survival-analysis-for-data-analysis-introduction_2024-06.ipynb

survival-analysis-for-data-analysis-introduction_2024-06.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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    "colab": {
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    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/1599bf39c72bda16beac7b03bec05e11/survival-analysis-for-data-analysis-introduction_2024-06.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEj365e4uTvD"
      },
      "source": [
        "# Survival Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "heading_collapsed": true,
        "id": "0Z1wEfUFuTvG"
      },
      "source": [
        "* Historically, survival analysis was developed and used by actuaries\n",
        "and medical researchers to measure the lifetime of populations.\n",
        "* What's the expected lifetime of patients that were given drug A? drug B?\n",
        "* What's the life-expectancy of a baby born today in France?\n",
        "\n",
        "These researchers wanted to measure the duration between *Birth* and *Death*\n",
        "\n",
        "\n",
        "Source: [Lifelines: Survival Analysis in Python](https://www.youtube.com/watch?v=XQfxndJH4UA)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxSbfi7QuTvI"
      },
      "source": [
        "# Survival function and hazard function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXfTmltSuTvK"
      },
      "source": [
        "**Definition:** Let $T$ be a random variable called failure time.\n",
        "\n",
        "- $f(t)$ be its probability density function\n",
        "- $F(t):=\\mathcal{P}(T\\le t)$ its cumulative distribution function\n",
        "\n",
        "Then we define\n",
        "\n",
        "- The *survival function* $S(t):=\\mathcal{P}(T>t)=1-F(t)$.\n",
        "- The *hazard function* (probability of failure between $t$ and $t+\\delta t$ knowing that it was working at time $t$):\n",
        "$$\n",
        "h(t):=\\lim_{\\delta t\\to0}\\frac{\\mathcal{P}(T<t+\\delta t|T>t)}{\\delta t}=\n",
        "\\lim_{\\delta t\\to0}\\frac{F(t+\\delta t)-F(t)}{\\delta t}\\times\\frac{1}{1-F(t)}=\\frac{f(t)}{1-F(t)}.\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBY8J3sxuTvM"
      },
      "source": [
        "**Properties:**\n",
        "- $S(t)=\\exp(-\\int_0^t h(s)\\,ds)$.\n",
        "- $h(t)=-\\frac{d}{dt}\\ln(S(t))$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VYNZWpmuTvO"
      },
      "source": [
        "# Right censoring\n",
        "\n",
        "By the end of the study, the event of interest (for example, in medicine \"death of a patient\" or \"churn of a customer\") has only occurred for a subset of the observations.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vao5BhTYuTvP"
      },
      "source": [
        "# Modern Survival Analysis\n",
        "\n",
        "+ **Birth:** Customer joins Netflix  \n",
        "**Death:** Customer leaves Netflix  \n",
        "**Censorship:** At the current time, I cannot see all cancelations  \n",
        "  \n",
        "  \n",
        "+ **Birth:** Leader forms government  \n",
        "**Death:** Government dissolves  \n",
        "**Censorship:** Death of leader or current time do not allow me to see all dissolvements  \n",
        "\n",
        "\n",
        "+ **Birth:** Couple starts dating  \n",
        "**Death:** Couple breaks-up  \n",
        "**Censorship:** Some couples never break-up (partner's death comes first)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVUDoqXOuTvU"
      },
      "source": [
        "First, let's take a dataset from lifelines to see what does it mean in practice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcqsnDunuZ3x",
        "outputId": "30688795-5be3-45f9-85d6-5ff7a8d53f90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%pip install --quiet --upgrade lifelines"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m349.3/349.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.5/94.5 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:10.331529Z",
          "start_time": "2020-01-09T22:37:03.811697Z"
        },
        "id": "A5qmUxSxuTvW"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "plt.style.use('seaborn-v0_8-bright')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hz_oM2IuTve"
      },
      "source": [
        "from lifelines.datasets import load_dd\n",
        "\n",
        "df = load_dd()\n",
        "df = df[['ctryname', 'un_region_name', 'un_continent_name', 'ehead',\\\n",
        "         'democracy', 'regime', 'start_year', 'duration', 'observed']]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCN-nZXDuTvk"
      },
      "source": [
        "# Democracy and dictatorship\n",
        "\n",
        "This dataset contains a classification of political regimes as democracy and dictatorship.\n",
        "* Classification of democracies as\n",
        "  + parliamentary,\n",
        "  + semi-presidential (mixed), and\n",
        "  + presidential.\n",
        "  \n",
        "* Classification of dictatorships as\n",
        "  + military,\n",
        "  + civilian, and\n",
        "  + royal.\n",
        "  \n",
        "Coverage: 202 countries, from 1946 or year of independence to 2008.\n",
        "\n",
        "**References**\n",
        "\n",
        "José Antonio Cheibub, Jennifer Gandhi, and James Raymond Vreeland. [\"Democracy and Dictatorship Revisited.\"](https://doi.org/10.1007/s11127-009-9491-2) Public Choice, vol. 143, no. 2-1, pp. 67-101, 2010."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:10.515360Z",
          "start_time": "2020-01-09T22:37:10.466697Z"
        },
        "id": "9b51kQDjuTvm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "93f3573e-1914-404d-a6b2-40ff30fc2583"
      },
      "source": [
        "df.tail(10).style.hide(axis=\"index\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e761bd11f60>"
