01_preprocessing.ipynb

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        "<a href=\"https://colab.research.google.com/github/weclaprz/machineLearning/blob/main/01_preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "### scikit-learn\n",
        "Strona biblioteki: [https://scikit-learn.org](https://scikit-learn.org)  \n",
        "\n",
        "Dokumentacja/User Guide: [https://scikit-learn.org/stable/user_guide.html](https://scikit-learn.org/stable/user_guide.html)\n",
        "\n",
        "Podstawowa biblioteka do uczenia maszynowego w języku Python.\n",
        "\n",
        "Aby zainstalować bibliotekę scikit-learn, użyj polecenia poniżej:\n",
        "```\n",
        "!pip install scikit-learn\n",
        "```\n",
        "Aby zaktualizować do najnowszej wersji bibliotekę scikit-learn, użyj polecenia poniżej:\n",
        "```\n",
        "!pip install --upgrade scikit-learn\n",
        "```\n",
        "Kurs stworzony w oparciu o wersję `0.22.1`\n",
        "\n",
        "### Preprocessing danych:\n",
        "1. [Import bibliotek](#0)\n",
        "2. [Wygenerowanie danych](#1)\n",
        "3. [Utworzenie kopii danych](#2)\n",
        "4. [Zmiana typu danych i wstępna eksploracja](#3)\n",
        "5. [LabelEncoder](#4)\n",
        "6. [OneHotEncoder](#5)\n",
        "7. [Pandas *get_dummies()*](#6)\n",
        "8. [Standaryzacja - StandardScaler](#7)\n",
        "9. [Przygotowanie danych do modelu](#8)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkIALacsUnj7"
      },
      "source": [
        "### <a name='0'></a> Import bibliotek"
      ]
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "\n",
        "sklearn.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
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              "'1.2.2'"
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      "source": [
        "### <a name='1'></a> Wygenerowanie danych"
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        "data = {\n",
        "    'size': ['XL', 'L', 'M', 'L', 'M'],\n",
        "    'color': ['red', 'green', 'blue', 'green', 'red'],\n",
        "    'gender': ['female', 'male', 'male', 'female', 'female'],\n",
        "    'price': [199.0, 89.0, 99.0, 129.0, 79.0],\n",
        "    'weight': [500, 450, 300, 380, 410],\n",
        "    'bought': ['yes', 'no', 'yes', 'no', 'yes']\n",
        "}\n",
        "\n",
        "df_raw = pd.DataFrame(data=data)\n",
        "df_raw"
      ],
      "execution_count": 2,
      "outputs": [
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              "  size  color  gender  price  weight bought\n",
              "0   XL    red  female  199.0     500    yes\n",
              "1    L  green    male   89.0     450     no\n",
              "2    M   blue    male   99.0     300    yes\n",
              "3    L  green  female  129.0     380     no\n",
              "4    M    red  female   79.0     410    yes"
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      "source": [
        "### <a name='2'></a> Utworzenie kopii danych\n",
        "\n"
      ]
    },
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      "cell_type": "code",
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        "colab": {
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      },
      "source": [
        "df = df_raw.copy()\n",
        "df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5 entries, 0 to 4\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   size    5 non-null      object \n",
            " 1   color   5 non-null      object \n",
            " 2   gender  5 non-null      object \n",
            " 3   price   5 non-null      float64\n",
            " 4   weight  5 non-null      int64  \n",
            " 5   bought  5 non-null      object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 368.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAvCBH3hWWzl"
      },
      "source": [
        "### <a name='3'></a> Zmiana typu danych i wstępna eksploracja\n",
        "\n"
      ]
    },
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        "for col in ['size', 'color', 'gender', 'bought']:\n",
        "    df[col] = df[col].astype('category')\n",
        "\n",
        "df['weight'] = df['weight'].astype('float')\n",
        "\n",
        "df.info()"
      ],
      "execution_count": 4,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5 entries, 0 to 4\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype   \n",
            "---  ------  --------------  -----   \n",
            " 0   size    5 non-null      category\n",
            " 1   color   5 non-null      category\n",
            " 2   gender  5 non-null      category\n",
            " 3   price   5 non-null      float64 \n",
            " 4   weight  5 non-null      float64 \n",
            " 5   bought  5 non-null      category\n",
            "dtypes: category(4), float64(2)\n",
            "memory usage: 740.0 bytes\n"
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      "source": [
        "df.describe()"
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      "execution_count": 5,
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              "            price      weight\n",
              "count    5.000000    5.000000\n",
              "mean   119.000000  408.000000\n",
              "std     48.476799   75.299402\n",
              "min     79.000000  300.000000\n",
              "25%     89.000000  380.000000\n",
              "50%     99.000000  410.000000\n",
              "75%    129.000000  450.000000\n",
              "max    199.000000  500.000000"
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              "      <th>mean</th>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <th>25%</th>\n",
              "      <td>89.000000</td>\n",
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              "            + ' 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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
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        "df.describe().T"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        count   mean        std    min    25%    50%    75%    max\n",
