Survival Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches #for custom legends\n",
    "import seaborn as sns\n",
    "from lifelines import KaplanMeierFitter #survival analysis library\n",
    "from lifelines.statistics import logrank_test #survival statistical testing\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>7038</th>\n",
       "      <th>7039</th>\n",
       "      <th>7040</th>\n",
       "      <th>7041</th>\n",
       "      <th>7042</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>3186-AJIEK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Fiber optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>One year</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Two year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>84.8</td>\n",
       "      <td>103.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>74.4</td>\n",
       "      <td>105.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>1990.5</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>346.45</td>\n",
       "      <td>306.6</td>\n",
       "      <td>6844.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          7038                     7039              7040  \\\n",
       "customerID          6840-RESVB               2234-XADUH        4801-JZAZL   \n",
       "gender                    Male                   Female            Female   \n",
       "SeniorCitizen                0                        0                 0   \n",
       "Partner                    Yes                      Yes               Yes   \n",
       "Dependents                 Yes                      Yes               Yes   \n",
       "tenure                      24                       72                11   \n",
       "PhoneService               Yes                      Yes                No   \n",
       "MultipleLines              Yes                      Yes  No phone service   \n",
       "InternetService            DSL              Fiber optic               DSL   \n",
       "OnlineSecurity             Yes                       No               Yes   \n",
       "OnlineBackup                No                      Yes                No   \n",
       "DeviceProtection           Yes                      Yes                No   \n",
       "TechSupport                Yes                       No                No   \n",
       "StreamingTV                Yes                      Yes                No   \n",
       "StreamingMovies            Yes                      Yes                No   \n",
       "Contract              One year                 One year    Month-to-month   \n",
       "PaperlessBilling           Yes                      Yes               Yes   \n",
       "PaymentMethod     Mailed check  Credit card (automatic)  Electronic check   \n",
       "MonthlyCharges            84.8                    103.2              29.6   \n",
       "TotalCharges            1990.5                   7362.9            346.45   \n",
       "Churn                       No                       No                No   \n",
       "\n",
       "                            7041                       7042  \n",
       "customerID            8361-LTMKD                 3186-AJIEK  \n",
       "gender                      Male                       Male  \n",
       "SeniorCitizen                  1                          0  \n",
       "Partner                      Yes                         No  \n",
       "Dependents                    No                         No  \n",
       "tenure                         4                         66  \n",
       "PhoneService                 Yes                        Yes  \n",
       "MultipleLines                Yes                         No  \n",
       "InternetService      Fiber optic                Fiber optic  \n",
       "OnlineSecurity                No                        Yes  \n",
       "OnlineBackup                  No                         No  \n",
       "DeviceProtection              No                        Yes  \n",
       "TechSupport                   No                        Yes  \n",
       "StreamingTV                   No                        Yes  \n",
       "StreamingMovies               No                        Yes  \n",
       "Contract          Month-to-month                   Two year  \n",
       "PaperlessBilling             Yes                        Yes  \n",
       "PaymentMethod       Mailed check  Bank transfer (automatic)  \n",
       "MonthlyCharges              74.4                     105.65  \n",
       "TotalCharges               306.6                     6844.5  \n",
       "Churn                        Yes                         No  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"https://community.watsonanalytics.com/wp-content/uploads/2015/03/WA_Fn-UseC_-Telco-Customer-Churn.csv?cm_mc_uid=51304980933215218170416&cm_mc_sid_50200000=92178841521817041648&cm_mc_sid_52640000=98592221521817041652\"\n",
    "df = pd.read_csv(url)\n",
    "print(df.shape)\n",
    "df.tail().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"b_Churn\"] = df.Churn.apply(lambda x: 1 if x == \"Yes\" else 0) #recode churn var\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>7038</th>\n",
       "      <th>7039</th>\n",
       "      <th>7040</th>\n",
       "      <th>7041</th>\n",
       "      <th>7042</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>3186-AJIEK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Fiber optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>One year</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Two year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>84.8</td>\n",
       "      <td>103.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>74.4</td>\n",
       "      <td>105.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>1990.5</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>346.45</td>\n",
       "      <td>306.6</td>\n",
       "      <td>6844.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_Churn</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          7038                     7039              7040  \\\n",
       "customerID          6840-RESVB               2234-XADUH        4801-JZAZL   \n",
       "gender                    Male                   Female            Female   \n",
       "SeniorCitizen                0                        0                 0   \n",
       "Partner                    Yes                      Yes               Yes   \n",
       "Dependents                 Yes                      Yes               Yes   \n",
       "tenure                      24                       72                11   \n",
       "PhoneService               Yes                      Yes                No   \n",
       "MultipleLines              Yes                      Yes  No phone service   \n",
       "InternetService            DSL              Fiber optic               DSL   \n",
       "OnlineSecurity             Yes                       No               Yes   \n",