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              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_a8a13\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_a8a13_level0_col0\" class=\"col_heading level0 col0\" >ctryname</th>\n",
              "      <th id=\"T_a8a13_level0_col1\" class=\"col_heading level0 col1\" >un_region_name</th>\n",
              "      <th id=\"T_a8a13_level0_col2\" class=\"col_heading level0 col2\" >un_continent_name</th>\n",
              "      <th id=\"T_a8a13_level0_col3\" class=\"col_heading level0 col3\" >ehead</th>\n",
              "      <th id=\"T_a8a13_level0_col4\" class=\"col_heading level0 col4\" >democracy</th>\n",
              "      <th id=\"T_a8a13_level0_col5\" class=\"col_heading level0 col5\" >regime</th>\n",
              "      <th id=\"T_a8a13_level0_col6\" class=\"col_heading level0 col6\" >start_year</th>\n",
              "      <th id=\"T_a8a13_level0_col7\" class=\"col_heading level0 col7\" >duration</th>\n",
              "      <th id=\"T_a8a13_level0_col8\" class=\"col_heading level0 col8\" >observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row0_col0\" class=\"data row0 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_a8a13_row0_col1\" class=\"data row0 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_a8a13_row0_col2\" class=\"data row0 col2\" >Europe</td>\n",
              "      <td id=\"T_a8a13_row0_col3\" class=\"data row0 col3\" >Stipe Suvar</td>\n",
              "      <td id=\"T_a8a13_row0_col4\" class=\"data row0 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row0_col5\" class=\"data row0 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row0_col6\" class=\"data row0 col6\" >1988</td>\n",
              "      <td id=\"T_a8a13_row0_col7\" class=\"data row0 col7\" >1</td>\n",
              "      <td id=\"T_a8a13_row0_col8\" class=\"data row0 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row1_col0\" class=\"data row1 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_a8a13_row1_col1\" class=\"data row1 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_a8a13_row1_col2\" class=\"data row1 col2\" >Europe</td>\n",
              "      <td id=\"T_a8a13_row1_col3\" class=\"data row1 col3\" >Milan Pancevski</td>\n",
              "      <td id=\"T_a8a13_row1_col4\" class=\"data row1 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row1_col5\" class=\"data row1 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row1_col6\" class=\"data row1 col6\" >1989</td>\n",
              "      <td id=\"T_a8a13_row1_col7\" class=\"data row1 col7\" >1</td>\n",
              "      <td id=\"T_a8a13_row1_col8\" class=\"data row1 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row2_col0\" class=\"data row2 col0\" >Yugoslavia</td>\n",
              "      <td id=\"T_a8a13_row2_col1\" class=\"data row2 col1\" >Southern Europe</td>\n",
              "      <td id=\"T_a8a13_row2_col2\" class=\"data row2 col2\" >Europe</td>\n",
              "      <td id=\"T_a8a13_row2_col3\" class=\"data row2 col3\" >Borisav Jovic</td>\n",
              "      <td id=\"T_a8a13_row2_col4\" class=\"data row2 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row2_col5\" class=\"data row2 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row2_col6\" class=\"data row2 col6\" >1990</td>\n",
              "      <td id=\"T_a8a13_row2_col7\" class=\"data row2 col7\" >1</td>\n",
              "      <td id=\"T_a8a13_row2_col8\" class=\"data row2 col8\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row3_col0\" class=\"data row3 col0\" >Zambia</td>\n",
              "      <td id=\"T_a8a13_row3_col1\" class=\"data row3 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row3_col2\" class=\"data row3 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row3_col3\" class=\"data row3 col3\" >Kenneth Kaunda</td>\n",
              "      <td id=\"T_a8a13_row3_col4\" class=\"data row3 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row3_col5\" class=\"data row3 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row3_col6\" class=\"data row3 col6\" >1964</td>\n",
              "      <td id=\"T_a8a13_row3_col7\" class=\"data row3 col7\" >27</td>\n",
              "      <td id=\"T_a8a13_row3_col8\" class=\"data row3 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row4_col0\" class=\"data row4 col0\" >Zambia</td>\n",
              "      <td id=\"T_a8a13_row4_col1\" class=\"data row4 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row4_col2\" class=\"data row4 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row4_col3\" class=\"data row4 col3\" >Frederick Chiluba</td>\n",
              "      <td id=\"T_a8a13_row4_col4\" class=\"data row4 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row4_col5\" class=\"data row4 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row4_col6\" class=\"data row4 col6\" >1991</td>\n",
              "      <td id=\"T_a8a13_row4_col7\" class=\"data row4 col7\" >11</td>\n",
              "      <td id=\"T_a8a13_row4_col8\" class=\"data row4 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row5_col0\" class=\"data row5 col0\" >Zambia</td>\n",
              "      <td id=\"T_a8a13_row5_col1\" class=\"data row5 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row5_col2\" class=\"data row5 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row5_col3\" class=\"data row5 col3\" >Levy Patrick Mwanawasa</td>\n",
              "      <td id=\"T_a8a13_row5_col4\" class=\"data row5 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row5_col5\" class=\"data row5 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row5_col6\" class=\"data row5 col6\" >2002</td>\n",
              "      <td id=\"T_a8a13_row5_col7\" class=\"data row5 col7\" >6</td>\n",
              "      <td id=\"T_a8a13_row5_col8\" class=\"data row5 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row6_col0\" class=\"data row6 col0\" >Zambia</td>\n",
              "      <td id=\"T_a8a13_row6_col1\" class=\"data row6 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row6_col2\" class=\"data row6 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row6_col3\" class=\"data row6 col3\" >Rupiah Bwezani Banda</td>\n",
