
              "      <td>95</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>173</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.58</td>\n",
              "      <td>2.87</td>\n",
              "      <td>8.8</td>\n",
              "      <td>134</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>23</td>\n",
              "      <td>21485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>-1</td>\n",
              "      <td>95</td>\n",
              "      <td>volvo</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>145</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.01</td>\n",
              "      <td>3.4</td>\n",
              "      <td>23.0</td>\n",
              "      <td>106</td>\n",
              "      <td>4800</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>22470</td>\n",
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              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>-1</td>\n",
              "      <td>95</td>\n",
              "      <td>volvo</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "      <td>141</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.5</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>22625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c08a30d-ccc3-4870-b38f-71e7f24a6138')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "            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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmOLK5gSn5k8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "357ededa-42f6-4941-f76f-98170b237c2d"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symboling</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>205.000000</td>\n",
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              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
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              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>205.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.834146</td>\n",
              "      <td>98.756585</td>\n",
              "      <td>174.049268</td>\n",
              "      <td>65.907805</td>\n",
              "      <td>53.724878</td>\n",
              "      <td>2555.565854</td>\n",
              "      <td>126.907317</td>\n",
              "      <td>10.142537</td>\n",
              "      <td>25.219512</td>\n",
              "      <td>30.751220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.245307</td>\n",
              "      <td>6.021776</td>\n",
              "      <td>12.337289</td>\n",
              "      <td>2.145204</td>\n",
              "      <td>2.443522</td>\n",
              "      <td>520.680204</td>\n",
              "      <td>41.642693</td>\n",
              "      <td>3.972040</td>\n",
              "      <td>6.542142</td>\n",
              "      <td>6.886443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.000000</td>\n",
              "      <td>86.600000</td>\n",
              "      <td>141.100000</td>\n",
              "      <td>60.300000</td>\n",
              "      <td>47.800000</td>\n",
              "      <td>1488.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.500000</td>\n",
              "      <td>166.300000</td>\n",
              "      <td>64.100000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2145.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>173.200000</td>\n",
              "      <td>65.500000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>2414.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>102.400000</td>\n",
              "      <td>183.100000</td>\n",
              "      <td>66.900000</td>\n",
              "      <td>55.500000</td>\n",
              "      <td>2935.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.900000</td>\n",
              "      <td>208.100000</td>\n",
              "      <td>72.300000</td>\n",
              "      <td>59.800000</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>326.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        symboling  wheel-base  ...    city-mpg  highway-mpg\n",
              "count  205.000000  205.000000  ...  205.000000   205.000000\n",
              "mean     0.834146   98.756585  ...   25.219512    30.751220\n",
              "std      1.245307    6.021776  ...    6.542142     6.886443\n",
              "min     -2.000000   86.600000  ...   13.000000    16.000000\n",
              "25%      0.000000   94.500000  ...   19.000000    25.000000\n",
              "50%      1.000000   97.000000  ...   24.000000    30.000000\n",
              "75%      2.000000  102.400000  ...   30.000000    34.000000\n",
              "max      3.000000  120.900000  ...   49.000000    54.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IsMaZp_oIFu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "outputId": "38d35d3e-fe2a-4d57-b4a3-cdac5e665a8e"
      },
      "source": [
        "df_make = df.groupby('make')\n",
        "df_make.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              symboling                                                 \\\n",
              "                  count      mean       std  min   25%  50%   75%  max   \n",
              "make                                                                     \n",
              "alfa-romero         3.0  2.333333  1.154701  1.0  2.00  3.0  3.00  3.0   \n",
              "audi                7.0  1.285714  0.755929  0.0  1.00  1.0  2.00  2.0   \n",
              "bmw                 8.0  0.375000  0.744024  0.0  0.00  0.0  0.25  2.0   \n",
              "chevrolet           3.0  1.000000  1.000000  0.0  0.50  1.0  1.50  2.0   \n",
              "dodge               9.0  1.000000  1.000000 -1.0  1.00  1.0  1.00  3.0   \n",
              "honda              13.0  0.615385  0.767948  0.0  0.00  0.0  1.00  2.0   \n",
              "isuzu               4.0  0.750000  0.957427  0.0  0.00  0.5  1.25  2.0   \n",
              "jaguar              3.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0   \n",
              "mazda              17.0  1.117647  1.166316  0.0  0.00  1.0  1.00  3.0   \n",
              "mercedes-benz       8.0  0.000000  1.414214 -1.0 -1.00 -0.5  0.25  3.0   \n",
