DROPm_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/clizarraga-UAD7/Notebooks/blob/main/DROPm_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d524694",
      "metadata": {
        "id": "9d524694"
      },
      "source": [
        "## Analysis of the DROPm vs. count data\n",
        "\n",
        "C. Lizarraga (04/14/2023)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WJApMywHIsNs"
      },
      "id": "WJApMywHIsNs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "9cf4ab85",
      "metadata": {
        "id": "9cf4ab85"
      },
      "outputs": [],
      "source": [
        "# Load needed libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.stattools import medcouple # Skewed datasets\n",
        "from scipy.stats import kstest # Kolmogorov-Smirnov Normality test\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cef343d4",
      "metadata": {
        "id": "cef343d4"
      },
      "source": [
        "### Reading the data and transform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1665ca29",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1665ca29",
        "outputId": "b2985314-5e00-4083-f410-fd255a2c199d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DROP  count\n",
              "0   -62      1\n",
              "1   -55      1\n",
              "2   -50      1\n",
              "3   -47      1\n",
              "4   -43      3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0af6508b-072a-49d7-a410-2940932272d3\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-62</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-55</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-43</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0af6508b-072a-49d7-a410-2940932272d3')\"\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-0af6508b-072a-49d7-a410-2940932272d3 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-0af6508b-072a-49d7-a410-2940932272d3');\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": 3
        }
      ],
      "source": [
        "# Read the original data\n",
        "\n",
        "df0 = pd.read_csv('dropm.txt', sep='\\s+', header=0)\n",
        "df0.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "4cfa4ac1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4cfa4ac1",
        "outputId": "18c0f656-828d-4d29-aa46-3e8ca80dde72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DROP  count\n",
              "0    62      1\n",
              "1    55      1\n",
              "2    50      1\n",
              "3    47      1\n",
              "4    43      3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e7bfb60-051d-41eb-832f-8d0dbafd9b5e\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e7bfb60-051d-41eb-832f-8d0dbafd9b5e')\"\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-7e7bfb60-051d-41eb-832f-8d0dbafd9b5e 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-7e7bfb60-051d-41eb-832f-8d0dbafd9b5e');\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": 4
        }
      ],
      "source": [
        "# Reflect to positive values\n",
        "# Possibly it's a decaying exponential (?)\n",
        "\n",
        "df0['DROP'] *= -1\n",
        "df0.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "156447d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "156447d7",
        "outputId": "eadf592b-74fb-4319-b303-a2984c6a8569"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41 entries, 0 to 40\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   DROP    41 non-null     int64\n",
            " 1   count   41 non-null     int64\n",
            "dtypes: int64(2)\n",
            "memory usage: 784.0 bytes\n"
          ]
        }
      ],
      "source": [
        "# Check genera information\n",
        "\n",
        "df0.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "84673ad8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "84673ad8",
        "outputId": "4bf4f695-bf38-4e30-9b9d-7fb7e25e07b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DROP\n",
              "0     0\n",
              "1     1\n",
              "2     2\n",
              "3     3\n",
              "4     4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e1ed4be-f586-4f71-800a-87cbfc5e564c\">\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>DROP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e1ed4be-f586-4f71-800a-87cbfc5e564c')\"\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-6e1ed4be-f586-4f71-800a-87cbfc5e564c 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-6e1ed4be-f586-4f71-800a-87cbfc5e564c');\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": 6
        }
      ],
      "source": [
        "# Create a sequential data column\n",
        "\n",
        "df1 = pd.DataFrame(np.arange(0,63), columns=['DROP'])\n",
        "df1.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "3a90cf7f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3a90cf7f",
        "outputId": "9a9b966a-36a2-4794-efe9-2be00d22f596"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DROP\n",
              "58    58\n",
              "59    59\n",
              "60    60\n",
              "61    61\n",
              "62    62"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b96fd02-2c10-43d2-bc89-ca4dc1c279e0\">\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>DROP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b96fd02-2c10-43d2-bc89-ca4dc1c279e0')\"\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-6b96fd02-2c10-43d2-bc89-ca4dc1c279e0 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-6b96fd02-2c10-43d2-bc89-ca4dc1c279e0');\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": 7
        }
      ],
      "source": [
        "df1.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "eeb282f4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "eeb282f4",
        "outputId": "45b9e898-2a15-472d-8856-d536094376d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DROP   count\n",
              "0      0  1361.0\n",
              "1      1    89.0\n",
              "2      2   114.0\n",
              "3      3   111.0\n",
              "4      4    69.0\n",
              "5      5   130.0\n",
              "6      6    80.0\n",
              "7      7    58.0\n",
              "8      8    71.0\n",
              "9      9     8.0\n",
              "10    10    58.0\n",
              "11    11    28.0\n",
              "12    12    27.0\n",
              "13    13    18.0\n",
              "14    14    10.0\n",
              "15    15    11.0\n",
              "16    16    14.0\n",
              "17    17     5.0\n",
              "18    18     5.0\n",