              "      <td id=\"T_a8a13_row6_col4\" class=\"data row6 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row6_col5\" class=\"data row6 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row6_col6\" class=\"data row6 col6\" >2008</td>\n",
              "      <td id=\"T_a8a13_row6_col7\" class=\"data row6 col7\" >1</td>\n",
              "      <td id=\"T_a8a13_row6_col8\" class=\"data row6 col8\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row7_col0\" class=\"data row7 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_a8a13_row7_col1\" class=\"data row7 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row7_col2\" class=\"data row7 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row7_col3\" class=\"data row7 col3\" >Ian Smith</td>\n",
              "      <td id=\"T_a8a13_row7_col4\" class=\"data row7 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row7_col5\" class=\"data row7 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row7_col6\" class=\"data row7 col6\" >1965</td>\n",
              "      <td id=\"T_a8a13_row7_col7\" class=\"data row7 col7\" >14</td>\n",
              "      <td id=\"T_a8a13_row7_col8\" class=\"data row7 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row8_col0\" class=\"data row8 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_a8a13_row8_col1\" class=\"data row8 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row8_col2\" class=\"data row8 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row8_col3\" class=\"data row8 col3\" >Abel Muzorewa</td>\n",
              "      <td id=\"T_a8a13_row8_col4\" class=\"data row8 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row8_col5\" class=\"data row8 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row8_col6\" class=\"data row8 col6\" >1979</td>\n",
              "      <td id=\"T_a8a13_row8_col7\" class=\"data row8 col7\" >1</td>\n",
              "      <td id=\"T_a8a13_row8_col8\" class=\"data row8 col8\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_a8a13_row9_col0\" class=\"data row9 col0\" >Zimbabwe</td>\n",
              "      <td id=\"T_a8a13_row9_col1\" class=\"data row9 col1\" >Eastern Africa</td>\n",
              "      <td id=\"T_a8a13_row9_col2\" class=\"data row9 col2\" >Africa</td>\n",
              "      <td id=\"T_a8a13_row9_col3\" class=\"data row9 col3\" >Robert Mugabe</td>\n",
              "      <td id=\"T_a8a13_row9_col4\" class=\"data row9 col4\" >Non-democracy</td>\n",
              "      <td id=\"T_a8a13_row9_col5\" class=\"data row9 col5\" >Civilian Dict</td>\n",
              "      <td id=\"T_a8a13_row9_col6\" class=\"data row9 col6\" >1980</td>\n",
              "      <td id=\"T_a8a13_row9_col7\" class=\"data row9 col7\" >29</td>\n",
              "      <td id=\"T_a8a13_row9_col8\" class=\"data row9 col8\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-o57OZxvuTvu"
      },
      "source": [
        "Let's look at right-censored samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:12.185066Z",
          "start_time": "2020-01-09T22:37:10.519270Z"
        },
        "scrolled": false,
        "id": "vzaXN9QWuTvv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "88a4f722-ad3a-4e84-e17f-02c9f03a4e5f"
      },
      "source": [
        "format_dict = {'ehead':'{}','duration':'{}', 'observed':'{}'}\n",
        "(df.query('ctryname == \"United States of America\"')[['ehead', 'duration', 'observed']].style.format(format_dict)\n",
        "  .hide(axis=\"index\")\n",
        "  .highlight_min('observed', color='lightgreen'))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table id=\"T_08b5f\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_08b5f_level0_col0\" class=\"col_heading level0 col0\" >ehead</th>\n",
              "      <th id=\"T_08b5f_level0_col1\" class=\"col_heading level0 col1\" >duration</th>\n",
              "      <th id=\"T_08b5f_level0_col2\" class=\"col_heading level0 col2\" >observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row0_col0\" class=\"data row0 col0\" >Harry Truman</td>\n",
              "      <td id=\"T_08b5f_row0_col1\" class=\"data row0 col1\" >7</td>\n",
              "      <td id=\"T_08b5f_row0_col2\" class=\"data row0 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row1_col0\" class=\"data row1 col0\" >Dwight D. Eisenhower</td>\n",
              "      <td id=\"T_08b5f_row1_col1\" class=\"data row1 col1\" >8</td>\n",
              "      <td id=\"T_08b5f_row1_col2\" class=\"data row1 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row2_col0\" class=\"data row2 col0\" >John Kennedy</td>\n",
              "      <td id=\"T_08b5f_row2_col1\" class=\"data row2 col1\" >2</td>\n",
              "      <td id=\"T_08b5f_row2_col2\" class=\"data row2 col2\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row3_col0\" class=\"data row3 col0\" >Lyndon Johnson</td>\n",
              "      <td id=\"T_08b5f_row3_col1\" class=\"data row3 col1\" >6</td>\n",
              "      <td id=\"T_08b5f_row3_col2\" class=\"data row3 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row4_col0\" class=\"data row4 col0\" >Richard Nixon</td>\n",
              "      <td id=\"T_08b5f_row4_col1\" class=\"data row4 col1\" >5</td>\n",
              "      <td id=\"T_08b5f_row4_col2\" class=\"data row4 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row5_col0\" class=\"data row5 col0\" >Gerald Ford</td>\n",
              "      <td id=\"T_08b5f_row5_col1\" class=\"data row5 col1\" >3</td>\n",
              "      <td id=\"T_08b5f_row5_col2\" class=\"data row5 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row6_col0\" class=\"data row6 col0\" >Jimmy Carter</td>\n",
              "      <td id=\"T_08b5f_row6_col1\" class=\"data row6 col1\" >4</td>\n",
              "      <td id=\"T_08b5f_row6_col2\" class=\"data row6 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row7_col0\" class=\"data row7 col0\" >Ronald Reagan</td>\n",