              "mercury             1.0  1.000000       NaN  1.0  1.00  1.0  1.00  1.0   \n",
              "mitsubishi         13.0  1.846154  1.214232 -1.0  1.00  2.0  3.00  3.0   \n",
              "nissan             18.0  1.000000  0.907485  0.0  0.25  1.0  1.00  3.0   \n",
              "peugot             11.0  0.000000  0.000000  0.0  0.00  0.0  0.00  0.0   \n",
              "plymouth            7.0  1.000000  1.154701 -1.0  1.00  1.0  1.00  3.0   \n",
              "porsche             5.0  2.600000  0.894427  1.0  3.00  3.0  3.00  3.0   \n",
              "renault             2.0  1.000000  1.414214  0.0  0.50  1.0  1.50  2.0   \n",
              "saab                6.0  2.500000  0.547723  2.0  2.00  2.5  3.00  3.0   \n",
              "subaru             12.0  0.500000  0.904534  0.0  0.00  0.0  0.50  2.0   \n",
              "toyota             32.0  0.562500  1.216486 -1.0  0.00  0.0  1.25  3.0   \n",
              "volkswagen         12.0  1.666667  1.073087  0.0  1.50  2.0  2.00  3.0   \n",
              "volvo              11.0 -1.272727  0.467099 -2.0 -1.50 -1.0 -1.00 -1.0   \n",
              "\n",
              "              wheel-base              ... city-mpg       highway-mpg  \\\n",
              "                   count        mean  ...      75%   max       count   \n",
              "make                                  ...                              \n",
              "alfa-romero          3.0   90.566667  ...     21.0  21.0         3.0   \n",
              "audi                 7.0  102.271429  ...     19.0  24.0         7.0   \n",
              "bmw                  8.0  103.162500  ...     21.5  23.0         8.0   \n",
              "chevrolet            3.0   92.466667  ...     42.5  47.0         3.0   \n",
              "dodge                9.0   95.011111  ...     31.0  37.0         9.0   \n",
              "honda               13.0   94.330769  ...     30.0  49.0        13.0   \n",
              "isuzu                4.0   94.825000  ...     38.0  38.0         4.0   \n",
              "jaguar               3.0  109.333333  ...     15.0  15.0         3.0   \n",
              "mazda               17.0   97.017647  ...     31.0  36.0        17.0   \n",
              "mercedes-benz        8.0  110.925000  ...     22.0  22.0         8.0   \n",
              "mercury              1.0  102.700000  ...     19.0  19.0         1.0   \n",
              "mitsubishi          13.0   95.353846  ...     25.0  37.0        13.0   \n",
              "nissan              18.0   95.722222  ...     31.0  45.0        18.0   \n",
              "peugot              11.0  110.200000  ...     26.5  28.0        11.0   \n",
              "plymouth             7.0   95.385714  ...     31.0  37.0         7.0   \n",
              "porsche              5.0   92.280000  ...     17.0  19.0         5.0   \n",
              "renault              2.0   96.100000  ...     23.0  23.0         2.0   \n",
              "saab                 6.0   99.100000  ...     21.0  21.0         6.0   \n",
              "subaru              12.0   96.175000  ...     28.0  32.0        12.0   \n",
              "toyota              32.0   98.103125  ...     30.0  38.0        32.0   \n",
              "volkswagen          12.0   97.608333  ...     34.0  37.0        12.0   \n",
              "volvo               11.0  106.481818  ...     23.5  26.0        11.0   \n",
              "\n",
              "                                                                   \n",
              "                    mean       std   min   25%   50%    75%   max  \n",
              "make                                                               \n",
              "alfa-romero    26.666667  0.577350  26.0  26.5  27.0  27.00  27.0  \n",
              "audi           24.142857  3.236694  20.0  22.0  25.0  25.00  30.0  \n",
              "bmw            25.375000  3.622844  20.0  22.0  26.5  28.25  29.0  \n",
              "chevrolet      46.333333  5.773503  43.0  43.0  43.0  48.00  53.0  \n",
              "dodge          34.111111  5.710614  24.0  30.0  38.0  38.00  41.0  \n",
              "honda          35.461538  6.462912  28.0  33.0  34.0  34.00  54.0  \n",
              "isuzu          36.000000  8.082904  29.0  29.0  36.0  43.00  43.0  \n",
              "jaguar         18.333333  1.154701  17.0  18.0  19.0  19.00  19.0  \n",
              "mazda          31.941176  6.339071  23.0  27.0  32.0  38.00  42.0  \n",
              "mercedes-benz  21.000000  4.342481  16.0  17.5  21.5  25.00  25.0  \n",
              "mercury        24.000000       NaN  24.0  24.0  24.0  24.00  24.0  \n",
              "mitsubishi     31.153846  5.413658  24.0  30.0  30.0  32.00  41.0  \n",
              "nissan         32.944444  7.549618  22.0  25.0  37.0  37.00  50.0  \n",
              "peugot         26.636364  4.105429  24.0  24.0  24.0  29.00  33.0  \n",
              "plymouth       34.142857  6.175990  24.0  30.0  38.0  38.00  41.0  \n",
              "porsche        26.000000  1.414214  25.0  25.0  25.0  27.00  28.0  \n",
              "renault        31.000000  0.000000  31.0  31.0  31.0  31.00  31.0  \n",
              "saab           27.333333  1.032796  26.0  26.5  28.0  28.00  28.0  \n",
              "subaru         30.750000  3.980064  23.0  29.0  31.0  32.25  37.0  \n",
              "toyota         32.906250  5.549539  24.0  30.0  32.0  36.25  47.0  \n",
              "volkswagen     34.916667  6.881574  24.0  30.5  34.0  39.00  46.0  \n",
              "volvo          25.818182  2.522625  22.0  24.0  27.0  28.00  28.0  \n",
              "\n",
              "[22 rows x 80 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4dcfd553-4ee1-4eee-92c1-9760de3b8edb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">symboling</th>\n",
              "      <th colspan=\"2\" halign=\"left\">wheel-base</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">city-mpg</th>\n",
              "      <th colspan=\"8\" halign=\"left\">highway-mpg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>...</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alfa-romero</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1.154701</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>90.566667</td>\n",
