poster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "This is a data analysis for \n",
    "\"Anxiety and Depression Rates Between Women with and without Endometriosis\" \n",
    "presented in a 2023 poster section for CENTER FOR RESEARCH IN WOMEN’S HEALTH at Cedars Sinai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import util\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from pathlib import Path\n",
    "from statannotations.Annotator import Annotator\n",
    "from upsetplot import UpSet,from_memberships\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import from RedCap API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_name = \"poster\"\n",
    "config_json = util.get_config_json(project_name)\n",
    "table_df = util.get_data(config_json)\n",
    "table_df = table_df.apply(pd.to_numeric, downcast=\"integer\", errors=\"ignore\").fillna(0)\n",
    "data_df = util.get_data_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract and Recode RedCap Variables for Downstream Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_detail_map = {1:\"confirmed\",\n",
    "                   2:\"confirmed\",\n",
    "                   3:\"suspected\",\n",
    "                   4:\"not found\"}\n",
    "table_df = (\n",
    "    table_df.assign(type_detail=table_df.type_detail.map(type_detail_map))\n",
    "    .assign(self_anxiety=np.where(table_df.mh_4_1 == 2, \"Yes\", \"No\"))\n",
    "    .assign(self_depression=np.where(table_df.mh_4_7 == 2, \"Yes\", \"No\"))\n",
    "    .assign(chart_anxiety=np.where(table_df.anxiety_depression___1 == 1,\"Yes\",\"No\"))\n",
    "    .assign(chart_depression=np.where(table_df.anxiety_depression___2 == 1,\"Yes\",\"No\"))\n",
    "    .filter(['type_detail','self_anxiety','self_depression','chart_anxiety','chart_depression'])\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sumamry Table \n",
    "\n",
    "This summary of anxiety and depression responses obtained from self-reported and chart review \n"   
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>type</th>\n",
       "      <th colspan=\"2\" halign=\"left\">chart_anxiety</th>\n",
       "      <th colspan=\"2\" halign=\"left\">chart_depression</th>\n",
       "      <th colspan=\"2\" halign=\"left\">self_anxiety</th>\n",
       "      <th colspan=\"2\" halign=\"left\">self_depression</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>response</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_detail</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>confirmed</th>\n",
       "      <td>226</td>\n",
       "      <td>192</td>\n",
       "      <td>286</td>\n",
       "      <td>132</td>\n",
       "      <td>276</td>\n",
       "      <td>142</td>\n",
       "      <td>240</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not found</th>\n",
       "      <td>95</td>\n",
       "      <td>39</td>\n",
       "      <td>109</td>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "      <td>76</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suspected</th>\n",
       "      <td>34</td>\n",
       "      <td>26</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>42</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Total</th>\n",
       "      <td>355</td>\n",
       "      <td>257</td>\n",
       "      <td>434</td>\n",
       "      <td>178</td>\n",
       "      <td>401</td>\n",
       "      <td>211</td>\n",
       "      <td>358</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type        chart_anxiety      chart_depression      self_anxiety       \\\n",