              "      <td id=\"T_08b5f_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "      <td id=\"T_08b5f_row7_col2\" class=\"data row7 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row8_col0\" class=\"data row8 col0\" >George Bush</td>\n",
              "      <td id=\"T_08b5f_row8_col1\" class=\"data row8 col1\" >4</td>\n",
              "      <td id=\"T_08b5f_row8_col2\" class=\"data row8 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row9_col0\" class=\"data row9 col0\" >Bill Clinton</td>\n",
              "      <td id=\"T_08b5f_row9_col1\" class=\"data row9 col1\" >8</td>\n",
              "      <td id=\"T_08b5f_row9_col2\" class=\"data row9 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_08b5f_row10_col0\" class=\"data row10 col0\" >George W. Bush</td>\n",
              "      <td id=\"T_08b5f_row10_col1\" class=\"data row10 col1\" >8</td>\n",
              "      <td id=\"T_08b5f_row10_col2\" class=\"data row10 col2\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:12.216114Z",
          "start_time": "2020-01-09T22:37:12.189453Z"
        },
        "id": "AVHDxLziuTv2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd106b64-f279-417c-d25d-5d370ca19635"
      },
      "source": [
        "print(f'samples: {len(df)}\\n')\n",
        "print(f'right censored samples: {len(df.query(\"observed == 0\"))}')\n",
        "print(f'right censored samples (%): {100*len(df.query(\"observed == 0\"))/len(df):.1f}%')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "samples: 1808\n",
            "\n",
            "right censored samples: 340\n",
            "right censored samples (%): 18.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v0OGKU_uTv9"
      },
      "source": [
        "# How can we estimate the probability of a government survival?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0UzmdpeuTv9"
      },
      "source": [
        "**Example:** I want to estimate the survival function of a new machine and I have 100 of these new machines. After the first year:\n",
        "\n",
        "Samples | I\n",
        "--- | ---\n",
        "Initial numbers | 100\n",
        "Deaths in first year of age | 70\n",
        "One-year survivors | `30`\n",
        "\n",
        "Therefore, a reasonable estimate of the survival probability of 1 year is 0.3.\n",
        "\n",
        "I have increased my production. So now I have 1000 new machines.\n",
        "\n",
        "Samples | I | II\n",
        "--- | --- | ---\n",
        "Initial numbers | 100 | 1000\n",
        "Deaths in first year of age | 70 | 750\n",
        "One-year survivors | `30` | `250`\n",
        "Deaths in second year of age | 15 |\n",
        "Two-year survivors | `15` |\n",
        "\n",
        "What would be a good estimate of the survival probability of 1 year? and 2 years?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVevvzzCuTv-"
      },
      "source": [
        "The estimate of the probability of survival of 1 year would be\n",
        "$\\hat{P}(1)=(30+250)/(100+1000) \\sim 0.255$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlBS5dveuTwA"
      },
      "source": [
        "$\\hat{P}(2|1) = 15/30 = 0.5$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVja8-P5uTwA"
      },
      "source": [
        "$\\hat{P}(2)=0.255\\times0.5=0.127$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pG5yhITUuTwB"
      },
      "source": [
        "# Kaplan-Meier estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6hn2l6PuTwC"
      },
      "source": [
        "**Definition:** Kaplan-Meier estimator of the survival function is given by\n",
        "\n",
        "$$\n",
        "\\hat{S}(t):=\\prod_{i:t_i\\le t}\\left(1-\\frac{d_i}{n_i}\\right)\n",
        "$$\n",
        "where $t_i$ is a time where at least one event happened, $d_i$ the number of events that happened at time $t_i$, and $n_i$ the individuals known to have survived up to time $t_i$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cojEUEjsuTwD"
      },
      "source": [
        "We use the [Kaplan-Meier estimator](https://en.wikipedia.org/wiki/Kaplan?Meier_estimator) to estimate the probability of a government survival."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:16.155051Z",
          "start_time": "2020-01-09T22:37:12.219801Z"
        },
        "scrolled": false,
        "id": "LCGcjoexuTwE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "ffebb8c4-18b4-45e8-f188-6f504c467469"
      },
      "source": [
        "from lifelines import KaplanMeierFitter\n",
        "kmf = KaplanMeierFitter()\n",
        "kmf.fit(df['duration'],df['observed'], label='Estimate for average government')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdrG0N6nuTwL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73c4aa3d-7b68-4579-c91b-6ba324b0c013"
      },
      "source": [
        "print(f'The median number of years of government survival is {kmf.median_survival_time_}')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median number of years of government survival is 4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "NQHPG2x6uTwQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "6bbec1e6-3a8f-4f6d-d7c3-d50638ec00dd"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "for r in df['democracy'].unique():\n",
        "    ix = df['democracy'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBKPtOFKuTwV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4eb001fe-5d39-4b06-912c-7e09d56c8834"
      },
      "source": [
        "for r in df['democracy'].unique():\n",
        "    ix = df['democracy'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    print(f'The median number of years for a {r} is {kmf.median_survival_time_}')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median number of years for a Non-democracy is 6.0\n",
            "The median number of years for a Democracy is 3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhK-X5-fuTwc"
      },
      "source": [