              "19    19     5.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e1076df-a572-4041-8739-0f4d63ad76b5\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1361.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>111.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>69.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>130.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>11</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>12</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>13</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>14</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>15</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>16</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>17</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e1076df-a572-4041-8739-0f4d63ad76b5')\"\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-0e1076df-a572-4041-8739-0f4d63ad76b5 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-0e1076df-a572-4041-8739-0f4d63ad76b5');\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": 8
        }
      ],
      "source": [
        "# Merge original data df0 and sequential data df1\n",
        "# Create a new dataframe combining two previous dataframe\n",
        "\n",
        "df_tmp = pd.merge_ordered(df1, df0, fill_method=\"ffill\", left_by=\"DROP\")\n",
        "df_tmp.head(20)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0a23b8b2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0a23b8b2",
        "outputId": "a1849f93-505f-43e7-b763-dcb733d799e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DROP  count\n",
              "53    53    NaN\n",
              "54    54    NaN\n",
              "55    55    1.0\n",
              "56    56    NaN\n",
              "57    57    NaN\n",
              "58    58    NaN\n",
              "59    59    NaN\n",
              "60    60    NaN\n",
              "61    61    NaN\n",
              "62    62    1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-454e0bc3-f6f0-4db6-985d-50e20a9d1a69\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>54</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>55</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>56</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>57</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>58</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>61</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>62</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-454e0bc3-f6f0-4db6-985d-50e20a9d1a69')\"\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-454e0bc3-f6f0-4db6-985d-50e20a9d1a69 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-454e0bc3-f6f0-4db6-985d-50e20a9d1a69');\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": 9
        }
      ],
      "source": [
        "df_tmp.tail(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "6507c29a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6507c29a",
        "outputId": "9cfee077-d590-4b33-d892-0bf82a65e708"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 63 entries, 0 to 62\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DROP    63 non-null     int64  \n",
            " 1   count   41 non-null     float64\n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 1.1 KB\n"
          ]
        }
      ],
      "source": [
        "# General dataframe info\n",
        "\n",
        "df_tmp.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "a2975609",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2975609",
        "outputId": "0ba16ad7-7c8c-4fdb-c289-1085288e3e2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 63 entries, 0 to 62\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DROP    63 non-null     int64  \n",
            " 1   count   63 non-null     float64\n",
            "dtypes: float64(1), int64(1)\n",
            "memory usage: 1.1 KB\n"
          ]
        }
      ],
      "source": [
        "# Replace NaN (missing numbers) with zero\n",
        "\n",
        "df_tmp = df_tmp.fillna(0)\n",
        "df_tmp.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "84cbb7f3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "84cbb7f3",
        "outputId": "b29eb0fe-ebb7-41aa-8313-63a93c8029f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DROP  count\n",
              "58    58    0.0\n",
              "59    59    0.0\n",
              "60    60    0.0\n",
              "61    61    0.0\n",
              "62    62    1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09261db3-b609-4c2e-b87e-e703502a6339\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>58</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>59</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>61</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>62</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09261db3-b609-4c2e-b87e-e703502a6339')\"\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-09261db3-b609-4c2e-b87e-e703502a6339 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-09261db3-b609-4c2e-b87e-e703502a6339');\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
        }
      ],
      "source": [
        "df_tmp.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "65f623ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "65f623ef",
        "outputId": "e33e3dc6-71b2-4a70-e54c-37d7550e91b3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            DROP        count\n",
              "count  63.000000    63.000000\n",
              "mean   31.000000    36.761905\n",
              "std    18.330303   172.401886\n",
              "min     0.000000     0.000000\n",
              "25%    15.500000     0.000000\n",
              "50%    31.000000     1.000000\n",
              "75%    46.500000     9.000000\n",
              "max    62.000000  1361.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31f201dd-7ee0-4fda-ab61-eb6463d67baf\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>36.761905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.330303</td>\n",
              "      <td>172.401886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.500000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>1361.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31f201dd-7ee0-4fda-ab61-eb6463d67baf')\"\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-31f201dd-7ee0-4fda-ab61-eb6463d67baf 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-31f201dd-7ee0-4fda-ab61-eb6463d67baf');\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": 13
        }
      ],
      "source": [
        "# Basic statistics\n",
        "\n",
        "df_tmp.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "fe35adfd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "fe35adfd",