       "OnlineBackup                No                      Yes                No   \n",
       "DeviceProtection           Yes                      Yes                No   \n",
       "TechSupport                Yes                       No                No   \n",
       "StreamingTV                Yes                      Yes                No   \n",
       "StreamingMovies            Yes                      Yes                No   \n",
       "Contract              One year                 One year    Month-to-month   \n",
       "PaperlessBilling           Yes                      Yes               Yes   \n",
       "PaymentMethod     Mailed check  Credit card (automatic)  Electronic check   \n",
       "MonthlyCharges            84.8                    103.2              29.6   \n",
       "TotalCharges            1990.5                   7362.9            346.45   \n",
       "Churn                       No                       No                No   \n",
       "b_Churn                      0                        0                 0   \n",
       "\n",
       "                            7041                       7042  \n",
       "customerID            8361-LTMKD                 3186-AJIEK  \n",
       "gender                      Male                       Male  \n",
       "SeniorCitizen                  1                          0  \n",
       "Partner                      Yes                         No  \n",
       "Dependents                    No                         No  \n",
       "tenure                         4                         66  \n",
       "PhoneService                 Yes                        Yes  \n",
       "MultipleLines                Yes                         No  \n",
       "InternetService      Fiber optic                Fiber optic  \n",
       "OnlineSecurity                No                        Yes  \n",
       "OnlineBackup                  No                         No  \n",
       "DeviceProtection              No                        Yes  \n",
       "TechSupport                   No                        Yes  \n",
       "StreamingTV                   No                        Yes  \n",
       "StreamingMovies               No                        Yes  \n",
       "Contract          Month-to-month                   Two year  \n",
       "PaperlessBilling             Yes                        Yes  \n",
       "PaymentMethod       Mailed check  Bank transfer (automatic)  \n",
       "MonthlyCharges              74.4                     105.65  \n",
       "TotalCharges               306.6                     6844.5  \n",
       "Churn                        Yes                         No  \n",
       "b_Churn                        1                          0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                  3390\n",
       "Yes                 2971\n",
       "No phone service     682\n",
       "Name: MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MultipleLines.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                  3390\n",
       "Yes                 2971\n",
       "No phone service     682\n",
       "Name: MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop \"No phone service\"\n",
    "df[df.MultipleLines != \"No phone service\"]\n",
    "df.MultipleLines.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#recode MultipleLines var to get our two comparison cohorts\n",
    "df[\"b_MultipleLines\"] = df.MultipleLines.apply(lambda x: 1 if x == \"Yes\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4072\n",
       "1    2971\n",
       "Name: b_MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.b_MultipleLines.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7043.000000\n",
       "mean       32.371149\n",
       "std        24.559481\n",
       "min         0.000000\n",
       "25%         9.000000\n",
       "50%        29.000000\n",
       "75%        55.000000\n",
       "max        72.000000\n",
       "Name: tenure, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter()\n",
    "T = df['tenure'] #duration\n",
    "C = df[\"b_Churn\"] #censorship - 1 if death/churn is seen, 0 if censored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.KaplanMeierFitter: fitted with 7043 observations, 5174 censored>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.fit(durations = df.tenure, \n",
    "        event_observed = df.b_Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>removed</th>\n",
       "      <th>observed</th>\n",
       "      <th>censored</th>\n",
       "      <th>entrance</th>\n",
       "      <th>at_risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>7043</td>\n",
       "      <td>7043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>613</td>\n",
       "      <td>380</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>7032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238</td>\n",
       "      <td>123</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>6419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>94</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>6181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176</td>\n",
       "      <td>83</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>5981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>133</td>\n",
       "      <td>64</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>5805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>5672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>131</td>\n",
       "      <td>51</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>123</td>\n",
       "      <td>42</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>5431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>119</td>\n",
       "      <td>46</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>5308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>116</td>\n",
       "      <td>45</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>5189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>99</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>5073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>117</td>\n",
       "      <td>38</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>4974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>4857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>4748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>99</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80</td>\n",
       "      <td>28</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>4573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>87</td>\n",
       "      <td>26</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>4493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>97</td>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>4406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>73</td>\n",