        "How can we tell if these survival functions are different?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uL59D8GAuTwc"
      },
      "source": [
        "# Log-rank test (not recommended but still very common statistical test)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "LDq8D5ovuTwe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27d154ce-f93f-4764-ab4f-0d93b3b9d481"
      },
      "source": [
        "df['democracy'].unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Non-democracy', 'Democracy'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaRMHNtSuTwi"
      },
      "source": [
        "from lifelines.statistics import logrank_test"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qrrTDDWuTwn"
      },
      "source": [
        "ix = df['democracy'] == 'Democracy'\n",
        "T_democracy, E_democracy = df.loc[ix, 'duration'], df.loc[ix, 'observed']\n",
        "T_non_democracy, E_non_democracy = df.loc[~ix, 'duration'], df.loc[~ix, 'observed']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Qw20wiiRuTws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a511caed-5e31-4caf-fd3e-a866b8e10885"
      },
      "source": [
        "results = logrank_test(T_democracy, T_non_democracy, event_observed_A=E_democracy, event_observed_B=E_non_democracy)\n",
        "results.print_summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.StatisticalResult: logrank_test>\n",
              "               t_0 = -1\n",
              " null_distribution = chi squared\n",
              "degrees_of_freedom = 1\n",
              "         test_name = logrank_test\n",
              "\n",
              "---\n",
              " test_statistic      p  -log2(p)\n",
              "         260.47 <0.005    192.23"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>t_0</th>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>null_distribution</th>\n",
              "      <td>chi squared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>degrees_of_freedom</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test_name</th>\n",
              "      <td>logrank_test</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>test_statistic</th>\n",
              "      <th>p</th>\n",
              "      <th>-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>260.47</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>192.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/latex": "\\begin{tabular}{lrrr}\n & test_statistic & p & -log2(p) \\\\\n0 & 260.47 & 0.00 & 192.23 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Oms68oOuTww",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "101c1315-c1bb-473c-a69c-64f83e41f462"
      },
      "source": [
        "print(results.p_value)\n",
        "print(results.test_statistic)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3557143218482446e-58\n",
            "260.46953907795944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTX47C6-uTw1"
      },
      "source": [
        "# Univariate Cox regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meyGKaYcuTw4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "49c76672-40b9-4383-b7af-2fd52dc18546"
      },
      "source": [
        "from lifelines import CoxPHFitter\n",
        "\n",
        "cph = CoxPHFitter()\n",
        "df_Uni_Cox = df.copy()\n",
        "df_Uni_Cox['indicator'] = df_Uni_Cox['democracy'] == 'Democracy'\n",
        "cph.fit(df_Uni_Cox[['indicator', 'duration', 'observed']], 'duration', 'observed')\n",
        "cph.print_summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
              "             duration col = 'duration'\n",
              "                event col = 'observed'\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 1808\n",
              "number of events observed = 1468\n",
              "   partial log-likelihood = -9614.27\n",
              "         time fit was run = 2024-12-04 06:22:50 UTC\n",
              "\n",
              "---\n",
              "           coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
              "covariate                                                                                                  \n",
              "indicator  0.96      2.62      0.06            0.84            1.09                2.32                2.96\n",
              "\n",
              "           cmp to     z      p  -log2(p)\n",
              "covariate                               \n",
              "indicator    0.00 15.40 <0.005    175.43\n",
              "---\n",
              "Concordance = 0.59\n",
              "Partial AIC = 19230.53\n",
              "log-likelihood ratio test = 264.03 on 1 df\n",
              "-log2(p) of ll-ratio test = 194.81"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'duration'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'observed'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>1808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>1468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-9614.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2024-12-04 06:22:50 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">cmp to</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>indicator</th>\n",
              "      <td>0.96</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.32</td>\n",
              "      <td>2.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15.40</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>175.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Concordance</th>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>19230.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>264.03 on 1 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>194.81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate &  &  &  &  &  &  &  &  &  &  &  \\\\\nindicator & 0.96 & 2.62 & 0.06 & 0.84 & 1.09 & 2.32 & 2.96 & 0.00 & 15.40 & 0.00 & 175.43 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.131185Z",
          "start_time": "2020-01-09T22:37:16.573714Z"
        },