        "outputId": "6cf57464-41da-4ba9-9610-482fdd8ac7da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Scatterplot of original dataset (No transformation)\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.scatterplot(data=df_tmp, x=\"DROP\", y=\"count\");\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "8c873b96",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8c873b96",
        "outputId": "9e0d396c-71b5-4991-afc7-c03e44c65e0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean= 36.76190476190476\n",
            "StdDev= 172.40188554145467\n",
            "Skewness= 7.548533725204234\n",
            "Kurtosis= 58.70444671174729\n",
            "Lambda= 0.027202072538860107\n"
          ]
        }
      ],
      "source": [
        "# General dataset statistics\n",
        "\n",
        "ave_count = df_tmp['count'].mean()\n",
        "std_count = df_tmp['count'].std()\n",
        "skew_count = df_tmp['count'].skew()\n",
        "kurt_count = df_tmp['count'].kurtosis()\n",
        "\n",
        "print('Mean=', ave_count)\n",
        "print('StdDev=', std_count)\n",
        "print('Skewness=', skew_count)\n",
        "print('Kurtosis=', kurt_count)\n",
        "\n",
        "# lambda = 1/average in exponential distribution\n",
        "lmbd = 1.0/ave_count\n",
        "print('Lambda=', lmbd)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03971528",
      "metadata": {
        "id": "03971528"
      },
      "source": [
        "**The data do not a pure exponential probability distribution.**\n",
        "\n",
        "(See: [Exponential Distribution properties](https://en.wikipedia.org/wiki/Exponential_distribution))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "941ac064",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "941ac064",
        "outputId": "1d1ae1f7-aa66-492e-8661-95f721357790"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Empirical Cummulative Distribution Function\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.ecdfplot(data=df_tmp, x=\"count\");"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7292b12",
      "metadata": {
        "id": "d7292b12"
      },
      "source": [
        "### Use a Log transform for the count variable\n",
        "\n",
        "Since count can have zero values will use $\\log (x+1)$ transformation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "103c23df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "103c23df",
        "outputId": "2b78bc88-c0ae-45cc-ea60-a0def947c99b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DROP   count    transf\n",
              "0     0  1361.0  7.216709\n",
              "1     1    89.0  4.499810\n",
              "2     2   114.0  4.744932\n",
              "3     3   111.0  4.718499\n",
              "4     4    69.0  4.248495"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ab84af5-7969-4a67-8f0a-3737371fa82e\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1361.0</td>\n",
              "      <td>7.216709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89.0</td>\n",
              "      <td>4.499810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>114.0</td>\n",
              "      <td>4.744932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>111.0</td>\n",
              "      <td>4.718499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>4.248495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ab84af5-7969-4a67-8f0a-3737371fa82e')\"\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-8ab84af5-7969-4a67-8f0a-3737371fa82e 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-8ab84af5-7969-4a67-8f0a-3737371fa82e');\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
        }
      ],
      "source": [
        "# Make a variable transformation for 'count' using Log(x+1) \n",
        "# Define a new column of the dataframe df_tmp\n",
        "\n",
        "df_tmp['transf'] = np.log(df_tmp['count'] + 1.0)\n",
        "df_tmp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "169a6bcf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "169a6bcf",
        "outputId": "8f4f9168-0ec4-4941-eeb0-6f60d9c507f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 63 entries, 0 to 62\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DROP    63 non-null     int64  \n",
            " 1   count   63 non-null     float64\n",
            " 2   transf  63 non-null     float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 1.6 KB\n"
          ]
        }
      ],
      "source": [
        "df_tmp.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "d65a9603",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d65a9603",
        "outputId": "0c911076-faa5-43e5-984b-0d96e7708679"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 63 entries, 0 to 62\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DROP    63 non-null     int64  \n",
            " 1   count   63 non-null     float64\n",
            " 2   transf  63 non-null     float64\n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 1.6 KB\n"
          ]
        }
      ],
      "source": [
        "# Replace NaN (missing numbers) with zero\n",
        "df_tmp = df_tmp.fillna(0)\n",
        "df_tmp.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "e971b4f8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "e971b4f8",
        "outputId": "09fb03d1-a80b-46e5-c085-801cfa712ae8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            DROP        count     transf\n",
              "count  63.000000    63.000000  63.000000\n",
              "mean   31.000000    36.761905   1.484551\n",
              "std    18.330303   172.401886   1.711787\n",
              "min     0.000000     0.000000   0.000000\n",
              "25%    15.500000     0.000000   0.000000\n",
              "50%    31.000000     1.000000   0.693147\n",
              "75%    46.500000     9.000000   2.297560\n",
              "max    62.000000  1361.000000   7.216709"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-907f0fd1-1d2f-4492-b932-fb4cc6ddf9b0\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>36.761905</td>\n",
              "      <td>1.484551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.330303</td>\n",
              "      <td>172.401886</td>\n",
              "      <td>1.711787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.297560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>1361.000000</td>\n",
              "      <td>7.216709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-907f0fd1-1d2f-4492-b932-fb4cc6ddf9b0')\"\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-907f0fd1-1d2f-4492-b932-fb4cc6ddf9b0 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-907f0fd1-1d2f-4492-b932-fb4cc6ddf9b0');\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": 20
        }
      ],
      "source": [
        "# General statistics\n",