       "      <td>19</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>4309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>71</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>63</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>90</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>4102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>85</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>4012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>3927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>79</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>3833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>79</td>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>3675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>3603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>72</td>\n",
       "      <td>15</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>3546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>65</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>2557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>2506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>2445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>68</td>\n",
       "      <td>14</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>2371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>2303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>2239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>2173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>68</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>2105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>2037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>68</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>67</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>1483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>1261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>100</td>\n",
       "      <td>9</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>95</td>\n",
       "      <td>8</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>119</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>170</td>\n",
       "      <td>6</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>362</td>\n",
       "      <td>6</td>\n",
       "      <td>356</td>\n",
       "      <td>0</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          removed  observed  censored  entrance  at_risk\n",
       "event_at                                                \n",
       "0              11         0        11      7043     7043\n",
       "1             613       380       233         0     7032\n",
       "2             238       123       115         0     6419\n",
       "3             200        94       106         0     6181\n",
       "4             176        83        93         0     5981\n",
       "5             133        64        69         0     5805\n",
       "6             110        40        70         0     5672\n",
       "7             131        51        80         0     5562\n",
       "8             123        42        81         0     5431\n",
       "9             119        46        73         0     5308\n",
       "10            116        45        71         0     5189\n",
       "11             99        31        68         0     5073\n",
       "12            117        38        79         0     4974\n",
       "13            109        38        71         0     4857\n",
       "14             76        24        52         0     4748\n",
       "15             99        37        62         0     4672\n",
       "16             80        28        52         0     4573\n",
       "17             87        26        61         0     4493\n",
       "18             97        24        73         0     4406\n",
       "19             73        19        54         0     4309\n",
       "20             71        18        53         0     4236\n",
       "21             63        17        46         0     4165\n",
       "22             90        27        63         0     4102\n",
       "23             85        13        72         0     4012\n",
       "24             94        23        71         0     3927\n",
       "25             79        23        56         0     3833\n",
       "26             79        15        64         0     3754\n",
       "27             72        13        59         0     3675\n",
       "28             57        12        45         0     3603\n",
       "29             72        15        57         0     3546\n",
       "...           ...       ...       ...       ...      ...\n",
       "43             65        15        50         0     2622\n",
       "44             51         6        45         0     2557\n",
       "45             61         6        55         0     2506\n",
       "46             74        12        62         0     2445\n",
       "47             68        14        54         0     2371\n",
       "48             64         9        55         0     2303\n",
       "49             66        15        51         0     2239\n",
       "50             68        10        58         0     2173\n",
       "51             68         8        60         0     2105\n",
       "52             80         8        72         0     2037\n",
       "53             70        14        56         0     1957\n",
       "54             68        13        55         0     1887\n",
       "55             64         9        55         0     1819\n",
       "56             80        10        70         0     1755\n",
       "57             65         8        57         0     1675\n",
       "58             67        11        56         0     1610\n",
       "59             60         8        52         0     1543\n",
       "60             76         6        70         0     1483\n",
       "61             76         8        68         0     1407\n",
       "62             70         5        65         0     1331\n",
       "63             72         4        68         0     1261\n",
       "64             80         4        76         0     1189\n",
       "65             76         9        67         0     1109\n",
       "66             89        13        76         0     1033\n",
       "67             98        10        88         0      944\n",
       "68            100         9        91         0      846\n",
       "69             95         8        87         0      746\n",
       "70            119        11       108         0      651\n",
       "71            170         6       164         0      532\n",
       "72            362         6       356         0      362\n",
       "\n",
       "[73 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.event_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the values for time = 0 from the survival table\n",
    "event_at_0 = kmf.event_table.iloc[0, :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "removed       11\n",
       "observed       0\n",
       "censored      11\n",
       "entrance    7043\n",