        "id": "USPOd2fOuTw8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "fe195a8c-7b94-48e9-e4a0-abc2acef31d8"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "\n",
        "for r in df['regime'].unique():\n",
        "    ix = df['regime'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.survival_function_.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.690930Z",
          "start_time": "2020-01-09T22:37:17.134877Z"
        },
        "scrolled": false,
        "id": "ydmaYBqnuTxA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "6e613bfc-70b5-445f-8e13-795442d4aef1"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "for r in df['un_continent_name'].unique():\n",
        "    ix = df['un_continent_name'] == r\n",
        "    kmf.fit(df['duration'].loc[ix], df['observed'].loc[ix], label=r)\n",
        "    kmf.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.723699Z",
          "start_time": "2020-01-09T22:37:17.694880Z"
        },
        "scrolled": false,
        "id": "NXIl1sBfuTxF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "1376fd90-d54d-4407-faa8-748f4a22daa2"
      },
      "source": [
        "df.query('ctryname == \"United States of America\"').tail(3)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      ctryname    un_region_name un_continent_name  \\\n",
              "1721  United States of America  Northern America          Americas   \n",
              "1722  United States of America  Northern America          Americas   \n",
              "1723  United States of America  Northern America          Americas   \n",
              "\n",
              "               ehead  democracy            regime  start_year  duration  \\\n",
              "1721     George Bush  Democracy  Presidential Dem        1989         4   \n",
              "1722    Bill Clinton  Democracy  Presidential Dem        1993         8   \n",
              "1723  George W. Bush  Democracy  Presidential Dem        2001         8   \n",
              "\n",
              "      observed  \n",
              "1721         1  \n",
              "1722         1  \n",
              "1723         0  "
            ],
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              "      <th></th>\n",
              "      <th>ctryname</th>\n",
              "      <th>un_region_name</th>\n",
              "      <th>un_continent_name</th>\n",
              "      <th>ehead</th>\n",
              "      <th>democracy</th>\n",
              "      <th>regime</th>\n",
              "      <th>start_year</th>\n",
              "      <th>duration</th>\n",
              "      <th>observed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>1721</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>Northern America</td>\n",
              "      <td>Americas</td>\n",
              "      <td>George Bush</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Presidential Dem</td>\n",
              "      <td>1989</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>Northern America</td>\n",
              "      <td>Americas</td>\n",
              "      <td>Bill Clinton</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Presidential Dem</td>\n",
              "      <td>1993</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>Northern America</td>\n",
              "      <td>Americas</td>\n",
              "      <td>George W. Bush</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Presidential Dem</td>\n",
              "      <td>2001</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3641d4f3-70ef-4450-ab24-c212f0e98338')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
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              "        document.querySelector('#df-3641d4f3-70ef-4450-ab24-c212f0e98338 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-3641d4f3-70ef-4450-ab24-c212f0e98338');\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",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a051ef99-8227-4ff8-b806-612efc3b0941 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"ctryname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States of America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"un_region_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Northern America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"un_continent_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Americas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ehead\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"George Bush\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"democracy\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Democracy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"regime\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Presidential Dem\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1989,\n        \"max\": 2001,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"observed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:17.748036Z",
          "start_time": "2020-01-09T22:37:17.725349Z"
        },
        "scrolled": false,
        "id": "Qzy7VJ_luTxI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "b5500e0c-f7b3-4959-d8e5-2bf1d8f32a3d"
      },
      "source": [
        "df.query('ctryname == \"United Kingdom\"').tail(3)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ctryname   un_region_name un_continent_name         ehead  \\\n",
              "1710  United Kingdom  Northern Europe            Europe    John Major   \n",
              "1711  United Kingdom  Northern Europe            Europe    Tony Blair   \n",
              "1712  United Kingdom  Northern Europe            Europe  Gordon Brown   \n",
              "\n",
              "      democracy             regime  start_year  duration  observed  \n",
              "1710  Democracy  Parliamentary Dem        1990         7         1  \n",
              "1711  Democracy  Parliamentary Dem        1997        10         1  \n",