        "df_tmp.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "9bbe2133",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "9bbe2133",
        "outputId": "0e8ad96d-6401-4a54-b68b-df1155ba7867"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Scatterplot in the transformed Log variable\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.scatterplot(data=df_tmp, x=\"DROP\", y=\"transf\");\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "f005d05b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "f005d05b",
        "outputId": "fcf2ac74-d561-4552-b782-3c4a5702023f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Line plot (not nice)\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.lineplot(data=df_tmp, x=\"DROP\", y=\"transf\");\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "49d7d33c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "49d7d33c",
        "outputId": "8b51ecec-d998-4ba3-c9ec-f38f7712553d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Fit a linear function \n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.lmplot(x=\"DROP\", y=\"transf\", data=df_tmp, x_estimator=np.mean);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "fed52d10",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "fed52d10",
        "outputId": "9d07f204-7c28-4628-f232-8ac3ee827a1a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Fit a second order polynomial\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.lmplot(x=\"DROP\", y=\"transf\", data=df_tmp,\n",
        "           order=2,  x_estimator=np.mean);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a86949d2",
      "metadata": {
        "id": "a86949d2"
      },
      "source": [
        "Comparison between transformed variable and using simply a log y scale."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "a1420b19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "a1420b19",
        "outputId": "addbfa3c-ab89-4cb7-918f-0075448d8c2a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Barplot (histogram) of transformed variable\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "g=sns.barplot(data=df_tmp, x=\"DROP\", y=\"transf\")\n",
        "\n",
        "# Use Log scale\n",
        "#g.set_yscale(\"log\")\n",
        "\n",
        "plt.locator_params(nbins=12, axis='x')\n",
        "\n",
        "# Rotating X-axis labels\n",
        "plt.xticks(rotation = 30);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "2c3c7320",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2c3c7320",
        "outputId": "e519716d-dce6-4821-cfea-c3915e8678cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# For illustration purposes, only used y-log scale in plot\n",
        "# Barplot (histogram)\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "g=sns.barplot(data=df_tmp, x=\"DROP\", y=\"count\")\n",
        "\n",
        "# Use Log scale\n",
        "g.set_yscale(\"log\")\n",
        "\n",
        "plt.locator_params(nbins=12, axis='x')\n",
        "\n",
        "# Rotating X-axis labels\n",
        "plt.xticks(rotation = 30);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "83051bd2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "83051bd2",
        "outputId": "4709ce63-0028-41bc-ef15-bfe7be61f46e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Boxplot of the transformed variable\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.boxplot(x=df_tmp['transf']);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "37362fcc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "37362fcc",
        "outputId": "efe009e8-5ee7-46da-ac90-11d57e8adaf6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Boxplot with scatterplot of transformed data\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "\n",
        "# Initialize the figure with a logarithmic x axis\n",
        "f, ax = plt.subplots(figsize=(10, 6))\n",
        "#ax.set_xscale(\"log\")\n",
        "\n",
        "# Plot the orbital period with horizontal boxes\n",
        "sns.boxplot(x=\"transf\", data=df_tmp)\n",
        "\n",
        "# Add in points to show each observation\n",
        "sns.stripplot(x='transf', data=df_tmp,\n",
        "              size=4, color=\".3\", linewidth=0)\n",
        "\n",
        "# Tweak the visual presentation\n",
        "ax.xaxis.grid(True)\n",
        "ax.set(ylabel=\" \")\n",
        "sns.despine(trim=True, left=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "9112831d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9112831d",
        "outputId": "8923e602-9715-46e4-9867-20a789b5e7cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.48808395038036895\n"
          ]
        }
      ],
      "source": [
        "# Followed paper: An adjusted boxplot for skewed distributions. \n",
        "# M. Hubert, E. Vandervieren / Computational Statistics and Data Analysis 52 (2008) 5186–5201\n",
        "   \n",
        "# Using statsmodels.medcouple() method\n",
        "\n",
        "MC = medcouple(df_tmp['transf'])\n",
        "   \n",
        "print(MC)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "35200f77",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35200f77",
        "outputId": "2275f547-3ca1-4308-b60e-47683708d97e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.25    0.000000\n",
              "0.50    0.693147\n",
              "0.75    2.297560\n",
              "Name: transf, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Outlier detection method for skewed distributions\n",
        "# For MC > 0\n",
        "# [Q1 − 1.5e(−4 MC) IQR; Q3 + 1.5e(+3 MC) IQR]\n",
        "\n",
        "#calculate quartiles for count column\n",
        "\n",
        "df_tmp['transf'].quantile([0.25, 0.5, 0.75])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "1a272c99",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a272c99",
        "outputId": "11a1a7cf-5f02-42c5-e5ff-d1c4157feaf0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1, Q2, Q3, IQR 0.0 0.6931471805599453 2.2975599250672953 2.2975599250672953\n"
          ]
        }
      ],
      "source": [
        "Q1 = df_tmp['transf'].quantile([0.25]).values[0]  # Q1\n",
        "Q2 = df_tmp['transf'].quantile([0.50]).values[0]  # Q2\n",
        "Q3 = df_tmp['transf'].quantile([0.75]).values[0]  # Q3\n",
        "IQR = Q3 - Q1\n",
        "print('Q1, Q2, Q3, IQR', Q1, Q2, Q3, IQR)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "1b6c8a57",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b6c8a57",
        "outputId": "e2ac180c-f499-4f1f-c29e-1f0faa92521e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LL, UL: -0.4891808430052974 17.20059095192427\n"
          ]
        }
      ],
      "source": [
        "# Lower and Upper Fences if MC >  0\n",