       "response               No  Yes               No  Yes           No  Yes   \n",
       "type_detail                                                              \n",
       "confirmed             226  192              286  132          276  142   \n",
       "not found              95   39              109   25           83   51   \n",
       "suspected              34   26               39   21           42   18   \n",
       "Grand Total           355  257              434  178          401  211   \n",
       "\n",
       "type        self_depression       \n",
       "response                 No  Yes  \n",
       "type_detail                       \n",
       "confirmed               240  178  \n",
       "not found                76   58  \n",
       "suspected                42   18  \n",
       "Grand Total             358  254  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_df = table_df.pipe(\n",
    "    pd.melt, id_vars=\"type_detail\", var_name=\"type\", value_name=\"response\"\n",
    ")\n",
    "table_df.pipe(\n",
    "    pd.pivot_table,\n",
    "    columns=[\"type\", \"response\"],\n",
    "    index=[\"type_detail\"],\n",
    "    aggfunc=len,\n",
    "    margins=True,\n",
    "    margins_name=\"Grand Total\",\n",
    ").iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "confirmed vs. not found: t-test independent samples, P_val:3.667e-01 t=9.761e-01\n",
      "not found vs. suspected: t-test independent samples, P_val:7.247e-01 t=-3.691e-01\n",
      "confirmed vs. suspected: t-test independent samples, P_val:4.297e-01 t=8.465e-01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f25d33259d0>,\n",
       " [<statannotations.Annotation.Annotation at 0x7f25ec3bfed0>,\n",
       "  <statannotations.Annotation.Annotation at 0x7f25eb17d890>,\n",
       "  <statannotations.Annotation.Annotation at 0x7f25eb8ee490>])"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = (\n",
    "    table_df\n",
    "    .groupby([\"type_detail\", \"type\"])[\"response\"]\n",
    "    .value_counts(normalize=True)\n",
    "    .unstack()\n",
    "    .drop(\"No\", axis=1)\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "ax=sns.barplot(data=plot_df, x=\"type_detail\", y=\"Yes\",order=[\"confirmed\",\"suspected\",\"not found\"],palette=\"Set2\")\n",
    "ann=Annotator(\n",
    "            ax, \n",
    "            pairs=[(\"confirmed\",\"not found\"),(\"confirmed\",\"suspected\"),(\"suspected\",\"not found\")],\n",
    "            data=plot_df,\n",
    "            x=\"type_detail\",\n",
    "            y=\"Yes\"\n",
    "        )\n",
    "ann.configure(test='t-test_ind', text_format='star',loc='inside').apply_and_annotate()\n",
    "            \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breakdown of Anxiety and Depression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f25ec095f90>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAADBCAYAAADSHhG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcM0lEQVR4nO3de5RlZXnn8e+PBhQBZYTGUQFFJDIYCUqLMoKXBF2AOkDECEoQE4dgQggTiZroclg6XvAWxygwoNhRVJREDCIIBgWiiHYrLdAYSAdI7EACKJqgROjmmT/223Ioq6qruvfu09X1/ax1Vu3Le/Z+3lP7Ofs8+3JOqgpJkiRJ0vrbbNwBSJIkSdKmwgJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTC6w5LMniJEes5zL+rK94ZrCutyU5cC1tjk3yuA0VkzYd48iHJKckOXl91tmnJB9Nsue449D8M5p/SQ5IsjzJsiRbDbzetW7zSU5K8ogh45BGrU8+9LEv61OSi5JsN+445hoLrHkqnc2ADVZgVdVbq+pv19LsWMACSxvUOPJhwvo372M5VfXaqrqhj2VJ6+FVwPuqau+qunfIFc1wmz8JsMDSuGywfBjV437lkKr6cR/Lmk8ssOaQJMckuTbJ95J8sk1+bpKrktw8crRkmySXJflukuuSHNqmPzHJ95OcBnwX+BiwVTuq8qlp1vuFJN9pR2COG5l+T5