example_notebooks/BlinkingExperimentResults.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Blinking Experiment - Results\n",
    "\n",
    "You can use this notebook to analyze the data we collected during the workshop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from lifelines import KaplanMeierFitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data and add a 'censored' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# blinking_data = pd.read_csv('../data/Blinking experiment - results.csv')\n",
    "blinking_data = pd.read_csv('~/Downloads/Blinking experiment - results.csv').dropna(subset=['Number of seconds without blinking'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Name/Pseudoname</th>\n",
       "      <th>Number of seconds without blinking</th>\n",
       "      <th>Wearing glasses / contacts / None</th>\n",
       "      <th>censored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Julianna</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kat</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Glasses</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>diana</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Teja</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Glasses</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name/Pseudoname  Number of seconds without blinking  \\\n",
       "0        Julianna                                 6.0   \n",
       "1             Kat                                11.0   \n",
       "2           diana                                 8.0   \n",
       "3            Teja                                21.0   \n",
       "4               6                                12.0   \n",
       "\n",
       "  Wearing glasses / contacts / None  censored  \n",
       "0                               NaN     False  \n",
       "1                           Glasses     False  \n",
       "2                               NaN     False  \n",
       "3                           Glasses     False  \n",
       "4                               NaN     False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blinking_data = blinking_data.assign(\n",
    "    censored=lambda df: df['Number of seconds without blinking'] >= 30)\n",
    "\n",
    "blinking_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival curve using the Kaplan-Meier fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall survival curve for all people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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.fit(blinking_data['Number of seconds without blinking'],\n",
    "        ~blinking_data['censored'],  # note that observed is the opposite of censored, so we need to add the tilde (~)\n",
    "        label='KM estimate for all observations')\n",
    "kmf.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survival curve separated by eyewear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glasses = blinking_data['Wearing glasses / contacts / None'] == 'Glasses'\n",
    "contacts = blinking_data['Wearing glasses / contacts / None'] == 'Contacts'\n",
    "no_eyewear = (blinking_data['Wearing glasses / contacts / None'].isnull() | blinking_data['Wearing glasses / contacts / None'].isin(\n",
    "    ['none', 'None']))\n",
    "\n",
    "kmf.fit(blinking_data.loc[glasses, 'Number of seconds without blinking'],\n",
    "        ~blinking_data.loc[glasses, 'censored'],  # note that observed is the opposite of censored, so we need to add the tilde (~)\n",
    "        label='KM estimate with glasses')\n",
    "ax1 = kmf.plot()\n",
    "\n",
    "kmf.fit(blinking_data.loc[contacts, 'Number of seconds without blinking'],\n",
    "        ~blinking_data.loc[contacts, 'censored'],  # note that observed is the opposite of censored, so we need to add the tilde (~)\n",
    "        label='KM estimate with contacts')\n",
    "ax2 = kmf.plot(ax=ax1)\n",
    "\n",
    "kmf.fit(blinking_data.loc[no_eyewear, 'Number of seconds without blinking'],\n",
    "        ~blinking_data.loc[no_eyewear, 'censored'],  # note that observed is the opposite of censored, so we need to add the tilde (~)\n",
    "        label='KM estimate with no eyewear')\n",
    "kmf.plot(ax=ax2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}