              "price     5.0  119.0  48.476799   79.0   89.0   99.0  129.0  199.0\n",
              "weight    5.0  408.0  75.299402  300.0  380.0  410.0  450.0  500.0"
            ],
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              "      <td>119.0</td>\n",
              "      <td>48.476799</td>\n",
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              "      <td>89.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>199.0</td>\n",
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              "    <tr>\n",
              "      <th>weight</th>\n",
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              "      <td>75.299402</td>\n",
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              "      <td>380.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>450.0</td>\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",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
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        "colab": {
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      "source": [
        "df.describe(include=['category']).T"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count unique     top freq\n",
              "size       5      3       L    2\n",
              "color      5      3   green    2\n",
              "gender     5      2  female    3\n",
              "bought     5      2     yes    3"
            ],
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              "                                                     [key], {});\n",
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              "\n",
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    {
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      "source": [
        "df"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender  price  weight bought\n",
              "0   XL    red  female  199.0   500.0    yes\n",
              "1    L  green    male   89.0   450.0     no\n",
              "2    M   blue    male   99.0   300.0    yes\n",
              "3    L  green  female  129.0   380.0     no\n",
              "4    M    red  female   79.0   410.0    yes"
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEjOtDqIWim6"
      },
      "source": [
        "### <a name='4'></a> LabelEncoder\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdXinbCdQn9w",
        "outputId": "3469a224-94cc-46bb-caaf-42fab4aa8a13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(df['bought'])\n",
        "le.transform(df['bought'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ao4XQz4TXzP",
        "outputId": "278e081b-952c-4259-f3c7-d960b4d0675a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "le.fit_transform(df['bought'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk6eIwFDSdLO",
        "outputId": "68d0b582-889e-4ba3-f805-7b39f221592c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "le.classes_"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxoP3kglTbjO",
        "outputId": "2f8ac0b9-f689-459e-dcec-55e24ff560a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df['bought'] = le.fit_transform(df['bought'])\n",
        "df"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender  price  weight  bought\n",
              "0   XL    red  female  199.0   500.0       1\n",
              "1    L  green    male   89.0   450.0       0\n",
              "2    M   blue    male   99.0   300.0       1\n",
              "3    L  green  female  129.0   380.0       0\n",
              "4    M    red  female   79.0   410.0       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83b230d7-4afa-4bab-8cd8-3e5c38615ec7\">\n",
              "    <div 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",
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              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>color</th>\n",
              "      <th>gender</th>\n",
              "      <th>price</th>\n",
              "      <th>weight</th>\n",
              "      <th>bought</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XL</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>199.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>male</td>\n",
              "      <td>89.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>blue</td>\n",
              "      <td>male</td>\n",
              "      <td>99.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>female</td>\n",
              "      <td>129.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83b230d7-4afa-4bab-8cd8-3e5c38615ec7')\"\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-83b230d7-4afa-4bab-8cd8-3e5c38615ec7 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-83b230d7-4afa-4bab-8cd8-3e5c38615ec7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rY4NnuAiTp-d",
        "outputId": "2acb3eda-ff61-4382-d575-fc879aa28510",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "le.inverse_transform(df['bought'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['yes', 'no', 'yes', 'no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZRYERYrTfUy",
        "outputId": "617ae7c9-5f10-4d53-97df-36aa7937c7fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df['bought'] = le.inverse_transform(df['bought'])\n",
        "df"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender  price  weight bought\n",
              "0   XL    red  female  199.0   500.0    yes\n",
              "1    L  green    male   89.0   450.0     no\n",
              "2    M   blue    male   99.0   300.0    yes\n",
              "3    L  green  female  129.0   380.0     no\n",
              "4    M    red  female   79.0   410.0    yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d13c255e-c3ea-4f25-9749-5a6498e3ecc6\">\n",
              "    <div 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>size</th>\n",
              "      <th>color</th>\n",
              "      <th>gender</th>\n",
              "      <th>price</th>\n",
              "      <th>weight</th>\n",