J3tHiuTvKYNv1vkhzThn9vzbInHNHZJ8kVbbmXJHlsm7cI+FSL6cVJzh9Z3wuTfL6/V1Rz2Rjz4c1Jbkzyt8BTRqbvluTLbZv+uyR7tOmLk5zRpt2U5CVt+rFJzkvyReDSJFsnOTvJkiTXjMT51CTfbnFdm2T31vZLre/XJ3lFa3t5kkVt+KjW3+uTnDoS56R5K0002XY22Xv3hOe8Fvgt4K1T5dEMcvKstr+5NMlWSTZvefH81u5dSd7Rhke3+Rcl+WZb7nltPSfSHbT7WpKvJfndJH8+Esv/TPKBAV4+bWIGzIck+XCSG5J8CdhxZN6ky2/b/Qfb/u76JPu26ackOTPJpcAnkixM8tctf5YkeU5r97y2T1nW9jfbpvscdmWbdn2SA1rbW5Ps0Ib/uM27PslJbdqkedv36z/nVJWPOfAAngrcCOzQxh8NLAbOoyuU9wRWtHmbA49swzsAK4AATwQeAJ49stx7ZrDuR7e/WwHXA9u38QJe2obfA7ylDT+mrfMA4KaR5y8GjgC2AK4CFrbprwDObsOXA4vacIC/H2n36TXr8zG/H+PKB2Af4Dq6o+GPbMs6uc27DNi9DT8L+GobXgx8ucW1O7ASeDjd2dqVI/nxTuDoNrxdy52tgb8AXtWmb9ny8GXAWSNxPar9vZzuIMXjgH8GFrb+fxU4rLWZNG99+Jj4mGw7m+a9ezFwxMThKZY7XU6uAvZu8z43khNPBb4PvBC4BtiyTV+zze8AXAls3aa/EXhrG76VB98rtgb+EdiijV8FPG3cr7WPjf8xYD78JvAVYEF77/4xM/usdFYbfi5wfRs+BfgOsFUb/zSwfxveBfh+G/4i8Jw2vE3LydcDb27TFgDbtuFbW36t2f9t3Z6zHHj6dHk7nx+9nD7UBvHrwF9V1V0AVfWjJABfqKoHgBtGjkQHeGeS59J9gHw8XdED8E9VdfUs131iksPb8M50HxJ/CNwHXNimf4dux0dV/VuStwJfAw6vqh9NWN5TgF8FvtL6sAC4feJKq6rSnZk4OsnHgf2AY2YZuzZN48qHA4Dzq+pnAEkuaH+3Af47cF6LA+BhI8/7XIvrH5LcDOzRpn9lJD9eBPyPPHhP18PpdojfBN6cZCfg81X1D0muA97XzkxdWFV/NyHOZwKXV9WdLb5P0e2Ev8AUeStN4iHbGXA3M3jvnoHpcvKWqlrWhr9D9+GNqlre9gdfBParqvsmLPPZdAdWvtFi25Iudx6iqn6a5KvAS5J8n67Qum4d+qD5Z6h8eC7wmapaDdzWtk9Y+2elzwBU1ZVJHpkH75O6oB68FPFAYM+R/dIjk2wLfAP4QNs3fL6qViZZApydZAu6fekyHmp/uv3fTwHSXVF0AHABU+TtfGaBNXeE7sjzRD+f0Aa6630XAvtU1f1JbqX7sAbw01mttLsk40C6HdrPklw+sqz7qx2uAFbz0O3paXRF2GT3UwVYXlX7zSCEj9PtUP8TOK+qVs0mfm2yxpIPzWTr3Qz4cVXtPcPnrBkfXX+Al1XVjRPafj/Jt4AXA5ckeW1VfTXJPsAhwLuSXFpVb5uwrKlMl7fSL1TVTaPbGd1R9pm+d09nupwczeHVdGds13ga3dH9yS5rDd0Bi6NmsP6P0t1v+fd0+xhprQbMB5h8v7K2z0oz2a9sRvf5beK9X+9ulyMeAlyd5MBWqD2Xbl/zySTvrapPTIhnKtPl7bzkPVhzx2XAbyXZHiDJo6dp+yjgjrbjegHwhGna3t+OVky3rLtbcbUH3VHCabVrgQ+mO3V8cpJdJzS5EViYZL/WfoskT23z/gPYdk3DqroNuA14C91pdgnGlw9XAoenuy9kW+ClAFX178AtSV7e4kmSXxt53suTbJZkN+BJdDkw0SXAH6Ydakzy9Pb3ScDNVfUhuiOFe6X7ps2fVdU5wPuAZ0xY1reA5yXZIckC4Cjgimn6Jf2SSbazZzH1e/dszCYn18Tym8D2dEf7P5Rf/lazq4HnJHlya/+IJL/S5k3cr3yL7mqMV9LOAkhrM2A+XAkcmWRBunusXtCmT/dZCbpLBkmyP/CTqvrJJMu+FDhhpA97t7+7VdV1VXUqsBTYI8kT6PLyLLp7kifuV64EDmu5tTVwODDx6gk1HrmcI9rlEe8Arkiymu4a9Kl8CvhikqXAMrqjdFM5E7g2yXer6lWTzP8ycHySa+mSfdrLqZI8DDgLeE1V3Zbk9XSnnH99pC/3pfsCgg8leRTddvhBuut5FwNnJLmXB4+6fIruGmS/HU3A+PKhqr6b5LNtOf/EQ3curwJOT/IWumvnzwW+1+bdSFfgPAY4vqr+c+SSjTXeTpcH17Yi61bgJXQ70aOT3A/8K/A2uksA35vkAeB+4HUT4rw9yZ/SXaYb4KKq+ptp+i1N5mn88na2isnfu2djNjlJuhvs3w38RlX9IMmHgf8LvHpNm6q6M8mxwGfafgi6A3M30eX1xUlur6o1H14/R3fPyN2zjF3z11D5cD7dZe/X0W2vV8BaPysB3J3kKrr7gX9nimWfCHykfYbbnK5IOh44qR3cWA3cAFwMHAn8SdvX3MOEWzLa/m8x8O026aNVdU2SJ86yv/NCHrxSRNo4tZ3pNVX1sXHHIs1W2yFdWFV/Ne5YJHWSXAj8eVVdNu5YpNlqt2ucXFVLxx2LJuclgtqoJfkOsBdwzrhjkSTNbUm2S3ITcK/FlaSheAZLALR7WSbb2fxGVf1wQ8cjjZP5IK2/JE8DPjlh8s+r6lnjiEcaJ/NhfrHAkiRJkqSezLlLBA866KCi+ypKHz7m22OdmDM+5vFjnZgzPubxY52YMz7m8WNSc67Auuuuu8YdgjSnmDPS7Jgz0uyYM9JDzbkCS5IkSZI2VhZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknqy+bgD6NPrL/7EuEPoxfsPPmbcIUiSJElaB57BkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknqySf0O1nx22jlfH3cIvfn9o/cfdwiSJEnSOrHAkiRJktSbO05/w7hD6M2Or3vPrJ/jJYKSJEmS1BPPYEmSJKl3r7/4E+MOoTfvP/iYcYegOcQzWJIkSZLUEwssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6MmiBleSgJDcmWZHkTdO0e2aS1UmOGDIeSZIkSRrSYAVWkgXAR4CDgT2Bo5LsOUW7U4FLhopFkiRJkjaEIc9g7QusqKqbq+o+4Fzg0Ena/SHw18AdA8YiSZIkSYMbssB6PPCDkfGVbdovJHk8cDhwxnQLSnJckqVJlt555529ByptaswZaXbMGWl2zBlpakMWWJlkWk0Y/yDwxqpaPd2CqurMqlpUVYsWLlzYW4DSpsqckWbHnJFmx5yRprb5gMteCew8Mr4TcNuENouAc5MA7AAckmRVVX1hwLgkSZIkaRBDFlhLgN2T7Ar8C3Ak8MrRBlW165rhJIuBCy2uJEmSJM1VgxVYVbUqyQl03w64ADi7qpYnOb7Nn/