Lecture_1(Basics_of_Clustering).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cx7EiZYpihsT",
        "outputId": "af7ab20e-ee8d-4f66-93cc-3fdcfed5c69f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi!!!!!\n"
          ]
        }
      ],
      "source": [
        "print(\"Hi!!!!!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Unsupervised learning\n",
        "A group of maching learning algorithms that find the patterns.\n",
        "#### Clustering\n",
        "Clustering is the process of grouping items with similar characteristics. Cluster is a group of items with similar characteristics."
      ],
      "metadata": {
        "id": "3tCzzq97iqjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#example\n",
        "x = [9, 6, 2, 3, 1, 7, 1, 6, 1, 7, 23, 26, 25, 23, 21, 23, 23, 20, 30, 23]\n",
        "y = [8, 4, 10, 6, 0, 4, 10, 10, 6, 1, 29, 25, 30, 29, 29, 30, 25, 27, 26, 30]\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.scatter(x, y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "o9Di3OWCkHhn",
        "outputId": "0d5dca1c-9d2b-489d-c5a3-f9eaf2bf6f57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CLustering algorithms\n",
        "- Hierarchical clustering  \n",
        "- K Means clustering\n",
        "- DBSCAN\n",
        "- Gaussian Method\n"
      ],
      "metadata": {
        "id": "R1eDN7Xck6pI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Clusterinng basics"
      ],
      "metadata": {
        "id": "bj23aUgyqPxa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hierarchical clustering"
      ],
      "metadata": {
        "id": "GpaWbDkjngHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = {'x': x, 'y': y}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v08EzlDwkcDE",
        "outputId": "2a8ec128-3a9b-44d9-e935-42eaace9a8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     x   y\n",
            "0    9   8\n",
            "1    6   4\n",
            "2    2  10\n",
            "3    3   6\n",
            "4    1   0\n",
            "5    7   4\n",
            "6    1  10\n",
            "7    6  10\n",
            "8    1   6\n",
            "9    7   1\n",
            "10  23  29\n",
            "11  26  25\n",
            "12  25  30\n",
            "13  23  29\n",
            "14  21  29\n",
            "15  23  30\n",
            "16  23  25\n",
            "17  20  27\n",
            "18  30  26\n",
            "19  23  30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.cluster.hierarchy import linkage, fcluster\n",
        "import seaborn as sns\n",
        "\n",
        "Z = linkage(df, 'ward')\n",
        "df['cluster_labels'] = fcluster(Z, 3, criterion='maxclust')\n",
        "\n",
        "sns.scatterplot(x='x', y='y', data=df, hue='cluster_labels')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "3oksetsBpZJR",
        "outputId": "3762cba6-c1e2-485d-ca1a-127d741b3731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VYy-wQfCqd6l",
        "outputId": "a07ea04d-f32f-4b92-a097-172faa45a3bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   x   y  cluster_labels\n",
              "0  9   8               3\n",
              "1  6   4               2\n",
              "2  2  10               3\n",
              "3  3   6               3\n",
              "4  1   0               2"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-79a226f6-44e7-4e07-9def-c6e853fbd4af\">\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>x</th>\n",
              "      <th>y</th>\n",
              "      <th>cluster_labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79a226f6-44e7-4e07-9def-c6e853fbd4af')\"\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",
              "\n",
              "\n",
              "    <div id=\"df-41ceb53d-d7b4-40d8-bd71-bd8c04369e82\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41ceb53d-d7b4-40d8-bd71-bd8c04369e82')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-41ceb53d-d7b4-40d8-bd71-bd8c04369e82 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-79a226f6-44e7-4e07-9def-c6e853fbd4af 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-79a226f6-44e7-4e07-9def-c6e853fbd4af');\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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K Means clustering"
      ],
      "metadata": {
        "id": "iPuUWwi4qWpA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.cluster.vq import kmeans, vq\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "0D7vwKEvu1ny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_array = df.values.astype(float)\n",
        "centroids, _ = kmeans(data_array, 3)\n",
        "cluster_labels, _ = vq(data_array, centroids)\n",
        "\n",
        "clustered_df = df.copy()\n",
        "clustered_df['cluster_labels'] = cluster_labels\n",
        "\n",
        "sns.scatterplot(x='x', y='y', hue='cluster_labels', data=clustered_df)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "smCLNlTAukWz",
        "outputId": "51e6e757-45ed-4a89-da58-4ad2c11c4f38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clustered_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8R8Jzrx_vC_Q",
        "outputId": "e637583f-69f7-4965-d3e6-a599d609a435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   x   y  cluster_labels\n",
              "0  9   8               2\n",
              "1  6   4               2\n",
              "2  2  10               1\n",
              "3  3   6               1\n",
              "4  1   0               2"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9b59c2e0-b0d1-4939-8ceb-fce724143951\">\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>x</th>\n",
              "      <th>y</th>\n",
              "      <th>cluster_labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b59c2e0-b0d1-4939-8ceb-fce724143951')\"\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",
              "\n",
              "\n",
              "    <div id=\"df-fa4dfdd2-6480-4ad8-a57d-d11dc87d4a66\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fa4dfdd2-6480-4ad8-a57d-d11dc87d4a66')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fa4dfdd2-6480-4ad8-a57d-d11dc87d4a66 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-9b59c2e0-b0d1-4939-8ceb-fce724143951 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-9b59c2e0-b0d1-4939-8ceb-fce724143951');\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": 17
        }
      ]
    }
  ]
}