              "      <th>bought</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XL</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>199.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>male</td>\n",
              "      <td>89.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>blue</td>\n",
              "      <td>male</td>\n",
              "      <td>99.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>female</td>\n",
              "      <td>129.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d13c255e-c3ea-4f25-9749-5a6498e3ecc6')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-d13c255e-c3ea-4f25-9749-5a6498e3ecc6 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-d13c255e-c3ea-4f25-9749-5a6498e3ecc6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqXZ2JgyW482"
      },
      "source": [
        "### <a name='5'></a> OneHotEncoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWCDB5xOTi-a",
        "outputId": "52e5a746-4e83-42e9-d5aa-308fdf478cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder(sparse_output=False)\n",
        "encoder.fit(df[['size']])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder(sparse_output=False)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 16
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          "base_uri": "https://localhost:8080/"
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      "source": [
        "encoder.transform(df[['size']])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.]])"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      "source": [
        "encoder.categories_"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array(['L', 'M', 'XL'], dtype=object)]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
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      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
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      "source": [
        "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
        "encoder.fit(df[['size']])\n",
        "encoder.transform(df[['size']])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [0., 0.],\n",
              "       [1., 0.],\n",
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      "source": [
        "df"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender  price  weight bought\n",
              "0   XL    red  female  199.0   500.0    yes\n",
              "1    L  green    male   89.0   450.0     no\n",
              "2    M   blue    male   99.0   300.0    yes\n",
              "3    L  green  female  129.0   380.0     no\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opfi219t0Uz0"
      },
      "source": [
        "### <a name='6'></a> Pandas *get_dummies()*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2hNUAJ7zdsx",
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      "source": [
        "pd.get_dummies(data=df)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price  weight  size_L  size_M  size_XL  color_blue  color_green  color_red  \\\n",
              "0  199.0   500.0       0       0        1           0            0          1   \n",
              "1   89.0   450.0       1       0        0           0            1          0   \n",
              "2   99.0   300.0       0       1        0           1            0          0   \n",
              "3  129.0   380.0       1       0        0           0            1          0   \n",
              "4   79.0   410.0       0       1        0           0            0          1   \n",
              "\n",
              "   gender_female  gender_male  bought_no  bought_yes  \n",
              "0              1            0          0           1  \n",
              "1              0            1          1           0  \n",
              "2              0            1          0           1  \n",
              "3              1            0          1           0  \n",
              "4              1            0          0           1  "
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "pd.get_dummies(data=df, drop_first=True)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price  weight  size_M  size_XL  color_green  color_red  gender_male  \\\n",
              "0  199.0   500.0       0        1            0          1            0   \n",
              "1   89.0   450.0       0        0            1          0            1   \n",
              "2   99.0   300.0       1        0            0          0            1   \n",
              "3  129.0   380.0       0        0            1          0            0   \n",
              "4   79.0   410.0       1        0            0          1            0   \n",
              "\n",
              "   bought_yes  \n",
              "0           1  \n",
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              "\n",
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              "          document.querySelector('#df-071c10f1-283b-4cb4-9aee-00aac9dfa706 button.colab-df-convert');\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-071c10f1-283b-4cb4-9aee-00aac9dfa706');\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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            ]
          },
          "metadata": {},
          "execution_count": 22
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "ec7d79a7-d986-466f-909d-b7e55272107b",
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      },
      "source": [
        "pd.get_dummies(data=df, drop_first=True, prefix='new')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price  weight  new_M  new_XL  new_green  new_red  new_male  new_yes\n",
              "0  199.0   500.0      0       1          0        1         0        1\n",
              "1   89.0   450.0      0       0          1        0         1        0\n",
              "2   99.0   300.0      1       0          0        0         1        1\n",