       "at_risk     7043\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_at_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now calculate the survival probability for t = 0\n",
    "surv_for_0 =  (event_at_0.at_risk - event_at_0.observed) / event_at_0.at_risk\n",
    "surv_for_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9459613196814562"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the survival probability for t = 1\n",
    "event_at_1 = kmf.event_table.iloc[1, :]\n",
    "surv_for_1 =  (event_at_1.at_risk - event_at_1.observed) / event_at_1.at_risk\n",
    "surv_for_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9808381367814302"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the survival probability for t = 2\n",
    "event_at_2 = kmf.event_table.iloc[2, :]\n",
    "surv_for_2 =  (event_at_2.at_risk - event_at_2.observed) / event_at_2.at_risk\n",
    "surv_for_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9278349382636624"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The probability that is longer than 2 years\n",
    "surv_after_2 = surv_for_0 * surv_for_1 * surv_for_2\n",
    "surv_after_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9278349382636628"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.predict(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9459613196814566"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.predict(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.predict(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KM_estimate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.945961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.927835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.913725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.901045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.891111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.884826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.876713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.869933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.862394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.854915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.849691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.843200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.836603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.832374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.825782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.820726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.815976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.811531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>0.807953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.804520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.801236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.795962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.793383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>0.788736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>0.784004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>0.780871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>0.778109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>0.775517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>0.772237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>0.722801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44.0</th>\n",
       "      <td>0.721105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.719379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.0</th>\n",
       "      <td>0.715848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.0</th>\n",
       "      <td>0.711621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>0.708840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>0.704091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.700851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.0</th>\n",
       "      <td>0.698188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52.0</th>\n",
       "      <td>0.695446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>0.690471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54.0</th>\n",
       "      <td>0.685714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55.0</th>\n",
       "      <td>0.682321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>0.678433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57.0</th>\n",
       "      <td>0.675193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58.0</th>\n",
       "      <td>0.670580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59.0</th>\n",
       "      <td>0.667103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.664404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61.0</th>\n",
       "      <td>0.660626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62.0</th>\n",
       "      <td>0.658145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63.0</th>\n",
       "      <td>0.656057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64.0</th>\n",
       "      <td>0.653850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65.0</th>\n",
       "      <td>0.648543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66.0</th>\n",
       "      <td>0.640382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67.0</th>\n",
       "      <td>0.633598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68.0</th>\n",
       "      <td>0.626858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69.0</th>\n",
       "      <td>0.620135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>0.609657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>0.602781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72.0</th>\n",
       "      <td>0.592790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          KM_estimate\n",
       "timeline             \n",
       "0.0          1.000000\n",
       "1.0          0.945961\n",
       "2.0          0.927835\n",
       "3.0          0.913725\n",
       "4.0          0.901045\n",
       "5.0          0.891111\n",
       "6.0          0.884826\n",
       "7.0          0.876713\n",
       "8.0          0.869933\n",
       "9.0          0.862394\n",
       "10.0         0.854915\n",
       "11.0         0.849691\n",
       "12.0         0.843200\n",
       "13.0         0.836603\n",
       "14.0         0.832374\n",
       "15.0         0.825782\n",
       "16.0         0.820726\n",
       "17.0         0.815976\n",
       "18.0         0.811531\n",
       "19.0         0.807953\n",
       "20.0         0.804520\n",
       "21.0         0.801236\n",
       "22.0         0.795962\n",
       "23.0         0.793383\n",
       "24.0         0.788736\n",
       "25.0         0.784004\n",
       "26.0         0.780871\n",
       "27.0         0.778109\n",
       "28.0         0.775517\n",
       "29.0         0.772237\n",
       "...               ...\n",
       "43.0         0.722801\n",
       "44.0         0.721105\n",
       "45.0         0.719379\n",
       "46.0         0.715848\n",
       "47.0         0.711621\n",