              "      <td>...</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.666667</td>\n",
              "      <td>0.577350</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.00</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>audi</th>\n",
              "      <td>7.0</td>\n",
              "      <td>1.285714</td>\n",
              "      <td>0.755929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>102.271429</td>\n",
              "      <td>...</td>\n",
              "      <td>19.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>24.142857</td>\n",
              "      <td>3.236694</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.00</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmw</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.744024</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>103.162500</td>\n",
              "      <td>...</td>\n",
              "      <td>21.5</td>\n",
              "      <td>23.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>25.375000</td>\n",
              "      <td>3.622844</td>\n",
              "      <td>20.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>28.25</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chevrolet</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>92.466667</td>\n",
              "      <td>...</td>\n",
              "      <td>42.5</td>\n",
              "      <td>47.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>5.773503</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>48.00</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dodge</th>\n",
              "      <td>9.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>95.011111</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>34.111111</td>\n",
              "      <td>5.710614</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>honda</th>\n",
              "      <td>13.0</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>0.767948</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>94.330769</td>\n",
              "      <td>...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>35.461538</td>\n",
              "      <td>6.462912</td>\n",
              "      <td>28.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>34.00</td>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>isuzu</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.957427</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>94.825000</td>\n",
              "      <td>...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>8.082904</td>\n",
              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>43.00</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jaguar</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>109.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>18.333333</td>\n",
              "      <td>1.154701</td>\n",
              "      <td>17.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.00</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mazda</th>\n",
              "      <td>17.0</td>\n",
              "      <td>1.117647</td>\n",
              "      <td>1.166316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>97.017647</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>31.941176</td>\n",
              "      <td>6.339071</td>\n",
              "      <td>23.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mercedes-benz</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>110.925000</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>4.342481</td>\n",
              "      <td>16.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>21.5</td>\n",
              "      <td>25.00</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mercury</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>102.700000</td>\n",
              "      <td>...</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.00</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mitsubishi</th>\n",
              "      <td>13.0</td>\n",
              "      <td>1.846154</td>\n",
              "      <td>1.214232</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>95.353846</td>\n",
              "      <td>...</td>\n",
              "      <td>25.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>31.153846</td>\n",
              "      <td>5.413658</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>32.00</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nissan</th>\n",
              "      <td>18.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.907485</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>95.722222</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>32.944444</td>\n",
              "      <td>7.549618</td>\n",
              "      <td>22.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.00</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>peugot</th>\n",
              "      <td>11.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.200000</td>\n",
              "      <td>...</td>\n",
              "      <td>26.5</td>\n",
              "      <td>28.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>26.636364</td>\n",
              "      <td>4.105429</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>29.00</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>plymouth</th>\n",
              "      <td>7.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.154701</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>95.385714</td>\n",
              "      <td>...</td>\n",
              "      <td>31.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>34.142857</td>\n",
              "      <td>6.175990</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>porsche</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.894427</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>92.280000</td>\n",
              "      <td>...</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>27.00</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>renault</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.414214</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>96.100000</td>\n",
