Survival Analysis P2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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": 142,
     "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": 143,
   "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": 144,
   "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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                  3390\n",
       "Yes                 2971\n",
       "No phone service     682\n",
       "Name: MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.MultipleLines.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                  3390\n",
       "Yes                 2971\n",
       "No phone service     682\n",
       "Name: MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 146,
     "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": 147,
   "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": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4072\n",
       "1    2971\n",
       "Name: b_MultipleLines, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.b_MultipleLines.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaplan Meier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.KaplanMeierFitter: fitted with 7043 observations, 5174 censored>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.fit(durations = df.tenure, \n",
    "        event_observed = df.b_Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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",
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       "24             94        23        71         0     3927\n",
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       "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": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.event_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>KM_estimate</th>\n",
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       "      <td>0.648543</td>\n",
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       "    <tr>\n",
       "      <th>67.0</th>\n",
       "      <td>0.633598</td>\n",
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      ],
      "text/plain": [
       "          KM_estimate\n",
       "timeline             \n",
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       "8.0          0.869933\n",
       "9.0          0.862394\n",
       "10.0         0.854915\n",
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       "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",
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       "22.0         0.795962\n",
       "23.0         0.793383\n",
       "24.0         0.788736\n",
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       "26.0         0.780871\n",
       "27.0         0.778109\n",
       "28.0         0.775517\n",
       "29.0         0.772237\n",
       "...               ...\n",
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       "44.0         0.721105\n",
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       "46.0         0.715848\n",
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       "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",
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       "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": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.survival_function_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.median_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ecf6400>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.survival_function_.plot()\n",
    "plt.title('Survival function of political regimes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1197ff048>"
      ]
     },
     "execution_count": 230,
     "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": [
    "kmf.fit(T, C, label=\"kmf.plot(show_censors=True)\")\n",
    "kmf.plot(show_censors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KM by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "T = df['tenure'] #duration\n",
    "E = df[\"b_Churn\"] #censorship - 1 if death/churn is seen, 0 if censored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118393978>"
      ]
     },
     "execution_count": 157,
     "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": [
    "groups = df['MultipleLines']\n",
    "ix = (groups == 'Yes')\n",
    "\n",
    "kmf.fit(T[~ix], E[~ix], label='control')\n",
    "ax = kmf.plot()\n",
    "\n",
    "kmf.fit(T[ix], E[ix], label='Yes')\n",
    "kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "\n",
    "dem = (df[\"MultipleLines\"] == \"Yes\")\n",
    "kmf.fit(T[dem], event_observed=E[dem], label=\"Yes\")\n",
    "kmf.plot(ax=ax, ci_force_lines=True)\n",
    "kmf.fit(T[~dem], event_observed=E[~dem], label=\"No\")\n",
    "kmf.plot(ax=ax, ci_force_lines=True)\n",
    "\n",
    "plt.ylim(0, 1);\n",
    "plt.title(\"Lifespans of different Lines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median survival time of Yes: inf\n",
      "Median survival time of No: inf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "\n",
    "t = np.linspace(0, 50, 51)\n",
    "kmf.fit(T[dem], event_observed=E[dem], timeline=t, label=\"Yes\")\n",
    "ax = kmf.plot(ax=ax)\n",
    "print(\"Median survival time of Yes:\", kmf.median_)\n",
    "\n",
    "kmf.fit(T[~dem], event_observed=E[~dem], timeline=t, label=\"No\")\n",
    "ax = kmf.plot(ax=ax)\n",
    "print(\"Median survival time of No:\", kmf.median_)\n",
    "\n",
    "plt.ylim(0,1)\n",
    "plt.title(\"Lifespans of different lines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": [
    "from lifelines.statistics import logrank_test\n",
    "\n",
    "results = logrank_test(T[dem], T[~dem], E[dem], E[~dem], alpha=.99)\n",
    "\n",
    "results.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regime_types = df['MultipleLines'].unique()\n",
    "\n",
    "for i,regime_type in enumerate(regime_types):\n",
    "    ax = plt.subplot(1, 3, i+1)\n",
    "    ix = df['MultipleLines'] == regime_type\n",
    "    kmf.fit( T[ix], E[ix], label=regime_type)\n",
    "    kmf.plot(ax=ax, legend=False)\n",
    "    plt.title(regime_type)\n",
    "    plt.xlim(0, 150)\n",
    "    if i==0:\n",
    "        plt.ylabel('$n$ years')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weibull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.004539494588420575 0.644703245441282\n",
      "n=7043, number of events=1869\n",
      "\n",
      "          coef  se(coef)  lower 0.95  upper 0.95\n",
      "lambda_ 0.0045    0.0002      0.0041      0.0050\n",
      "rho_    0.6447    0.0133      0.6187      0.6707\n"
     ]
    }
   ],
   "source": [
    "from lifelines import WeibullFitter\n",
    "\n",
    "T = df['tenure']+.00001\n",
    "E = df['b_Churn']\n",
    "\n",
    "wf = WeibullFitter()\n",
    "wf.fit(T, E)\n",
    "print(wf.lambda_, wf.rho_)\n",
    "wf.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survival from events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          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"
     ]
    }
   ],
   "source": [
    "from lifelines.utils import survival_table_from_events\n",
    "\n",
    "table = survival_table_from_events(T, E)\n",
    "print(table.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CoxPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp=pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp2=dfp[dfp.describe().columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n=7043, number of events=1869\n",
      "\n",
      "                   coef  exp(coef)  se(coef)        z      p  lower 0.95  upper 0.95     \n",
      "SeniorCitizen    0.4998     1.6483    0.0545   9.1658 0.0000      0.3929      0.6066  ***\n",
      "MonthlyCharges   0.0093     1.0094    0.0009  10.0505 0.0000      0.0075      0.0111  ***\n",
      "b_MultipleLines -0.5680     0.5667    0.0535 -10.6144 0.0000     -0.6728     -0.4631  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "\n",
      "Concordance = 0.596\n",
      "Likelihood ratio test = 245.056 on 3 df, p=0.00000\n"
     ]
    }
   ],
   "source": [
    "from lifelines import CoxPHFitter\n",
    "\n",
    "# Using Cox Proportional Hazards model\n",
    "cph = CoxPHFitter()\n",
    "cph.fit(dfp2, 'tenure', event_col='b_Churn')\n",
    "cph.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117901978>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9vXR0dPDXv/51vfS+vvT5bMmSJXR0dLhHwsa1ofZE3ABcS+p5+EVOe30u58fAFysZJU0g9U7sQLqY3JqP+5GkwyJiTk3ZxwG9wKnANqSL9veBqXn/ycCWwHZA5aJ/7xDLWE9E3CTpN6Qhmq/U7D4UWAlclp+/FlgWEb+vV1Y/Xgm8iXQBvRc4mvRJ/X8i4s6cZw/gpcA84J9AO/AeYJGkXSLivzVlngXcDpwAbAIgaTJwKXBbTm8FjgdW1VZI0gl531zgbNIF/IPAHpKmRMRaSZNyu5eThmjuA7YHDqoprg/YIEip0jvA/urjY4C8Zo8ql156Kd3d3f3u7+vro7u7m4ULF9LR0dFvPrMxFREDbsDhpH/wuwEfAe4ENsn7uoCf5J/XALPzzx/Mx0yvKmczYCnw76rj98r5/lxJy+lH5/TnVaVdDKyoU7+hlLEIWFT1/MicZ6eqtAnA3cDX8vOtc56LB/N65WMCuB/YvirtBTn9/dXnqnPsi3O+w+q8B4uAlpr8PwXuAZ5SlfYs0hyVqEprBx4CPlrnfH3AUfn5G/K5tu2nbU8Gfk8KfPbIddobuJ4UdEwAfg5cTgo+VuTfnWuB83IZs/Pz3fL+HYAr8nGb15xvzQBbtLW1hdnGZMaMGdHS0lIJnutuLS0t0dnZOdZVtUeptra2ANbEIK9r9baSJZ4/BCYC+0t6IrAvdYYygA7SJ+P5lYSIWAd8M1+EaudPnBsR1RMzf5UfdxhC3UrK+CFpwuihVWmvBbYCKr0lW+fHe4ZQF4CFEbG88iQi/kQKTnaoSltb+VnSJvk1vZF0cfx/dco8O6rmo+TJifsAF0YaZqiUeyPpglztIEDAhZKeVNny+W4nBWPkcwMcJKne78gdwHERMYPc2xERvwAm5zavBb4SEftW2h8RS0nBSuU1PR+YmtOJiJsiYp98nIcz7FFv5cqVDw9d9Kevr4+VKzcYPTUbN4YcRETEbaSL81tIXfUPkD4J19oO+EdE1HZRX1+1v9otNc9X58dthlC9IZcREWuAS0hDLRWHAjdExB/y87vz41ZDqEu9+lTq9HB98oqGEyXdSgpm7iBdmCeSVrzUWl7zfBLpk/+NdfLWpu1Ees9vyueo3p4GbJvz/ZI0PHUWsCrP0TissoQ1Inoj4orak0WaC/Gr/PNldfY/FBFX5p+vjIgNBnv7OW5iow3oqdN2s3Ft0qRJtLQ0/hfc0tLiiZU2rg11TkTFXNKs/B2Bn8aG4/Yl+ps9pFEo43vAT5TugXETacnqyZWdEXG3pNuB5w2hLoOtzxmklQqnA78jXRCDNFRQ7z/M2jppg9WS63QA9ecfrIY8/gGHSJpK6pXZj/QaHSPpZRHx8FyUiFjBIz0YdUVE+3D2mz0azZw5k3nz5jXM09fXx8yZM0epRmZDVxpE/Ih08dsDOLGfPDcDu0hSTW/Ec6r2D9VITbz7OakH4FBST8kmpAmZ1S4B3i1pakQsbuK5DwG+GxEfrSRI2pzUEzEYK0lzL+qtQqlNW0aadHlDvvg3lNu5GDhO0ptJQz/Tge8Msm5m1o/999+fKVOmsGTJkrrDGi0tLUyePJn99ttvDGpnNjhFt72OiLtIN1qaRVrmV88C4BmkiySQ7mxJWnnwH6D/acn9u4/6XfzDkudRzCNdIA8DFuc5BdVOBf4LfFvStjX7kPRcSUcWnL6XDXtKPkC62A8oDwlcAbxR0lOq6vMsUo9DtYtIEyiPry0n30DqCfnnbSTV1mlpftx8MPUys8ZaW1tZsGABu+yyy3rplSGOyZMns2DBAlpbB/WvwGxMlPZEEBHnDJDlbNLKh/Ml7cEjSzx3I606KLm7ZTdwqKQvA9cA90bEzwrKqed84P2kJY8fqN0ZETdKOpQUbPxN0veAv5DmI7ycdBfLLxec9xLgMEk9wF+Bl5AmSt7Z8Kj1zQJ+C/xG0lmkAOT9pPtI7FbThuOBk5RunvUzUmC2Y67/yaQbQr0NeK+ki0m9F1sA7yLNDVlQ0EYzq2PSpEksXbqUhQsXMmfOHFauXMmkSZOYOXMm++23nwMIG/eKg4iBRLrfwN7A54F3kCYl/hV4c0T8qLDYs0grFg4n3SviZtKFcNgi4g+S/k66oNYdqIyIiyW9kHT/iTeSemPuJ31Kfx9wXsGpP0jqjTiU9Cn/N6Qgor8ennr16pZ0AOk+HZ8lBWzHA88Fdq7Je7KkG0g3evoMqWfiFtLy2ctztl+SbiA1nbSSpgf4Ayn4q53YaWbD0NraSkdHh+8FYRslbbh44rFL0p+BmyPiwLGuSzPknoRdI2Knsa7LSJK0pq2trW3NmjUDZzYzMwAmTpxIT09PT17lVuRR8VXgzSDpxaTVF+ePdV1K5MmY1c93It2rY9GYVMjMzB71Rmw4Y2Mh6XnA7sAxpG79C8e2RkMn6XHATZK+S1qi2g4cRbqHx6ljWDUzM3sUe8wHEaTVI8eTlnYeWjjhc6z1kr7rohN4CummVb8FPhURN4xlxczM7NHLcyJso+c5EWZmQ+c5EWZmZjZmHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRUZdhAhaZakkDSxGRUaSZJWSJrd7LzNPHYQZbfn1/vwkSjfzMxssMZNT0RVMPKQpCfX2f+UvC8kzWrSOXfO521vRnnDVRUgfGis6zJYkvZqFDDlNi0aoIwVTa7WuNDb20tXVxednZ1MmzaNzs5Ourq66O3tHeuqmZk1xbgJIqo8BEyvkz4deLDJ59oZOAFor7PvOcC7m3y+