              "1712  Democracy  Parliamentary Dem        2007         2         0  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ctryname</th>\n",
              "      <th>un_region_name</th>\n",
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              "      <th>democracy</th>\n",
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              "      <th>observed</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1710</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>Europe</td>\n",
              "      <td>John Major</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Parliamentary Dem</td>\n",
              "      <td>1990</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1711</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>Northern Europe</td>\n",
              "      <td>Europe</td>\n",
              "      <td>Tony Blair</td>\n",
              "      <td>Democracy</td>\n",
              "      <td>Parliamentary Dem</td>\n",
              "      <td>1997</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1712</th>\n",
              "      <td>United Kingdom</td>\n",
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              "      <td>Europe</td>\n",
              "      <td>Gordon Brown</td>\n",
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              "      <td>Parliamentary Dem</td>\n",
              "      <td>2007</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba9bd7c6-4929-4960-b7e5-e1ae6fb0b788')\"\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 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ba9bd7c6-4929-4960-b7e5-e1ae6fb0b788 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-ba9bd7c6-4929-4960-b7e5-e1ae6fb0b788');\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",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df115887-6ad2-4939-abd4-b398f4da4f40 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"ctryname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"un_region_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Northern Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"un_continent_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ehead\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"John Major\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"democracy\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Democracy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"regime\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Parliamentary Dem\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1990,\n        \"max\": 2007,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"observed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2020-01-09T22:37:18.129226Z",
          "start_time": "2020-01-09T22:37:17.749639Z"
        },
        "id": "010659VeuTxM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "7741b122-cfd5-4825-cbc0-d2b58f2b0cf0"
      },
      "source": [
        "ix_US = df['ctryname'] == 'United States of America'\n",
        "ix_UK = df['ctryname'] == 'United Kingdom'\n",
        "\n",
        "kmf_US = KaplanMeierFitter()\n",
        "kmf_US.fit(df['duration'].loc[ix_US], df['observed'].loc[ix_US], label='USA')\n",
        "\n",
        "kmf_UK = KaplanMeierFitter()\n",
        "kmf_UK.fit(df['duration'].loc[ix_UK], df['observed'].loc[ix_UK], label='UK')\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "ax = plt.subplot(111)\n",
        "kmf_US.plot(ax=ax)\n",
        "kmf_UK.plot(ax=ax)\n",
        "plt.title('Estimated probability of government survival vs number of years')\n",
        "plt.xlabel('Time (in years)')\n",
        "plt.ylabel('Estimated probability of government survival')\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaQCN1ChuTxs"
      },
      "source": [
        "# Multivariate Cox regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "nCPi7YY1uTxQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f56cb3c-c03b-41ff-c3d0-7d2e446e492d"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ctryname', 'un_region_name', 'un_continent_name', 'ehead', 'democracy',\n",
              "       'regime', 'start_year', 'duration', 'observed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "xphf-zpauTxU"
      },
      "source": [
        "df = df.drop(columns=['ctryname', 'un_region_name', 'ehead', 'regime', 'start_year'])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOuRvPJXuTxZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d0ff229-11c0-4275-b921-a882699fe9d3"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['un_continent_name', 'democracy', 'duration', 'observed'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzcg35DtuTxd"
      },
      "source": [
        "# df_hazard = pd.get_dummies(df, drop_first=True, columns=df.columns.drop(['duration', 'observed']))\n",
        "df_hazard = pd.get_dummies(df, columns=df.columns.drop(['duration', 'observed']))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGxRvM20uTxg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79468c5b-bdb9-4ca6-d615-01c97d3841a4"
      },
      "source": [
        "df_hazard.columns"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['duration', 'observed', 'un_continent_name_Africa',\n",
              "       'un_continent_name_Americas', 'un_continent_name_Asia',\n",
              "       'un_continent_name_Europe', 'un_continent_name_Oceania',\n",
              "       'democracy_Democracy', 'democracy_Non-democracy'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9b7PSd2uTxl"
      },
      "source": [
        "df_hazard = df_hazard.drop(columns=['un_continent_name_Americas', 'democracy_Democracy'])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-O2MTLPuTxo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9166057a-9d66-49cb-ee09-32e3b9493f10"
      },
      "source": [
        "df_hazard.columns"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['duration', 'observed', 'un_continent_name_Africa',\n",
              "       'un_continent_name_Asia', 'un_continent_name_Europe',\n",
