Chi_Square_Test_Cluster_shared.ipynb

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        "<a href=\"https://colab.research.google.com/github/LilianYou/Sea_Hero_Quest/blob/main/Chi_Square_Test_Cluster_shared.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "v0KOmPux77K9",
        "outputId": "273dcf1f-059a-4584-bcc0-678d37c14729"
      },
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "drive.mount('/content/drive')\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYS2n6Gyxjo8"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L80ydzbvbBo"
      },
      "source": [
        "# df = pd.read_csv('/content/drive/My Drive/SHQ/Data/cfa_test_df.csv')\n",
        "\n",
        "\n",
        "df0 = pd.read_csv('/content/drive/My Drive/SHQ/Data/2019_cleaned_demographics_3121970.csv')\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6dlfzGb_GaB"
      },
      "source": [
        "df = df0.drop(['navigating_skills'], axis = 1)"
      ],
      "execution_count": 21,
      "outputs": []
    },
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        "outputId": "ed9763c4-f11b-49e4-ed6f-b38c3ec5a9b2"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
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            "text/plain": [
              "    age  education  gender  hand  home_environment  sleep  travel_time  \\\n",
              "0  68.0          1       0     0                 2    8.0            0   \n",
              "1  21.0          1       0     0                 1    7.0            1   \n",
              "2  36.0          1       1     0                 0    5.0            1   \n",
              "3  19.0          0       1     0                 1    8.0            0   \n",
              "4  61.0          1       1     0                 1    7.0            0   \n",
              "\n",
              "   nav_num  \n",
              "0        1  \n",
              "1        2  \n",
              "2        3  \n",
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              "4        2  "
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7Wxqme88Zc-"
      },
      "source": [
        "## Step1. Clustering\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4Ux8UGVPt1K"
      },
      "source": [
        "from sklearn.metrics.cluster import adjusted_rand_score\n",
        "from sklearn.preprocessing import scale,RobustScaler,StandardScaler, MinMaxScaler\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
        "#import mglearn\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "2ftZWWpIGQmi",
        "outputId": "bf1597f9-5a4d-4261-fe97-6d0bf9498b7b"
      },
      "source": [
        "# MinMax scaled\n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaler.fit(df)\n",
        "df2 = scaler.transform(df)\n",
        "df2 = pd.DataFrame(df2,index=df.index,columns=['age',\t'gender',\t'hand',\t'nav_num',\t'sleep',\t'edu_level',\t'commute_level',\t'home_env'])\n",
        "df2[:10]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  nav_num  sleep  edu_level  commute_level  home_env\n",
              "0  0.960784     1.0   0.0      0.0    1.0   0.555556            0.0  0.333333\n",
              "1  0.039216     1.0   0.0      0.0    0.5   0.444444            0.5  0.666667\n",
              "2  0.333333     1.0   1.0      0.0    0.0   0.222222            0.5  1.000000\n",
              "3  0.000000     0.0   1.0      0.0    0.5   0.555556            0.0  0.666667\n",
              "4  0.823529     1.0   1.0      0.0    0.5   0.444444            0.0  0.666667\n",
              "5  0.196078     1.0   1.0      0.0    0.0   0.444444            1.0  1.000000\n",
              "6  0.000000     1.0   0.0      0.0    0.5   0.555556            0.0  0.333333\n",
              "7  0.372549     1.0   1.0      0.0    0.0   0.444444            0.0  0.666667\n",
              "8  0.392157     1.0   1.0      0.0    1.0   0.333333            0.0  1.000000\n",
              "9  0.490196     0.0   1.0      0.0    0.5   0.444444            0.5  0.666667"
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              "      <th>7</th>\n",
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              "\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-7d989b88-5375-434a-b655-a738613fb6a0 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-7d989b88-5375-434a-b655-a738613fb6a0');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNNwOeutdKoI"
      },
      "source": [
        "test = scaler.transform(df)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ceJcA-eVWWIT",
        "outputId": "31d94164-9441-47d4-82a7-1decca35ddfb"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  nav_num  sleep  edu_level  commute_level  home_env\n",
              "0  0.960784     1.0   0.0      0.0    1.0   0.555556            0.0  0.333333\n",
              "1  0.039216     1.0   0.0      0.0    0.5   0.444444            0.5  0.666667\n",
              "2  0.333333     1.0   1.0      0.0    0.0   0.222222            0.5  1.000000\n",
              "3  0.000000     0.0   1.0      0.0    0.5   0.555556            0.0  0.666667\n",
              "4  0.823529     1.0   1.0      0.0    0.5   0.444444            0.0  0.666667"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>hand</th>\n",