ZrgZmACcP1YVkLSlpJfWSX+CpD0ktUjap87+CZL2zD/vW2d/q6RpI1Pr+lasWIEkJLFixYqmlbty5UqmTp3KgQceyPz587nqqquYP38+Bx54IFOnTmXlypVNO9dItcHMbCDjMYhYAHTWSe/M+0ZFRKyLiGYHLcMWyf0RMZYfZ58JfEfS14AtASS9CbgW2AOYBJwiaS6wbd6/N/BHYD9JE4CPSbpc0vZ5/27AYuCw0W5Ms/X29tLR0cGSJUsA6OvrW+9xyZIldHR0uEfCzDZ6zQwiJkm6QNI9klZKOlXSJgXlzAVeXLm4AEjaAZia962nMgxSJ/3wPDTQXu8keU7BRfnpL3LekLRX3r/evIaq8l4m6RxJqyWtkfRtSVsN1Kj8Kf0MSf+UtE7S3yUdPdBxdcrZYE6EpNm5Ls+U9FNJ90paJemLklprjm+RdIykv+V63J7rtWVNvn0l/TqXe2+u7+cAIuJvwAuAFcBZwCHAwcArI+LMiPg38CJgIfCD/PMngIMj4riIWBsRrwbOAC4EngqcCXwgIg5nI3fppZfS3d39cNBQq6+vj+7ubhYuXDjKNTMza65mBhEXAK2ki8WVwMeArxaUcxXwH9bvjegE/g38Yph1rHY1cHr++XOkT8CHAX8b4LhvAs8CPk0Kat4BzGt0gKQtgF+ShmTOBY4GlgBflXRCYf1rbQJcBqwEjiG176PAETX5vgOcDCzK9fg+adjmJ5KU67srcAnwAPCpXN4lwMurygmgLz9S9Vitr5/0it4B9pPrs6bRBrQNVMZomjNnDi0tjf+0WlpamDNnzijVyMxsZDyuiWXdEBEH55+/IWktcJSk0yJi+RDK6QXmkwKHz+W0zpzWtP7fiLhJ0i+BDwGXR8SiQR66Ftg3Ih4CkHQ78BlJr4yIX/ZzzEeA7YAXVr0WZ0laDXxC0tciYnVxY5LHA9+LiFPy829J+iPwTlLgQ56PcDhwSET8uHKgpGtIgdB+wKXAvsD9uZ0bvOaSdgZ+TAr4jiL1RCwAfinpNFLvwk9IPRWHAqcBnwculHQBKYi5kPT7dzApOHwv8G1Jf46Itw/ztSiy/fbbD5ypSfr6+pg7dy5z527QuWZmttFoZk/EN+o8F+nCNFRzgedJer6kFwC7UmcoY4ycVQkgskq7D2hwzCGknoh7JD2pspF6DjYnDdU0pW41z38F7FBTj7tIF/vqelxNCtD2yvnWAFsA+/dzntuAd0fEB4B7ASJiPrA7aV7EHcBxETEDWJX3/wKYDCyMiLXAVyJi30pQFRFLgRcDG3w8j4iJjTagZ7AvUCPLly8nIoa9zZgxY1A9EZ2dnU053/LlQ4nRzcyap6k9Ef08bx9qQRHxu7zs7y2kQGR5RPxe4+NeFOu1MyLuzD0K7Q2O2Yk0h2BVP/u3bUK97o2Iu2rSVgPb1NTjCYOoxw+BdwGX5J6WK0jzRy7OEzvvAX5be3A+f6UOV9TZv5YU2BARl9XZ/xBpKGyjNnPmTObNazjCRV9fHzNnzhylGpmZjYxmBhHNNg+YQQoiftAgX39j6q39pI+FFtIwwZf62f+XJpxjMEM9LcDtwFv72f8vSBd7Sa8A9gY6SD0ShwGXSzqgeogjDwMt6u+EEbGCR3o4+svTPoi6bzT2339/pkyZwpIlS+pOrmxpaWHy5Mnst19JJ52Z2fjRzCBiJ+DWmueQ7mtQ4gekSZqVn/uzGkDSxIhYU5W+3SDOMeCkvjp2In+azud9IunTfqN2LgMmRMQGn85H2TJSYPCriFjXKGNE9JF6Ba4EPirpWOALpIBgo+8tGEmtra0sWLCAjo4Ouru7aWlpoa+v7+HHyZMns2DBAlpbx1Oca2Y2dM2cE/G+Os+DtMxvyCLiz8CxwLERcV2DrMvy4ysqCXk1xNsGcZr78uNQhkmOlFQdfFXa/fMGx1wAvKKyfLRanpegIZx/OC4ANuWR4Ky6HptJ2jr//MQ6xy7Nj5uPXPVGX3t7+8NzC9rb25tW7qRJk1i8eDFdXV1Mnz6dadOmMX36dLq6uli8eDGTJk1q2rlGqg1mZgNpak+EpIuAy4E9SUMRZ0XETaUFRsRpg8h2GXAL6eZHp5G69d9BGvf/nwGO/T/SHTI/LqkNWAdcFRGNbic4gdSt/2PShM8jSZMFFzU45lTg9cBCSeeSLshbAy8krU7YKtejYt/a+zZk82ryDUlE/ELSt4FZkqaQehT6gGcDbyatpLgC+HQezlhAWmGxLWn1xD+BX5ee/7GmtbWVjo4OOjo6xroqZmYjoplBxCHAKaQu7/tJ4///28Ty64qIByUdRLpZ0WdJ95M4nTTMcd4Ax66SdARwHOn+Ca2k7v5GQcR7ScskP0vqyZlNWiba6Dz35Yvyp0iv0ztz/a4HPs6G8xk68lZrKdCoV2YwjgC68+PnSYHTcuAcHult+ClpoujbgSeRVlv8EjghIpqyEsLMzDZ+iiiZFvDYk+8QeR4wOS9HtHFC0pq2tra2NWvWDJzZzMwAmDhxIj09PT15qXyR8fjdGWZmZrYRGPElnnmuwYRGefJ3LZiZmdlGZDTuE/FVBl4pMVqrE8zMzKxJRnxOhKRdgKc1yjMO7p9gGzHPiTAzG7pmzInwxErb6EnqA9TWNq6+zNPMbFzr6ekBiIgonh/pIMI2epIeIk0Svnus6zKCKhHSY2GJ7WOprfDYau9jqa0w/tu7NdAXEcVTGxxEmG0EJK2ODQZAAAAL1UlEQVSB9I2mY12XkfZYais8ttr7WGorPDba6yWeZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFfJ8IMzMzK+KeCDMzMyviIMLMzMyKOIgwMzOzIg4izMzMrIiDCLMxJmmipLMlrZJ0n6SrJO02yGNnS4o62+/r5G2RdKyk5ZLul/QnSdOb36IB61zU3lz/t0v6maRb87HXSfqkpM1q8rb387qEpP2b3J7NJH1B0r8krZX0e0nTBnns0yXNl7RG0t2SLpa0fT953ynpb/m9+4ek9zWzHYNV2l5Jb5T0w/z7919J10s6TVJbnbz9vXdHjUyr+q1zaVtn9VP/f/eTf1y8tyWKv/7TzIZPUgvQBTwf+CJwJ/BeYJGkKRGxbBDF/Bc4siZtVZ18JwOfAM4GrgVeD8yT1BsRFxQ2YUiG2d7HA+cCvwe+BawEXgJ8FngVsE+dY+YAC2vS/m84bahjNnAwcDpwI3A48HNJr4yI3/V3kKQtgV8AW5Hem4eAD5Nei90iYnVV3iNJbf4R8GVgT+DrkjaPiC81uT0DmU1Be0m/d/8CzgduIf0OHA0cIGn3iLi/Jv9C0vtXbfGwaz80sylra8WRpL/PirW1GcbZezt0EeHNm7cx2oA3AwG8oSptW2A18L1BHD8bWDOIfE8HHgBOr0oTcDWwAmgZ7+0FNgVeWif9+FzmXlVp7TntQyPcnhfVngfYnHTBuXqAY48F+oDJVWk7k4KJE6vSJgB3ABfXHD8HuBtoG8Xf1+G0d686aW/N5R1ekx7Vv6tjsQ2zrbPysRMHyDdu3tvSzcMZZmPrENKns59UEiJiFTAfeIOkTQZTiKRWSVs1yPJ6YBPgzKrzBPBNYDvSP8zRUNzeiHggIn5bZ9dF+fG59Y6TtIWkTcur3NAhwIPAtysJkT5Rfwd4uaSnDnDs7yNiSdWx1wNXkoKtir2BJ1L13mXfIPViHDCcBgxRcXsjYlGd5IHeuwmSNi+u7fAM572tkKStJamf/ePpvS3iIMJsbE0GuvMFvdofSP9EnjWIMrYifWq5W9Idkr5c5x/vZODuiPhHnfNU9o+GZrS31lPy4x119n0WuBe4X9LvJL2ioPxGJgPXR8S9Nel/IPX01J3rkYd1XkAaVqr1B+DZkh5fdQ7q5O0m92QU1LtUUXsbaPTevQu4D1ib5+8cNMSyh6sZbb0F6AF6JJ0r6Ql1zgHj470t4iDCbGw9Fbi9Tnol7WkDHH87cCrwduAtwGWkcfWLavI9Fag3qWuw52mW4ba3nmNJ/6gvq0rrI42pHwO8Lj9uB1whac+Cc/SntD1PADZrcKxy2ZVzrIuIu6ozRcQDpDklo/XeVerSzPfv40AvcGFN+m+BT5J60N5Heq0ulNQ5xPKHYzhtXQ2cQZoT8SbS8MTbgKtqJgGPp/e2iCdWmjVJ/nQ5qG7zeGQS2QRgXZ0s1fsblfO/NUlzJf0T+JikfSPi8macp56xaG+dOnySNKHyyIjoqTrfLcD+NXnnAX8FPg+8bCjnaaC0PZX0wRw7gTSfpZ77G5xjJDTt/ZP0FuCdwClRM6E2Il5Wk/e7wHXAqZLm1enJGgnFbY2Ir9YkXSDpOtIwxVuBc6rKGC/vbRH3RJg1zytIs68H3CQ9KR+zlvQpq9bmVfuHqjKju3op2kicZ0zbq7Q89STgrIg4e6D8EfEvYC7w4qqhguEqbU8lfTDH9neOSt6S965UU96/3Bv0HdJKnU8PlD8i7iOtYHgG8JxB1XT4mv038y3SSo3B/F1WzjOa720R90SYNc/1pGGFwbgnP97OI93W1Spp/xpqJSLiP5IeIHWZV9xOWjrWtPMwhu2VtC/wPeBnpO7uwbqV9OFpIusvvStV2p67SJ9y+zs2eKTb/HZgU0lPqO72zpNFn9jgHCNh2O+fpBcCPwX+BEyPiN5BnvvW/Fg7r2CkNPVvMyL6JN3Ghn+X4+W9LeIgwqxJIuLfpCWXQ7EUeKkk1XTRTiVNCLxxqPWQ9AzSMEP1vSKWAu+S9OyayZVTq/YPyVi1V9JU0pyPa4AZQ7gIAexAGoNfPVDGQVoKfFDSljUT8Cqva917UuQLyp+B3evsngrcEBGVIKfy3uzO+vM+dicFREN+74ahqL0VknYELiXd4+M1uYdhsHbIj/XugTIShtXWWnnl0TNJv7fV54Dx8d6WGes1pt68PZY3YDob3jfhSaSL3JyavDsCO1Y93xzYqk6ZZ+UyX1GV9gz6v0/EzYzefSKK25vTnkuayX8dsE2D82xbJ+1ZpNn+VzWxPVPZ8F4CmwE3AL+uSvsfYOeaYz/OhveJeA7pPhEnVaVNIE2yq72XwPmkHp6G9yJo8vs3nPY+BVgG3Aa0NzjHk+qkVT6V37SRtLXe798nc3lvHY/vbemmXGEzGwOSWoFfA7uS7uB4B+kOjs8EpkTEjVV5VwBERHt+3g4sAX5AGlpoAV5LGnP9YUTMqDnXqaRVCpU7Vr4BeA2pS3n+yLRwfcNs71bAX0g3zvok6WJU7U8R8aec9zzSJ9crSV3GOwJHke6VsWdE/LGJbZpPei2/QrpIvg3YA9g7In6T8ywCXhkRqjpuK9L7twVpHstDwEfIywcj4s6qvO8lTcr7EekT656kCXofj4hTm9WWwRhGe5cCLyStJvpzTbHLIt8BUtIs0qqMS0hLJJ8OHAFMIgWfl4xU22oNo63/BeaRgt11pPtBHEz63d87Ih6qyjtu3tsiYx3FePP2WN+AbUg3tLmD9En5F8D/q5NvBbCi6vlE0ieWG/Jxa0ldrB8BWusc3wL8by5nHekfeedG1N520ie5/rZZVXk7gV+Sur4fJHWfzwd2HYH2bA6cRgpW7ifdR2CfmjyLyPf3qkl/Buni0UP65PlTYId+zvNuUrC4jjTsc/QY/b4WtXeA9252Vb5XA5eTliQ/QJo/0gW8bCNq6zmklUD35Pfr78CJwITx/N6WbO6JMDMzsyJe4mlmZmZFHESYmZlZEQcRZmZmVsRBhJmZmRVxEGFmZmZFHESYmZlZEQcRZmZmVsRBhJnZOCZpe0kXS1olKSTNHus6mVX4C7jMzMa32cALgJNJd3FcNqa1MaviO1aamY1TkjYj3c786xFx9FjXx6yWhzPMzMavJ5O+kOuusa6IWT0OIszMGpC0qaRjJS2V9F9JPZKulfT+mnztks6X9B9J6yQtk/Q5SY+vU2abpC9IujHnXSVprqQdqvLMJn1NO8AJeT5ESNprJNtrNhQezjAz64ekTYGFwF6kr2m+nPRtjs8HdoqIV+V82wHXAG3AmaRvVt0LeBNwNTAt8tc/S2oDfgv8D3Au6evNn0r6SvQWYPeIuFnSS4CppK+hvgi4MFfr8oj4z0i222ywHESYmfVD0rHAF4BTIuKTNftaIqIv//x94C3AayJiQVWe04BjgHdFxHdy2leBI4AXR8T/VeXdjvT17BdGxOE5rR1YDnwmImaNTCvNynk4w8ysf4cCq4ETa3dUBRAtwOuAJdUBRHYK0AcclPMql3k1cJukJ1U24D7g98CrR6gtZk3nJZ5mZv3bCVgaEfc3yLMtsCVpWGI9EXGXpNuBHaryPpEUKKzqp7y+8uqajS4HEWZmo0f58QrSMInZRs1BhJlZ//4B7Cxps4hY10+eVcA9wK61OyRtQ5o0ubQq7xpg64i4YgTqazaqPCfCzKx/3we2AY6r3ZHnN1TmRvwMmCxp/5psnyD9n72oKu/3gRdJOqTeCSVNalrtzUaYV2eYmfUjL/G8AtiTtNTzMtISz12B50TEPjlfZYnn1qQlnjcCrwCmU3+J5yLghcB80mTKB4DtgA6g26szbGPh4Qwzs35ExAOSXg18lLSE83OkIOIG4LyqfDdLmkpaxTETmAj8k7Q646RKAJHz9kh6WS7zzcDrgYdy/l8D3x6Fppk1hXsizMzMrIjnRJiZmVkRBxFmZmZWxEGEmZmZFXEQYWZmZkUcRJiZmVkRBxFmZmZWxEGEmZmZFXEQYWZmZkUcRJiZmVkRBxFmZmZW5P8D+qr9h1wku3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cph.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>b_Churn</th>\n",
       "      <th>b_MultipleLines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>0.265370</td>\n",
       "      <td>0.421837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>0.441561</td>\n",
       "      <td>0.493888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges      b_Churn  \\\n",
       "count    7043.000000  7043.000000     7043.000000  7043.000000   \n",
       "mean        0.162147    32.371149       64.761692     0.265370   \n",
       "std         0.368612    24.559481       30.090047     0.441561   \n",
       "min         0.000000     0.000000       18.250000     0.000000   \n",
       "25%         0.000000     9.000000       35.500000     0.000000   \n",
       "50%         0.000000    29.000000       70.350000     0.000000   \n",
       "75%         0.000000    55.000000       89.850000     1.000000   \n",
       "max         1.000000    72.000000      118.750000     1.000000   \n",
       "\n",
       "       b_MultipleLines  \n",
       "count      7043.000000  \n",
       "mean          0.421837  \n",
       "std           0.493888  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           0.000000  \n",
       "75%           1.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>7033</th>\n",
       "      <th>7034</th>\n",
       "      <th>7035</th>\n",
       "      <th>7036</th>\n",
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       "      <th>7039</th>\n",
       "      <th>7040</th>\n",
       "      <th>7041</th>\n",
       "      <th>7042</th>\n",
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       "    <tr>\n",
       "      <th>event_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.957963</td>\n",
       "      <td>0.946214</td>\n",
       "      <td>0.947705</td>\n",
       "      <td>0.952914</td>\n",
       "      <td>0.939089</td>\n",
       "      <td>0.954428</td>\n",
       "      <td>0.958606</td>\n",
       "      <td>0.958001</td>\n",
       "      <td>0.952242</td>\n",
       "      <td>0.946603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939746</td>\n",
       "      <td>0.953039</td>\n",
       "      <td>0.934532</td>\n",
       "      <td>0.944381</td>\n",
       "      <td>0.961172</td>\n",
       "      <td>0.960199</td>\n",
       "      <td>0.952932</td>\n",
       "      <td>0.958058</td>\n",
       "      <td>0.941048</td>\n",
       "      <td>0.916638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.943475</td>\n",
       "      <td>0.927833</td>\n",
       "      <td>0.929814</td>\n",
       "      <td>0.936745</td>\n",
       "      <td>0.918380</td>\n",
       "      <td>0.938762</td>\n",
       "      <td>0.944334</td>\n",
       "      <td>0.943526</td>\n",
       "      <td>0.935850</td>\n",
       "      <td>0.928349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919250</td>\n",
       "      <td>0.936911</td>\n",
       "      <td>0.912347</td>\n",
       "      <td>0.925398</td>\n",
       "      <td>0.947760</td>\n",
       "      <td>0.946461</td>\n",
       "      <td>0.936768</td>\n",
       "      <td>0.943603</td>\n",
       "      <td>0.920976</td>\n",
       "      <td>0.888760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.932104</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.915820</td>\n",
       "      <td>0.924076</td>\n",
       "      <td>0.902228</td>\n",
       "      <td>0.926480</td>\n",
       "      <td>0.933129</td>\n",
       "      <td>0.932165</td>\n",
       "      <td>0.923008</td>\n",
       "      <td>0.914077</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903261</td>\n",
       "      <td>0.924273</td>\n",
       "      <td>0.895071</td>\n",
       "      <td>0.910566</td>\n",
       "      <td>0.937222</td>\n",
       "      <td>0.935670</td>\n",
       "      <td>0.924103</td>\n",
       "      <td>0.932257</td>\n",
       "      <td>0.905312</td>\n",
       "      <td>0.867184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.921842</td>\n",
       "      <td>0.900536</td>\n",
       "      <td>0.903227</td>\n",
       "      <td>0.912658</td>\n",
       "      <td>0.887729</td>\n",
       "      <td>0.915407</td>\n",
       "      <td>0.923015</td>\n",
       "      <td>0.921912</td>\n",
       "      <td>0.911438</td>\n",
       "      <td>0.901237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888906</td>\n",