              "3  129.0   380.0      0       0          1        0         0        0\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      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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              "      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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              "    }\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-9e1adf2b-62b3-46bb-bec3-b4f4f20cb814 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-9e1adf2b-62b3-46bb-bec3-b4f4f20cb814');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyUypogfz7km",
        "outputId": "b54e21bd-fae1-48d5-a6cf-e659588f2147",
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      },
      "source": [
        "pd.get_dummies(data=df, drop_first=True, prefix_sep='-')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   price  weight  size-M  size-XL  color-green  color-red  gender-male  \\\n",
              "0  199.0   500.0       0        1            0          1            0   \n",
              "1   89.0   450.0       0        0            1          0            1   \n",
              "2   99.0   300.0       1        0            0          0            1   \n",
              "3  129.0   380.0       0        0            1          0            0   \n",
              "4   79.0   410.0       1        0            0          1            0   \n",
              "\n",
              "   bought-yes  \n",
              "0           1  \n",
              "1           0  \n",
              "2           1  \n",
              "3           0  \n",
              "4           1  "
            ],
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              "\n",
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              "    }\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>weight</th>\n",
              "      <th>size-M</th>\n",
              "      <th>size-XL</th>\n",
              "      <th>color-green</th>\n",
              "      <th>color-red</th>\n",
              "      <th>gender-male</th>\n",
              "      <th>bought-yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>199.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>89.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>99.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>129.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>79.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2f3ed86-a001-4cab-aaf2-21c953f317ad')\"\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2f3ed86-a001-4cab-aaf2-21c953f317ad');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NJve2sV0Eem",
        "outputId": "b72c172f-eece-4752-d757-7611b996cfc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "pd.get_dummies(data=df, drop_first=True, columns=['size','gender'])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   color  price  weight bought  size_M  size_XL  gender_male\n",
              "0    red  199.0   500.0    yes       0        1            0\n",
              "1  green   89.0   450.0     no       0        0            1\n",
              "2   blue   99.0   300.0    yes       1        0            1\n",
              "3  green  129.0   380.0     no       0        0            0\n",
              "4    red   79.0   410.0    yes       1        0            0"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\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-9f47139e-8822-4544-aeea-4817e38ee94c button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f47139e-8822-4544-aeea-4817e38ee94c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGQ33Qjo1sfu"
      },
      "source": [
        "### <a name='7'></a> Standaryzacja - StandardScaler\n",
        "\n",
        "std() - pandas nieobciążony  \n",
        "std() - numpy obciążony"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFffHPh81us9",
        "outputId": "380e65e6-96b3-496f-dcad-1bea6a10e4a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(f\"{df['price']}\\n\")\n",
        "print(f\"Średnia: {df['price'].mean()}\")\n",
        "print(f\"Odchylenie standardowe: {df['price'].std():.4f}\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    199.0\n",
            "1     89.0\n",
            "2     99.0\n",
            "3    129.0\n",
            "4     79.0\n",
            "Name: price, dtype: float64\n",
            "\n",
            "Średnia: 119.0\n",
            "Odchylenie standardowe: 48.4768\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iimzXIPp2BgZ",
        "outputId": "e3129c7c-827b-4242-a63a-e44580576774",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "(df['price'] - df['price'].mean()) / df['price'].std()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.650274\n",
              "1   -0.618853\n",
              "2   -0.412568\n",
              "3    0.206284\n",
              "4   -0.825137\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxbQs-0X2QcQ",
        "outputId": "2445337c-cfa0-4aa6-dfcf-2d10fac59b5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def standardize(x):\n",
        "    return (x - x.mean()) / x.std()\n",
        "\n",
        "standardize(df['price'])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1.650274\n",
              "1   -0.618853\n",
              "2   -0.412568\n",
              "3    0.206284\n",
              "4   -0.825137\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kOGadH82lao",
        "outputId": "4aa5e42d-c266-41e8-ddfe-b7af4b814321",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.preprocessing import scale\n",
        "\n",
        "scale(df['price'])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.84506242, -0.69189841, -0.4612656 ,  0.2306328 , -0.92253121])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrpITpt53SoH",
        "outputId": "14ea89ea-c906-4caf-b7e5-b6857d91fb80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(df[['price']])\n",
        "scaler.transform(df[['price']])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.84506242],\n",
              "       [-0.69189841],\n",
              "       [-0.4612656 ],\n",
              "       [ 0.2306328 ],\n",
              "       [-0.92253121]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4ZWXT6lWMap",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2597613c-f18e-4f1e-e99a-11f6923c1a76"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "df[['price', 'weight']] = scaler.fit_transform(df[['price', 'weight']])\n",