              "      <th>nav_num</th>\n",
              "      <th>sleep</th>\n",
              "      <th>edu_level</th>\n",
              "      <th>commute_level</th>\n",
              "      <th>home_env</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.039216</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.5</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.823529</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87669af2-f380-4df1-8707-24c330b5f033')\"\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-87669af2-f380-4df1-8707-24c330b5f033 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-87669af2-f380-4df1-8707-24c330b5f033');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8hDZ2q5WRk6"
      },
      "source": [
        "df2 = df2.drop(columns=\"nav_num\", axis=1)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SCcIO9m7WeLE",
        "outputId": "a8d9247b-f560-4422-cebb-9e786566115a"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  gender  hand  sleep  edu_level  commute_level  home_env\n",
              "0  0.960784     1.0   0.0    1.0   0.555556            0.0  0.333333\n",
              "1  0.039216     1.0   0.0    0.5   0.444444            0.5  0.666667\n",
              "2  0.333333     1.0   1.0    0.0   0.222222            0.5  1.000000\n",
              "3  0.000000     0.0   1.0    0.5   0.555556            0.0  0.666667\n",
              "4  0.823529     1.0   1.0    0.5   0.444444            0.0  0.666667"
            ],
            "text/html": [
              "\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",
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              "        vertical-align: top;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>hand</th>\n",
              "      <th>sleep</th>\n",
              "      <th>edu_level</th>\n",
              "      <th>commute_level</th>\n",
              "      <th>home_env</th>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.333333</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.039216</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>1.0</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc9584db-49cd-4b1b-a44a-afc24102b4f6')\"\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\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-bc9584db-49cd-4b1b-a44a-afc24102b4f6 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-bc9584db-49cd-4b1b-a44a-afc24102b4f6');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4vuQPjIIs8R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6af1c91d-6421-4c94-c94f-51c6c732dd24"
      },
      "source": [
        "km = KMeans(n_clusters= 4, n_init = 2, init = \"random\", random_state = 42)\n",
        "km.fit(df2)\n",
        "y_kmeans = km.predict(df2)\n",
        "y_kmeans"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, ..., 3, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_elQQX6DJxDF"
      },
      "source": [
        "df_model = df.copy()\n",
        "\n",
        "skill_map = {0:\"very bad\", 1:\"bad\", 2:\"good\", 3:\"very good\"}\n",
        "df_model['nav_num'] = df_model['nav_num'].replace(skill_map)\n",
        "\n",
        "df_model['cluster'] = y_kmeans"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ElWnwj7_VZh"
      },
      "source": [
        "### Step2. Chi-Square of Independence Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m_kDSCW3RZ7T",
        "outputId": "47cc1fd2-604c-45bd-d553-b42d0c30484d"
      },
      "source": [
        "myCrosstable = pd.crosstab(df_model['cluster'], df_model['nav_num'])\n",
        "myCrosstable"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nav_num    bad    good  very bad  very good\n",
              "cluster                                    \n",
              "0         6626   54794      1010      53592\n",
              "1        44129  153192      8574      59061\n",
              "2        15457  105254      2198      84819\n",
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              "                                                     [key], {});\n",
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              "\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUgB2ufFRsJt",
        "outputId": "68636c92-59db-46d6-ecb7-54260ac5468e"
      },
      "source": [
        "# !pip install scipy\n",
        "\n",
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "chiVal, pVal, df, exp = chi2_contingency(myCrosstable)\n",
        "chiVal, pVal, df, exp"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41069.5070743028,\n",
              " 0.0,\n",
              " 9,\n",
              " array([[ 13383.84592576,  63270.14235098,   2402.75308828,\n",
              "          36965.25863499],\n",
              "        [ 30564.29195415, 144488.14739227,   5487.09595816,\n",
              "          84416.46469542],\n",
              "        [ 23962.69282089, 113280.06869631,   4301.93492201,\n",
              "          66183.30356078],\n",
              "        [ 21101.1692992 ,  99752.64156044,   3788.21603156,\n",
              "          58279.9731088 ]]))"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4RfgeofRs1l",