       "      <td>0.912884</td>\n",
       "      <td>0.879584</td>\n",
       "      <td>0.897232</td>\n",
       "      <td>0.927703</td>\n",
       "      <td>0.925925</td>\n",
       "      <td>0.912690</td>\n",
       "      <td>0.922016</td>\n",
       "      <td>0.891242</td>\n",
       "      <td>0.847943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.913774</td>\n",
       "      <td>0.890403</td>\n",
       "      <td>0.893352</td>\n",
       "      <td>0.903692</td>\n",
       "      <td>0.876384</td>\n",
       "      <td>0.906709</td>\n",
       "      <td>0.915063</td>\n",
       "      <td>0.913851</td>\n",
       "      <td>0.902354</td>\n",
       "      <td>0.891171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.877671</td>\n",
       "      <td>0.903940</td>\n",
       "      <td>0.867478</td>\n",
       "      <td>0.886784</td>\n",
       "      <td>0.920214</td>\n",
       "      <td>0.918259</td>\n",
       "      <td>0.903727</td>\n",
       "      <td>0.913966</td>\n",
       "      <td>0.880227</td>\n",
       "      <td>0.832970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.908641</td>\n",
       "      <td>0.883970</td>\n",
       "      <td>0.887080</td>\n",
       "      <td>0.897994</td>\n",
       "      <td>0.869190</td>\n",
       "      <td>0.901180</td>\n",
       "      <td>0.910004</td>\n",
       "      <td>0.908722</td>\n",
       "      <td>0.896581</td>\n",
       "      <td>0.884780</td>\n",
       "      <td>...</td>\n",
       "      <td>0.870546</td>\n",
       "      <td>0.898256</td>\n",
       "      <td>0.859808</td>\n",
       "      <td>0.880154</td>\n",
       "      <td>0.915447</td>\n",
       "      <td>0.913381</td>\n",
       "      <td>0.898031</td>\n",
       "      <td>0.908844</td>\n",
       "      <td>0.873240</td>\n",
       "      <td>0.823515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.902007</td>\n",
       "      <td>0.875670</td>\n",
       "      <td>0.878987</td>\n",
       "      <td>0.890634</td>\n",
       "      <td>0.859918</td>\n",
       "      <td>0.894035</td>\n",
       "      <td>0.903462</td>\n",
       "      <td>0.902093</td>\n",
       "      <td>0.889125</td>\n",
       "      <td>0.876534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861363</td>\n",
       "      <td>0.890913</td>\n",
       "      <td>0.849931</td>\n",
       "      <td>0.871601</td>\n",
       "      <td>0.909281</td>\n",
       "      <td>0.907073</td>\n",
       "      <td>0.890673</td>\n",
       "      <td>0.902223</td>\n",
       "      <td>0.864233</td>\n",
       "      <td>0.811374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.896443</td>\n",
       "      <td>0.868723</td>\n",
       "      <td>0.872211</td>\n",
       "      <td>0.884467</td>\n",
       "      <td>0.852168</td>\n",
       "      <td>0.888048</td>\n",
       "      <td>0.897976</td>\n",
       "      <td>0.896534</td>\n",
       "      <td>0.882879</td>\n",
       "      <td>0.869631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.853685</td>\n",
       "      <td>0.884761</td>\n",
       "      <td>0.841680</td>\n",
       "      <td>0.864444</td>\n",
       "      <td>0.904108</td>\n",
       "      <td>0.901780</td>\n",
       "      <td>0.884508</td>\n",
       "      <td>0.896671</td>\n",
       "      <td>0.856700</td>\n",
       "      <td>0.801262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.890241</td>\n",
       "      <td>0.860993</td>\n",
       "      <td>0.864671</td>\n",
       "      <td>0.877597</td>\n",
       "      <td>0.843554</td>\n",
       "      <td>0.881377</td>\n",
       "      <td>0.891861</td>\n",
       "      <td>0.890337</td>\n",
       "      <td>0.875922</td>\n",
       "      <td>0.861951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.845152</td>\n",
       "      <td>0.877908</td>\n",
       "      <td>0.832518</td>\n",
       "      <td>0.856484</td>\n",
       "      <td>0.898338</td>\n",
       "      <td>0.895879</td>\n",
       "      <td>0.877641</td>\n",
       "      <td>0.890482</td>\n",
       "      <td>0.848326</td>\n",
       "      <td>0.790066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.884073</td>\n",
       "      <td>0.853321</td>\n",
       "      <td>0.857185</td>\n",
       "      <td>0.870772</td>\n",
       "      <td>0.835015</td>\n",
       "      <td>0.874746</td>\n",
       "      <td>0.885777</td>\n",
       "      <td>0.884174</td>\n",
       "      <td>0.869010</td>\n",
       "      <td>0.854327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836691</td>\n",
       "      <td>0.871098</td>\n",
       "      <td>0.823442</td>\n",
       "      <td>0.848586</td>\n",
       "      <td>0.892597</td>\n",
       "      <td>0.890008</td>\n",
       "      <td>0.870818</td>\n",
       "      <td>0.884326</td>\n",
       "      <td>0.840022</td>\n",
       "      <td>0.779011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.879754</td>\n",
       "      <td>0.847959</td>\n",
       "      <td>0.851951</td>\n",
       "      <td>0.865996</td>\n",
       "      <td>0.829053</td>\n",
       "      <td>0.870107</td>\n",
       "      <td>0.881518</td>\n",
       "      <td>0.879859</td>\n",
       "      <td>0.864175</td>\n",
       "      <td>0.848999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830784</td>\n",
       "      <td>0.866334</td>\n",
       "      <td>0.817109</td>\n",
       "      <td>0.843067</td>\n",
       "      <td>0.888576</td>\n",
       "      <td>0.885896</td>\n",
       "      <td>0.866044</td>\n",
       "      <td>0.880017</td>\n",
       "      <td>0.834223</td>\n",
       "      <td>0.771318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.874384</td>\n",
       "      <td>0.841302</td>\n",
       "      <td>0.845452</td>\n",
       "      <td>0.860062</td>\n",
       "      <td>0.821658</td>\n",
       "      <td>0.864340</td>\n",
       "      <td>0.876221</td>\n",
       "      <td>0.874493</td>\n",
       "      <td>0.858166</td>\n",
       "      <td>0.842382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823455</td>\n",
       "      <td>0.860413</td>\n",
       "      <td>0.809259</td>\n",
       "      <td>0.836217</td>\n",
       "      <td>0.883573</td>\n",
       "      <td>0.880781</td>\n",
       "      <td>0.860111</td>\n",
       "      <td>0.874657</td>\n",
       "      <td>0.827028</td>\n",
       "      <td>0.761806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.868926</td>\n",
       "      <td>0.834547</td>\n",
       "      <td>0.838857</td>\n",
       "      <td>0.854035</td>\n",
       "      <td>0.814164</td>\n",
       "      <td>0.858482</td>\n",
       "      <td>0.870837</td>\n",
       "      <td>0.869040</td>\n",
       "      <td>0.852065</td>\n",
       "      <td>0.835669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.816028</td>\n",
       "      <td>0.854400</td>\n",
       "      <td>0.801309</td>\n",
       "      <td>0.829269</td>\n",
       "      <td>0.878486</td>\n",
       "      <td>0.875581</td>\n",
       "      <td>0.854087</td>\n",
       "      <td>0.869210</td>\n",
       "      <td>0.819733</td>\n",
       "      <td>0.752199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.865414</td>\n",
       "      <td>0.830208</td>\n",
       "      <td>0.834619</td>\n",
       "      <td>0.850160</td>\n",
       "      <td>0.809353</td>\n",
       "      <td>0.854714</td>\n",
       "      <td>0.867372</td>\n",
       "      <td>0.865531</td>\n",
       "      <td>0.848142</td>\n",
       "      <td>0.831356</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811260</td>\n",
       "      <td>0.850534</td>\n",
       "      <td>0.796209</td>\n",
       "      <td>0.824806</td>\n",
       "      <td>0.875212</td>\n",
       "      <td>0.872234</td>\n",
       "      <td>0.850213</td>\n",
       "      <td>0.865705</td>\n",
       "      <td>0.815050</td>\n",
       "      <td>0.746050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.859939</td>\n",
       "      <td>0.823452</td>\n",
       "      <td>0.828020</td>\n",
       "      <td>0.844122</td>\n",
       "      <td>0.801871</td>\n",
       "      <td>0.848843</td>\n",
       "      <td>0.861970</td>\n",
       "      <td>0.860060</td>\n",
       "      <td>0.842031</td>\n",
       "      <td>0.824641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.803843</td>\n",
       "      <td>0.844509</td>\n",
       "      <td>0.788282</td>\n",
       "      <td>0.817860</td>\n",
       "      <td>0.870104</td>\n",
       "      <td>0.867014</td>\n",
       "      <td>0.844176</td>\n",
       "      <td>0.860241</td>\n",
       "      <td>0.807764</td>\n",
       "      <td>0.736515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.855729</td>\n",
       "      <td>0.818266</td>\n",
       "      <td>0.822954</td>\n",
       "      <td>0.839482</td>\n",
       "      <td>0.796133</td>\n",
       "      <td>0.844330</td>\n",
       "      <td>0.857816</td>\n",
       "      <td>0.855853</td>\n",
       "      <td>0.837335</td>\n",
       "      <td>0.819486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.798154</td>\n",
       "      <td>0.839880</td>\n",
       "      <td>0.782206</td>\n",
       "      <td>0.812529</td>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.863000</td>\n",
       "      <td>0.839538</td>\n",
       "      <td>0.856039</td>\n",
       "      <td>0.802175</td>\n",
       "      <td>0.729225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.851765</td>\n",
       "      <td>0.813391</td>\n",
       "      <td>0.818189</td>\n",
       "      <td>0.835117</td>\n",
       "      <td>0.790743</td>\n",
       "      <td>0.840084</td>\n",
       "      <td>0.853905</td>\n",
       "      <td>0.851893</td>\n",
       "      <td>0.832917</td>\n",
       "      <td>0.814639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.792810</td>\n",
       "      <td>0.835524</td>\n",
       "      <td>0.776502</td>\n",
       "      <td>0.807518</td>\n",
       "      <td>0.862476</td>\n",
       "      <td>0.859219</td>\n",
       "      <td>0.835174</td>\n",
       "      <td>0.852083</td>\n",
       "      <td>0.796923</td>\n",
       "      <td>0.722396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.848051</td>\n",
       "      <td>0.808827</td>\n",
       "      <td>0.813729</td>\n",
       "      <td>0.831028</td>\n",
       "      <td>0.785702</td>\n",
       "      <td>0.836106</td>\n",
       "      <td>0.850239</td>\n",
       "      <td>0.848181</td>\n",
       "      <td>0.828780</td>\n",
       "      <td>0.810103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787812</td>\n",
       "      <td>0.831444</td>\n",
       "      <td>0.771169</td>\n",
       "      <td>0.802829</td>\n",
       "      <td>0.859007</td>\n",
       "      <td>0.855675</td>\n",
       "      <td>0.831086</td>\n",
       "      <td>0.848376</td>\n",
       "      <td>0.792011</td>\n",
       "      <td>0.716025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.845057</td>\n",
       "      <td>0.805154</td>\n",
       "      <td>0.810138</td>\n",
       "      <td>0.827734</td>\n",