a+K0mSJEmaa4Y8g0VVXQRcNGHapIVVVR07ZCySJEmSNLRBCyxJkqS57o7T3zDuEHqz4+veM+4QpE3ekN8iKEmSJEnzigWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ5YYEmSJElSTyywJEmSJKknFliSJEmS1BN/aFjSvHTaOV8fdwi9+f2j9x93CJIkqfEMliRJkiT1xAJLkiRJknriJYKSJM3A6y/+xLhD6M37Dz5m3CFI0ibLM1iSJEmS1BMLLEmSJEnqiQWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ5YYEmSJElST/wdLGme8jd9JEmS+jfoGawkByW5McmKJG+aZP6hSa5NsizJ0iT7DxmPJEmSJA1psDNYSRYAHwFeCKwEliS5oKpuGGl2GXBBVVWSvYDPAXsMFZMkSZIkDWnIM1j7Aiuq6uaqug84Fzh0tEFV3VNV1Ua3BgpJkiRJmqOGvAfr8cAPRsZXAs+a2CjJ4cC7gB2BF0+2oCTHAccB7LLLLr0HKm1qzBlpdsyZtTvtnK+PO4Te/P7R3pGwvsyZtTNn5q8hz2Blkmm/dIaqqs6vqj2Aw4C3T7agqjqzqhZV1aKFCxf2HKa06TFnpNkxZ6TZMWekqQ1ZYK0Edh4Z3wm4barGVXUlsFuSHQaMSZIkSZIGM+QlgkuA3ZPsCvwLcCTwytEGSZ4M/GP7kotnAFsCPxwwJm2i7jj9DeMOoRc7vu494w5BkiRJ62GwAquqViU5AbgEWACcXVXLkxzf5p8BvAw4Jsn9wL3AK0a+9EKSJEmS5pRBf2i4qi4CLpow7YyR4VOBU4eMQZIkSZI2lEF/aFiSJEmS5hMLLEmSJEnqiQWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ6stcBKsluSh7Xh5yc5Mcl2w4cmSZIkSXPLTM5g/TWwOsmTgY8BuwKfHjQqSZIkSZqDZlJgPVBVq4DDgQ9W1f8CHjtsWJIkSZI098ykwLo/yVHAq4EL27QthgtJkiRJkuammRRYrwH2A95RVbck2RU4Z9iwJEmSJGnu2XxtDarqhiRvBHZp47cA7x46MEmSJEmaa2byLYIvBZYBX27jeye5YOjAJEmSJGmumcklgqcA+wI/BqiqZXTfJChJkiRJGjGTAmtVVf1kwrQaIhhJkiRJmsumLLCSXNS+0OL6JK8EFiTZPclfAFdtsAglSZIkaY6Y7gzWYuAS4FbgV4Gf0/3A8E+APxo6MEmSJEmaa6YssKrqc8DTgW2AFwOfBc4F7gb+YINEJ0mSJElzyNq+pv1+4KfAw+gKLe+9kiRJkqQpTFlgJTkI+ABwAfCMqvrZBotKkiRJkuag6c5gvRl4eVUt31DBSJIkSdJcNmWBVVUHbMhAJEmSJGmum8nvYK2zJAcluTHJiiRvmmT+q5Jc2x5XJfm1IeORJEmSpCENVmAlWQB8BDgY2BM4KsmeE5rdAjyvqvYC3g6cOVQ8kiRJkjS0Ic9g7QusqKqbq+o+uq94P3S0QVVdVVV3t9GrgZ0GjEeSJEmSBjVkgfV44Acj4yvbtKn8LnDxZDOSHJdkaZKld955Z48hSpsmc0aaHXNGmh1zRprakAVWJpk26e9oJXkBXYH1xsnmV9WZVbWoqhYtXLiwxxClTZM5I82OOSPNjjkjTW1tPzS8PlYCO4+M7wTcNrFRkr2AjwIHV9UPB4xHkiRJkgY15BmsJcDuSXZNsiVwJN2PFv9Ckl2AzwO/XVU3DRiLJEmSJA1usDNYVbUqyQnAJcAC4OyqWp7k+Db/DOCtwPbAaUkAVlXVoqFikiR17jj9DeMOoRc7vu494w5BkqSHGPISQarqIuCiCdPOGBl+LfDaIWOQJEmSpA1l0B8aliRJkqT5xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJ4MWWEkOSnJjkhVJ3jTJ/D2SfDPJz5OcPGQskiRJkjS0zYdacJIFwEeAFwIrgSVJLqiqG0aa/Qg4EThsqDgkSZIkaUMZ8gzWvsCKqrq5qu4DzgUOHW1QVXdU1RLg/gHjkCRJkqQNYsgC6/HAD0bGV7Zps5bkuCRLkyy98847ewlO2pSZM9LsmDPS7Jgz0tSGLLAyybRalwVV1ZlVtaiqFi1cuHA9w5I2feaMNDvmjDQ75ow0tSELrJXAziPjOwG3Dbg+SZIkSRqrIQusJcDuSXZNsiVwJHDBgOuTJEmSpLEa7FsEq2pVkhOAS4AFwNlVtTzJ8W3+GUn+K7AUeCTwQJKTgD2r6t+HikuSJEmShjJYgQVQVRcBF02YdsbI8L/SXTooSZIkSXPeoD80LEmSJEnziQWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ5YYEmSJElSTyywJEmSJKknFliSJEmS1BMLLEmSJEnqiQWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ5YYEmSJElSTyywJEmSJKknFliSJEmS1BMLLEmSJEnqiQWWJEmSJPXEAkuSJEmSemKBJUmSJEk9scCSJEmSpJ5YYEmSJElSTwYtsJIclOTGJCuSvGmS+UnyoTb/2iTPGDIeSZIkSRrSYAVWkgXAR4CDgT2Bo5LsOaHZwcDu7XEccPpQ8UiSJEnS0IY8g7UvsKKqbq6q+4BzgUMntDkU+ER1rga2S/LYAWOSJEmSpMGkqoZZcHIEcFBVvbaN/zbwrKo6YaTNhcC7q+rrbfwy4I1VtXTCso6jO8MF8BTgxkGCnpkdgLvGuP5xm8/9H3ff76qqg2bS0JzZaMznvsP4+2/OzD3zue8w/v6bM3PPfO47jL//k+bM5gOuMJNMm1jNzaQNVXUmcGYfQa2vJEuratG44xiX+dz/udR3c2bjMJ/7DnOr/+bMxmE+9x3mVv/NmY3DfO47bLz9H/ISwZXAziPjOwG3rUMbSZIkSZoThiywlgC7J9k1yZbAkcAFE9pcABzTvk3w2cBPqur2AWOSJEmSpMEMdolgVa1KcgJwCbAAOLuqlic5vs0/A7gIOARYAfwMeM1Q8fRoozgdPkbzuf/zue/rYz6/bvO572D/19V8ft3mc9/B/q+r+fy6zee+w0ba/8G+5EKSJEmS5ptBf2hYkiRJkuYTCyxJkiRJ6okF1iSSvDfJ8vb3+CTHbIB1Hpvkw0OvZ0NIsneSQ9bheZcn2aBftdle98dNMW+PJMuSXJNktwFj2OD97ps5s+7mUr609ZozPTBn1p05s04xmDPrtk5zxpxZJ0P+DtZc9nvAwqr6+VQNkmxeVas2YExzyd7AIrovMdnYHQtcz+Q/D3AY8DdV9b83aERzkzmz7uZSvoA50xdzZt2ZM/OTObPuzJkNrao2qQdwDHAt8D3gk8ATgMvatMuAXVq7xcCHgKuAm4Ej2vQLgNXAMuAVwCnAyW3e5cA7gSuA17fxPweuBL4PPBP4PPAPwP8Zielo4Nttmf8PWNCmvwa4qS