VAERS.IPYNB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAERS\n",
    "[https://github.com/jgarza9788/vaers](https://github.com/jgarza9788/vaers)\n",
    "---- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "\n",
    "# Disclaimer\n",
    "**THIS IS NOT MEDICAL ADVICE**   \n",
    "if you want medical advice please go see your doctor.  \n",
    "\n",
    "please also read the disclaimer on the VAERS data on their website.  \n",
    "[VAERS website](https://vaers.hhs.gov/data.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# get started\n",
    "## 1. get the data\n",
    "\n",
    "download the data from VAERS website  \n",
    "[link to VAERS website](https://vaers.hhs.gov/data/datasets.html?)\n",
    "\n",
    "make sure to download the 2020-2022 data\n",
    "2022VAERSVAX.csv  \n",
    "2022VAERSSYMPTOMS.csv  \n",
    "2022VAERSDATA.csv  \n",
    "2021VAERSVAX.csv  \n",
    "2021VAERSSYMPTOMS.csv  \n",
    "2021VAERSDATA.csv  \n",
    "2020VAERSVAX.csv  \n",
    "2020VAERSSYMPTOMS.csv  \n",
    "2020VAERSDATA.csv \n",
    "\n",
    "\n",
    "## 2. move and unzip\n",
    "move the AllVAERSDataCSVS.zip to VAERS folder and unzip it  \n",
    "\n",
    "## 3. check the files\n",
    "\n",
    "you should have at least 6 files   \n",
    "```\n",
    ".../VAERS/AllVAERSDataCSVS/  \n",
    "    2022VAERSVAX.csv  \n",
    "    2022VAERSSYMPTOMS.csv  \n",
    "    2022VAERSDATA.csv  \n",
    "    ...\n",
    "    2020VAERSVAX.csv  \n",
    "    2020VAERSSYMPTOMS.csv  \n",
    "    2020VAERSDATA.csv  \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE CODE!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## setup/imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import needed libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, sys, re, json\n",
    "from collections import Counter\n",
    "\n",
    "# used for graphs/charts\n",
    "# import plotly.express as px  # too large \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display, HTML # needed for IPYNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set pandas options\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## directory variables\n",
    "\n",
    "DIR (String) : the current directory this file is in  \n",
    "DATAPATH (String) : the location where the data files are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATAPATH:  .\\AllVAERSDataCSVS\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# DIR = os.path.dirname(os.path.realpath(__file__)) #doesn't work due to IPYNB file\n",
    "DIR = '.' #this works in IPYNB\n",
    "DATAPATH = os.path.join(DIR,'AllVAERSDataCSVS')\n",
    "\n",
    "print('DATAPATH: ',DATAPATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving and Loading JSON files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOADING AND SAVING JSON FILES\n",
    "\n",
    "def load_json(path_to_file):\n",
    "    \"\"\"\n",
    "    loads json from file\n",
    "    \"\"\"\n",
    "    with open(path_to_file, 'r') as f:\n",
    "        return json.load(f)\n",
    "\n",
    "def save_json(path_to_file,data):\n",
    "    \"\"\"\n",
    "    writes dict/json to file\n",
    "    \"\"\"\n",
    "    with open(path_to_file, 'w') as f:\n",
    "        json.dump(data, f,indent=4)\n",
    "\n",
    "## test\n",
    "# save_json(os.path.join(DATAPATH,'test.json'),{\"HELLO\":0})\n",
    "# print(load_json(os.path.join(DATAPATH,'test.json')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## processing files\n",
    "\n",
    "the functions flow will process the csv files into one all_data.json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def has_covid(text):\n",
    "    \"\"\"\n",
    "    returns 1 or 0 if text has \\'COVID\\' in it\n",
    "    \"\"\"\n",
    "    if re.search('COVID',text.upper()):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "        \n",
    "def compile_files(directory,files):\n",
    "    \"\"\"\n",
    "    compiles/adds/unions multiple csv files together and returns a dataframe\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame()\n",
    "    for i,f in enumerate(files):\n",
    "        df0 = pd.read_csv(os.path.join(directory,f),encoding='cp1252',low_memory=False)\n",
    "        df = pd.concat([df,df0])\n",
    "    return df\n",
    "\n",
    "def process_to_one_file():\n",
    "    \"\"\"\n",
    "    processes VAERS data from 2020 - 2023, creates all_data.json, and returns a dataframe\n",
    "    \"\"\"\n",
    "    print('process to one file\\n\\tthis might take a while...go get a drink☕🍷🍸🍹🍶🍺\\n')\n",