       "      <td>0.781647</td>\n",
       "      <td>0.832901</td>\n",
       "      <td>0.847285</td>\n",
       "      <td>0.845190</td>\n",
       "      <td>0.825447</td>\n",
       "      <td>0.806451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783791</td>\n",
       "      <td>0.828158</td>\n",
       "      <td>0.766882</td>\n",
       "      <td>0.799055</td>\n",
       "      <td>0.856210</td>\n",
       "      <td>0.852818</td>\n",
       "      <td>0.827794</td>\n",
       "      <td>0.845389</td>\n",
       "      <td>0.788058</td>\n",
       "      <td>0.710912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.842184</td>\n",
       "      <td>0.801631</td>\n",
       "      <td>0.806694</td>\n",
       "      <td>0.824574</td>\n",
       "      <td>0.777760</td>\n",
       "      <td>0.829825</td>\n",
       "      <td>0.844448</td>\n",
       "      <td>0.842318</td>\n",
       "      <td>0.822249</td>\n",
       "      <td>0.802948</td>\n",
       "      <td>...</td>\n",
       "      <td>0.779937</td>\n",
       "      <td>0.825004</td>\n",
       "      <td>0.762775</td>\n",
       "      <td>0.795436</td>\n",
       "      <td>0.853525</td>\n",
       "      <td>0.850075</td>\n",
       "      <td>0.824634</td>\n",
       "      <td>0.842520</td>\n",
       "      <td>0.784269</td>\n",
       "      <td>0.706020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.839428</td>\n",
       "      <td>0.798256</td>\n",
       "      <td>0.803395</td>\n",
       "      <td>0.821545</td>\n",
       "      <td>0.774039</td>\n",
       "      <td>0.826877</td>\n",
       "      <td>0.841729</td>\n",
       "      <td>0.839565</td>\n",
       "      <td>0.819184</td>\n",
       "      <td>0.799593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.776247</td>\n",
       "      <td>0.821982</td>\n",
       "      <td>0.758844</td>\n",
       "      <td>0.791970</td>\n",
       "      <td>0.850950</td>\n",
       "      <td>0.847445</td>\n",
       "      <td>0.821606</td>\n",
       "      <td>0.839770</td>\n",
       "      <td>0.780641</td>\n",
       "      <td>0.701346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.834999</td>\n",
       "      <td>0.792838</td>\n",
       "      <td>0.798096</td>\n",
       "      <td>0.816678</td>\n",
       "      <td>0.768070</td>\n",
       "      <td>0.822139</td>\n",
       "      <td>0.837356</td>\n",
       "      <td>0.835139</td>\n",
       "      <td>0.814261</td>\n",
       "      <td>0.794206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770327</td>\n",
       "      <td>0.817126</td>\n",
       "      <td>0.752540</td>\n",
       "      <td>0.786407</td>\n",
       "      <td>0.846808</td>\n",
       "      <td>0.843216</td>\n",
       "      <td>0.816741</td>\n",
       "      <td>0.835349</td>\n",
       "      <td>0.774820</td>\n",
       "      <td>0.693866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.832827</td>\n",
       "      <td>0.790185</td>\n",
       "      <td>0.795502</td>\n",
       "      <td>0.814293</td>\n",
       "      <td>0.765149</td>\n",
       "      <td>0.819818</td>\n",
       "      <td>0.835213</td>\n",
       "      <td>0.832969</td>\n",
       "      <td>0.811848</td>\n",
       "      <td>0.791568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767430</td>\n",
       "      <td>0.814746</td>\n",
       "      <td>0.749457</td>\n",
       "      <td>0.783683</td>\n",
       "      <td>0.844778</td>\n",
       "      <td>0.841142</td>\n",
       "      <td>0.814357</td>\n",
       "      <td>0.833182</td>\n",
       "      <td>0.771971</td>\n",
       "      <td>0.690214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.828917</td>\n",
       "      <td>0.785411</td>\n",
       "      <td>0.790832</td>\n",
       "      <td>0.810000</td>\n",
       "      <td>0.759897</td>\n",
       "      <td>0.815638</td>\n",
       "      <td>0.831352</td>\n",
       "      <td>0.829062</td>\n",
       "      <td>0.807506</td>\n",
       "      <td>0.786822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762221</td>\n",
       "      <td>0.810463</td>\n",
       "      <td>0.743916</td>\n",
       "      <td>0.778783</td>\n",
       "      <td>0.841119</td>\n",
       "      <td>0.837406</td>\n",
       "      <td>0.810065</td>\n",
       "      <td>0.829279</td>\n",
       "      <td>0.766847</td>\n",
       "      <td>0.683661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.824932</td>\n",
       "      <td>0.780555</td>\n",
       "      <td>0.786081</td>\n",
       "      <td>0.805629</td>\n",
       "      <td>0.754558</td>\n",
       "      <td>0.811380</td>\n",
       "      <td>0.827418</td>\n",
       "      <td>0.825080</td>\n",
       "      <td>0.803085</td>\n",
       "      <td>0.781992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756925</td>\n",
       "      <td>0.806101</td>\n",
       "      <td>0.738287</td>\n",
       "      <td>0.773799</td>\n",
       "      <td>0.837390</td>\n",
       "      <td>0.833599</td>\n",
       "      <td>0.805696</td>\n",
       "      <td>0.825302</td>\n",
       "      <td>0.761637</td>\n",
       "      <td>0.677017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.822289</td>\n",
       "      <td>0.777336</td>\n",
       "      <td>0.782932</td>\n",
       "      <td>0.802731</td>\n",
       "      <td>0.751023</td>\n",
       "      <td>0.808557</td>\n",
       "      <td>0.824809</td>\n",
       "      <td>0.822439</td>\n",
       "      <td>0.800153</td>\n",
       "      <td>0.778792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.753418</td>\n",
       "      <td>0.803209</td>\n",
       "      <td>0.734561</td>\n",
       "      <td>0.770497</td>\n",
       "      <td>0.834916</td>\n",
       "      <td>0.831073</td>\n",
       "      <td>0.802798</td>\n",
       "      <td>0.822664</td>\n",
       "      <td>0.758187</td>\n",
       "      <td>0.672628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.819952</td>\n",
       "      <td>0.774494</td>\n",
       "      <td>0.780151</td>\n",
       "      <td>0.800169</td>\n",
       "      <td>0.747902</td>\n",
       "      <td>0.806062</td>\n",
       "      <td>0.822501</td>\n",
       "      <td>0.820104</td>\n",
       "      <td>0.797563</td>\n",
       "      <td>0.775965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750322</td>\n",
       "      <td>0.800653</td>\n",
       "      <td>0.731272</td>\n",
       "      <td>0.767581</td>\n",
       "      <td>0.832728</td>\n",
       "      <td>0.828839</td>\n",
       "      <td>0.800237</td>\n",
       "      <td>0.820331</td>\n",
       "      <td>0.755140</td>\n",
       "      <td>0.668759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.817757</td>\n",
       "      <td>0.771825</td>\n",
       "      <td>0.777539</td>\n",
       "      <td>0.797764</td>\n",
       "      <td>0.744973</td>\n",
       "      <td>0.803718</td>\n",
       "      <td>0.820333</td>\n",
       "      <td>0.817910</td>\n",
       "      <td>0.795130</td>\n",
       "      <td>0.773312</td>\n",
       "      <td>...</td>\n",
       "      <td>0.747416</td>\n",
       "      <td>0.798252</td>\n",
       "      <td>0.728187</td>\n",
       "      <td>0.764844</td>\n",
       "      <td>0.830671</td>\n",
       "      <td>0.826740</td>\n",
       "      <td>0.797832</td>\n",
       "      <td>0.818140</td>\n",
       "      <td>0.752281</td>\n",
       "      <td>0.665135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.814975</td>\n",
       "      <td>0.768446</td>\n",
       "      <td>0.774232</td>\n",
       "      <td>0.794716</td>\n",
       "      <td>0.741267</td>\n",
       "      <td>0.800749</td>\n",
       "      <td>0.817586</td>\n",
       "      <td>0.815130</td>\n",
       "      <td>0.792048</td>\n",
       "      <td>0.769951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.743739</td>\n",
       "      <td>0.795211</td>\n",
       "      <td>0.724285</td>\n",
       "      <td>0.761378</td>\n",
       "      <td>0.828065</td>\n",
       "      <td>0.824080</td>\n",
       "      <td>0.794786</td>\n",
       "      <td>0.815363</td>\n",
       "      <td>0.748663</td>\n",
       "      <td>0.660557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>0.772627</td>\n",
       "      <td>0.717433</td>\n",
       "      <td>0.724250</td>\n",
       "      <td>0.748492</td>\n",
       "      <td>0.685589</td>\n",
       "      <td>0.755662</td>\n",
       "      <td>0.775750</td>\n",
       "      <td>0.772813</td>\n",
       "      <td>0.745325</td>\n",
       "      <td>0.719205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688473</td>\n",
       "      <td>0.749079</td>\n",
       "      <td>0.665846</td>\n",
       "      <td>0.709123</td>\n",
       "      <td>0.788307</td>\n",
       "      <td>0.783527</td>\n",
       "      <td>0.748574</td>\n",
       "      <td>0.773091</td>\n",
       "      <td>0.694224</td>\n",
       "      <td>0.592846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.771159</td>\n",
       "      <td>0.715678</td>\n",
       "      <td>0.722529</td>\n",
       "      <td>0.746895</td>\n",
       "      <td>0.683683</td>\n",
       "      <td>0.754103</td>\n",
       "      <td>0.774299</td>\n",
       "      <td>0.771346</td>\n",
       "      <td>0.743711</td>\n",
       "      <td>0.717459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.686581</td>\n",
       "      <td>0.747485</td>\n",
       "      <td>0.663852</td>\n",
       "      <td>0.707328</td>\n",
       "      <td>0.786925</td>\n",
       "      <td>0.782119</td>\n",
       "      <td>0.746978</td>\n",
       "      <td>0.771626</td>\n",
       "      <td>0.692358</td>\n",
       "      <td>0.590565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.769661</td>\n",
       "      <td>0.713889</td>\n",
       "      <td>0.720774</td>\n",
       "      <td>0.745265</td>\n",
       "      <td>0.681740</td>\n",
       "      <td>0.752512</td>\n",
       "      <td>0.772818</td>\n",
       "      <td>0.769849</td>\n",
       "      <td>0.742065</td>\n",
       "      <td>0.715678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.684651</td>\n",
       "      <td>0.745859</td>\n",
       "      <td>0.661820</td>\n",
       "      <td>0.705497</td>\n",
       "      <td>0.785516</td>\n",
       "      <td>0.780682</td>\n",
       "      <td>0.745349</td>\n",
       "      <td>0.770130</td>\n",
       "      <td>0.690456</td>\n",
       "      <td>0.588242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.766597</td>\n",
       "      <td>0.710232</td>\n",
       "      <td>0.717186</td>\n",
       "      <td>0.741934</td>\n",
       "      <td>0.677772</td>\n",
       "      <td>0.749259</td>\n",
       "      <td>0.769789</td>\n",
       "      <td>0.766787</td>\n",
       "      <td>0.738699</td>\n",
       "      <td>0.712039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.680710</td>\n",
       "      <td>0.742534</td>\n",
       "      <td>0.657670</td>\n",
       "      <td>0.701757</td>\n",
       "      <td>0.782631</td>\n",
       "      <td>0.777742</td>\n",
       "      <td>0.742018</td>\n",