        "df"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender     price    weight bought\n",
              "0   XL    red  female  1.845062  1.366002    yes\n",
              "1    L  green    male -0.691898  0.623610     no\n",
              "2    M   blue    male -0.461266 -1.603567    yes\n",
              "3    L  green  female  0.230633 -0.415740     no\n",
              "4    M    red  female -0.922531  0.029696    yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43f16e6a-6a98-4d3f-a36a-d7c3c77081e7\">\n",
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              "      <div>\n",
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              "      <th>size</th>\n",
              "      <th>color</th>\n",
              "      <th>gender</th>\n",
              "      <th>price</th>\n",
              "      <th>weight</th>\n",
              "      <th>bought</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XL</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>1.845062</td>\n",
              "      <td>1.366002</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>male</td>\n",
              "      <td>-0.691898</td>\n",
              "      <td>0.623610</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>blue</td>\n",
              "      <td>male</td>\n",
              "      <td>-0.461266</td>\n",
              "      <td>-1.603567</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>female</td>\n",
              "      <td>0.230633</td>\n",
              "      <td>-0.415740</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>-0.922531</td>\n",
              "      <td>0.029696</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43f16e6a-6a98-4d3f-a36a-d7c3c77081e7')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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",
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              "          document.querySelector('#df-43f16e6a-6a98-4d3f-a36a-d7c3c77081e7 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-43f16e6a-6a98-4d3f-a36a-d7c3c77081e7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJA5B0h81Zjq"
      },
      "source": [
        "### <a name='8'></a> Przygotowanie danych do modelu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ym8YZ0m0LLr",
        "outputId": "5684e95d-3012-4311-fcd7-e0b5b6b382cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df = df_raw.copy()\n",
        "df"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  size  color  gender  price  weight bought\n",
              "0   XL    red  female  199.0     500    yes\n",
              "1    L  green    male   89.0     450     no\n",
              "2    M   blue    male   99.0     300    yes\n",
              "3    L  green  female  129.0     380     no\n",
              "4    M    red  female   79.0     410    yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-919ee052-65f3-4502-be48-d2eda1b0fd76\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>color</th>\n",
              "      <th>gender</th>\n",
              "      <th>price</th>\n",
              "      <th>weight</th>\n",
              "      <th>bought</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XL</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>199.0</td>\n",
              "      <td>500</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>male</td>\n",
              "      <td>89.0</td>\n",
              "      <td>450</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>blue</td>\n",
              "      <td>male</td>\n",
              "      <td>99.0</td>\n",
              "      <td>300</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L</td>\n",
              "      <td>green</td>\n",
              "      <td>female</td>\n",
              "      <td>129.0</td>\n",
              "      <td>380</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>red</td>\n",
              "      <td>female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>410</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-919ee052-65f3-4502-be48-d2eda1b0fd76')\"\n",
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              "              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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-919ee052-65f3-4502-be48-d2eda1b0fd76 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-919ee052-65f3-4502-be48-d2eda1b0fd76');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Opowqc1C1YsD",
        "outputId": "e1134e57-d0a7-4c2d-8284-4bd8e38ba9ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "df['bought'] = le.fit_transform(df['bought'])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "df[['price', 'weight']] = scaler.fit_transform(df[['price', 'weight']])\n",
        "\n",
        "df = pd.get_dummies(data=df, drop_first=True)\n",
        "df"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price    weight  bought  size_M  size_XL  color_green  color_red  \\\n",
              "0  1.845062  1.366002       1       0        1            0          1   \n",
              "1 -0.691898  0.623610       0       0        0            1          0   \n",
              "2 -0.461266 -1.603567       1       1        0            0          0   \n",
              "3  0.230633 -0.415740       0       0        0            1          0   \n",
              "4 -0.922531  0.029696       1       1        0            0          1   \n",
              "\n",
              "   gender_male  \n",
              "0            0  \n",
              "1            1  \n",
              "2            1  \n",
              "3            0  \n",
              "4            0  "
            ],
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ce71431-88f9-4349-b9c6-fedd41267144 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-0ce71431-88f9-4349-b9c6-fedd41267144');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0oKCwxrCKkB"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}