        "# M. Hubert, E. Vandervieren / Computational Statistics and Data Analysis 52 (2008) 5186–5201\n",
        "\n",
        "LL = Q1 - 1.5 * IQR * np.exp(-4 * MC)\n",
        "UL = Q3 + 1.5 * IQR * np.exp(3 * MC)\n",
        "print('LL, UL:', LL, UL)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "41dece89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "41dece89",
        "outputId": "31e0c2b1-7e59-4207-d98b-7e58e062d551"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [DROP, count, transf]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7391b37-87b1-483b-b7f1-3289f8b402da\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7391b37-87b1-483b-b7f1-3289f8b402da')\"\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-b7391b37-87b1-483b-b7f1-3289f8b402da 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-b7391b37-87b1-483b-b7f1-3289f8b402da');\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": 33
        }
      ],
      "source": [
        "# See if there are outliers (There are no extreme values)\n",
        "df_tmp[df_tmp['transf'] >= UL ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "412a6820",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "412a6820",
        "outputId": "d7a0c93a-667a-4baa-a42a-abaa1427addd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quartiles\n",
            "Q1 =  0.0\n",
            "Q2 =  0.6931471805599453\n",
            "Q3 =  2.2975599250672953\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot the distribution of data values\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "\n",
        "sns.displot(data=df_tmp, x='transf', kde=True).set(title='DROPm in transformed variable $\\log(count+1)$')\n",
        "plt.axvline(x=Q1, linewidth=2, color='g', label='Q1')\n",
        "plt.axvline(x=Q2, linewidth=2, color='k', label='Q2')\n",
        "plt.axvline(x=Q3, linewidth=2, color='m', label='Q3')\n",
        "\n",
        "#plt.axvline(x=UL, linewidth=2, color='r', label='UL', ls='--')\n",
        "plt.legend();\n",
        "print('Quartiles')\n",
        "print('Q1 = ', Q1)\n",
        "print('Q2 = ', Q2)\n",
        "print('Q3 = ', Q3)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "a4a9a50a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "a4a9a50a",
        "outputId": "a3090676-b2ae-4620-a51c-553d68a8dbac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#create Q-Q plot with 45-degree line added to plot\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "fig = sm.qqplot(df_tmp['transf'], line='45')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "9d8e2d16",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d8e2d16",
        "outputId": "aab31143-0eb3-47ed-8b3a-48250c52743c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KstestResult(statistic=0.5, pvalue=4.339398723240336e-15, statistic_location=0.0, statistic_sign=-1)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# Test normality hypothesis\n",
        "# perform Kolmogorov-Smirnov test\n",
        "\n",
        "kstest(df_tmp['transf'], 'norm')\n",
        "\n",
        "# if p-value < 0.5 we can reject the hypothesis that the data follow a normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "4d45610a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4d45610a",
        "outputId": "50ccb4bb-3cdf-4186-d97b-96ec00f2264a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DROP   count    transf\n",
              "0     0  1361.0  7.216709\n",
              "1     1    89.0  4.499810\n",
              "2     2   114.0  4.744932\n",
              "3     3   111.0  4.718499\n",
              "4     4    69.0  4.248495"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24d5d889-b8d3-4b31-8714-c34c7717eae9\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1361.0</td>\n",
              "      <td>7.216709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>89.0</td>\n",
              "      <td>4.499810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>114.0</td>\n",
              "      <td>4.744932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>111.0</td>\n",
              "      <td>4.718499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>4.248495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24d5d889-b8d3-4b31-8714-c34c7717eae9')\"\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-24d5d889-b8d3-4b31-8714-c34c7717eae9 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-24d5d889-b8d3-4b31-8714-c34c7717eae9');\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": 37
        }
      ],
      "source": [
        "df_tmp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "9dab1c70",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9dab1c70",
        "outputId": "38970477-a677-4d71-9b83-42cd8048a665"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    DROP  count    transf\n",
              "58    58    0.0  0.000000\n",
              "59    59    0.0  0.000000\n",
              "60    60    0.0  0.000000\n",
              "61    61    0.0  0.000000\n",
              "62    62    1.0  0.693147"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1fdf0c7-078a-475f-bc4d-f51a01dea82b\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>59</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>61</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>62</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.693147</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1fdf0c7-078a-475f-bc4d-f51a01dea82b')\"\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-a1fdf0c7-078a-475f-bc4d-f51a01dea82b 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-a1fdf0c7-078a-475f-bc4d-f51a01dea82b');\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": 38
        }
      ],
      "source": [
        "df_tmp.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee1fd016",
      "metadata": {
        "id": "ee1fd016"
      },
      "source": [
        "## Use a z-scale transform\n",
        "\n",
        "Explore if new results have better interpretation "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "ad1fd3d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ad1fd3d4",
        "outputId": "20326901-f633-4dc2-c208-e970638c2804"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        DROP     count    transf\n",
              "0  -1.691189  7.681111  3.348639\n",
              "1  -1.636634  0.303002  1.761468\n",
              "2  -1.582080  0.448012  1.904665\n",
              "3  -1.527525  0.430611  1.889223\n",
              "4  -1.472971  0.186994  1.614654\n",
              "..       ...       ...       ...\n",
              "58  1.472971 -0.213234 -0.867252\n",
              "59  1.527525 -0.213234 -0.867252\n",
              "60  1.582080 -0.213234 -0.867252\n",