       "      <td>0.767071</td>\n",
       "      <td>0.686570</td>\n",
       "      <td>0.583505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.762921</td>\n",
       "      <td>0.705851</td>\n",
       "      <td>0.712888</td>\n",
       "      <td>0.737940</td>\n",
       "      <td>0.673022</td>\n",
       "      <td>0.745357</td>\n",
       "      <td>0.766156</td>\n",
       "      <td>0.763113</td>\n",
       "      <td>0.734665</td>\n",
       "      <td>0.707680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675992</td>\n",
       "      <td>0.738547</td>\n",
       "      <td>0.652705</td>\n",
       "      <td>0.697277</td>\n",
       "      <td>0.779170</td>\n",
       "      <td>0.774215</td>\n",
       "      <td>0.738025</td>\n",
       "      <td>0.763402</td>\n",
       "      <td>0.681917</td>\n",
       "      <td>0.577848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.760498</td>\n",
       "      <td>0.702966</td>\n",
       "      <td>0.710057</td>\n",
       "      <td>0.735308</td>\n",
       "      <td>0.669896</td>\n",
       "      <td>0.742787</td>\n",
       "      <td>0.763760</td>\n",
       "      <td>0.760692</td>\n",
       "      <td>0.732006</td>\n",
       "      <td>0.704809</td>\n",
       "      <td>...</td>\n",
       "      <td>0.672887</td>\n",
       "      <td>0.735920</td>\n",
       "      <td>0.649440</td>\n",
       "      <td>0.694327</td>\n",
       "      <td>0.776888</td>\n",
       "      <td>0.771889</td>\n",
       "      <td>0.735394</td>\n",
       "      <td>0.760982</td>\n",
       "      <td>0.678855</td>\n",
       "      <td>0.574134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.756364</td>\n",
       "      <td>0.698051</td>\n",
       "      <td>0.705233</td>\n",
       "      <td>0.730821</td>\n",
       "      <td>0.664574</td>\n",
       "      <td>0.738403</td>\n",
       "      <td>0.759673</td>\n",
       "      <td>0.756561</td>\n",
       "      <td>0.727474</td>\n",
       "      <td>0.699917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.667601</td>\n",
       "      <td>0.731441</td>\n",
       "      <td>0.643883</td>\n",
       "      <td>0.689303</td>\n",
       "      <td>0.772993</td>\n",
       "      <td>0.767920</td>\n",
       "      <td>0.730908</td>\n",
       "      <td>0.756855</td>\n",
       "      <td>0.673640</td>\n",
       "      <td>0.567827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.753536</td>\n",
       "      <td>0.694693</td>\n",
       "      <td>0.701937</td>\n",
       "      <td>0.727753</td>\n",
       "      <td>0.660941</td>\n",
       "      <td>0.735405</td>\n",
       "      <td>0.756877</td>\n",
       "      <td>0.753735</td>\n",
       "      <td>0.724376</td>\n",
       "      <td>0.696575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.663992</td>\n",
       "      <td>0.728379</td>\n",
       "      <td>0.640091</td>\n",
       "      <td>0.685871</td>\n",
       "      <td>0.770328</td>\n",
       "      <td>0.765205</td>\n",
       "      <td>0.727841</td>\n",
       "      <td>0.754032</td>\n",
       "      <td>0.670079</td>\n",
       "      <td>0.563533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.751208</td>\n",
       "      <td>0.691931</td>\n",
       "      <td>0.699226</td>\n",
       "      <td>0.725228</td>\n",
       "      <td>0.657955</td>\n",
       "      <td>0.732938</td>\n",
       "      <td>0.754576</td>\n",
       "      <td>0.751408</td>\n",
       "      <td>0.721826</td>\n",
       "      <td>0.693827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.661026</td>\n",
       "      <td>0.725860</td>\n",
       "      <td>0.636976</td>\n",
       "      <td>0.683049</td>\n",
       "      <td>0.768133</td>\n",
       "      <td>0.762969</td>\n",
       "      <td>0.725317</td>\n",
       "      <td>0.751708</td>\n",
       "      <td>0.667152</td>\n",
       "      <td>0.560009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.748812</td>\n",
       "      <td>0.689092</td>\n",
       "      <td>0.696438</td>\n",
       "      <td>0.722631</td>\n",
       "      <td>0.654887</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>0.752207</td>\n",
       "      <td>0.749014</td>\n",
       "      <td>0.719204</td>\n",
       "      <td>0.691001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.657977</td>\n",
       "      <td>0.723267</td>\n",
       "      <td>0.633776</td>\n",
       "      <td>0.680147</td>\n",
       "      <td>0.765873</td>\n",
       "      <td>0.760668</td>\n",
       "      <td>0.722721</td>\n",
       "      <td>0.749316</td>\n",
       "      <td>0.664144</td>\n",
       "      <td>0.556395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.744463</td>\n",
       "      <td>0.683944</td>\n",
       "      <td>0.691383</td>\n",
       "      <td>0.717920</td>\n",
       "      <td>0.649328</td>\n",
       "      <td>0.725793</td>\n",
       "      <td>0.747906</td>\n",
       "      <td>0.744668</td>\n",
       "      <td>0.714446</td>\n",
       "      <td>0.685877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.652454</td>\n",
       "      <td>0.718564</td>\n",
       "      <td>0.627983</td>\n",
       "      <td>0.674889</td>\n",
       "      <td>0.761771</td>\n",
       "      <td>0.756489</td>\n",
       "      <td>0.718010</td>\n",
       "      <td>0.744974</td>\n",
       "      <td>0.658693</td>\n",
       "      <td>0.549865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.740291</td>\n",
       "      <td>0.679014</td>\n",
       "      <td>0.686541</td>\n",
       "      <td>0.713403</td>\n",
       "      <td>0.644010</td>\n",
       "      <td>0.721377</td>\n",
       "      <td>0.743780</td>\n",
       "      <td>0.740498</td>\n",
       "      <td>0.709885</td>\n",
       "      <td>0.680969</td>\n",
       "      <td>...</td>\n",
       "      <td>0.647170</td>\n",
       "      <td>0.714056</td>\n",
       "      <td>0.622443</td>\n",
       "      <td>0.669854</td>\n",
       "      <td>0.757833</td>\n",
       "      <td>0.752479</td>\n",
       "      <td>0.713495</td>\n",
       "      <td>0.740809</td>\n",
       "      <td>0.653477</td>\n",
       "      <td>0.543638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.737302</td>\n",
       "      <td>0.675486</td>\n",
       "      <td>0.683076</td>\n",
       "      <td>0.710169</td>\n",
       "      <td>0.640209</td>\n",
       "      <td>0.718214</td>\n",
       "      <td>0.740824</td>\n",
       "      <td>0.737511</td>\n",
       "      <td>0.706620</td>\n",
       "      <td>0.677458</td>\n",
       "      <td>...</td>\n",
       "      <td>0.643392</td>\n",
       "      <td>0.710827</td>\n",
       "      <td>0.618485</td>\n",
       "      <td>0.666252</td>\n",
       "      <td>0.755011</td>\n",
       "      <td>0.749606</td>\n",
       "      <td>0.710262</td>\n",
       "      <td>0.737825</td>\n",
       "      <td>0.649747</td>\n",
       "      <td>0.539198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.733872</td>\n",
       "      <td>0.671444</td>\n",
       "      <td>0.679104</td>\n",
       "      <td>0.706460</td>\n",
       "      <td>0.635855</td>\n",
       "      <td>0.714586</td>\n",
       "      <td>0.737431</td>\n",
       "      <td>0.734083</td>\n",
       "      <td>0.702876</td>\n",
       "      <td>0.673433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639065</td>\n",
       "      <td>0.707125</td>\n",
       "      <td>0.613955</td>\n",
       "      <td>0.662125</td>\n",
       "      <td>0.751772</td>\n",
       "      <td>0.746307</td>\n",
       "      <td>0.706553</td>\n",
       "      <td>0.734401</td>\n",
       "      <td>0.645474</td>\n",
       "      <td>0.534126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.731005</td>\n",
       "      <td>0.668070</td>\n",
       "      <td>0.675788</td>\n",
       "      <td>0.703362</td>\n",
       "      <td>0.632224</td>\n",
       "      <td>0.711555</td>\n",
       "      <td>0.734596</td>\n",
       "      <td>0.731219</td>\n",
       "      <td>0.699748</td>\n",
       "      <td>0.670074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635457</td>\n",
       "      <td>0.704032</td>\n",
       "      <td>0.610179</td>\n",
       "      <td>0.658682</td>\n",
       "      <td>0.749064</td>\n",
       "      <td>0.743550</td>\n",
       "      <td>0.703456</td>\n",
       "      <td>0.731539</td>\n",
       "      <td>0.641910</td>\n",
       "      <td>0.529907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.726908</td>\n",
       "      <td>0.663253</td>\n",
       "      <td>0.671054</td>\n",
       "      <td>0.698936</td>\n",
       "      <td>0.627046</td>\n",
       "      <td>0.707224</td>\n",
       "      <td>0.730542</td>\n",
       "      <td>0.727124</td>\n",
       "      <td>0.695281</td>\n",
       "      <td>0.665279</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630309</td>\n",
       "      <td>0.699614</td>\n",
       "      <td>0.604795</td>\n",
       "      <td>0.653767</td>\n",
       "      <td>0.745191</td>\n",
       "      <td>0.739608</td>\n",
       "      <td>0.699031</td>\n",
       "      <td>0.727448</td>\n",
       "      <td>0.636826</td>\n",
       "      <td>0.523904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.723817</td>\n",
       "      <td>0.659624</td>\n",
       "      <td>0.667486</td>\n",
       "      <td>0.695598</td>\n",
       "      <td>0.623147</td>\n",
       "      <td>0.703958</td>\n",
       "      <td>0.727484</td>\n",
       "      <td>0.724035</td>\n",
       "      <td>0.691912</td>\n",
       "      <td>0.661666</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626433</td>\n",
       "      <td>0.696282</td>\n",
       "      <td>0.600745</td>\n",
       "      <td>0.650064</td>\n",
       "      <td>0.742268</td>\n",
       "      <td>0.736633</td>\n",
       "      <td>0.695694</td>\n",
       "      <td>0.724361</td>\n",
       "      <td>0.632997</td>\n",
       "      <td>0.519398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.721414</td>\n",
       "      <td>0.656807</td>\n",
       "      <td>0.664717</td>\n",
       "      <td>0.693006</td>\n",
       "      <td>0.620123</td>\n",
       "      <td>0.701421</td>\n",
       "      <td>0.725107</td>\n",
       "      <td>0.721634</td>\n",
       "      <td>0.689296</td>\n",
       "      <td>0.658861</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623427</td>\n",
       "      <td>0.693695</td>\n",
       "      <td>0.597604</td>\n",
       "      <td>0.647191</td>\n",
       "      <td>0.739996</td>\n",
       "      <td>0.734321</td>\n",
       "      <td>0.693103</td>\n",
       "      <td>0.721963</td>\n",
       "      <td>0.630027</td>\n",
       "      <td>0.515910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.718033</td>\n",
       "      <td>0.652846</td>\n",
       "      <td>0.660822</td>\n",
       "      <td>0.689359</td>\n",
       "      <td>0.615874</td>\n",
       "      <td>0.697851</td>\n",
       "      <td>0.721761</td>\n",
       "      <td>0.718254</td>\n",
       "      <td>0.685616</td>\n",
       "      <td>0.654917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619203</td>\n",