       "48.0         0.708840\n",
       "49.0         0.704091\n",
       "50.0         0.700851\n",
       "51.0         0.698188\n",
       "52.0         0.695446\n",
       "53.0         0.690471\n",
       "54.0         0.685714\n",
       "55.0         0.682321\n",
       "56.0         0.678433\n",
       "57.0         0.675193\n",
       "58.0         0.670580\n",
       "59.0         0.667103\n",
       "60.0         0.664404\n",
       "61.0         0.660626\n",
       "62.0         0.658145\n",
       "63.0         0.656057\n",
       "64.0         0.653850\n",
       "65.0         0.648543\n",
       "66.0         0.640382\n",
       "67.0         0.633598\n",
       "68.0         0.626858\n",
       "69.0         0.620135\n",
       "70.0         0.609657\n",
       "71.0         0.602781\n",
       "72.0         0.592790\n",
       "\n",
       "[73 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.survival_function_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kkj4E7BARl0oaBQyJiKcyXHcQcD4wEPhZRJxVcHxb4BJgFPAi8IWIWFzh79BQ7o1kZt2kbFKQdBowAXgvcCkwCLgC2LfMdQOBi4ADgcXA/ZJmRsT8vNN+APwiIn4u6aPA94F/quYXaRT3RjKzbpKlpPApYA/gAYCI+JukoRmu2xNYGBFPAkiaAUwG8pPCzsA30td3AjdmjLtl3BvJzPqzLG0Kr0dEAAEgabOM9x4NPJO3vTjdl+8h4ND09aeAoZJGZrx/000eP5rRIzbJbS9a/ipzF73UwojMzOorS0nhGkn/DYyQdBTwz8BP6/T8bwIXSjoCuAtYArxZeJKko4GjAcaOHVunR1euWG+ktevWu43BzPqNskkhIn4g6UBgJUm7wqkR8ZsM914CbJO3PSbdl3/vv5GWFCQNAT4dESuKxDAdmA4wYcKEyPDsptl40AC3MZhZv5GlofkbwNUZE0G++4EdJG1HkgymAJ8vuPcWwIsRsR44iaQnUlsr7I20vq1SlJlZbbJUHw0Fbpf0InA1cG1EPF/uooh4Q9JXgNtIuqReEhGPSDoTmBMRM4EPA9+XFCTVR8dV+Xs0TWFvpD8tXtGr4XmnrYb1Ot/VSWbWSbJUH50BnCFpd+BzwO8lLY6IAzJcOwuYVbDv1LzX1wHXVRx1myg2DQbAZ/7urVozVyeZWSepZJqLpcBzwHLAf/pSvOHZzKyTZWlT+FfgsySjjq8FjioYgGZ5Fi1/1eMYzKxjZSkpbAMcHxHzGh1MJ8pveN599HDWrntrwrxFy1/doMtqzzVuZzCzdtRnUpA0LCJWAuek25vnH4+IFxscW0fI/3KfNH40M+ct2aA6Kb/LKridwczaV6mSwv8AE4G5JKOZlXcsgHc1MK5+o7A6CVylZGbtq8+kEBET03+3a144na9UdRIUr1JydZKZtYssDc2zI2L/cvssUao6CYpXKbk6yczaRak2hcHApsAWkt7OW9VHw9hwYjurgHsomVm7KlVSOAY4HtiapF2hJymsBC5scFz91r7bbwG8kNt2dZKZtZNSbQrnA+dL+mpE/FcTY+pX8tsYAHYfM5z3vHMI27w9mYHc1Ulm1k6yjFNYL2lEz+ylaVXS1Ij4UWND6x+K/cVfOG7BzKxdZEkKR0XERT0bEfFSuq6Ck0KduI3BzNpFlqQwUJLS1dd61l5+W2PD6t8qHQXtNgYza5YsSeFW4Op09TVIGqBvbVxI/V+lo6DdxmBmzZIlKXyLZCnM/5Nu/4b6LcdpRRRWJ3mNBjNrlizrKawHfpL+IGkb4N9J50Sy+irWZRW8RoOZNUem9RQkjQI+A0wlGbdwQyOD6ja92hj66LJqZtYMpUY0DwUOJVlX+T3AL4HtImJMk2LrGoVVQYVdVl2dZGbNUqqksBS4D/g28L8REZI+1ZywrIerk8ysmUolhZOAKSTjEa6SdHVzQjJXJ5lZq5Sa5uI84DxJ7yJJDjcCW0v6FnBDRDzWpBi7Tq3VSeAqJTOrzoByJ0TEkxHxvYjYDZhAMkvqrIZHZkXtu/0WbDty09z2ouWvsuDZlYwaOrjXz6rX1rUwSjPrVJl6H/WIiIeBU9Ifa5JqqpPWvPGmR0WbWcUqSgrWGpVWJ0FSoth/p3fktt0YbWZZOCl0uMLeSdDTQ+mFXknBzCwLJ4UOVKo6CZIqJY9tMLNqlBq89mcgih0CIiJ2b1hUVlK56iSPbTCzapUqKUxsWhRWV/vv9I5eVUfFGqPdEG1mxZQap7ComYFY9QqX/ITkSz+/SqmwOskN0WZWTKnqo1WUrj7acMSUtUS5JT+LVyf1boh2ycHMoHRJYWgzA7H6Kje2wSUHMyumVElhWESslLR5seMR8WLjwrJalWqMrqbkAC49mHWDUg3N/0PS2DyXpBpJeccCeFcD47IGKtYQXa7kAC49mHWDUtVHE9N/t2teONYohY3R+Q3RWUoOPde43cGsfys7eE3S7IjYv9w+a2+lqpOydGEFevVmAnjmpVecJMz6mVJtCoOBTYEtJL2dt6qPhgGjmxCbtVCW+ZQKk4Srl8w6X6mSwjHA8SRrMs/lraSwEriwwXFZg1VSnQSw4NlVLHh2FXc/8db+wiTh6iWzzleqTeF84HxJX42I/2piTNYElVQnAcxe8HyvhFCs3cElB7POp4hi49NA0geAZyLiuXT7i8CngUXA6Vm6pEo6CDgfGAj8LCLOKjg+Fvg5MCI958SIKLmAz4QJE2LOnDnlHm0VuvPRpb0W5ikcEV2op8dS/oI/hSWHZ156hcEbDcxtu+Rg1jqS5kbEhLLnlUgKDwAHRMSLkvYDZgBfBcYDO0XEYWUCGAg8BhwILAbuB6ZGxPy8c6YDD0bEjyXtDMyKiHGl7uuk0Bwz5y1h1NDBfR4vLDkseHYVADtt9daYRycJs/aRNSmUalMYmFca+BwwPSKuB66XNC9DDHsCCyPiyTSgGcBkYH7eOUHScA0wHPhbhvtaE5SbT6mwisnVS2b9Q8mkIGmjiHgD2B84OuN1PUYDz+RtLwb2KjjndOB2SV8FNgMOKHYjSUf3PH/s2LEZHm21KjefUqGs3VrzedS0Wfsp9eV+FfB7SS8ArwF/AJD0buDlOj1/KnBZRPynpH2AyyXtGhHr80+KiOnAdEiqj+r0bKtQqR5LxZQbJV3sWpcezFqrVO+j70qaDWwF3B5vNT4MIGlbKGcJsE3e9ph0X74jgYPS5/0xHRuxBbA0W/jWTIV/wd/56NLM3VqzdGk1s9YrWQ0UEfcU2fdYxnvfD+wgaTuSZDAF+HzBOX8lqZq6TNJOwGBgWcb7W4tV0q01S5sDeKyDWav12fuoLjeXDgHOI+luekla+jgTmBMRM9MeRz8FhpA0Ov9HRNxe6p7ufdS+KunWWqxLK7jHklmj1KP3Uc3SMQezCvadmvd6PrBvI2Ow5im3dnS+YqOms/RY8nxLZo1VUUlB0sSIuLmB8ZTlkkLnaMSAuEIuSZhl06iSwplAS5OCdY5KSg6QfQrvfB77YFZflSYFlT/FrLhyXVqrXfwnnxuqzWpTaVI4piFRWFeoteSQpVtruTYIcKIwK6WipBAR9zUqEOs+tU6lUU2SAFcxmZXS0N5HZqXUOpVG1rEPhVzFZNY3JwVrK5VMpVFtG4Qbp836lmWN5l8CFwO3FM5JZFZvlbY75MvSBtFznleMMysuS0nhR8CXgQskXQtcGhGPNjYss0QtJYfC6iXwlN5m5WQevCZpOMmspqeQTIn9U+CKiFhX8sI68+C17lbpgLhCXjHOulVdB69JGgl8Afgn4EHgSuBDwJeAD1cfplllKpmptZgsA+Q8tYZ1s7IlBUk3AO8FLidZ++DZvGNzsmSeenJJwUopt4xoIU+tYd2iniWFn6YT2+XffOOIWNvshGBWTqULAXlqDbPespQUHoiI95fb1ywuKVglCtsgoPIpvSstOYBLD9Z+ai4pSHonyTrLm0jag7fmPRoGbNrXdWbtpNIBcvWYWgPcDmGdq1T10ceBI0iW0Tw3b/8q4OQGxmTWUKWqmOo1atpVTNapslQffToirm9SPGW5+sjqrZLG6Wqql8CN09Z69ag++kJEXAGMk/SNwuMRcW6Ry8w6TiWN09WOmnY3V+sUpaqPev4rHtKMQMxapZKpNbKMmq6mHcLVS9Yu+kwKEfHf6b9nNC8cs9arZWoNqK4dwvMvWbvos01B0gWlLoyIrzUkojLcpmDN1oypNQotW7WGSeNHVxewWRH1GLw2t47xmHWsWmZuheoGyLnkYK2SeUK8duGSgrVaK0oOHiBntapH76PzIuJ4Sb8CNsgcETGpxhjNOlK5Sfmg9h5MXlbUWqVU9dHl6b8/aEYgZp3Ky4paf5Kp+kjS24AdSUoMj0bE640OrC+uPrJOUEsVkwfIWSPUbZZUSf8I/AR4gmT+o+0kHRMRt9Qepln/VMu6D9U0TIMHyFl9ZJnm4i/AxIhYmG5vD/w6InZsQnwbcEnB+oNap9aAbKWHfC5JdLd6rqewqichpJ4kmRTPzKpUy9QakL30kM+jqC2LUoPXDk1fHghsC1xD0qbwGeCvEfGvTYmwgEsK1h/VY8W4Ql5BzvLVo6TwibzXzwP/kL5eBmxSQ2xmVqDWFeMKVdPNtbANoicuJ4ruUmruoy83MxCzblZpw3SxOZfyVdPN1WMhDLL1PhoMHAnsAuTKtxHxzw2My6yr1Tq1RmHS6KluOvPmR3L7Km2otu6QpaH5cuAvJCuxnQkcDixoZFBm1lth9RJUNvah2m6uHiDXfbJ0SX0wIvaQ9KeI2F3SIOAPEbF3c0LszQ3NZolKG6fzeYBc96lnl9SeYZkrJO0KPAf4vwKzFqu0cTqfV5CzvmRJCtMlvR34v8BMkpXY/m9DozKzsmoZNZ1lBblqGqedJDpfQ6fOlnQQcD4wEPhZRJxVcPyHwEfSzU2BLSNiRKl7uvrILJtWTPFdyIsFtY96zn00Ejgd2Jdk8NofgO9ExPIy1w0ELiIZ/LYYuF/SzIiY33NORHw97/yvAnuUi8fMsmnF4kDW+bJUH80A7gI+nW4fDlwNHFDmuj2BhRHxJICkGcBkYH4f508FTssQj5lVodI2iHp0a3Xvpc6TJSlsFRHfydueJulzGa4bDTyTt70Y2KvYiZK2BbYDftvH8aOBowHGjh2b4dFmVqjeJYdqRk17MFz7y5IUbpc0hWTuI4DDgNvqHMcU4LqIeLPYwYiYDkyHpE2hzs8260q1lhwKG6ezJInCkkNPHC49tI9Sy3GuImlDEHA8cEV6aACwGvhmmXsvAbbJ2x6T7itmCnBchnjNrE5qXVa0mhXkit3LPZbaS6m5j4bWeO/7gR0kbUeSDKYAny88SdKOwNuBP9b4PDOrQaXLihYq1gaRhauY2kuW6iMkTQL2Szd/FxE3l7smIt6Q9BWSqqaBwCUR8YikM4E5ETEzPXUKMCMa2TfWzKpSywA5oKr5ltw43VpZprk4C/gAcGW6ayrJl/pJDY6tKI9TMGudSqbWKFadtO3ITTl14i4VPdNTa9RH1nEKWZLCn4DxEbE+3R4IPBgRu9cl0go5KZi1Ti0D4uq1rKgHxFWnnnMfAYwAXkxfD686KjPraLVMrVFsYaAsPZasubIkhe8DD0q6k6Qn0n7AiQ2Nysw6QiVJotjCQNUsBuQ2h8YqWX0kSSRdSd8gaVcAuC8inmtCbEW5+sisc9Rj7WmvNV0fdak+ioiQNCsidiOZIdXMrGGqmW/Ja03XV5bqowckfSAi7m94NGbWr7RiviWvNV2bLElhL+ALkp4GXiFpV4hW9T4ys87RLjO1uh0iuyxdUrcttj8iFjUkojLcpmDWuQq7tELt3Vqr6a3Uje0QNbcpSBoMHAu8G/gzcHFEvFG/EM2s29Q6lUY1M7UW4xXj+laq+ujnJOsz/wE4GNgZ+LdmBGVm3aOSdodqJuHLwkniLX1WH0n6c9rrCEkbkXRFfX8zgyvG1Udm/Vs7jJou1B9GUdejS2ruU0knt6tLYGZmpdTSON2oUdPd1FBdKim8T9LK9LWATdLtnt5HwxoenZl1vVqql6A+VUzdNL13qfUUBvZ1zMysWWrt1ppl7EOhbp5/KeuEeGZmbaHWNR6KVTHlq7axur8oO06h3bih2czyVTq/UjlZxkIUjnOA9m9nqPfU2WZmbanWkkOhLKOo+/NUGk4KZtbRyk3fDZUlinrMv9TJnBTMrF+pddR0oazzL/WXbqtOCmbW79VSxVSs5FBMf+m26qRgZv1eLcuIdhsnBTPrOrUmiWLjHArbGTq1OslJwcy6XiVJotg4hyw9lDqlOslJwcysQKlR1MWm0ig1OrrTOCmYmdVBuW6rnVKd5KRgZlZGud5L1Qx4a9fqJCcFM7Myyk3Kl7XbaidwUjAza4BOHQXtpGBmVmdZqpPatY3BScHMrEKFbQzQu50hS3VSu7YxOCmYmVWomvmVOqU6yUnBzKzBOqk6yUnBzKwOSnVb7aTqJCcFM7M6qHUt6XYxoNUBmJlZ+3BJwcysBSqdFgOa087gpGBm1gCl2hjaeR3ohiYFSQcB5wMDgZ9FxFlFzvkscDoQwEMR8flGxmRm1gyVzLTaTtNiNCwpSBoIXAQcCCwG7pc0MyI038HsAAAHhklEQVTm552zA3ASsG9EvCSp9cP5zMxaoF3GMTSypLAnsDAingSQNAOYDMzPO+co4KKIeAkgIpY2MB4zs5aptTqpWRqZFEYDz+RtLwb2KjjnPQCS7iapYjo9Im4tvJGko4GjAcaOHduQYM3MGqlTqpNa3SV1I2AH4MPAVOCnkkYUnhQR0yNiQkRMGDVqVJNDNDPrHo1MCkuAbfK2x6T78i0GZkbEuoh4CniMJEmYmVkLNDIp3A/sIGk7SW8DpgAzC865kaSUgKQtSKqTnmxgTGZmVkLD2hQi4g1JXwFuI2kvuCQiHpF0JjAnImamxz4maT7wJnBCRCxvVExmZu2iVMPztiM3HKPQLIqIlj28GhMmTIg5c+a0Ogwzs7qaOW8Jo4YOLnnOslVrmDR+dFX3lzQ3IiaUO6/VDc1mZtZGnBTMzCzHcx+ZmbWBUm0MzeSkYGbWBtplPQZXH5mZWY6TgpmZ5TgpmJlZjtsUzMzaUGHDc8++RnNSMDNrQ41edrMvrj4yM7McJwUzM8txUjAzsxwnBTMzy3FSMDOzHCcFMzPLcVIwM7McJwUzM8vpuJXXJC0DFlV5+RbAC3UMp1EcZ/10QozgOOupE2KE5se5bUSMKndSxyWFWkiak2U5ulZznPXTCTGC46ynTogR2jdOVx+ZmVmOk4KZmeV0W1KY3uoAMnKc9dMJMYLjrKdOiBHaNM6ualMwM7PSuq2kYGZmJTgpmJlZTtckBUkHSXpU0kJJJ7Y6nh6SLpG0VNLDefs2l/QbSY+n/769xTFuI+lOSfMlPSLp39o0zsGS7pP0UBrnGen+7STdm372V0t6WyvjTGMaKOlBSTe3cYxPS/qzpHmS5qT72uozT2MaIek6SX+RtEDSPu0Wp6T3pu9jz89KSce3W5zQJUlB0kDgIuBgYGdgqqSdWxtVzmXAQQX7TgRmR8QOwOx0u5XeAP49InYG9gaOS9+/dotzLfDRiHgfMB44SNLewNnADyPi3cBLwJEtjLHHvwEL8rbbMUaAj0TE+Lz+9O32mQOcD9waETsC7yN5X9sqzoh4NH0fxwN/B7wK3ECbxQlARPT7H2Af4La87ZOAk1odV14844CH87YfBbZKX28FPNrqGAvivQk4sJ3jBDYFHgD2Ihk1ulGx/xZaFNsYki+AjwI3A2q3GNM4nga2KNjXVp85MBx4irTTTLvGWRDbx4C72zXOrigpAKOBZ/K2F6f72tU7IuLZ9PVzwDtaGUw+SeOAPYB7acM402qZecBS4DfAE8CKiHgjPaUdPvvzgP8A1qfbI2m/GAECuF3SXElHp/va7TPfDlgGXJpWx/1M0ma0X5z5pgBXpa/bLs5uSQodK5I/Idqi37CkIcD1wPERsTL/WLvEGRFvRlJEHwPsCezY4pB6kTQRWBoRc1sdSwYfioj3k1S7Hidpv/yDbfKZbwS8H/hxROwBvEJBFUybxAlA2lY0Cbi28Fi7xNktSWEJsE3e9ph0X7t6XtJWAOm/S1scD5IGkSSEKyPil+nutouzR0SsAO4kqYoZIWmj9FCrP/t9gUmSngZmkFQhnU97xQhARCxJ/11KUv+9J+33mS8GFkfEven2dSRJot3i7HEw8EBEPJ9ut12c3ZIU7gd2SHt4vI2k+DazxTGVMhP4Uvr6SyR1+C0jScDFwIKIODfvULvFOUrSiPT1JiTtHgtIksNh6WktjTMiToqIMRExjuS/w99GxOG0UYwAkjaTNLTnNUk9+MO02WceEc8Bz0h6b7prf2A+bRZnnqm8VXUE7Rhnqxs1mti4cwjwGEkd8ymtjicvrquAZ4F1JH/1HElSxzwbeBy4A9i8xTF+iKRY+ydgXvpzSBvGuTvwYBrnw8Cp6f53AfcBC0mK7Ru3+nNP4/owcHM7xpjG81D680jP/zPt9pmnMY0H5qSf+43A29s0zs2A5cDwvH1tF6enuTAzs5xuqT4yM7MMnBTMzCzHScHMzHKcFMzMLMdJwczMcpwUrGuls2v+a/p6a0nX1em+p0v6Zvr6TEkH1OO+Zs3gLqnWtdJ5nG6OiF3rfN/TgdUR8YN63tesGVxSsG52FrB9Or/9tT1rWkg6QtKN6fz2T0v6iqRvpBOu3SNp8/S87SXdmk4Y9wdJG8yzJOkySYelr5+WdIakB9J1CnZM92+mZF2N+9JnTG7ie2DWi5OCdbMTgScimUDvhIJjuwKHAh8Avgu8GsmEa38EvpieMx34akT8HfBN4EcZnvlCJJPM/Ti9BuAUkuku9gQ+ApyTTi1h1nQblT/FrCvdGRGrgFWSXgZ+le7/M7B7OmPsB4Frk6mhANg4w317JhOcS5J0IJlXaFJPOwQwGBhL70V4zJrCScGsuLV5r9fnba8n+f9mAMkaCOOrvO+bvPX/n4BPR8SjVcZqVjeuPrJutgoYWs2Fkawn8ZSkz0Ayk6yk91UZx23AV9PZaJG0R5X3MauZk4J1rYhYDtydNjCfU8UtDgeOlNQzk2i1DcTfAQYBf5L0SLpt1hLukmpmZjkuKZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWc7/B3ZTSRZG3q82AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the KM estimate\n",
    "kmf.plot()\n",
    "# Add title and y-axis label\n",
    "plt.title(\"The Kaplan-Meier Estimate\")\n",
    "plt.ylabel(\"Probability - Still Active\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.median_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Partner.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     3641\n",
       "Yes    3402\n",
       "Name: Partner, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Partner.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.Partner.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new KMF object\n",
    "kmf_by_pos = KaplanMeierFitter()\n",
    "\n",
    "duration = df.tenure\n",
    "observed = df.b_Churn\n",
    "\n",
    "# Set the order that the positions will be plotted\n",
    "positions = [\"No\", \"Yes\"]\n",
    "\n",
    "# Set up the the 5x3 plotting grid by creating figure and axes objects\n",
    "# Set sharey to True so that each row of plots share the left most y-axis labels\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 2, sharey = True,\n",
    "                         figsize=(12,15))\n",
    "\n",
    "# flatten() creates a 1-D array of the individual axes (or subplots)\n",
    "# that we will plot on in our grid\n",
    "# We zip together the two 1-D arrays containing the positions and axes\n",
    "# so we can iterate over each postion and plot its KM estimate onto \n",
    "# its respective axes\n",
    "for pos, ax in zip(positions, axes.flatten()):\n",
    "    # get indices for players with the matching position label\n",
    "    idx = df.Partner == pos\n",
    "    # fit the kmf for the those players\n",
    "    kmf_by_pos.fit(duration[idx], observed[idx])\n",
    "    # plot the KM estimate for that position on its respective axes\n",
    "    kmf_by_pos.plot(ax=ax, legend=False)\n",
    "    # place text indicating the median for the position\n",
    "    # the xy-coord passed in represents the fractional value for each axis\n",
    "    # for example (.5, .5) places text at the center of the plot\n",
    "    ax.annotate(\"Median = {:.0f} yrs\".format(kmf_by_pos.median_), xy = (.47, .85), \n",
    "                xycoords = \"axes fraction\")\n",
    "    # get rid the default \"timeline\" x-axis label set by kmf.plot() \n",
    "    ax.set_xlabel(\"\")\n",
    "    # label each plot by its position\n",
    "    ax.set_title(pos)\n",
    "    # set a common x and y axis across all plots\n",
    "    ax.set_xlim(0,25)\n",
    "    ax.set_ylim(0,1)\n",
    "\n",
    "# tighten up the padding for the subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "# https://stackoverflow.com/questions/16150819/common-xlabel-ylabel-for-matplotlib-subplots\n",
    "# set a common x-axis label\n",
    "fig.text(0.5, -0.01, \"Timeline (Years)\", ha=\"center\")\n",
    "# set a common y-axis label\n",
    "fig.text(-0.01, 0.5, \"Probability - Still Active\", \n",
    "         va=\"center\", rotation=\"vertical\")\n",
    "# add the title for the whole plot\n",
    "fig.suptitle(\"Survival Curve\",\n",
    "             fontsize=20)\n",
    "# add some padding between the title and the rest of the plot to avoid overlap\n",
    "fig.subplots_adjust(top=0.92)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No phone service', 'No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MultipleLines.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new KMF object\n",
    "kmf_by_pos = KaplanMeierFitter()\n",
    "\n",
    "duration = df.tenure\n",
    "observed = df.b_Churn\n",
    "\n",
    "# Set the order that the positions will be plotted\n",
    "positions = [\"No phone service\", \"No\", \"Yes\"]\n",
    "\n",
    "# Set up the the 5x3 plotting grid by creating figure and axes objects\n",
    "# Set sharey to True so that each row of plots share the left most y-axis labels\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 2, sharey = True,\n",
    "                         figsize=(12,15))\n",
    "\n",
    "# flatten() creates a 1-D array of the individual axes (or subplots)\n",
    "# that we will plot on in our grid\n",
    "# We zip together the two 1-D arrays containing the positions and axes\n",
    "# so we can iterate over each postion and plot its KM estimate onto \n",
    "# its respective axes\n",
    "for pos, ax in zip(positions, axes.flatten()):\n",