3vLODD69n3rYEvtb5f3+K/FdihzV8EXN6Gn9fiWQZcA2wLPL/15XzgBuAMYLPW/kXAN4HvAucB27Tpz2yv4fdaHx8F/DNw58hruDVwNt1X918DHNqeuxVwbvvffBb4FrBoPfr/xPZ/OAtYDlwKbNXm7Q1c3dZ1PvBfgCOAe+h+fX7Zmrat/SHAvwL/AnytTfvj9rpeD5w0ss7rR553MnDKyPZyantdbgIOGKLf5oz5Ys6YM5gz5ow5Y86YMxtNzowl0QZM4Ke2f8iaDffRwBeBV7fx3wG+MJLE59FdJrknsGJkOfeMDJ/CQ5P4tJF5lwOntuE/oqu0Hws8jO5HlLcH/luLYYvW7jS6N5rHtg1+IbAl8A3Wv8B6GXDWyPijmDqRvwg8pw1vQ3c28/nAfwJPovtq/a+0DX0HugTfurV/I/DWFvfNwDPb9Ee25Rw72he6N76j2/B2bYPeuiXF2W36XsCq9dqYu4RaBezdxj83st5rgee14bcBHxz5H066zgn/+32A61rc29C9STydtSfx+9vwIcDftuFe+23OmC/mjDmDOWPOmDPmjDmz0eTMpnYP1q8Df1VVdwFU1Y+A/YBPt/mfBPYfaf+Fqnqgqm4AHjPDdXx2wviaH0++DlheVbdXd/r6ZmBn4DfoNoAlSZa18ScBz6JLqjur6r5JlrsurgMOTHJqkgOq6ifTtP0G8IEkJwLb1YOn1L9dVTdX1WrgM3Sv17Pp3ui+0frwarqjT08Bbq+qJQBV9e81+an5FwFvas+9HHg4sAvwXOCc9txr6RJtfd1SVcva8HeAJyZ5VOvjFW36X7Z1z8b+wPlV9dOquofuaNgBM3je50djacND9HtdzeecMV865szsmDPmjDkzO+aMOTPvcmZTuwcrQK2lzej80et4M8N1/HTC+JplPDBheQ/Qvb4B/rKq/vQhgSaHzSDWWamqm5LsQ1eRvyvJpXQV+JpC+uEjbd+d5Eut7dVJDlwza+JiWx++UlVHTejDXjPsQ4CXVdWNE54/2frW1+j/YDXdKd8+TLV9jL6+MPIaT4hnNQ/Nt777va7mbc6YL79gzsyOOWPOmDOzY86YM/MuZza1M1iXAb+VZHuAJI+muw71yDb/VcDXxxDTEUl2XBNTkifQXdv5/CTbJ9kCePn6rqh948rPquoc4H3AM+hORe/TmrxspO1uVXVdVZ0KLAX2aLP2TbJrks3ortP9Ot31sc9J8uT23Eck+RXg74HHJXlmm75tks2B/6C7dniNS4A/TMvcJE9v06+k+5+Q5FfpTsn2rh0xujvJmqMav013bTWTxDqVK4HDWt+3Bg4H/g74N2DH9n98GPCSGS5r8H7P0LzNGfNlaubMtMwZc+aXmDPTMmfMmV+yqefMJnUGq6qWJ3kHcEWS1XQ37p0InJ3kT+hu8HvNBo7phiRvAS5tyXE/8AdVdXWSU+huULyd7ibFBeu5uqcB703yQFvP6+iOEnwsyZ/RvXGscVKSF9BV7zcAF9Odsv8m8O62rCvpTr0+kORY4DNtQwV4Szsy8wrgL5JsBdwLHAh8jQdPPb8LeDvwQeDalsy30m3spwMfT3It3Y2M317P/k/n1cAZSR5Bd4nAmu1gcZt+L7BfVd072ZOr6rtJFo/E+NGqugYgydvoXttb6N7c1mZD9nta8zxnzJfpmTOTMGfMmWmYM5MwZ8yZaWyyOZOqjeUMssYtyfPpbhycSaUvzWvmizQ75ow0O+bM3LWpXSIoSZIkSWPjGSxJkiRJ6olnsCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknry/wGz7FeLfKhuCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.FacetGrid(plot_df, col=\"type\")\n",
    "plot.map_dataframe(\n",
    "    sns.barplot,\n",
    "    x=\"type_detail\",\n",
    "    y=\"Yes\",\n",
    "    hue=\"type_detail\",\n",
    "    palette=\"Set2\",\n",
    "    order=[\"confirmed\", \"suspected\", \"not found\"],\n",
    "    dodge=False,\n",
    ")\n",
    "plot.set_titles(\"{col_name}\")\n",
    "plot.set_xlabels(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combined both patients with confirmed and suspected endometriosis as one group to compare with control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f25edb208d0>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAADBCAYAAADSHhG+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAa1UlEQVR4nO3df5QldXnn8ffHQQxB1FUG1wAKQVYWfwR1giECkkRzADVI1AiiiIlh8YQQNrJqVo/r6jEBjcYYFRYNmfgTMYoZEARFkUREZ5BhGEAIQRMnaBgMmqBGGHj2j/qOXNrunu6hqu/c7vfrnHv6W9+qrnpu3fvcuk/9uqkqJEmSJEn33wPGHYAkSZIkLRYWWJIkSZLUEwssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6YoElSZIkST2xwJIkSZKknlhgTbAkK5O84H7O43/3Fc8clvWmJM/cwjTHJfm5hYpJi8c48iHJG5Occn+W2ack70+y77jj0NIzmn9JDkpybZK1SXYYeLlbfM8nOTnJzw4ZhzTq/uRDH9uyPiW5IMnDxh3HpLHAWqLSeQCwYAVWVb2hqj63hcmOAyywtKDGkQ9Tlr9dH/OpqldU1XV9zEu6H44B/rSq9quqHw25oDm+508GLLA0LguWD6N63K4cXlXf62NeS4kF1gRJcmySdUmuTvLB1n1wksuT3Dyyt+TBSS5J8rUk1yQ5ovXvkeT6JO8Fvgb8JbBD26vy4VmW+6kkV7Y9MMeP9N+R5C0tniuSPLL1/22SY1v7f2ye95Q9Ok9N8sU234uSPKqNWwF8uMX07CTnjizvWUk+2d8a1SQbYz68LskNST4HPG6kf68kn2nv6b9Lsk/rX5nkjNZ3Y5LntP7jknw8yXnAxUl2THJWktVJrhqJ8/FJvtriWpdk7zbtp9tzX5/kRW3aS5OsaO2j2/Ndn+S0kTinzVtpquneZ9N9dk/5n1cAvwW8YaY8mkNOvq9tby5OskOS7VpeHNKm+5Mkb2nt0ff8ryf5cpvvx9tyTqLbafeFJF9I8jtJ/mwklt9N8o4BVp8WmQHzIUneneS6JJ8GdhkZN+382/v+nW17tz7J/q3/jUnOTHIx8IEky5N8ouXP6iRPb9M9o21T1rbtzU7pvodd1vrWJzmoTfvNJDu39h+2ceuTnNz6ps3bvtf/xKkqHxPwAB4P3ADs3IYfDqwEPk5XKO8L3NTGbQc8pLV3Bm4CAuwB3AP80sh875jDsh/e/u4ArAce0YYLeG5rvxV4fWs/si3zIODGkf9fCbwAeCBwObC89b8IOKu1LwVWtHaAr49M95HNy/OxtB/jygfgqcA1dHvDH9LmdUobdwmwd2s/Dfh8a68EPtPi2hvYAPwM3dHaDSP58cfAS1r7YS13dgT+Ajim9W/f8vD5wPtG4npo+3sp3U6KnwP+GVjenv/ngee1aabNWx8+pj6me5/N8tm9EnjB1PYM850tJzcB+7Vx54zkxOOB64FnAVcB27f+ze/5nYHLgB1b/2uAN7T2N7n3s2JH4B+BB7bhy4Enjntd+9j2HwPmw28CnwWWtc/u7zG370rva+2DgfWt/UbgSmCHNvwR4MDWfjRwfWufBzy9tR/ccvJVwOta3zJgp9b+Zsuvzdu/Hdv/XAs8eba8XcqPXg4fakH8KvA3VXUbQFX9WxKAT1XVPcB1I3uiA/xxkoPpvkDuSlf0APxTVV0xz2WflOTI1t6d7kvid4E7gfNb/5V0Gz6q6l+TvAH4AnBkVf3blPk9DngC8Nn2HJYB35660KqqdEcmXpLkr4ADgGPnGbsWp3Hlw0HAuVX1Q4Akq9rfBwO/DHy8xQHwoJH/O6fF9Q9Jbgb2af2fHcmPXwd+I/de0/UzdBvELwOvS7Ib8Mmq+ock1wB/2o5MnV9Vfzclzl8ELq2qjS2+D9NthD/FDHkrTeM+7zPgdubw2T0Hs+XkN6pqbWtfSffljaq6tm0PzgMOqKo7p8zzl+h2rHypxbY9Xe7cR1X9IMnngeckuZ6u0LpmK56Dlp6h8uFg4KNVdTdwS3t/wpa/K30UoKouS/KQ3Hud1Kq691TEZwL7jmyXHpJkJ+BLwDvatuGTVbUhyWrgrCQPpNuWruW+DqTb/v0AIN0ZRQcBq5ghb5cyC6zJEbo9z1P9eMo00J3vuxx4alXdleSbdF/WAH4wr4V2p2Q8k26D9sMkl47M665quyuAu7nv++mJdEXYdNdTBbi2qg6YQwh/RbdB/U/g41W1aT7xa9EaSz400y33AcD3qmq/Of7P5uHR5Qd4flXdMGXa65N8BXg2cFGSV1TV55M8FTgc+JMkF1fVm6bMayaz5a30E1V14+j7jG4v+1w/u2czW06O5vDddEdsN3si3d796U5rDd0Oi6PnsPz3011v+XW6bYy0RQPmA0y/XdnSd6W5bFceQPf9beq1X6e20xEPB65I8sxWqB1Mt635YJK3VdUHpsQzk9nydknyGqzJcQnwW0keAZDk4bNM+1Dg1rbh+hXgMbNMe1fbWzHbvG5vxdU+dHsJZ9XOBT6M7tDxKUn2nDLJDcDyJAe06R+Y5PFt3H8AO22esKpuAW4BXk93mF2C8eXDZcCR6a4L2Ql4LkBV/TvwjSQvbPEkyS+M/N8LkzwgyV7Az9PlwFQXAb+ftqsxyZPb358Hbq6qd9HtKXxSujtt/rCqPgT8KfCUKfP6CvCMJDsnWQYcDXxxlucl/ZRp3mdPY+bP7vmYT05ujuU3gUfQ7e1/V376rmZXAE9P8tg2/c8m+W9t3NTtylfozsZ4Me0ogLQlA+bDZcBRSZalu8bqV1r/bN+VoDtlkCQHAt+vqu9PM++LgRNHnsN+7e9eVXVNVZ0GrAH2SfIYurx8H901yVO3K5cBz2u5tSNwJDD17Ak17rmcEO30iLcAX0xyN9056DP5MHBekjXAWrq9dDM5E1iX5GtVdcw04z8DnJBkHV2yz3o6VZIHAe8DXl5VtyR5Fd0h518deS53prsBwbuSPJTuffhOuvN5VwJnJPkR9+51+TDdOcjeHU3A+PKhqr6W5GNtPv/EfTcuxwCnJ3k93bnzZwNXt3E30BU4jwROqKr/HDllY7M30+XBulZkfRN4Dt1G9CVJ7gK+A7yJ7hTAtyW5B7gLeOWUOL+d5I/oTtMNcEFV/e0sz1uazhP56ffZJqb/7J6P+eQk6S6wPxX4tar6VpJ3A38OvGzzNFW1MclxwEfbdgi6HXM30uX1hUm+XVWbv7yeQ3fNyO3zjF1L11D5cC7dae/X0L1fvwhb/K4EcHuSy+muB/7tGeZ9EvCe9h1uO7oi6QTg5LZz427gOuBC4Cjgf7VtzR1MuSSjbf9WAl9tXe+vqquS7DHP57sk5N4zRaRtU9uYXlVVfznuWKT5ahuk86vqb8Ydi6ROkvOBP6uqS8YdizRf7XKNU6pqzbhj0fQ8RVDbtCRXAk8CPjTuWCRJky3Jw5LcCPzI4krSUDyCJQDatSzTbWx+raq+u9DxSONkPkj3X5InAh+c0v3jqnraOOKRxsl8WFossCRJkiSpJxN3iuChhx5adLei9OFjqT22ijnjYwk/too542MJP7aKOeNjCT+mNXEF1m233TbuEKSJYs5I82POSPNjzkj3NXEFliRJkiRtqyywJEmSJKknFliSJEmS1BMLLEmSJEnqiQWWJEmSJPVku3EHMJRXXfiBcYcwUd5+2LHjDkGSJEmaeB7BkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknqyaH8HS5IkSRLcevqrxx3CRNnllW+9X//vESxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8GvU17kkOBPweWAe+vqlNnmO4XgSuAF1XV3wwZkyRJkhbeqy78wLhDmChvP+zYcYegrTTYEawky4D3AIcB+wJHJ9l3hulOAy4aKhZJkiRJWghDniK4P3BTVd1cVXcCZwNHTDPd7wOfAG4dMBZJkiRJGtyQBdauwLdGhje0vp9IsitwJHDGbDNKcnySNUnWbNy4sfdApcXGnJHmx5yR5seckWY2ZIGVafpqyvA7gddU1d2zzaiqzqyqFVW1Yvny5b0FKC1W5ow0P+aMND/mjDSzIW9ysQHYfWR4N+CWKdOsAM5OArAzcHiSTVX1qQHjkiRJkqRBDFlgrQb2TrIn8C/AUcCLRyeoqj03t5OsBM63uJIkSZI0qQYrsKpqU5IT6e4OuAw4q6quTXJCGz/rdVeSJEmSNGkG/R2sqroAuGBK37SFVVUdN2QskiRJkjS0QQssSZKkvt16+qvHHcJE2eWVbx13CNKSMuRdBCVJkiRpSbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktST7cYdgCRJk+hVF35g3CFMlLcfduy4Q5CkBeERLEmSJEnqiQWWJEmSJPXEAkuSJEmSemKBJUmSJEk98SYXkgAv2J8vL9iXJEnT8QiWJEmSJPXEAkuSJEmSejJogZXk0CQ3JLkpyWunGX9EknVJ1iZZk+TAIeORJEmSpCENdg1WkmXAe4BnARuA1UlWVdV1I5NdAqyqqkryJOAcYJ+hYpIkSZKkIQ15BGt/4Kaqurmq7gTOBo4YnaCq7qiqaoM7AoUkSZIkTaghC6xdgW+NDG9offeR5MgkXwc+Dfz2dDNKcnw7hXDNxo0bBwlWWkzMGWl+zBlpfswZaWZDFliZpu+njlBV1blVtQ/wPODN082oqs6sqhVVtWL58uU9hyktPuaMND/mjDQ/5ow0syELrA3A7iPDuwG3zDRxVV0G7JVk5wFjkiRJkqTBDPlDw6uBvZPsCfwLcBTw4tEJkjwW+Md2k4unANsD3x0wJi2AW09/9bhDmCi7vPKt4w5BkiRJPRmswKqqTUlOBC4ClgFnVdW1SU5o488Ang8cm+Qu4EfAi0ZueiFJkiRJE2XII1hU1QXABVP6zhhpnwacNmQMkiRJkrRQBv2hYUmSJElaSiywJEmSJKknFliSJEmS1BMLLEmSJEnqiQWWJEmSJPXEAkuSJEmSerLFAivJXkke1NqHJDkpycOGD02SJEmSJstcjmB9Arg7yWOBvwT2BD4yaFSSJEmSNIHmUmDdU1WbgCOBd1bV/wQeNWxYkiRJkjR55lJg3ZXkaOBlwPmt74HDhSRJkiRJk2kuBdbLgQOAt1TVN5LsCXxo2LAkSZIkafJst6UJquq6JK8BHt2GvwGcOnRgkiRJkjRp5nIXwecCa4HPtOH9kqwaOjBJkiRJmjRzOUXwjcD+wPcAqmot3Z0EJUmSJEkj5lJgbaqq70/pqyGCkSRJkqRJNmOBleSCdkOL9UleDCxLsneSvwAuX7AIJUmSJGlCzHYEayVwEfBN4AnAj+l+YPj7wB8MHZgkSZIkTZoZC6yqOgd4MvBg4NnAx4CzgduB31uQ6CRJkiRpgmzpNu13AT8AHkRXaHntlSRJkiTNYMYCK8mhwDuAVcBTquqHCxaVJEmSJE2g2Y5gvQ54YVVdu1DBSJIkSdIkm7HAqqqDFjIQSZIkSZp0c/kdrK2W5NAkNyS5Kclrpxl/TJJ17XF5kl8YMh5JkiRJGtJgBVaSZcB7gMOAfYGjk+w7ZbJvAM+oqicBbwbOHCoeSZIkSRrakEew9gduqqqbq+pOulu8HzE6QVVdXlW3t8ErgN0GjEeSJEmSBjVkgbUr8K2R4Q2tbya/A1w43YgkxydZk2TNxo0bewxRWpzMGWl+zBlpfswZaWZDFliZpm/a39FK8it0BdZrphtfVWdW1YqqWrF8+fIeQ5QWJ3NGmh9zRpofc0aa2ZZ+aPj+2ADsPjK8G3DL1ImSPAl4P3BYVX13wHgkSZIkaVBDHsFaDeydZM8k2wNH0f1o8U8keTTwSeClVXXjgLFIkiRJ0uAGO4JVVZuSnAhcBCwDzqqqa5Oc0MafAbwBeATw3iQAm6pqxVAxSdK26NbTXz3uECbKLq9867hDkCRpRkOeIkhVXQBcMKXvjJH2K4BXDBmDJEmSJC2UQX9oWJIkSZKWEgssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6YoElSZIkST2xwJIkSZKknlhgSZIkSVJPLLAkSZIkqScWWJIkSZLUEwssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6YoElSZIkST2xwJIkSZKknlhgSZIkSVJPLLAkSZIkqScWWJIkSZLUEwssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6YoElSZIkST2xwJIkSZKkngxaYCU5NMkNSW5K8tppxu+T5MtJfpzklCFjkSRJkqShbTfUjJMsA94DPAvYAKxOsqqqrhuZ7N+Ak4DnDRWHJEmSJC2UIY9g7Q/cVFU3V9WdwNnAEaMTVNWtVbUauGvAOCRJkiRpQQxZYO0KfGtkeEPrm7ckxydZk2TNxo0bewlOWszMGWl+zBlpfswZaWZDFliZpq+2ZkZVdWZVraiqFcuXL7+fYUmLnzkjzY85I82POSPNbMgCawOw+8jwbsAtAy5PkiRJksZqyAJrNbB3kj2TbA8cBawacHmSJEmSNFaD3UWwqjYlORG4CFgGnFVV1yY5oY0/I8l/BdYADwHuSXIysG9V/ftQcUmSJEnSUAYrsACq6gLggil9Z4y0v0N36qAkSZIkTbxBf2hYkiRJkpYSCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeWGBJkiRJUk8ssCRJkiSpJxZYkiRJktQTCyxJkiRJ6okFliRJkiT1xAJLkiRJknpigSVJkiRJPbHAkiRJkqSeDFpgJTk0yQ1Jbkry2mnGJ8m72vh1SZ4yZDySJEmSNKTBCqwky4D3AIcB+wJHJ9l3ymSHAXu3x/HA6UPFI0mSJElDG/II1v7ATVV1c1XdCZwNHDFlmiOAD1TnCuBhSR41YEySJEmSNJhU1TAzTl4AHFpVr2jDLwWeVlUnjkxzPnBqVf19G74EeE1VrZkyr+PpjnABPA64YZCgF8bOwG3jDmKJmvR1f1tVHTqXCc0Z9WTS1705o4U26evenNFCm/R1P23ObDfgAjNN39Rqbi7TUFVnAmf2EdS4JVlTVSvGHcdStJTWvTmjPiyldW/OqA9Lad2bM+rDYl33Q54iuAHYfWR4N+CWrZhGkiRJkibCkAXWamDvJHsm2R44Clg1ZZpVwLHtboK/BHy/qr49YEySJEmSNJjBThGsqk1JTgQuApYBZ1XVtUlOaOPPAC4ADgduAn4IvHyoeLYhi+Jw+oRy3U8mX7fxcd1PJl+38XHdTyZft/FZlOt+sJtcSJIkSdJSM+gPDUuSJEnSUmKBJUmSJEk9scCagyR3DDz/Q5L88izjfyPJa7diviuSvOv+RbdtS3Jckp+bYdw+SdYmuSrJXgPGcGmSRXeL0fvDnNl2mTPbJnNm22XObJvMmW2XOTPs72Bp7g4B7gAunzoiyXZVtYqfvgPjFrUfbF6zxQkn23HAeqa/vf/zgL+tqv+zoBFpIRyCObO1jsOcWYoOwZzZWsdhzixFh2DObK3jWOo5U1VL4gG8BPgqsBb4f3R3NrwDeAtwNXAF8Mg27Z7Al+luNf9m4I7WH+BtdG+aa4AXtf5DgC8C5wA3AqcCx7TlXQPs1aZbDnyizXc18HRgD+A7wL+02A4CVgLvAL4AvJ3ujfruNo/HAJcA69rfR7f+F7a4rgYuG4nr/NZ+Rpv/WuAqYKdxvybTvEZ7ANcD7wOuBS4Gdmjj9muv0TrgXOC/AC9or+EN7XntMDKvw0fW6xda3x+2dbQeOHlkmetH/u8U4I2tfSlwWnsdbwQOav07AGe3WD4GfAVYMe71Z86YM+bM2F8Pc8acMWfMGXPGnFkaBRbw34HzgAe24fcCxwIFPLf1vRV4fWuvAo5t7d8bSeLnA5+l+wB4JPDPwKNasnyvtR/U3jj/t/3PHwDvbO2PAAe29qOB61v7jcApI/GuBM4HlrXh0SQ+D3hZa/828KnWvgbYtbUf1v6OJvF5wNNb+8HAduN+XaZ5nfYANgH7teFzgJe09jrgGa39ppF1eulMCTS6XoGntnW0Y3v+1wJPnkMSv721Dwc+19p/SPezAwBPajEvqg2fOWPOmDPmjDljzpgz5ow5s3WPpXIN1q/RvYirk6xtwz8P3EmXLABX0r2g0O29+Ghrf3BkPgcCH62qu6vqX+n2jPxiG7e6qr5dVT8G/pGuwofujbN5vs8E3t1iWAU8JMlOM8T88aq6e5r+A+g+DDbHdmBrfwlYmeR36T5kpvoS8I4kJ9El+aYZljtu36iqta19JbBHkofSxfzF1v/XwMHznO+BwLlV9YOqugP4JN0eqS355GgsrX0w8CGAqlpH9wGz2Jgz5ow5Mz/mjDljzsyPOWPOLNqcWSrXYAX466r6o/t0JqdUK3WBu7nv+ih+WmZZxo9H2veMDN8zMt8HAAdU1Y+mxDHd/H4wy7JGFUBVnZDkacCzgbVJ9rvPRFWnJvk0XbV/RZJnVtXX57iMhTS6Hu+mO+Tbh5leu03c92YvPzNDPHN5fywm5ow5Y87Mjzljzpgz82POmDOLNmeWyhGsS4AXJNkFIMnDkzxmlum/BBzV2seM9F8GvCjJsiTL6arlr84jjouBEzcPjCTafwAz7S2Z6vIpsf19m9deVfWVqnoDcBuw++g/tfHXVNVpdBdX7jOPuMeqqr4P3J5k816Nl9LtoYK5r7vLgOcl+dkkOwJHAn8H/CuwS5JHJHkQ8Jw5zusYgCRPoDsUvdiYM+aMOTM/5ow5Y87MjzljzizanFkSBVZVXQe8Hrg4yTq6c3UfNcu//AHwe0lWAw8d6T+X7pDj1cDngVdX1XfmEcpJwIok65JcB5zQ+s8Djmy3rdzSodGTgJe35/HSFivA25Jck2Q93Zvs6in/d3KS9UmuBn4EXDiPuLcFL6N7juvoLqp8U+tfCZzR1t2Me1Sq6mtt2q/SXfj4/qq6qqruavP6Ct0pCXPZc3Q68OAWy6uZ3wf5RDBnAHPGnJkHcwYwZ8yZeTBnAHNm0eZM7j0KK0mSJEm6P5bEESxJkiRJWggWWJIkSZLUEwssSZIkSeqJBZYkSZIk9cQCS5IkSZJ6YoElSZIkST2xwJIkSZKknvx/ReAMfDekM90AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = (\n",
    "    table_df.assign(\n",
    "        type_detail=np.where(\n",
    "            table_df.type_detail.str.match(\"confirmed|suspect\"),\n",
    "            \"endometriosis\",\n",
    "            \"not found\",\n",
    "        )\n",
    "    )\n",
    "    .groupby([\"type_detail\", \"type\"])[\"response\"]\n",
    "    .value_counts(normalize=True)\n",
    "    .unstack()\n",
    "    .drop(\"No\", axis=1)\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "\n",
    "plot = sns.FacetGrid(plot_df, col=\"type\")\n",
    "plot.map_dataframe(\n",
    "    sns.barplot,\n",
    "    x=\"type_detail\",\n",
    "    y=\"Yes\",\n",
    "    hue=\"type_detail\",\n",
    "    palette=\"Set2\",\n",
    "    order=[\"endometriosis\", \"not found\"],\n",
    "    dodge=False,\n",
    ")\n",
    "plot.set_titles(\"{col_name}\")\n",
    "plot.set_xlabels(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison between self-report and chart review \n",
    "There is a discrepancy between self-report and chart review anxiety/depression. Particularly, 153 patients self-report to have both anxiety and depression but not found in their medical charts. It highlights the need to differentiate the source of the data which may lead to a different conclusion. Further studies are need to examine and address the discrepancy      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'matrix': <matplotlib.axes._subplots.AxesSubplot at 0x7f25d2d05810>,\n",
       " 'shading': <matplotlib.axes._subplots.AxesSubplot at 0x7f25ec526ed0>,\n",
       " 'totals': <matplotlib.axes._subplots.AxesSubplot at 0x7f25ecf2c6d0>,\n",
       " 'intersections': <matplotlib.axes._subplots.AxesSubplot at 0x7f25d2d4fe50>}"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 672x320 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_df[[\"self_anxiety\", \"self_depression\", \"chart_anxiety\", \"chart_depression\"]] = (\n",
    "    table_df[\n",
    "        [\"self_anxiety\", \"self_depression\", \"chart_anxiety\", \"chart_depression\"]\n",
    "    ].applymap(lambda x: True if x == \"Yes\" else False)\n",
    ")\n",
    "up_plot = table_df.groupby(\n",
    "    [\"self_anxiety\", \"self_depression\", \"chart_anxiety\", \"chart_depression\"]\n",
    ").size()\n",
    "\n",
    "UpSet(up_plot, sort_by=\"cardinality\", sort_categories_by=\"input\").plot()\n",
    "# from_memberships(table_df)"
   ]
  }
 ],
 "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": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}