Joe - maybe pythonic survival rate curve.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# data is from https://docs.google.com/spreadsheets/d/1A5Sm5Zi_zUT7pE_wGbOZcV9XTlOWYABDev-6y8IcA3E/edit#gid=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Treatment</th>\n",
       "      <th>Week</th>\n",
       "      <th>Members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Treatment  Week  Members\n",
       "0         A     1       50\n",
       "1         A     2       40\n",
       "2         A     3       35\n",
       "3         A     4       30\n",
       "4         A     5       22"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_clipboard(sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1143c2908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140da470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(['Week', 'Treatment']).unstack('Treatment').plot(kind='line', stacked=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Members</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Members        \n",
       "Treatment       A   B   C\n",
       "Week                     \n",
       "1              50  50  50\n",
       "2              40  34  23\n",
       "3              35  22  22\n",
       "4              30  13  19\n",
       "5              22   9   5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(['Week', 'Treatment']).unstack('Treatment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146ccf60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114367588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index(['Week', 'Treatment']).unstack('Treatment').plot(kind='bar', stacked=True)\n",
    "# eh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}