              "61  1.636634 -0.213234 -0.867252\n",
              "62  1.691189 -0.207433 -0.462326\n",
              "\n",
              "[63 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d75def0b-4539-4533-a842-a50dd5e2b4b1\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.691189</td>\n",
              "      <td>7.681111</td>\n",
              "      <td>3.348639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.636634</td>\n",
              "      <td>0.303002</td>\n",
              "      <td>1.761468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.582080</td>\n",
              "      <td>0.448012</td>\n",
              "      <td>1.904665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.527525</td>\n",
              "      <td>0.430611</td>\n",
              "      <td>1.889223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.472971</td>\n",
              "      <td>0.186994</td>\n",
              "      <td>1.614654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1.472971</td>\n",
              "      <td>-0.213234</td>\n",
              "      <td>-0.867252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>1.527525</td>\n",
              "      <td>-0.213234</td>\n",
              "      <td>-0.867252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1.582080</td>\n",
              "      <td>-0.213234</td>\n",
              "      <td>-0.867252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1.636634</td>\n",
              "      <td>-0.213234</td>\n",
              "      <td>-0.867252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1.691189</td>\n",
              "      <td>-0.207433</td>\n",
              "      <td>-0.462326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d75def0b-4539-4533-a842-a50dd5e2b4b1')\"\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-d75def0b-4539-4533-a842-a50dd5e2b4b1 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-d75def0b-4539-4533-a842-a50dd5e2b4b1');\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": {}
        }
      ],
      "source": [
        "# copy the data\n",
        "df_z_scaled = df_tmp.copy()\n",
        "\n",
        "# apply normalization techniques\n",
        "for column in df_z_scaled.columns:\n",
        "    df_z_scaled[column] = (df_z_scaled[column] -\n",
        "                        df_z_scaled[column].mean()) / df_z_scaled[column].std()\n",
        "\n",
        "# view normalized data\n",
        "display(df_z_scaled)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatterplot in the transformed  z scale\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.scatterplot(data=df_z_scaled, x=\"DROP\", y=\"transf\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "h-0FVanuLStz",
        "outputId": "aab75979-b1b7-464b-8991-579b9eaa14ad"
      },
      "id": "h-0FVanuLStz",
      "execution_count": 43,
      "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",
      "execution_count": 40,
      "id": "8cd032ad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8cd032ad",
        "outputId": "61c7ca30-38c2-4c99-a793-c5f1e5d58e5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       DROP     count    transf   transfz\n",
              "0 -1.691189  7.681111  3.348639  2.161150\n",
              "1 -1.636634  0.303002  1.761468  0.264671\n",
              "2 -1.582080  0.448012  1.904665  0.370192\n",
              "3 -1.527525  0.430611  1.889223  0.358101\n",
              "4 -1.472971  0.186994  1.614654  0.171424"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01ec10e6-a911-435a-ad56-7843f2d8ca82\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "      <th>transfz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.691189</td>\n",
              "      <td>7.681111</td>\n",
              "      <td>3.348639</td>\n",
              "      <td>2.161150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.636634</td>\n",
              "      <td>0.303002</td>\n",
              "      <td>1.761468</td>\n",
              "      <td>0.264671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.582080</td>\n",
              "      <td>0.448012</td>\n",
              "      <td>1.904665</td>\n",
              "      <td>0.370192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.527525</td>\n",
              "      <td>0.430611</td>\n",
              "      <td>1.889223</td>\n",
              "      <td>0.358101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.472971</td>\n",
              "      <td>0.186994</td>\n",
              "      <td>1.614654</td>\n",
              "      <td>0.171424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01ec10e6-a911-435a-ad56-7843f2d8ca82')\"\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-01ec10e6-a911-435a-ad56-7843f2d8ca82 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-01ec10e6-a911-435a-ad56-7843f2d8ca82');\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": 40
        }
      ],
      "source": [
        "# Make a variable transformation for 'count' using Log(x+1) \n",
        "# USe log scale to reduce differences between large values and small\n",
        "\n",
        "df_z_scaled['transfz'] = np.log(df_z_scaled['count'] + 1.0)\n",
        "df_z_scaled.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "dfcbd098",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "dfcbd098",
        "outputId": "8be174e2-e905-4544-9dfc-4df1c2194c08"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            DROP         count        transf    transfz\n",
              "count  63.000000  6.300000e+01  6.300000e+01  63.000000\n",
              "mean    0.000000  4.493760e-17 -2.907727e-17  -0.112509\n",
              "std     1.000000  1.000000e+00  1.000000e+00   0.338727\n",
              "min    -1.691189 -2.132338e-01 -8.672521e-01  -0.239824\n",
              "25%    -0.845594 -2.132338e-01 -8.672521e-01  -0.239824\n",
              "50%     0.000000 -2.074334e-01 -4.623261e-01  -0.232479\n",
              "75%     0.845594 -1.610302e-01  4.749473e-01  -0.175604\n",
              "max     1.691189  7.681111e+00  3.348639e+00   2.161150"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35572504-42eb-42ed-b65f-2d69e28eee0f\">\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>DROP</th>\n",
              "      <th>count</th>\n",
              "      <th>transf</th>\n",
              "      <th>transfz</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>63.000000</td>\n",
              "      <td>6.300000e+01</td>\n",
              "      <td>6.300000e+01</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.493760e-17</td>\n",
              "      <td>-2.907727e-17</td>\n",
              "      <td>-0.112509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.338727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.691189</td>\n",