    "    df_data = compile_files(DATAPATH,[\n",
    "        '2020VAERSDATA.csv',\n",
    "        '2021VAERSDATA.csv',\n",
    "        '2022VAERSDATA.csv',\n",
    "        '2023VAERSDATA.csv'\n",
    "        ])\n",
    "    df_vax = compile_files(DATAPATH,[\n",
    "        '2020VAERSVAX.csv',\n",
    "        '2021VAERSVAX.csv',\n",
    "        '2022VAERSVAX.csv',\n",
    "        '2023VAERSVAX.csv'\n",
    "        ])\n",
    "    df_sym = compile_files(DATAPATH,[\n",
    "        '2020VAERSSYMPTOMS.csv',\n",
    "        '2021VAERSSYMPTOMS.csv',\n",
    "        '2022VAERSSYMPTOMS.csv',\n",
    "        '2023VAERSSYMPTOMS.csv'\n",
    "        ])\n",
    "\n",
    "    # print(\"\"\"\n",
    "    # symptoms are contained in columns (up to 5 symptoms per event)\n",
    "    # we must transform these symptoms into a single list for each event\n",
    "    # \"\"\")\n",
    "    print('dedup-ing Symptoms')\n",
    "    vid = list(df_sym['VAERS_ID'].unique())\n",
    "\n",
    "    symptom_columns = [\n",
    "        'SYMPTOM1',\n",
    "        'SYMPTOM2',\n",
    "        'SYMPTOM3',\n",
    "        'SYMPTOM4',\n",
    "        'SYMPTOM5'\n",
    "    ]\n",
    "\n",
    "    idf_sym = []\n",
    "    for index,v in enumerate(vid):\n",
    "        if index%5000 == 0:\n",
    "            print('{:.2f}'.format(index/len(vid)), end='\\r')\n",
    "        temp = df_sym[df_sym['VAERS_ID'] == v]\n",
    "        temp = temp.to_dict(orient='records')\n",
    "        syms = []\n",
    "        for t in temp:\n",
    "            for sc in symptom_columns:\n",
    "                if isinstance(t[sc],str):\n",
    "                    syms.append(t[sc])\n",
    "        idf_sym.append({'VAERS_ID':v,'SYMPTOMS':syms})\n",
    "    df_sym = pd.DataFrame(idf_sym)\n",
    "\n",
    "    print('merge data')\n",
    "    df = pd.merge(df_data,df_vax,how='outer',on='VAERS_ID')\n",
    "    df = df.drop_duplicates(ignore_index = True)\n",
    "    df = pd.merge(df,df_sym,how='outer',on='VAERS_ID')\n",
    "\n",
    "    df.reset_index()\n",
    "\n",
    "    # creating a new column depending if this is the covid vaccine or not\n",
    "    df['COVID_VAX'] = df['VAX_TYPE'].apply(has_covid)\n",
    "    df = df[df['COVID_VAX'] == 1]\n",
    "    # print(len(df))\n",
    "\n",
    "    #save json file\n",
    "    json_file = os.path.join(DATAPATH,'all_data.json')\n",
    "    save_json(json_file,df.to_dict(orient='records'))\n",
    "    print('saved: ',json_file)\n",
    "\n",
    "    #save out csv file (not needed), but people might like a csv\n",
    "    csv_file = os.path.join(DATAPATH,'all_data.csv') \n",
    "    df.to_csv(csv_file)\n",
    "    print('saved: ',csv_file)\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get data\n",
    "the below will get data from the files or from all_data.json.\n",
    "\n",
    "> please note\n",
    "i am only using 25% of the data below, but you can see the .pdf for how this will look with all the data, or edit and run the notebook on your own.\n",
    "\n",
    "\n",
    "df (DataFrame) :  contains all the datafrom VAERS files listed below\n",
    "* .../VAERS/AllVAERSDataCSVS/  \n",
    "    * 2022VAERSVAX.csv  \n",
    "    * 2022VAERSSYMPTOMS.csv  \n",
    "    * 2022VAERSDATA.csv  \n",
    "    * ...\n",
    "    * 2020VAERSVAX.csv  \n",
    "    * 2020VAERSSYMPTOMS.csv  \n",
    "    * 2020VAERSDATA.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing the 2020-202X files\n",
      "\n",
      "        .../VAERS/AllVAERSDataCSVS/  \n",
      "            20??VAERSVAX.csv  \n",
      "            20??VAERSSYMPTOMS.csv  \n",
      "            20??VAERSDATA.csv  \n",
      "        \n",
      "process to one file\n",
      "\tthis might take a while...go get a drink☕🍷🍸🍹🍶🍺\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dedup-ing Symptoms\n",
      "merge data\n",
      "saved:  .\\AllVAERSDataCSVS\\all_data.json\n",
      "saved:  .\\AllVAERSDataCSVS\\all_data.csv\n",
      "\n",
      "loaded 1,060,660 records/rows\n",
      "\n",
      "columns:\n",
      " ['VAERS_ID', 'RECVDATE', 'STATE', 'AGE_YRS', 'CAGE_YR', 'CAGE_MO', 'SEX', 'RPT_DATE', 'SYMPTOM_TEXT', 'DIED', 'DATEDIED', 'L_THREAT', 'ER_VISIT', 'HOSPITAL', 'HOSPDAYS', 'X_STAY', 'DISABLE', 'RECOVD', 'VAX_DATE', 'ONSET_DATE', 'NUMDAYS', 'LAB_DATA', 'V_ADMINBY', 'V_FUNDBY', 'OTHER_MEDS', 'CUR_ILL', 'HISTORY', 'PRIOR_VAX', 'SPLTTYPE', 'FORM_VERS', 'TODAYS_DATE', 'BIRTH_DEFECT', 'OFC_VISIT', 'ER_ED_VISIT', 'ALLERGIES', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES', 'VAX_ROUTE', 'VAX_SITE', 'VAX_NAME', 'SYMPTOMS', 'COVID_VAX']\n",