              "       'un_continent_name_Oceania', 'democracy_Non-democracy'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUKzqcFvuTxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "01d2412e-dbab-4851-c419-56931ffd14a9"
      },
      "source": [
        "from lifelines import CoxPHFitter\n",
        "\n",
        "cph = CoxPHFitter(penalizer=0.1)\n",
        "cph.fit(df_hazard, 'duration', 'observed')\n",
        "cph.print_summary()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 1808 total observations, 340 right-censored observations>\n",
              "             duration col = 'duration'\n",
              "                event col = 'observed'\n",
              "                penalizer = 0.1\n",
              "                 l1 ratio = 0.0\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 1808\n",
              "number of events observed = 1468\n",
              "   partial log-likelihood = -9613.12\n",
              "         time fit was run = 2024-12-04 06:24:28 UTC\n",
              "\n",
              "---\n",
              "                           coef exp(coef)  se(coef)  coef lower 95%  coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n",
              "covariate                                                                                                                  \n",
              "un_continent_name_Africa  -0.20      0.82      0.08           -0.36           -0.04                0.70                0.96\n",
              "un_continent_name_Asia    -0.06      0.95      0.07           -0.20            0.09                0.82                1.09\n",
              "un_continent_name_Europe   0.23      1.26      0.06            0.11            0.35                1.12                1.43\n",
              "un_continent_name_Oceania -0.12      0.89      0.11           -0.33            0.10                0.72                1.10\n",
              "democracy_Non-democracy   -0.72      0.49      0.06           -0.84           -0.60                0.43                0.55\n",
              "\n",
              "                           cmp to      z      p  -log2(p)\n",
              "covariate                                                \n",
              "un_continent_name_Africa     0.00  -2.48   0.01      6.25\n",
              "un_continent_name_Asia       0.00  -0.77   0.44      1.19\n",
              "un_continent_name_Europe     0.00   3.79 <0.005     12.70\n",
              "un_continent_name_Oceania    0.00  -1.07   0.29      1.80\n",
              "democracy_Non-democracy      0.00 -11.48 <0.005     98.97\n",
              "---\n",
              "Concordance = 0.62\n",
              "Partial AIC = 19236.25\n",
              "log-likelihood ratio test = 266.31 on 5 df\n",
              "-log2(p) of ll-ratio test = 181.91"
            ],
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'duration'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'observed'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>penalizer</th>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l1 ratio</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>1808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>1468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-9613.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2024-12-04 06:24:28 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">cmp to</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Africa</th>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.08</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>0.01</td>\n",
              "      <td>6.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Asia</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.07</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.77</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Europe</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.79</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>12.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>un_continent_name_Oceania</th>\n",
              "      <td>-0.12</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.11</td>\n",
              "      <td>-0.33</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.07</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>democracy_Non-democracy</th>\n",
              "      <td>-0.72</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.06</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-11.48</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>98.97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Concordance</th>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>19236.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>266.31 on 5 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>181.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/latex": "\\begin{tabular}{lrrrrrrrrrrr}\n & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\ncovariate &  &  &  &  &  &  &  &  &  &  &  \\\\\nun_continent_name_Africa & -0.20 & 0.82 & 0.08 & -0.36 & -0.04 & 0.70 & 0.96 & 0.00 & -2.48 & 0.01 & 6.25 \\\\\nun_continent_name_Asia & -0.06 & 0.95 & 0.07 & -0.20 & 0.09 & 0.82 & 1.09 & 0.00 & -0.77 & 0.44 & 1.19 \\\\\nun_continent_name_Europe & 0.23 & 1.26 & 0.06 & 0.11 & 0.35 & 1.12 & 1.43 & 0.00 & 3.79 & 0.00 & 12.70 \\\\\nun_continent_name_Oceania & -0.12 & 0.89 & 0.11 & -0.33 & 0.10 & 0.72 & 1.10 & 0.00 & -1.07 & 0.29 & 1.80 \\\\\ndemocracy_Non-democracy & -0.72 & 0.49 & 0.06 & -0.84 & -0.60 & 0.43 & 0.55 & 0.00 & -11.48 & 0.00 & 98.97 \\\\\n\\end{tabular}\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYkJ1coruTxx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "52043275-fbb9-4212-e030-6ba39995f831"
      },
      "source": [
        "fig_coef, ax_coef = plt.subplots(figsize=(12,7))\n",
        "ax_coef.set_title('Survival Regression: Coefficients and Confident Intervals')\n",
        "cph.plot(ax=ax_coef)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KfaBnER0VoXT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}