              "      <td>-2.132338e-01</td>\n",
              "      <td>-8.672521e-01</td>\n",
              "      <td>-0.239824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.845594</td>\n",
              "      <td>-2.132338e-01</td>\n",
              "      <td>-8.672521e-01</td>\n",
              "      <td>-0.239824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.074334e-01</td>\n",
              "      <td>-4.623261e-01</td>\n",
              "      <td>-0.232479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.845594</td>\n",
              "      <td>-1.610302e-01</td>\n",
              "      <td>4.749473e-01</td>\n",
              "      <td>-0.175604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.691189</td>\n",
              "      <td>7.681111e+00</td>\n",
              "      <td>3.348639e+00</td>\n",
              "      <td>2.161150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35572504-42eb-42ed-b65f-2d69e28eee0f')\"\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-35572504-42eb-42ed-b65f-2d69e28eee0f 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-35572504-42eb-42ed-b65f-2d69e28eee0f');\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
        }
      ],
      "source": [
        "# General statistics\n",
        "df_z_scaled.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatterplot in the transformed z scale Log variable\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.scatterplot(data=df_z_scaled, x=\"DROP\", y=\"transfz\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "IC7cqfjjLp9F",
        "outputId": "4b0a2372-566e-41ef-f605-9871c2ac1ce9"
      },
      "id": "IC7cqfjjLp9F",
      "execution_count": 45,
      "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": [
        "So, the additional Log scales is not needed. Stay with the z-scale only"
      ],
      "metadata": {
        "id": "XuV2xRKqMFO4"
      },
      "id": "XuV2xRKqMFO4"
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "3e4e19b6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3e4e19b6",
        "outputId": "3fd37656-3821-4da6-ec87-974dad11b715"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean= -2.9077269692563625e-17\n",
            "StdDev= 1.0\n",
            "Skewness= 1.2234221323968875\n",
            "Kurtosis= 0.869018145926697\n",
            "Lambda= -3.4391124427192876e+16\n"
          ]
        }
      ],
      "source": [
        "# General dataset statistics\n",
        "\n",
        "ave_count_z = df_z_scaled['transf'].mean()\n",
        "std_count_z = df_z_scaled['transf'].std()\n",
        "skew_count_z = df_z_scaled['transf'].skew()\n",
        "kurt_count_z = df_z_scaled['transf'].kurtosis()\n",
        "\n",
        "print('Mean=', ave_count_z)\n",
        "print('StdDev=', std_count_z)\n",
        "print('Skewness=', skew_count_z)\n",
        "print('Kurtosis=', kurt_count_z)\n",
        "\n",
        "# lambda = 1/average in exponential distribution\n",
        "lmbd_z = 1.0/ave_count_z\n",
        "print('Lambda=', lmbd_z)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "d006a16b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "d006a16b",
        "outputId": "6e279c86-ef25-41ac-af9e-2387ee822649"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Barplot (histogram)\n",
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "\n",
        "g=sns.barplot(data=df_z_scaled, x=\"DROP\", y=\"transf\")\n",
        "\n",
        "# Use Log scale\n",
        "#g.set_yscale(\"log\")\n",
        "\n",
        "plt.locator_params(nbins=8, axis='x')\n",
        "\n",
        "# Rotating X-axis labels\n",
        "plt.xticks(rotation = 30);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "68d5e7c7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "68d5e7c7",
        "outputId": "3ff98ade-180b-4d84-8ebc-d29f94dd0030"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgYAAAG5CAYAAAD1S/ydAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAT1ElEQVR4nO3df4zXBf3A8dcH7k5C+T1gTYUGbAehFqY5bXKIuIwMsuZmnmZbibVo6WITlkiun7O1dNgU6KJya9mWc2QEIWOHg7ZSidCA8SND2USigCPA+xz3+f7R19dATvSU4/35HI/HXx/enzefz+tz7/t87nnv9/s+n1KlUqkEAEBE9Cl6AACgeggDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACDVdfc/bNiwISqVStTX1/fEPABADyiXy1EqlWLSpEmnXK/bewwqlUqc6s0SK5VKtLe3n3IdqoNtVTtsq9phW9WOs21bvd3P7zd0e4/BG3sKLr744i6vP3z4cGzevDnGjRsX/fv37+7NcwbZVrXDtqodtlXtONu21aZNm97Res4xAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAFJd0QNERFQqlSiXy0WP0S2VSiUiIkqlUsGTvHvt7e3R0dER7e3tUVdXFd8K71l9fX1NbxOAolXFT4NyuRz3339/0WPQCyxYsCAaGhqKHgOgZjmUAACkqthjcLyLrp8VffrWFz3GKXV2lOOFlYsjIuKij8+KPnXVPW9v13msHC+sWFz0GAC9QtWFQZ++9dG3hn7Q9qmrrXkB4FQcSgAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAACSMAAAkjAAAJIwAABSXdEDAPQGlUql6BHgtKiKMDj+CeXJBdSaSqUSixcvjlKpFM3NzUWPA+9JVYRBuVzOy5VjHRH1DQVOA9A95XI5du3alZehljnHAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAAqsTmzZvjhz/8YWzevLmwGYQBAFSB9vb2WLZsWezfvz+WLVsW7e3thcwhDACgCrS2tkZbW1tERLS1tcXatWsLmaOukHuFHlJUYfe09vb26OjoiPb29qir87StNr31+44zZ9++fbF27dqoVCoREVGpVKK1tTUmTZoUw4YNO6OzeIWh5r3xRIqI+P73v1/gJHDi9yO8E5VKJZYtW9bldcuWLYsvfOELUSqVztg8DiUAQIH27t0b27dvj87OzhOWd3Z2xvbt22Pv3r1ndB57DKh5x5f0vHnzoqGhocBpesbhw4dj69at0djYGP379y96HN6kvb0991adyd/s6B2GDx8e48aNi507d54QB3369ImxY8fG8OHDz+g8woBepaGhoVeGQUdHR9TV1fXaxwdns1KpFDNmzIgHH3zwpOs+9alPnfHYdCgBAAo2bNiwmDx5ckZAqVSKpqamM37iYYQwAICq0NTUFAMGDIiIiAEDBsTkyZMLmUMYAEAVaGhoiBkzZsTgwYNjxowZhR02dI4BAFSJCRMmxIQJEwqdwR4DACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACDVFT1ARER9fX1eLvWtipEA3rH6+voYPXp0XoZaVhU/hUulUpeXAWpBqVSKO+64IyIijhw5UvA08N5URRgA1Dq/1NBbOMcAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASMIAAEjCAABIwgAASHVFD/BmncfKRY/wtjo7yl1ephi18D0DUCuqLgxeWLG46BG65YWVtTUvAJyKQwkAQKqKPQb19fWxYMGCosfolkqlEhERpVKp4EnevcOHD8fWrVujsbEx+vfvX/Q4p0V9fX3RIwDUtKoIg1KpFA0NDUWPcdbp6OiIurq6aGho8PUHICIcSgAAjiMMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAVKpUKpXu/Ifnn38+KpVKNDQ0dHl9pVKJcrkc9fX1USqVTsuQ9AzbqnbYVrXDtqodZ9u2am9vj1KpFJdeeukp16vr7g2/3RevVCq9ZTRQXWyr2mFb1Q7bqnacbduqVCq9owDq9h4DAKD3co4BAJCEAQCQhAEAkIQBAJCEAQCQhAEAkIQBAJCEAQCQhAEAkIQBAJCEAQCQhAEAkLr96YrdtW7dunjiiSdi48aN8fLLL0dzc3Pcd999PX23nMKOHTviO9/5TmzYsCHOPffcmDlzZtx1111n1aeM1Yp//vOf0dLSEhs3boxt27bFmDFj4qmnnip6LN7kD3/4QyxbtixefPHFOHjwYIwePTpuu+22+OxnP3tWfJxvLWltbY0lS5bE9u3b49ChQzFy5MiYNm1azJ49OwYMGFD0eFWhx8PgmWeeiS1btsTll18eBw4c6Om7420cOHAgbr/99vjABz4QCxcujD179sQPfvCDOHr0qGCrQtu2bYvW1tb40Ic+FJ2dneHDUKvTz3/+8zj//PNj7ty5MWTIkFi/fn3Mnz8/Xn311Zg9e3bR43Gc/fv3xyWXXBK33XZbDB48OLZt2xYLFy6Mbdu2xc9+9rOix6sKPf6xy52dndGnz/+OWEydOjWmTJniB1CBFi1aFI8++misWbMmBg8eHBERjz/+eNx///2xZs2aGDlyZLEDcoLjnz9z586NF154wR6DKvTvf/87hg4desKy+fPnx/Lly+Mvf/lLbkOq029+85uYP39+rF271mtgnIFzDDwhqsvatWvjyiuvzCiIiPjEJz4RnZ2dsW7duuIGo0ueP7XhzVEQETFhwoQ4