              "      <td>...</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>31.00</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>saab</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.547723</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>99.100000</td>\n",
              "      <td>...</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.333333</td>\n",
              "      <td>1.032796</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>28.0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subaru</th>\n",
              "      <td>12.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.904534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>96.175000</td>\n",
              "      <td>...</td>\n",
              "      <td>28.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>30.750000</td>\n",
              "      <td>3.980064</td>\n",
              "      <td>23.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>32.25</td>\n",
              "      <td>37.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>toyota</th>\n",
              "      <td>32.0</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>1.216486</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>98.103125</td>\n",
              "      <td>...</td>\n",
              "      <td>30.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.906250</td>\n",
              "      <td>5.549539</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>36.25</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volkswagen</th>\n",
              "      <td>12.0</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.073087</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>97.608333</td>\n",
              "      <td>...</td>\n",
              "      <td>34.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34.916667</td>\n",
              "      <td>6.881574</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.5</td>\n",
              "      <td>34.0</td>\n",
              "      <td>39.00</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volvo</th>\n",
              "      <td>11.0</td>\n",
              "      <td>-1.272727</td>\n",
              "      <td>0.467099</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.50</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>106.481818</td>\n",
              "      <td>...</td>\n",
              "      <td>23.5</td>\n",
              "      <td>26.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>25.818182</td>\n",
              "      <td>2.522625</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22 rows × 80 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4dcfd553-4ee1-4eee-92c1-9760de3b8edb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-4dcfd553-4ee1-4eee-92c1-9760de3b8edb 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-4dcfd553-4ee1-4eee-92c1-9760de3b8edb');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LR5rOYmo5FY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ecd90da-51f8-4fee-e63a-866f1918594f"
      },
      "source": [
        "print(df['make'].unique())\n",
        "print(df['fuel-type'].unique())\n",
        "print(df['num-of-doors'].unique())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['alfa-romero' 'audi' 'bmw' 'chevrolet' 'dodge' 'honda' 'isuzu' 'jaguar'\n",
            " 'mazda' 'mercedes-benz' 'mercury' 'mitsubishi' 'nissan' 'peugot'\n",
            " 'plymouth' 'porsche' 'renault' 'saab' 'subaru' 'toyota' 'volkswagen'\n",
            " 'volvo']\n",
            "['gas' 'diesel']\n",
            "['two' 'four' '?']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4aD_Gxcpkld"
      },
      "source": [
        "## ***Matplotlib***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgLd_VlEpkFW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "6e119ed7-8ed5-4c48-f5a6-a9383c34323a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "labels = ['G1', 'G2', 'G3', 'G4', 'G5']\n",
        "men_means = [20, 35, 30, 35, 27]\n",
        "women_means = [25, 32, 34, 20, 25]\n",
        "men_std = [2, 3, 4, 1, 2]\n",
        "women_std = [3, 5, 2, 3, 3]\n",
        "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.bar(labels, men_means, width, yerr=men_std, label='Men')\n",
        "ax.bar(labels, women_means, width, yerr=women_std, bottom=men_means,\n",
        "       label='Women')\n",
        "\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Scores by group and gender')\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv39WAOnq48M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6f44601d-7054-42c3-ba77-340c5dee4b71"
      },
      "source": [
        "x = [int(x) for x in range(10)]\n",
        "y= [int(i)**2 for i in x]\n",
        "plt.plot(x,y)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-L1OBnarliy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "8d77d1ab-4197-41c1-9585-23a8a67bf090"
      },
      "source": [
        "height = [160,170,180,190,200]\n",
        "weight = [60,70,80,90,100]\n",
        "plt.bar(height,weight)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wK4M/Uc8fWv+PGfx2vpP2SoN5ZmLwxM5twM1t/Fdnua/a+HuA31li/bnsq1GZZrWvRvz7PQvYx+DVHnuAp7d1w+CLaD4P3AIsroFMr2776TPAjcAvzPKYAi5j8GqQzwGX0971Ps9jalSmGR5Tz2JwyeWzbVs3M3hFzBOB3Qx+MX8cOGU1jys/fkCSOtTDZRlJ0jEsd0nqkOUuSR2y3CWpQ5a7JHXIcpekDlnuktSh/wE0CIrddb168AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlRKfX0FuPi5"
      },
      "source": [
        "## ***Seaborn ***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAFSvDNTuO5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "44f8aed4-dbed-482b-c84d-b1714d652c0f"
      },
      "source": [
        "import seaborn as sns\n",
        "heights = [160,170,180,190,200]\n",
        "names = ['a','b','c','d','f']\n",
        "\n",
        "sns.barplot(names,heights)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f14e2801350>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8HhVUIOvA3K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bb075606-0e0f-42e7-d855-524cfd6ba622"
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/st119848/dataset/master/Automobile_data.csv\")\n",
        "sns.countplot(x='make',data= df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f14e279e150>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}