    "    # get indices for players with the matching position label\n",
    "    idx = df.MultipleLines == pos\n",
    "    # fit the kmf for the those players\n",
    "    kmf_by_pos.fit(duration[idx], observed[idx])\n",
    "    # plot the KM estimate for that position on its respective axes\n",
    "    kmf_by_pos.plot(ax=ax, legend=False)\n",
    "    # place text indicating the median for the position\n",
    "    # the xy-coord passed in represents the fractional value for each axis\n",
    "    # for example (.5, .5) places text at the center of the plot\n",
    "    ax.annotate(\"Median = {:.0f} yrs\".format(kmf_by_pos.median_), xy = (.47, .85), \n",
    "                xycoords = \"axes fraction\")\n",
    "    # get rid the default \"timeline\" x-axis label set by kmf.plot() \n",
    "    ax.set_xlabel(\"\")\n",
    "    # label each plot by its position\n",
    "    ax.set_title(pos)\n",
    "    # set a common x and y axis across all plots\n",
    "    ax.set_xlim(0,25)\n",
    "    ax.set_ylim(0,1)\n",
    "\n",
    "# tighten up the padding for the subplots\n",
    "fig.tight_layout()\n",
    "\n",
    "# https://stackoverflow.com/questions/16150819/common-xlabel-ylabel-for-matplotlib-subplots\n",
    "# set a common x-axis label\n",
    "fig.text(0.5, -0.01, \"Timeline (Years)\", ha=\"center\")\n",
    "# set a common y-axis label\n",
    "fig.text(-0.01, 0.5, \"Probability - Still Active\", \n",
    "         va=\"center\", rotation=\"vertical\")\n",
    "# add the title for the whole plot\n",
    "fig.suptitle(\"Survival Curve\",\n",
    "             fontsize=20)\n",
    "# add some padding between the title and the rest of the plot to avoid overlap\n",
    "fig.subplots_adjust(top=0.92)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"b_Partner\"] = df.Partner.apply(lambda x: 0 if x == \"No\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "t_0=-1, alpha=0.99, null_distribution=chi squared, df=1\n",
      "\n",
      "test_statistic      p     \n",
      "      423.5431 0.0000  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n"
     ]
    }
   ],
   "source": [
    "partner = (df[\"b_Partner\"] == 1)\n",
    "results = logrank_test(T[partner], T[~partner], C[partner], C[~partner], alpha=0.99 ) #at 99% confidence level\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "323268072.40851665\n"
     ]
    }
   ],
   "source": [
    "Z = results.test_statistic\n",
    "D = C.sum() #number of events observed\n",
    "\n",
    "hazard_ratio = np.exp(Z*np.sqrt(4/D))\n",
    "print(hazard_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "t_0=-1, alpha=0.99, null_distribution=chi squared, df=1\n",
      "\n",
      "test_statistic      p     \n",
      "       24.8504 0.0000  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n"
     ]
    }
   ],
   "source": [
    "multi = (df[\"b_MultipleLines\"] == 1)\n",
    "results = logrank_test(T[multi], T[~multi], C[multi], C[~multi], alpha=0.99 ) #at 99% confidence level\n",
    "results.print_summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1570355740947194\n"
     ]
    }
   ],
   "source": [
    "Z = results.test_statistic\n",
    "D = C.sum() #number of events observed\n",
    "\n",
    "hazard_ratio = np.exp(Z*np.sqrt(4/D))\n",
    "print(hazard_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "                 1\n",
      "timeline          \n",
      "0.0       1.000000\n",
      "1.0       0.980548\n",
      "2.0       0.974596\n",
      "3.0       0.967693\n",
      "4.0       0.961040\n",
      "5.0       0.955552\n",
      "6.0       0.951259\n",
      "7.0       0.945389\n",
      "8.0       0.941334\n",
      "9.0       0.937547\n",
      "10.0      0.933098\n",
      "11.0      0.928612\n",
      "12.0      0.925708\n",
      "13.0      0.921143\n",
      "14.0      0.917525\n",
      "15.0      0.915203\n",
      "16.0      0.912188\n",
      "17.0      0.906443\n",
      "18.0      0.903374\n",
      "19.0      0.901307\n",
      "20.0      0.898180\n",
      "21.0      0.894324\n",
      "22.0      0.890446\n",
      "23.0      0.889020\n",
      "24.0      0.885033\n",
      "25.0      0.882089\n",
      "26.0      0.879855\n",
      "27.0      0.877224\n",
      "28.0      0.874945\n",
      "29.0      0.871485\n",
      "...            ...\n",
      "43.0      0.828126\n",
      "44.0      0.826725\n",
      "45.0      0.826251\n",
      "46.0      0.824321\n",
      "47.0      0.819394\n",
      "48.0      0.816882\n",
      "49.0      0.812269\n",
      "50.0      0.808082\n",
      "51.0      0.805408\n",
      "52.0      0.803218\n",
      "53.0      0.797586\n",
      "54.0      0.794128\n",
      "55.0      0.790599\n",
      "56.0      0.787567\n",
      "57.0      0.784427\n",
      "58.0      0.779266\n",
      "59.0      0.774592\n",
      "60.0      0.772524\n",
      "61.0      0.768205\n",
      "62.0      0.766688\n",
      "63.0      0.764317\n",
      "64.0      0.763491\n",
      "65.0      0.756454\n",
      "66.0      0.747172\n",
      "67.0      0.740299\n",
      "68.0      0.731741\n",
      "69.0      0.727005\n",
      "70.0      0.716392\n",
      "71.0      0.708467\n",
      "72.0      0.694799\n",
      "\n",
      "[73 rows x 1 columns]\n",
      "0\n",
      "                 0\n",
      "timeline          \n",
      "0.0       1.000000\n",
      "1.0       0.913713\n",
      "2.0       0.883587\n",
      "3.0       0.862205\n",
      "4.0       0.843293\n",
      "5.0       0.828671\n",
      "6.0       0.820242\n",
      "7.0       0.809671\n",
      "8.0       0.799887\n",
      "9.0       0.788190\n",
      "10.0      0.777277\n",
      "11.0      0.771163\n",
      "12.0      0.760493\n",
      "13.0      0.751448\n",
      "14.0      0.746431\n",
      "15.0      0.734682\n",
      "16.0      0.727100\n",
      "17.0      0.723452\n",
      "18.0      0.717244\n",
      "19.0      0.711714\n",
      "20.0      0.707813\n",
      "21.0      0.705164\n",
      "22.0      0.697954\n",
      "23.0      0.693805\n",
      "24.0      0.688157\n",
      "25.0      0.680928\n",
      "26.0      0.676504\n",
      "27.0      0.673461\n",
      "28.0      0.670338\n",
      "29.0      0.667171\n",
      "...            ...\n",
      "43.0      0.605701\n",
      "44.0      0.603383\n",
      "45.0      0.599414\n",
      "46.0      0.592872\n",
      "47.0      0.589474\n",
      "48.0      0.585965\n",
      "49.0      0.580514\n",
      "50.0      0.578645\n",
      "51.0      0.575722\n",
      "52.0      0.571654\n",
      "53.0      0.567347\n",
      "54.0      0.559452\n",
      "55.0      0.555873\n",
      "56.0      0.549711\n",
      "57.0      0.545793\n",
      "58.0      0.541638\n",
      "59.0      0.540197\n",
      "60.0      0.535683\n",
      "61.0      0.532475\n",
      "62.0      0.527452\n",
      "63.0      0.525639\n",
      "64.0      0.519666\n",
      "65.0      0.517510\n",
      "66.0      0.510388\n",
      "67.0      0.502021\n",
      "68.0      0.498761\n",
      "69.0      0.483175\n",