dOhQHD58uICJ6I43Xg/L5XKxg1QJrzpnmZ07d8aYMWNOWDZw4MAYPnx47Ny5s6CpoPd57rnnYuTIkXHeeecVPQpdOHbsWLz++uvx4osvxk9+8pOYOnVqXHDBBUWPVRV6/BwDqsvBgwdj4MCBJy0fNGiQc0DgNHn22Wdj+fLlcc899xQ9Cm/hmmuuiT179kRExNVXXx0/+tGPCp6oenQ7DNra2uK111572/UuvPBCZ7kDZ51XX3017r777rjiiivi85//fNHj8BYWL14cR44cie3bt8cjjzwSX/7yl2Pp0qXRt2/fokcrXLfDYMWKFXHvvfe+7XrLly+PsWPHvquh6DkDBw6Mtra2k5YfOHAgBg0aVMBE0HscPHgw7rjjjhg8eHAsXLjQOSJVbPz48RERMWnSpLj44otj5syZsWrVqrj++usLnqx43Q6Dm266KW666aaemIUzYMyYMSedS9DW1hZ79+496dwD4J07evRo3HnnndHW1haPP/64v4mvIY2NjVFfXx+7du0qepSqIGfPMpMnT47169fHwYMHc9mKFSuiT58+8bGPfazAyaB2dXR0xF133RU7d+6Mn/70p/7krcZs3LgxyuWykw//X4+ffLh79+7YtGlTREQcOXIkdu3aFStWrIiIsMumADfffHM89thj8dWvfjXuvPPO2LNnTzzwwANx8803ezGrQkeOHInW1taI+N9z6dChQ/n8+ehHP9rln8lx5r3xPiBz586NQ4cOxV//+te87oMf/KDzrarI7Nmz46KLLorGxsbo169fbNmyJVpaWqKxsTGmTZtW9HhVocff4OiJJ56IefPmdXnd1q1be/KueQs7duyIb3/72ye8JfLdd9/txasKvfLKK3Httdd2ed0vf/nLuOKKK87wRHRl6tSpsXv37i6vW716td9Eq8jixYtj+fLlsWvXrqhUKnH++efHddddF1/84hf9aen/6/EwAABqh3MMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDIAe9eSTT8b1118fEydOjMsuu6zocYC30eNviQycfs8//3ysW7cubr/99hg4cGDR47ylHTt2xLx58+Lqq6+OWbNmRb9+/YoeCXgbwgBq0IYNG+Lhhx+OG2+8sarD4M9//nN0dnbGN7/5zRg9enTR4wDvgEMJ0It1dnbG66+/Xtj979u3LyLCRxBDDfFZCVBjFi5cGA8//PBJy1evXh3XXnttNDc3x4c//OFYtGhRvPTSS/HQQw/FtGnToqWlJVatWhX/+Mc/4siRIzFu3LiYNWvWSZ9y2tjYGM3NzXHVVVfFgw8+GC+99FKMHj067rnnnpg8eXKud+jQoXjooYdi9erV8dprr8WAAQNi/PjxMWfOnJg4cWKXHyw0e/bs+NrXvtYzXxjgtBAGUGO2bNkSS5YsiaeeeirmzZsXQ4YMiYiI6667LiZNmhRjx46N//znP9Hc3BxDhgyJSy+9NCZMmBBNTU0xderUGDduXJTL5fj9738ff/vb32LRokUxZcqUvP3GxsYYP3587Nu3L2655ZY499xz47HHHot//etfsWbNmry/b3zjG7Fy5cq49dZbY+zYsbF///547rnnYvr06TFjxox4+umn48knn4xVq1bFt771rejfv3/eNlC9hAHUoJaWlnjggQdO+kjfxsbG6NOnT/zud7+LcePGnfB/jh49esLJf+VyOT7zmc/E0KFD4xe/+MUJt1FfXx/Lly+PUaNGRcT/YmTmzJkxf/78uPXWWyMi4rLLLosZM2bEfffd95ZzvrF3409/+lMMHTr0tDx2oGc5xwB6mcsvv/ykKIiIE6LgwIED0dbWFh/5yEfi73//+0nrXnXVVRkFERHjx4+P8847L15++eVcNnDgwNi4cWPs2bPnND8CoEj+KgF6meP3IBxvzZo18cgjj8TmzZujvb09l5dKpZPWff/733/SskGDBsXBgwfz33PmzIm5c+fGlClTYuLEidHU1BSf/vSn48ILLzwNjwIoij0G0Mt09V4Bzz77bHzlK1+Jc845JxYsWBCLFy+OpUuXxg033BBdHU3s27dvl7d9/LrTp0+Pp59+Ou69994YMWJEtLS0xCc/+clobW09fQ8GOOPsMYAa1NVv+aeycuXKOOecc6KlpSUaGhpy+W9/+9v3NMeIESOiubk5mpubY9++fXHjjTfGo48+Gk1NTe/pdoHi2GMANeh973tfRES0tbW9o/X79u0bpVIpjh07lsteeeWVWL169bu6/2PHjp1038OGDYsRI0accJgCqD32GEANmjhxYkRE/PjHP47p06dHfX19XHPNNW+5flNTUyxdujS+9KUvxQ033BD79u2LX/3qVzFq1KjYunVrt+//v//9bzQ1NcXHP/7xGD9+fPTv3z/Wr18fmzZtirlz577rxwUUTxhADbrkkkvi61//evz617+OZ555Jjo7O0/52/+VV14Z3/3ud2PJkiXxve99Ly644IKYM2dO7N69+12FQb9+/eJzn/tcrFu3Lv74xz9GpVKJUaNGxYIFC+KWW255Lw8NKJj3MQAAknMMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAIAkDACAJAwAgCQMAID0f2cWh/L8oGjxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "sns.boxplot(x=df_z_scaled['transf']);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "69f6308e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "69f6308e",
        "outputId": "683eabe8-3b1b-4c9e-de64-774b9f22fe01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, ' ')]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set_theme(style=\"whitegrid\", palette=\"pastel\")\n",
        "\n",
        "# Initialize the figure with a logarithmic x axis\n",
        "f, ax = plt.subplots(figsize=(10, 6))\n",
        "#ax.set_xscale(\"log\")\n",
        "\n",
        "# Plot the orbital period with horizontal boxes\n",
        "sns.boxplot(x=\"transf\", data=df_z_scaled)\n",
        "\n",
        "# Add in points to show each observation\n",
        "sns.stripplot(x='transf', data=df_z_scaled,\n",
        "              size=4, color=\".3\", linewidth=0)\n",
        "\n",
        "# Tweak the visual presentation\n",
        "ax.xaxis.grid(True)\n",
        "ax.set(ylabel=\" \")\n",
        "#sns.despine(trim=True, left=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "1f2d3764",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f2d3764",
        "outputId": "82fa0ac9-03f8-4a31-95b6-00fd4ab8e560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.48808395038036895\n"
          ]
        }
      ],
      "source": [
        "# Using statsmodels.medcouple() method\n",
        "\n",
        "MCz = medcouple(df_z_scaled['transf'])\n",
        "   \n",
        "print(MCz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "c7aa3eae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7aa3eae",
        "outputId": "2d162c26-a4ea-409e-a5dd-d7e8df36fd6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1z, Q2z, Q3z, IQRz -0.8672521092868013 -0.46232612162511316 0.4749472791868421 1.3421993884736434\n"
          ]
        }
      ],
      "source": [
        "Q1z = df_z_scaled['transf'].quantile([0.25]).values[0]  # Q1\n",
        "Q2z = df_z_scaled['transf'].quantile([0.50]).values[0]  # Q2\n",
        "Q3z = df_z_scaled['transf'].quantile([0.75]).values[0]  # Q3\n",
        "IQRz = Q3z - Q1z\n",
        "print('Q1z, Q2z, Q3z, IQRz', Q1z, Q2z, Q3z, IQRz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "f41bb3c4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f41bb3c4",
        "outputId": "e6be1564-1f6e-4e11-93d0-502f016f8e03"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLz, ULz: -1.153024080311888 9.181070202211487\n"
          ]
        }
      ],
      "source": [
        "# Lower and Upper Fences if MC >  0\n",
        "# M. Hubert, E. Vandervieren / Computational Statistics and Data Analysis 52 (2008) 5186–5201\n",
        "LLz = Q1z - 1.5 * IQRz * np.exp(-4 * MCz)\n",
        "ULz = Q3z + 1.5 * IQRz * np.exp(3 * MCz)\n",
        "print('LLz, ULz:', LLz, ULz)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "394e7cdd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "394e7cdd",
        "outputId": "d12b4aef-4935-4183-9278-445f2ebeda76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quartiles (z-scale)\n",
            "Q1z =  -0.8672521092868013\n",
            "Q2z =  -0.46232612162511316\n",
            "Q3z =  0.4749472791868421\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot the distribution of data values\n",
        "\n",
        "sns.displot(data=df_z_scaled, x='transf', kde=True).set(title='DROPm in transformed z-scale')\n",
        "plt.axvline(x=Q1z, linewidth=2, color='g', label='Q1z', alpha=0.3)\n",
        "plt.axvline(x=Q2z, linewidth=2, color='k', label='Q2z', alpha=0.3)\n",
        "plt.axvline(x=Q3z, linewidth=2, color='m', label='Q3z', alpha=0.3)\n",
        "\n",
        "#plt.axvline(x=UL, linewidth=2, color='r', label='ULz', ls='--')\n",
        "plt.legend();\n",
        "print('Quartiles (z-scale)')\n",
        "print('Q1z = ', Q1z)\n",
        "print('Q2z = ', Q2z)\n",
        "print('Q3z = ', Q3z)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "986c77c8",
      "metadata": {
        "id": "986c77c8"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}