covid_venn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import upsetplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Anosmia</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Diarrhoea</th>\n",
       "      <th>Breath shortness</th>\n",
       "      <th>Fever</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Anosmia  Cough  Fatigue  Diarrhoea  Breath shortness  Fever  count\n",
       "0      True  False    False      False             False  False    140\n",
       "1     False   True    False      False             False  False     57\n",
       "2     False   True     True      False             False  False    179\n",
       "3     False   True     True      False              True  False     22\n",
       "4     False  False     True      False              True  False     10\n",
       "5     False  False     True       True             False  False     43\n",
       "6      True  False     True       True             False  False     64\n",
       "7      True  False     True      False             False  False    281\n",
       "8      True   True     True       True             False  False     41\n",
       "9      True   True     True       True              True  False     18\n",
       "10     True   True     True      False              True  False     26\n",
       "11     True  False     True      False             False   True     46\n",
       "12    False  False     True      False             False  False    198\n",
       "13     True  False     True       True             False   True     17\n",
       "14    False  False    False      False             False   True     11\n",
       "15    False  False     True      False             False   True     28\n",
       "16     True   True     True      False             False   True     69\n",
       "17    False   True     True      False             False   True     54\n",
       "18     True   True     True      False              True   True     17\n",
       "19     True   True     True       True              True   True     23\n",
       "20     True   True     True       True             False   True     50\n",
       "21    False  False     True       True             False   True     12\n",
       "22    False   True     True       True             False   True     17\n",
       "23    False   True     True       True              True   True      8\n",
       "24    False   True     True      False              True   True     11\n",
       "25    False   True     True       True              True  False      7\n",
       "26    False   True     True       True             False  False     31\n",
       "27    False   True    False      False              True  False      1\n",
       "28    False  False    False      False              True  False      5\n",
       "29    False  False    False       True             False  False     12\n",
       "30    False   True    False       True             False  False      7\n",
       "31     True   True     True      False             False  False    259"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('covid_venn.csv').fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby(['Anosmia', 'Cough', 'Fatigue', 'Diarrhoea', 'Breath shortness', 'Fever']).agg({'count': 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'matrix': <matplotlib.axes._subplots.AxesSubplot at 0x7fdc59ff8350>,\n",
       " 'shading': <matplotlib.axes._subplots.AxesSubplot at 0x7fdc59ff8a50>,\n",
       " 'totals': <matplotlib.axes._subplots.AxesSubplot at 0x7fdc59f74250>,\n",
       " 'intersections': <matplotlib.axes._subplots.AxesSubplot at 0x7fdc59f3d2d0>}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1183x384 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "upsetplot.plot(grouped['count'], sort_by='cardinality')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}