      "70.0      0.469102\n",
      "71.0      0.463238\n",
      "72.0      0.463238\n",
      "\n",
      "[73 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = [\"windows blue\", \"amber\"]\n",
    "sns.set_palette(sns.xkcd_palette(palette))\n",
    "\n",
    "##SET UP PLOT\n",
    "ax = plt.subplot(111)\n",
    "plt.title('Kaplan-Meier Estimate of Driver Retention by Partner')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "d={} #to store the models\n",
    "vlines = []\n",
    "i=0\n",
    "\n",
    "##PLOT FITTED GRAPH\n",
    "#loop through segmentation variable, plot on same axes\n",
    "for segment in df.b_Partner.unique(): \n",
    "    ix = df.b_Partner == segment\n",
    "    print(segment)\n",
    "    d['kmf{}'.format(i+1)] = kmf.fit(T.loc[ix],C.loc[ix], label=segment)\n",
    "    ax = kmf.plot(ax=ax, figsize=(12,6))\n",
    "    ax.set_xlim([T.min(),T.max()])\n",
    "    ax.set_ylim([0.5,1])\n",
    "    print(kmf.survival_function_)\n",
    "    y_ = kmf.survival_function_[kmf.survival_function_.round(2) ==.77].dropna().index[0]\n",
    "    #print(y_.head())\n",
    "    #ymax_ = kmf.survival_function_[kmf.survival_function_.round(2) ==.77].dropna()[i][y_]\n",
    "    #print(ymax_)\n",
    "    #vlines.append([y_, ymax_])\n",
    "    i+=1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = [\"windows blue\", \"amber\"]\n",
    "sns.set_palette(sns.xkcd_palette(palette))\n",
    "\n",
    "##SET UP PLOT\n",
    "ax = plt.subplot(111)\n",
    "plt.title('Kaplan-Meier Estimate of Driver Retention by Multiple Lines')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "d={} #to store the models\n",
    "vlines = []\n",
    "i=0\n",
    "\n",
    "##PLOT FITTED GRAPH\n",
    "#loop through segmentation variable, plot on same axes\n",
    "for segment in df.b_MultipleLines.unique(): \n",
    "    ix = df.b_MultipleLines == segment\n",
    "    d['kmf{}'.format(i+1)] = kmf.fit(T.loc[ix],C.loc[ix], label=segment)\n",
    "    ax = kmf.plot(ax=ax, figsize=(12,6))\n",
    "\n",
    "    ax.set_xlim([T.min(),T.max()])\n",
    "    ax.set_ylim([0.5,1])\n",
    "    #print(kmf.survival_function_)\n",
    "    y_ = kmf.survival_function_[kmf.survival_function_.round(2) ==.75].dropna().index[0]\n",
    "    ymax_ = kmf.survival_function_[kmf.survival_function_.round(2) ==.75].dropna()[i][y_]\n",
    "    #print (y_, ymax_)\n",
    "    vlines.append([y_, ymax_])\n",
    "    i+=1\n",
    "\n",
    "##PLOT ANNOTATION\n",
    "#for each intercept, plot a horizontal and a vertical line up to the fitted curve\n",
    "xmin_=0\n",
    "for i, xy in enumerate(vlines):\n",
    "    xmax_ = xy[0]\n",
    "    color=\"xkcd:{}\".format(palette[i])\n",
    "    \n",
    "    plt.axvline(x=xy[0], ymax=.5, alpha=.8, color=color, linestyle='dotted') #axes fractional\n",
    "    plt.hlines(y=.75, xmin=xmin_, xmax=xmax_, alpha=.8, color=color, linestyle='dotted') #axes data coordinates\n",
    "    xmin_ += xmax_ #to avoid overlapping hlines\n",
    "\n",
    "## position text label\n",
    "difference = vlines[1][0]-vlines[0][0]\n",
    "label_frac_x = (vlines[0][0]+(difference)/2)/T.max()-.07 #midpoint (minus .07 centering factor)\n",
    "label_frac_y = 0.2\n",
    "\n",
    "#label first line\n",
    "ax.annotate('Difference:\\n{} Months'.format(difference), \n",
    "  xy=(vlines[0][0],.62), xycoords='data', color='white',\n",
    "  xytext=(label_frac_x,label_frac_y), textcoords='axes fraction',\n",
    "  arrowprops=dict(arrowstyle=\"-|>\", #arrows removed for reability\n",
    "                  fc=\"k\", ec=\"k\")\n",
    "           )\n",
    "\n",
    "#label second line    \n",
    "ax.annotate('Difference:\\n{} Months'.format(difference), \n",
    "      xy=(vlines[1][0],.62), xycoords='data', color='black',\n",
    "      xytext=(label_frac_x, label_frac_y), textcoords='axes fraction',\n",
    "      arrowprops=dict(arrowstyle=\"-|>\", #arrows removed for reability\n",
    "                      fc=\"k\", ec='k')\n",
    "           )\n",
    "\n",
    "##LEGEND\n",
    "#override default legend\n",
    "patches = [ mpatches.Patch(color=\"xkcd:windows blue\", label='Has One Phoneline'),\n",
    "            mpatches.Patch(color=\"xkcd:amber\", label='Has Multiple Phonelines')\n",
    "          ]\n",
    "plt.legend(handles=[patches[0],patches[1]], title=\"User Segmentation\", loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df[df.describe().columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1: norm_delta = 0.61287, step_size = 0.95000, ll = -15653.03964, seconds_since_start = 0.1\n",
      "Iteration 2: norm_delta = 0.05826, step_size = 0.95000, ll = -15325.78709, seconds_since_start = 0.3\n",
      "Iteration 3: norm_delta = 0.00313, step_size = 0.95000, ll = -15323.50324, seconds_since_start = 0.4\n",
      "Iteration 4: norm_delta = 0.00016, step_size = 0.95000, ll = -15323.49644, seconds_since_start = 0.5\n",
      "Iteration 5: norm_delta = 0.00001, step_size = 0.95000, ll = -15323.49642, seconds_since_start = 0.7\n",
      "Convergence completed after 5 iterations.\n",
      "n=7043, number of events=1869\n",
      "\n",
      "                   coef  exp(coef)  se(coef)        z      p  lower 0.95  upper 0.95     \n",
      "SeniorCitizen    0.4870     1.6275    0.0545   8.9339 0.0000      0.3802      0.5939  ***\n",
      "MonthlyCharges   0.0100     1.0101    0.0009  10.7140 0.0000      0.0082      0.0119  ***\n",
      "b_MultipleLines -0.5098     0.6006    0.0539  -9.4543 0.0000     -0.6155     -0.4041  ***\n",
      "b_Partner       -0.9773     0.3763    0.0493 -19.8080 0.0000     -1.0740     -0.8806  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "\n",
      "Concordance = 0.679\n",
      "Likelihood ratio test = 659.086 on 4 df, p=0.00000\n"
     ]
    }
   ],
   "source": [
    "from lifelines.datasets import load_rossi\n",
    "from lifelines import CoxPHFitter\n",
    "\n",
    "rossi_dataset = load_rossi()\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(df2, duration_col='tenure', event_col='b_Churn', show_progress=True)\n",
    "\n",
    "cph.print_summary()  # access the results using cph.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1: norm_delta = 0.61287, step_size = 0.95000, ll = -15653.03964, seconds_since_start = 0.1\n",
      "Iteration 2: norm_delta = 0.05826, step_size = 0.95000, ll = -15325.78709, seconds_since_start = 0.3\n",
      "Iteration 3: norm_delta = 0.00313, step_size = 0.95000, ll = -15323.50324, seconds_since_start = 0.4\n",
      "Iteration 4: norm_delta = 0.00016, step_size = 0.95000, ll = -15323.49644, seconds_since_start = 0.5\n",
      "Iteration 5: norm_delta = 0.00001, step_size = 0.95000, ll = -15323.49642, seconds_since_start = 0.6\n",
      "Convergence completed after 5 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11832d780>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(df2, duration_col='tenure', event_col='b_Churn', show_progress=True)\n",
    "\n",
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}