        "outputId": "d63ac1c8-b1fe-4ef8-a1bb-d66fc456053d"
      },
      "source": [
        "colTotals = myCrosstable.sum()\n",
        "nCols = len(colTotals)\n",
        "\n",
        "nCols, colTotals"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, nav_num\n",
              " bad           89012\n",
              " good         420791\n",
              " very bad      15980\n",
              " very good    245845\n",
              " dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7qo12inRtBf",
        "outputId": "225483f2-cd3d-40eb-b71b-4f9733ae09e8"
      },
      "source": [
        "rowTotals = myCrosstable.sum(axis=1)\n",
        "nRows = len(rowTotals)\n",
        "\n",
        "nRows, rowTotals"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, cluster\n",
              " 0    116022\n",
              " 1    264956\n",
              " 2    207728\n",
              " 3    182922\n",
              " dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZwdTWIpSAUs",
        "outputId": "6dec6312-9028-4ed6-cbf1-a1b7c8f4f322"
      },
      "source": [
        "n = sum(rowTotals)\n",
        "n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "771628"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iy9GpZ7_CceP"
      },
      "source": [
        "## Step3. Posthoc Analyses with Multiple Comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDXC3hZtSAYz",
        "outputId": "a49d22f9-1df8-464c-dd5a-a866cabd53e3"
      },
      "source": [
        "for i in range(nRows):\n",
        "    for j in range(nCols):\n",
        "        AdjRes = (myCrosstable.iloc[i,j] - exp[i,j]) / (exp[i,j]*(1-rowTotals[i]/n)*(1-colTotals[j]/n))**0.5\n",
        "        print(AdjRes)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-67.37768864228899\n",
            "-54.21664762211999\n",
            "-31.14915775095124\n",
            "113.65630039951444\n",
            "101.80276538994966\n",
            "41.90706526609468\n",
            "51.968072523382304\n",
            "-130.46657165223644\n",
            "-68.3377354825272\n",
            "-41.36950815454613\n",
            "-37.918185487175606\n",
            "102.65380885045312\n",
            "14.235323974771413\n",
            "41.9224874604567\n",
            "7.702586318613608\n",
            "-56.91631708396889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "6pzeqX5MSH_y",
        "outputId": "62feeeb6-c7c3-4a8e-8431-ee5de94fc529"
      },
      "source": [
        "phRes = pd.DataFrame(columns=[ 'Group', 'Skills','Adj. Res.'])\n",
        "for i in range(nRows):\n",
        "    for j in range(nCols):\n",
        "        AdjRes = (myCrosstable.iloc[i,j] - exp[i,j]) / (exp[i,j]*(1-rowTotals[i]/n)*(1-colTotals[j]/n))**0.5\n",
        "        phRes = phRes.append({'Group':myCrosstable.index[i], 'Skills':myCrosstable.columns[j], 'Adj. Res.':AdjRes}, ignore_index=True)\n",
        "\n",
        "phRes"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Group     Skills   Adj. Res.\n",
              "0      0        bad  -67.377689\n",
              "1      0       good  -54.216648\n",
              "2      0   very bad  -31.149158\n",
              "3      0  very good  113.656300\n",
              "4      1        bad  101.802765\n",
              "5      1       good   41.907065\n",
              "6      1   very bad   51.968073\n",
              "7      1  very good -130.466572\n",
              "8      2        bad  -68.337735\n",
              "9      2       good  -41.369508\n",
              "10     2   very bad  -37.918185\n",
              "11     2  very good  102.653809\n",
              "12     3        bad   14.235324\n",
              "13     3       good   41.922487\n",
              "14     3   very bad    7.702586\n",
              "15     3  very good  -56.916317"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <td>7.702586</td>\n",
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              "      <th>15</th>\n",
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              "      <td>very good</td>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a09b1633-06ad-422d-9a13-8de111240d23');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJ4cycLmSIWT"
      },
      "source": [
        "# !pip install scipy\n",
        "from scipy.stats import norm"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "c8Rh46MQUhWr",
        "outputId": "07b3a56e-9b39-4304-aebd-3ca0422ab54c"
      },
      "source": [
        "phRes['Sig.'] = 2*(1-norm.cdf(abs(phRes['Adj. Res.'])))\n",
        "phRes"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Group     Skills   Adj. Res.          Sig.\n",
              "0      0        bad  -67.377689  0.000000e+00\n",
              "1      0       good  -54.216648  0.000000e+00\n",
              "2      0   very bad  -31.149158  0.000000e+00\n",
              "3      0  very good  113.656300  0.000000e+00\n",
              "4      1        bad  101.802765  0.000000e+00\n",
              "5      1       good   41.907065  0.000000e+00\n",
              "6      1   very bad   51.968073  0.000000e+00\n",
              "7      1  very good -130.466572  0.000000e+00\n",