       "      <td>0.690054</td>\n",
       "      <td>0.593194</td>\n",
       "      <td>0.643152</td>\n",
       "      <td>0.736797</td>\n",
       "      <td>0.731065</td>\n",
       "      <td>0.689457</td>\n",
       "      <td>0.718586</td>\n",
       "      <td>0.625853</td>\n",
       "      <td>0.511021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.715815</td>\n",
       "      <td>0.650251</td>\n",
       "      <td>0.658270</td>\n",
       "      <td>0.686968</td>\n",
       "      <td>0.613092</td>\n",
       "      <td>0.695510</td>\n",
       "      <td>0.719566</td>\n",
       "      <td>0.716038</td>\n",
       "      <td>0.683203</td>\n",
       "      <td>0.652333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616437</td>\n",
       "      <td>0.687667</td>\n",
       "      <td>0.590307</td>\n",
       "      <td>0.640506</td>\n",
       "      <td>0.734698</td>\n",
       "      <td>0.728929</td>\n",
       "      <td>0.687066</td>\n",
       "      <td>0.716372</td>\n",
       "      <td>0.623119</td>\n",
       "      <td>0.507826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.713948</td>\n",
       "      <td>0.648069</td>\n",
       "      <td>0.656124</td>\n",
       "      <td>0.684956</td>\n",
       "      <td>0.610754</td>\n",
       "      <td>0.693540</td>\n",
       "      <td>0.717719</td>\n",
       "      <td>0.714172</td>\n",
       "      <td>0.681173</td>\n",
       "      <td>0.650160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614112</td>\n",
       "      <td>0.685658</td>\n",
       "      <td>0.587882</td>\n",
       "      <td>0.638281</td>\n",
       "      <td>0.732931</td>\n",
       "      <td>0.727131</td>\n",
       "      <td>0.685055</td>\n",
       "      <td>0.714508</td>\n",
       "      <td>0.620821</td>\n",
       "      <td>0.505146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.711975</td>\n",
       "      <td>0.645765</td>\n",
       "      <td>0.653858</td>\n",
       "      <td>0.682832</td>\n",
       "      <td>0.608286</td>\n",
       "      <td>0.691460</td>\n",
       "      <td>0.715767</td>\n",
       "      <td>0.712201</td>\n",
       "      <td>0.679029</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.611658</td>\n",
       "      <td>0.683537</td>\n",
       "      <td>0.585324</td>\n",
       "      <td>0.635933</td>\n",
       "      <td>0.731064</td>\n",
       "      <td>0.725232</td>\n",
       "      <td>0.682931</td>\n",
       "      <td>0.712539</td>\n",
       "      <td>0.618396</td>\n",
       "      <td>0.502321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.707232</td>\n",
       "      <td>0.640232</td>\n",
       "      <td>0.648414</td>\n",
       "      <td>0.677725</td>\n",
       "      <td>0.602365</td>\n",
       "      <td>0.686458</td>\n",
       "      <td>0.711073</td>\n",
       "      <td>0.707460</td>\n",
       "      <td>0.673877</td>\n",
       "      <td>0.642356</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605770</td>\n",
       "      <td>0.678439</td>\n",
       "      <td>0.579187</td>\n",
       "      <td>0.630293</td>\n",
       "      <td>0.726571</td>\n",
       "      <td>0.720661</td>\n",
       "      <td>0.677825</td>\n",
       "      <td>0.707802</td>\n",
       "      <td>0.612575</td>\n",
       "      <td>0.495561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.699901</td>\n",
       "      <td>0.631701</td>\n",
       "      <td>0.640019</td>\n",
       "      <td>0.669840</td>\n",
       "      <td>0.593250</td>\n",
       "      <td>0.678733</td>\n",
       "      <td>0.703816</td>\n",
       "      <td>0.700133</td>\n",
       "      <td>0.665923</td>\n",
       "      <td>0.633860</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596705</td>\n",
       "      <td>0.670567</td>\n",
       "      <td>0.569750</td>\n",
       "      <td>0.621602</td>\n",
       "      <td>0.719624</td>\n",
       "      <td>0.713595</td>\n",
       "      <td>0.669942</td>\n",
       "      <td>0.700482</td>\n",
       "      <td>0.603611</td>\n",
       "      <td>0.485205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.693767</td>\n",
       "      <td>0.624583</td>\n",
       "      <td>0.633011</td>\n",
       "      <td>0.663251</td>\n",
       "      <td>0.585657</td>\n",
       "      <td>0.672275</td>\n",
       "      <td>0.697744</td>\n",
       "      <td>0.694003</td>\n",
       "      <td>0.659277</td>\n",
       "      <td>0.626771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.589152</td>\n",
       "      <td>0.663989</td>\n",
       "      <td>0.561898</td>\n",
       "      <td>0.614354</td>\n",
       "      <td>0.713806</td>\n",
       "      <td>0.707679</td>\n",
       "      <td>0.663355</td>\n",
       "      <td>0.694358</td>\n",
       "      <td>0.596140</td>\n",
       "      <td>0.476626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.687651</td>\n",
       "      <td>0.617505</td>\n",
       "      <td>0.626040</td>\n",
       "      <td>0.656689</td>\n",
       "      <td>0.578118</td>\n",
       "      <td>0.665841</td>\n",
       "      <td>0.691689</td>\n",
       "      <td>0.687891</td>\n",
       "      <td>0.652659</td>\n",
       "      <td>0.619720</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581652</td>\n",
       "      <td>0.657437</td>\n",
       "      <td>0.554108</td>\n",
       "      <td>0.607148</td>\n",
       "      <td>0.708002</td>\n",
       "      <td>0.701778</td>\n",
       "      <td>0.656794</td>\n",
       "      <td>0.688251</td>\n",
       "      <td>0.588719</td>\n",
       "      <td>0.468149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.681545</td>\n",
       "      <td>0.610455</td>\n",
       "      <td>0.619095</td>\n",
       "      <td>0.650143</td>\n",
       "      <td>0.570621</td>\n",
       "      <td>0.659422</td>\n",
       "      <td>0.685643</td>\n",
       "      <td>0.681788</td>\n",
       "      <td>0.646059</td>\n",
       "      <td>0.612697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.574193</td>\n",
       "      <td>0.650902</td>\n",
       "      <td>0.546370</td>\n",
       "      <td>0.599975</td>\n",
       "      <td>0.702202</td>\n",
       "      <td>0.695883</td>\n",
       "      <td>0.650250</td>\n",
       "      <td>0.682153</td>\n",
       "      <td>0.581336</td>\n",
       "      <td>0.459762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.672036</td>\n",
       "      <td>0.599512</td>\n",
       "      <td>0.608311</td>\n",
       "      <td>0.639965</td>\n",
       "      <td>0.559008</td>\n",
       "      <td>0.649436</td>\n",
       "      <td>0.676225</td>\n",
       "      <td>0.672284</td>\n",
       "      <td>0.635798</td>\n",
       "      <td>0.601795</td>\n",
       "      <td>...</td>\n",
       "      <td>0.562636</td>\n",
       "      <td>0.640739</td>\n",
       "      <td>0.534400</td>\n",
       "      <td>0.588846</td>\n",
       "      <td>0.693163</td>\n",
       "      <td>0.686698</td>\n",
       "      <td>0.640074</td>\n",
       "      <td>0.672657</td>\n",
       "      <td>0.569894</td>\n",
       "      <td>0.446853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.665766</td>\n",
       "      <td>0.592321</td>\n",
       "      <td>0.601221</td>\n",
       "      <td>0.633263</td>\n",
       "      <td>0.551393</td>\n",
       "      <td>0.642858</td>\n",
       "      <td>0.670014</td>\n",
       "      <td>0.666018</td>\n",
       "      <td>0.629043</td>\n",
       "      <td>0.594630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555056</td>\n",
       "      <td>0.634047</td>\n",
       "      <td>0.526560</td>\n",
       "      <td>0.581537</td>\n",
       "      <td>0.687197</td>\n",
       "      <td>0.680637</td>\n",
       "      <td>0.633374</td>\n",
       "      <td>0.666396</td>\n",
       "      <td>0.562386</td>\n",
       "      <td>0.438444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.656553</td>\n",
       "      <td>0.581791</td>\n",
       "      <td>0.590835</td>\n",
       "      <td>0.623431</td>\n",
       "      <td>0.540264</td>\n",
       "      <td>0.633202</td>\n",
       "      <td>0.660887</td>\n",
       "      <td>0.656811</td>\n",
       "      <td>0.619134</td>\n",
       "      <td>0.584137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.543976</td>\n",
       "      <td>0.624229</td>\n",
       "      <td>0.515119</td>\n",
       "      <td>0.570839</td>\n",
       "      <td>0.678424</td>\n",
       "      <td>0.671727</td>\n",
       "      <td>0.623543</td>\n",
       "      <td>0.657196</td>\n",
       "      <td>0.551407</td>\n",
       "      <td>0.426235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 7043 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5     \\\n",
       "event_at                                                               \n",
       "0         1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1         0.957963  0.946214  0.947705  0.952914  0.939089  0.954428   \n",
       "2         0.943475  0.927833  0.929814  0.936745  0.918380  0.938762   \n",
       "3         0.932104  0.913462  0.915820  0.924076  0.902228  0.926480   \n",
       "4         0.921842  0.900536  0.903227  0.912658  0.887729  0.915407   \n",
       "5         0.913774  0.890403  0.893352  0.903692  0.876384  0.906709   \n",
       "6         0.908641  0.883970  0.887080  0.897994  0.869190  0.901180   \n",
       "7         0.902007  0.875670  0.878987  0.890634  0.859918  0.894035   \n",
       "8         0.896443  0.868723  0.872211  0.884467  0.852168  0.888048   \n",
       "9         0.890241  0.860993  0.864671  0.877597  0.843554  0.881377   \n",
       "10        0.884073  0.853321  0.857185  0.870772  0.835015  0.874746   \n",
       "11        0.879754  0.847959  0.851951  0.865996  0.829053  0.870107   \n",
       "12        0.874384  0.841302  0.845452  0.860062  0.821658  0.864340   \n",
       "13        0.868926  0.834547  0.838857  0.854035  0.814164  0.858482   \n",
       "14        0.865414  0.830208  0.834619  0.850160  0.809353  0.854714   \n",
       "15        0.859939  0.823452  0.828020  0.844122  0.801871  0.848843   \n",
       "16        0.855729  0.818266  0.822954  0.839482  0.796133  0.844330   \n",
       "17        0.851765  0.813391  0.818189  0.835117  0.790743  0.840084   \n",
       "18        0.848051  0.808827  0.813729  0.831028  0.785702  0.836106   \n",
       "19        0.845057  0.805154  0.810138  0.827734  0.781647  0.832901   \n",
       "20        0.842184  0.801631  0.806694  0.824574  0.777760  0.829825   \n",
       "21        0.839428  0.798256  0.803395  0.821545  0.774039  0.826877   \n",
       "22        0.834999  0.792838  0.798096  0.816678  0.768070  0.822139   \n",
       "23        0.832827  0.790185  0.795502  0.814293  0.765149  0.819818   \n",
       "24        0.828917  0.785411  0.790832  0.810000  0.759897  0.815638   \n",
       "25        0.824932  0.780555  0.786081  0.805629  0.754558  0.811380   \n",
       "26        0.822289  0.777336  0.782932  0.802731  0.751023  0.808557   \n",