              "8      2        bad  -68.337735  0.000000e+00\n",
              "9      2       good  -41.369508  0.000000e+00\n",
              "10     2   very bad  -37.918185  0.000000e+00\n",
              "11     2  very good  102.653809  0.000000e+00\n",
              "12     3        bad   14.235324  0.000000e+00\n",
              "13     3       good   41.922487  0.000000e+00\n",
              "14     3   very bad    7.702586  1.332268e-14\n",
              "15     3  very good  -56.916317  0.000000e+00"
            ],
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              "      <th></th>\n",
              "      <th>Group</th>\n",
              "      <th>Skills</th>\n",
              "      <th>Adj. Res.</th>\n",
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              "      <td>-54.216648</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>-31.149158</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>very good</td>\n",
              "      <td>113.656300</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>101.802765</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>good</td>\n",
              "      <td>41.907065</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>51.968073</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>very good</td>\n",
              "      <td>-130.466572</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>bad</td>\n",
              "      <td>-68.337735</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>good</td>\n",
              "      <td>-41.369508</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>very bad</td>\n",
              "      <td>-37.918185</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>very good</td>\n",
              "      <td>102.653809</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>bad</td>\n",
              "      <td>14.235324</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>good</td>\n",
              "      <td>41.922487</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3</td>\n",
              "      <td>very bad</td>\n",
              "      <td>7.702586</td>\n",
              "      <td>1.332268e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3</td>\n",
              "      <td>very good</td>\n",
              "      <td>-56.916317</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c489b3e-1f95-4fd8-8e7c-65f5eb3aa77c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-6c489b3e-1f95-4fd8-8e7c-65f5eb3aa77c 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-6c489b3e-1f95-4fd8-8e7c-65f5eb3aa77c');\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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "fLUVPas8Uhan",
        "outputId": "60021dd1-50da-46c6-fd47-37b5a036027c"
      },
      "source": [
        "phRes['Adj. Sig.'] = phRes.shape[0]*phRes['Sig.']\n",
        "phRes"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Group     Skills   Adj. Res.          Sig.     Adj. Sig.\n",
              "0      0        bad  -67.377689  0.000000e+00  0.000000e+00\n",
              "1      0       good  -54.216648  0.000000e+00  0.000000e+00\n",
              "2      0   very bad  -31.149158  0.000000e+00  0.000000e+00\n",
              "3      0  very good  113.656300  0.000000e+00  0.000000e+00\n",
              "4      1        bad  101.802765  0.000000e+00  0.000000e+00\n",
              "5      1       good   41.907065  0.000000e+00  0.000000e+00\n",
              "6      1   very bad   51.968073  0.000000e+00  0.000000e+00\n",
              "7      1  very good -130.466572  0.000000e+00  0.000000e+00\n",
              "8      2        bad  -68.337735  0.000000e+00  0.000000e+00\n",
              "9      2       good  -41.369508  0.000000e+00  0.000000e+00\n",
              "10     2   very bad  -37.918185  0.000000e+00  0.000000e+00\n",
              "11     2  very good  102.653809  0.000000e+00  0.000000e+00\n",
              "12     3        bad   14.235324  0.000000e+00  0.000000e+00\n",
              "13     3       good   41.922487  0.000000e+00  0.000000e+00\n",
              "14     3   very bad    7.702586  1.332268e-14  2.131628e-13\n",
              "15     3  very good  -56.916317  0.000000e+00  0.000000e+00"
            ],
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              "      <th></th>\n",
              "      <th>Group</th>\n",
              "      <th>Skills</th>\n",
              "      <th>Adj. Res.</th>\n",
              "      <th>Sig.</th>\n",
              "      <th>Adj. Sig.</th>\n",
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              "      <th>1</th>\n",
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              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0.000000e+00</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.000000e+00</td>\n",
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              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>51.968073</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>very good</td>\n",
              "      <td>-130.466572</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>8</th>\n",
              "      <td>2</td>\n",