       "27        0.819952  0.774494  0.780151  0.800169  0.747902  0.806062   \n",
       "28        0.817757  0.771825  0.777539  0.797764  0.744973  0.803718   \n",
       "29        0.814975  0.768446  0.774232  0.794716  0.741267  0.800749   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "43        0.772627  0.717433  0.724250  0.748492  0.685589  0.755662   \n",
       "44        0.771159  0.715678  0.722529  0.746895  0.683683  0.754103   \n",
       "45        0.769661  0.713889  0.720774  0.745265  0.681740  0.752512   \n",
       "46        0.766597  0.710232  0.717186  0.741934  0.677772  0.749259   \n",
       "47        0.762921  0.705851  0.712888  0.737940  0.673022  0.745357   \n",
       "48        0.760498  0.702966  0.710057  0.735308  0.669896  0.742787   \n",
       "49        0.756364  0.698051  0.705233  0.730821  0.664574  0.738403   \n",
       "50        0.753536  0.694693  0.701937  0.727753  0.660941  0.735405   \n",
       "51        0.751208  0.691931  0.699226  0.725228  0.657955  0.732938   \n",
       "52        0.748812  0.689092  0.696438  0.722631  0.654887  0.730399   \n",
       "53        0.744463  0.683944  0.691383  0.717920  0.649328  0.725793   \n",
       "54        0.740291  0.679014  0.686541  0.713403  0.644010  0.721377   \n",
       "55        0.737302  0.675486  0.683076  0.710169  0.640209  0.718214   \n",
       "56        0.733872  0.671444  0.679104  0.706460  0.635855  0.714586   \n",
       "57        0.731005  0.668070  0.675788  0.703362  0.632224  0.711555   \n",
       "58        0.726908  0.663253  0.671054  0.698936  0.627046  0.707224   \n",
       "59        0.723817  0.659624  0.667486  0.695598  0.623147  0.703958   \n",
       "60        0.721414  0.656807  0.664717  0.693006  0.620123  0.701421   \n",
       "61        0.718033  0.652846  0.660822  0.689359  0.615874  0.697851   \n",
       "62        0.715815  0.650251  0.658270  0.686968  0.613092  0.695510   \n",
       "63        0.713948  0.648069  0.656124  0.684956  0.610754  0.693540   \n",
       "64        0.711975  0.645765  0.653858  0.682832  0.608286  0.691460   \n",
       "65        0.707232  0.640232  0.648414  0.677725  0.602365  0.686458   \n",
       "66        0.699901  0.631701  0.640019  0.669840  0.593250  0.678733   \n",
       "67        0.693767  0.624583  0.633011  0.663251  0.585657  0.672275   \n",
       "68        0.687651  0.617505  0.626040  0.656689  0.578118  0.665841   \n",
       "69        0.681545  0.610455  0.619095  0.650143  0.570621  0.659422   \n",
       "70        0.672036  0.599512  0.608311  0.639965  0.559008  0.649436   \n",
       "71        0.665766  0.592321  0.601221  0.633263  0.551393  0.642858   \n",
       "72        0.656553  0.581791  0.590835  0.623431  0.540264  0.633202   \n",
       "\n",
       "              6         7         8         9       ...         7033  \\\n",
       "event_at                                            ...                \n",
       "0         1.000000  1.000000  1.000000  1.000000    ...     1.000000   \n",
       "1         0.958606  0.958001  0.952242  0.946603    ...     0.939746   \n",
       "2         0.944334  0.943526  0.935850  0.928349    ...     0.919250   \n",
       "3         0.933129  0.932165  0.923008  0.914077    ...     0.903261   \n",
       "4         0.923015  0.921912  0.911438  0.901237    ...     0.888906   \n",
       "5         0.915063  0.913851  0.902354  0.891171    ...     0.877671   \n",
       "6         0.910004  0.908722  0.896581  0.884780    ...     0.870546   \n",
       "7         0.903462  0.902093  0.889125  0.876534    ...     0.861363   \n",
       "8         0.897976  0.896534  0.882879  0.869631    ...     0.853685   \n",
       "9         0.891861  0.890337  0.875922  0.861951    ...     0.845152   \n",
       "10        0.885777  0.884174  0.869010  0.854327    ...     0.836691   \n",
       "11        0.881518  0.879859  0.864175  0.848999    ...     0.830784   \n",
       "12        0.876221  0.874493  0.858166  0.842382    ...     0.823455   \n",
       "13        0.870837  0.869040  0.852065  0.835669    ...     0.816028   \n",
       "14        0.867372  0.865531  0.848142  0.831356    ...     0.811260   \n",
       "15        0.861970  0.860060  0.842031  0.824641    ...     0.803843   \n",
       "16        0.857816  0.855853  0.837335  0.819486    ...     0.798154   \n",
       "17        0.853905  0.851893  0.832917  0.814639    ...     0.792810   \n",
       "18        0.850239  0.848181  0.828780  0.810103    ...     0.787812   \n",
       "19        0.847285  0.845190  0.825447  0.806451    ...     0.783791   \n",
       "20        0.844448  0.842318  0.822249  0.802948    ...     0.779937   \n",
       "21        0.841729  0.839565  0.819184  0.799593    ...     0.776247   \n",
       "22        0.837356  0.835139  0.814261  0.794206    ...     0.770327   \n",
       "23        0.835213  0.832969  0.811848  0.791568    ...     0.767430   \n",
       "24        0.831352  0.829062  0.807506  0.786822    ...     0.762221   \n",
       "25        0.827418  0.825080  0.803085  0.781992    ...     0.756925   \n",
       "26        0.824809  0.822439  0.800153  0.778792    ...     0.753418   \n",
       "27        0.822501  0.820104  0.797563  0.775965    ...     0.750322   \n",
       "28        0.820333  0.817910  0.795130  0.773312    ...     0.747416   \n",
       "29        0.817586  0.815130  0.792048  0.769951    ...     0.743739   \n",
       "...            ...       ...       ...       ...    ...          ...   \n",
       "43        0.775750  0.772813  0.745325  0.719205    ...     0.688473   \n",
       "44        0.774299  0.771346  0.743711  0.717459    ...     0.686581   \n",
       "45        0.772818  0.769849  0.742065  0.715678    ...     0.684651   \n",
       "46        0.769789  0.766787  0.738699  0.712039    ...     0.680710   \n",
       "47        0.766156  0.763113  0.734665  0.707680    ...     0.675992   \n",
       "48        0.763760  0.760692  0.732006  0.704809    ...     0.672887   \n",
       "49        0.759673  0.756561  0.727474  0.699917    ...     0.667601   \n",
       "50        0.756877  0.753735  0.724376  0.696575    ...     0.663992   \n",
       "51        0.754576  0.751408  0.721826  0.693827    ...     0.661026   \n",
       "52        0.752207  0.749014  0.719204  0.691001    ...     0.657977   \n",
       "53        0.747906  0.744668  0.714446  0.685877    ...     0.652454   \n",
       "54        0.743780  0.740498  0.709885  0.680969    ...     0.647170   \n",
       "55        0.740824  0.737511  0.706620  0.677458    ...     0.643392   \n",
       "56        0.737431  0.734083  0.702876  0.673433    ...     0.639065   \n",
       "57        0.734596  0.731219  0.699748  0.670074    ...     0.635457   \n",
       "58        0.730542  0.727124  0.695281  0.665279    ...     0.630309   \n",
       "59        0.727484  0.724035  0.691912  0.661666    ...     0.626433   \n",
       "60        0.725107  0.721634  0.689296  0.658861    ...     0.623427   \n",
       "61        0.721761  0.718254  0.685616  0.654917    ...     0.619203   \n",
       "62        0.719566  0.716038  0.683203  0.652333    ...     0.616437   \n",
       "63        0.717719  0.714172  0.681173  0.650160    ...     0.614112   \n",
       "64        0.715767  0.712201  0.679029  0.647866    ...     0.611658   \n",
       "65        0.711073  0.707460  0.673877  0.642356    ...     0.605770   \n",
       "66        0.703816  0.700133  0.665923  0.633860    ...     0.596705   \n",
       "67        0.697744  0.694003  0.659277  0.626771    ...     0.589152   \n",
       "68        0.691689  0.687891  0.652659  0.619720    ...     0.581652   \n",
       "69        0.685643  0.681788  0.646059  0.612697    ...     0.574193   \n",
       "70        0.676225  0.672284  0.635798  0.601795    ...     0.562636   \n",
       "71        0.670014  0.666018  0.629043  0.594630    ...     0.555056   \n",
       "72        0.660887  0.656811  0.619134  0.584137    ...     0.543976   \n",
       "\n",
       "              7034      7035      7036      7037      7038      7039  \\\n",
       "event_at                                                               \n",
       "0         1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1         0.953039  0.934532  0.944381  0.961172  0.960199  0.952932   \n",
       "2         0.936911  0.912347  0.925398  0.947760  0.946461  0.936768   \n",
       "3         0.924273  0.895071  0.910566  0.937222  0.935670  0.924103   \n",
       "4         0.912884  0.879584  0.897232  0.927703  0.925925  0.912690   \n",
       "5         0.903940  0.867478  0.886784  0.920214  0.918259  0.903727   \n",
       "6         0.898256  0.859808  0.880154  0.915447  0.913381  0.898031   \n",
       "7         0.890913  0.849931  0.871601  0.909281  0.907073  0.890673   \n",
       "8         0.884761  0.841680  0.864444  0.904108  0.901780  0.884508   \n",
       "9         0.877908  0.832518  0.856484  0.898338  0.895879  0.877641   \n",
       "10        0.871098  0.823442  0.848586  0.892597  0.890008  0.870818   \n",
       "11        0.866334  0.817109  0.843067  0.888576  0.885896  0.866044   \n",
       "12        0.860413  0.809259  0.836217  0.883573  0.880781  0.860111   \n",
       "13        0.854400  0.801309  0.829269  0.878486  0.875581  0.854087   \n",
       "14        0.850534  0.796209  0.824806  0.875212  0.872234  0.850213   \n",
       "15        0.844509  0.788282  0.817860  0.870104  0.867014  0.844176   \n",
       "16        0.839880  0.782206  0.812529  0.866176  0.863000  0.839538   \n",
       "17        0.835524  0.776502  0.807518  0.862476  0.859219  0.835174   \n",
       "18        0.831444  0.771169  0.802829  0.859007  0.855675  0.831086   \n",
       "19        0.828158  0.766882  0.799055  0.856210  0.852818  0.827794   \n",
       "20        0.825004  0.762775  0.795436  0.853525  0.850075  0.824634   \n",
       "21        0.821982  0.758844  0.791970  0.850950  0.847445  0.821606   \n",
       "22        0.817126  0.752540  0.786407  0.846808  0.843216  0.816741   \n",
       "23        0.814746  0.749457  0.783683  0.844778  0.841142  0.814357   \n",
       "24        0.810463  0.743916  0.778783  0.841119  0.837406  0.810065   \n",
       "25        0.806101  0.738287  0.773799  0.837390  0.833599  0.805696   \n",
       "26        0.803209  0.734561  0.770497  0.834916  0.831073  0.802798   \n",
       "27        0.800653  0.731272  0.767581  0.832728  0.828839  0.800237   \n",
       "28        0.798252  0.728187  0.764844  0.830671  0.826740  0.797832   \n",
       "29        0.795211  0.724285  0.761378  0.828065  0.824080  0.794786   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "43        0.749079  0.665846  0.709123  0.788307  0.783527  0.748574   \n",
       "44        0.747485  0.663852  0.707328  0.786925  0.782119  0.746978   \n",
       "45        0.745859  0.661820  0.705497  0.785516  0.780682  0.745349   \n",
       "46        0.742534  0.657670  0.701757  0.782631  0.777742  0.742018   \n",
       "47        0.738547  0.652705  0.697277  0.779170  0.774215  0.738025   \n",