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              "      <td>-68.337735</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>good</td>\n",
              "      <td>-41.369508</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>very bad</td>\n",
              "      <td>-37.918185</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>very good</td>\n",
              "      <td>102.653809</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <th>12</th>\n",
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              "      <td>14.235324</td>\n",
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              "      <th>13</th>\n",
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              "      <td>0.000000e+00</td>\n",
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              "      <th>14</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eef177bb-86e6-4044-8963-679399e0688f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-eef177bb-86e6-4044-8963-679399e0688f 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-eef177bb-86e6-4044-8963-679399e0688f');\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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "ZNwSev0JUhgv",
        "outputId": "d7e049d9-f980-4f73-af0c-14dac8284d6d"
      },
      "source": [
        "phRes.loc[phRes['Adj. Sig.']> 1, 'Adj. Sig.'] = 1\n",
        "phRes"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Group     Skills   Adj. Res.          Sig.     Adj. Sig.\n",
              "0      0        bad  -67.377689  0.000000e+00  0.000000e+00\n",
              "1      0       good  -54.216648  0.000000e+00  0.000000e+00\n",
              "2      0   very bad  -31.149158  0.000000e+00  0.000000e+00\n",
              "3      0  very good  113.656300  0.000000e+00  0.000000e+00\n",
              "4      1        bad  101.802765  0.000000e+00  0.000000e+00\n",
              "5      1       good   41.907065  0.000000e+00  0.000000e+00\n",
              "6      1   very bad   51.968073  0.000000e+00  0.000000e+00\n",
              "7      1  very good -130.466572  0.000000e+00  0.000000e+00\n",
              "8      2        bad  -68.337735  0.000000e+00  0.000000e+00\n",
              "9      2       good  -41.369508  0.000000e+00  0.000000e+00\n",
              "10     2   very bad  -37.918185  0.000000e+00  0.000000e+00\n",
              "11     2  very good  102.653809  0.000000e+00  0.000000e+00\n",
              "12     3        bad   14.235324  0.000000e+00  0.000000e+00\n",
              "13     3       good   41.922487  0.000000e+00  0.000000e+00\n",
              "14     3   very bad    7.702586  1.332268e-14  2.131628e-13\n",
              "15     3  very good  -56.916317  0.000000e+00  0.000000e+00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38947596-7412-4901-9d94-05ae4d178c05\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Group</th>\n",
              "      <th>Skills</th>\n",
              "      <th>Adj. Res.</th>\n",
              "      <th>Sig.</th>\n",
              "      <th>Adj. Sig.</th>\n",
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              "  <tbody>\n",
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              "      <td>-67.377689</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>good</td>\n",
              "      <td>-54.216648</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>very bad</td>\n",
              "      <td>-31.149158</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>very good</td>\n",
              "      <td>113.656300</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>good</td>\n",
              "      <td>41.907065</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>very bad</td>\n",
              "      <td>51.968073</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>very good</td>\n",
              "      <td>-130.466572</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>bad</td>\n",
              "      <td>-68.337735</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>good</td>\n",
              "      <td>-41.369508</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>very bad</td>\n",
              "      <td>-37.918185</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>very good</td>\n",
              "      <td>102.653809</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>bad</td>\n",
              "      <td>14.235324</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>good</td>\n",
              "      <td>41.922487</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3</td>\n",
              "      <td>very bad</td>\n",
              "      <td>7.702586</td>\n",
              "      <td>1.332268e-14</td>\n",
              "      <td>2.131628e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3</td>\n",
              "      <td>very good</td>\n",
              "      <td>-56.916317</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38947596-7412-4901-9d94-05ae4d178c05')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38947596-7412-4901-9d94-05ae4d178c05 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-38947596-7412-4901-9d94-05ae4d178c05');\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": 45
        }
      ]
    }
  ]
}