       "48        0.735920  0.649440  0.694327  0.776888  0.771889  0.735394   \n",
       "49        0.731441  0.643883  0.689303  0.772993  0.767920  0.730908   \n",
       "50        0.728379  0.640091  0.685871  0.770328  0.765205  0.727841   \n",
       "51        0.725860  0.636976  0.683049  0.768133  0.762969  0.725317   \n",
       "52        0.723267  0.633776  0.680147  0.765873  0.760668  0.722721   \n",
       "53        0.718564  0.627983  0.674889  0.761771  0.756489  0.718010   \n",
       "54        0.714056  0.622443  0.669854  0.757833  0.752479  0.713495   \n",
       "55        0.710827  0.618485  0.666252  0.755011  0.749606  0.710262   \n",
       "56        0.707125  0.613955  0.662125  0.751772  0.746307  0.706553   \n",
       "57        0.704032  0.610179  0.658682  0.749064  0.743550  0.703456   \n",
       "58        0.699614  0.604795  0.653767  0.745191  0.739608  0.699031   \n",
       "59        0.696282  0.600745  0.650064  0.742268  0.736633  0.695694   \n",
       "60        0.693695  0.597604  0.647191  0.739996  0.734321  0.693103   \n",
       "61        0.690054  0.593194  0.643152  0.736797  0.731065  0.689457   \n",
       "62        0.687667  0.590307  0.640506  0.734698  0.728929  0.687066   \n",
       "63        0.685658  0.587882  0.638281  0.732931  0.727131  0.685055   \n",
       "64        0.683537  0.585324  0.635933  0.731064  0.725232  0.682931   \n",
       "65        0.678439  0.579187  0.630293  0.726571  0.720661  0.677825   \n",
       "66        0.670567  0.569750  0.621602  0.719624  0.713595  0.669942   \n",
       "67        0.663989  0.561898  0.614354  0.713806  0.707679  0.663355   \n",
       "68        0.657437  0.554108  0.607148  0.708002  0.701778  0.656794   \n",
       "69        0.650902  0.546370  0.599975  0.702202  0.695883  0.650250   \n",
       "70        0.640739  0.534400  0.588846  0.693163  0.686698  0.640074   \n",
       "71        0.634047  0.526560  0.581537  0.687197  0.680637  0.633374   \n",
       "72        0.624229  0.515119  0.570839  0.678424  0.671727  0.623543   \n",
       "\n",
       "              7040      7041      7042  \n",
       "event_at                                \n",
       "0         1.000000  1.000000  1.000000  \n",
       "1         0.958058  0.941048  0.916638  \n",
       "2         0.943603  0.920976  0.888760  \n",
       "3         0.932257  0.905312  0.867184  \n",
       "4         0.922016  0.891242  0.847943  \n",
       "5         0.913966  0.880227  0.832970  \n",
       "6         0.908844  0.873240  0.823515  \n",
       "7         0.902223  0.864233  0.811374  \n",
       "8         0.896671  0.856700  0.801262  \n",
       "9         0.890482  0.848326  0.790066  \n",
       "10        0.884326  0.840022  0.779011  \n",
       "11        0.880017  0.834223  0.771318  \n",
       "12        0.874657  0.827028  0.761806  \n",
       "13        0.869210  0.819733  0.752199  \n",
       "14        0.865705  0.815050  0.746050  \n",
       "15        0.860241  0.807764  0.736515  \n",
       "16        0.856039  0.802175  0.729225  \n",
       "17        0.852083  0.796923  0.722396  \n",
       "18        0.848376  0.792011  0.716025  \n",
       "19        0.845389  0.788058  0.710912  \n",
       "20        0.842520  0.784269  0.706020  \n",
       "21        0.839770  0.780641  0.701346  \n",
       "22        0.835349  0.774820  0.693866  \n",
       "23        0.833182  0.771971  0.690214  \n",
       "24        0.829279  0.766847  0.683661  \n",
       "25        0.825302  0.761637  0.677017  \n",
       "26        0.822664  0.758187  0.672628  \n",
       "27        0.820331  0.755140  0.668759  \n",
       "28        0.818140  0.752281  0.665135  \n",
       "29        0.815363  0.748663  0.660557  \n",
       "...            ...       ...       ...  \n",
       "43        0.773091  0.694224  0.592846  \n",
       "44        0.771626  0.692358  0.590565  \n",
       "45        0.770130  0.690456  0.588242  \n",
       "46        0.767071  0.686570  0.583505  \n",
       "47        0.763402  0.681917  0.577848  \n",
       "48        0.760982  0.678855  0.574134  \n",
       "49        0.756855  0.673640  0.567827  \n",
       "50        0.754032  0.670079  0.563533  \n",
       "51        0.751708  0.667152  0.560009  \n",
       "52        0.749316  0.664144  0.556395  \n",
       "53        0.744974  0.658693  0.549865  \n",
       "54        0.740809  0.653477  0.543638  \n",
       "55        0.737825  0.649747  0.539198  \n",
       "56        0.734401  0.645474  0.534126  \n",
       "57        0.731539  0.641910  0.529907  \n",
       "58        0.727448  0.636826  0.523904  \n",
       "59        0.724361  0.632997  0.519398  \n",
       "60        0.721963  0.630027  0.515910  \n",
       "61        0.718586  0.625853  0.511021  \n",
       "62        0.716372  0.623119  0.507826  \n",
       "63        0.714508  0.620821  0.505146  \n",
       "64        0.712539  0.618396  0.502321  \n",
       "65        0.707802  0.612575  0.495561  \n",
       "66        0.700482  0.603611  0.485205  \n",
       "67        0.694358  0.596140  0.476626  \n",
       "68        0.688251  0.588719  0.468149  \n",
       "69        0.682153  0.581336  0.459762  \n",
       "70        0.672657  0.569894  0.446853  \n",
       "71        0.666396  0.562386  0.438444  \n",
       "72        0.657196  0.551407  0.426235  \n",
       "\n",
       "[73 rows x 7043 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dfp2.drop([\"tenure\", \"b_Churn\"], axis=1)\n",
    "cph.predict_partial_hazard(X)\n",
    "cph.predict_survival_function(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>b_Churn</th>\n",
       "      <th>b_MultipleLines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>0.265370</td>\n",
       "      <td>0.421837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>0.441561</td>\n",
       "      <td>0.493888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges      b_Churn  \\\n",
       "count    7043.000000  7043.000000     7043.000000  7043.000000   \n",
       "mean        0.162147    32.371149       64.761692     0.265370   \n",
       "std         0.368612    24.559481       30.090047     0.441561   \n",
       "min         0.000000     0.000000       18.250000     0.000000   \n",
       "25%         0.000000     9.000000       35.500000     0.000000   \n",
       "50%         0.000000    29.000000       70.350000     0.000000   \n",
       "75%         0.000000    55.000000       89.850000     1.000000   \n",
       "max         1.000000    72.000000      118.750000     1.000000   \n",
       "\n",
       "       b_MultipleLines  \n",
       "count      7043.000000  \n",
       "mean          0.421837  \n",
       "std           0.493888  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           0.000000  \n",
       "75%           1.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores [0.614380652824168, 0.589085653975167, 0.5853748670979442]\n",
      "mean 0.596280391299093\n",
      "stD 0.012888162146433015\n"
     ]
    }
   ],
   "source": [
    "from lifelines import CoxPHFitter\n",
    "from lifelines.utils import k_fold_cross_validation\n",
    "\n",
    "\n",
    "cph = CoxPHFitter()\n",
    "scores = k_fold_cross_validation(cph, dfp2, 'tenure', event_col='b_Churn', k=3)\n",
    "print(\"scores\", scores)\n",
    "print(\"mean\", np.mean(scores))\n",
    "print(\"stD\", np.std(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nelson Aalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.NelsonAalenFitter: fitted with 7043 observations, 5174 censored>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines import NelsonAalenFitter\n",
    "naf = NelsonAalenFitter()\n",
    "naf.fit(T, event_observed=E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          NA_estimate\n",
      "timeline             \n",
      "0.0          0.000000\n",
      "1.0          0.055550\n",
      "2.0          0.074896\n",
      "3.0          0.090219\n",
      "4.0          0.104193\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117aabda0>"
      ]
     },
     "execution_count": 212,
     "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": [
    "print(naf.cumulative_hazard_.head())\n",
    "naf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.AalenAdditiveFitter: fitted with 7043 observations, 5174 censored>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Aalen's Additive model\n",
    "from lifelines import AalenAdditiveFitter\n",
    "aaf = AalenAdditiveFitter(fit_intercept=False)\n",
    "aaf.fit(dfp2, 'tenure', event_col='b_Churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117bff978>"
      ]
     },
     "execution_count": 177,
     "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": [
    "X = dfp2.drop(['tenure', 'b_Churn'], axis=1)\n",
    "aaf.predict_survival_function(X.iloc[10:12]).plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117c304a8>"
      ]
     },
     "execution_count": 178,
     "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": [
    "aaf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naf.fit(T[dem], event_observed=E[dem], label=\"Yes\")\n",
    "ax = naf.plot(loc=slice(0, 20))\n",
    "naf.fit(T[~dem], event_observed=E[~dem], label=\"No\")\n",
    "naf.plot(ax=ax, loc=slice(0, 20))\n",
    "plt.title(\"Cumulative hazard function of different lines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = 3.\n",
    "naf.fit(T[dem], event_observed=E[dem], label=\"Yes\")\n",
    "ax = naf.plot_hazard(bandwidth=b)\n",
    "naf.fit(T[~dem], event_observed=E[~dem], label=\"No\")\n",
    "naf.plot_hazard(ax=ax, bandwidth=b)\n",
    "plt.title(\"Hazard function of different lines | bandwidth=%.1f\"%b);\n",
    "plt.ylim(0, 0.04)\n",
    "plt.xlim(0, 75);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.595248847921206\n",
      "0.5957145171827003\n",
      "0.5972051728832404\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#create the three models we'd like to compare.\n",
    "aaf_1 = AalenAdditiveFitter(coef_penalizer=0.5)\n",
    "aaf_2 = AalenAdditiveFitter(coef_penalizer=10)\n",
    "cph = CoxPHFitter()\n",
    "\n",
    "print(np.mean(k_fold_cross_validation(cph, dfp2, duration_col='tenure', event_col='b_Churn')))\n",
    "print(np.mean(k_fold_cross_validation(aaf_1, dfp2, duration_col='tenure', event_col='b_Churn')))\n",
    "print(np.mean(k_fold_cross_validation(aaf_2, dfp2, duration_col='tenure', event_col='b_Churn')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manual Checks - KM"
   ]
  },
  {
   "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": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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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\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": "markdown",
   "metadata": {},
   "source": [
    "# Log rank test & Hazard score"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "# KM plots by Partner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "# KM plots by Lines"
   ]
  },
  {
   "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": 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
}