      "\n",
      "\n",
      "df.head(10):\n",
      "\n"
     ]
    },
    {
     "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>VAERS_ID</th>\n",
       "      <th>RECVDATE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>AGE_YRS</th>\n",
       "      <th>CAGE_YR</th>\n",
       "      <th>CAGE_MO</th>\n",
       "      <th>SEX</th>\n",
       "      <th>RPT_DATE</th>\n",
       "      <th>SYMPTOM_TEXT</th>\n",
       "      <th>DIED</th>\n",
       "      <th>DATEDIED</th>\n",
       "      <th>L_THREAT</th>\n",
       "      <th>ER_VISIT</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>HOSPDAYS</th>\n",
       "      <th>X_STAY</th>\n",
       "      <th>DISABLE</th>\n",
       "      <th>RECOVD</th>\n",
       "      <th>VAX_DATE</th>\n",
       "      <th>ONSET_DATE</th>\n",
       "      <th>NUMDAYS</th>\n",
       "      <th>LAB_DATA</th>\n",
       "      <th>V_ADMINBY</th>\n",
       "      <th>V_FUNDBY</th>\n",
       "      <th>OTHER_MEDS</th>\n",
       "      <th>CUR_ILL</th>\n",
       "      <th>HISTORY</th>\n",
       "      <th>PRIOR_VAX</th>\n",
       "      <th>SPLTTYPE</th>\n",
       "      <th>FORM_VERS</th>\n",
       "      <th>TODAYS_DATE</th>\n",
       "      <th>BIRTH_DEFECT</th>\n",
       "      <th>OFC_VISIT</th>\n",
       "      <th>ER_ED_VISIT</th>\n",
       "      <th>ALLERGIES</th>\n",
       "      <th>VAX_TYPE</th>\n",
       "      <th>VAX_MANU</th>\n",
       "      <th>VAX_LOT</th>\n",
       "      <th>VAX_DOSE_SERIES</th>\n",
       "      <th>VAX_ROUTE</th>\n",
       "      <th>VAX_SITE</th>\n",
       "      <th>VAX_NAME</th>\n",
       "      <th>SYMPTOMS</th>\n",
       "      <th>COVID_VAX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46308</th>\n",
       "      <td>902418</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>NJ</td>\n",
       "      <td>56.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Patient experienced mild numbness traveling from injection site up and down arm that subsided over 20 minutes.</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>none</td>\n",
       "      <td>PVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>latex</td>\n",
       "      <td>none</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>none</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>EH9899</td>\n",
       "      <td>1</td>\n",
       "      <td>IM</td>\n",
       "      <td>LA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[Hypoaesthesia, Injection site hypoaesthesia]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46327</th>\n",
       "      <td>902440</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>AZ</td>\n",
       "      <td>35.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C/O Headache</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>none</td>\n",
       "      <td>PVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>EH 9899</td>\n",
       "      <td>1</td>\n",
       "      <td>SYR</td>\n",
       "      <td>LA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[Headache]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VAERS_ID    RECVDATE STATE  AGE_YRS  CAGE_YR  CAGE_MO SEX RPT_DATE  \\\n",
       "46308    902418  12/15/2020    NJ    56.00    56.00      NaN   F      NaN   \n",
       "46327    902440  12/15/2020    AZ    35.00    35.00      NaN   F      NaN   \n",
       "\n",
       "                                                                                                         SYMPTOM_TEXT  \\\n",
       "46308  Patient experienced mild numbness traveling from injection site up and down arm that subsided over 20 minutes.   \n",
       "46327                                                                                                    C/O Headache   \n",
       "\n",
       "      DIED DATEDIED L_THREAT ER_VISIT HOSPITAL  HOSPDAYS X_STAY DISABLE  \\\n",
       "46308    N      NaN        N        N        N       NaN    NaN     NaN   \n",
       "46327    N      NaN        N        N        N       NaN    NaN     NaN   \n",
       "\n",
       "      RECOVD    VAX_DATE  ONSET_DATE  NUMDAYS LAB_DATA V_ADMINBY V_FUNDBY  \\\n",
       "46308      Y  12/15/2020  12/15/2020     0.00     none       PVT      NaN   \n",
       "46327      Y  12/15/2020  12/15/2020     0.00     none       PVT      NaN   \n",
       "\n",
       "      OTHER_MEDS CUR_ILL HISTORY PRIOR_VAX SPLTTYPE  FORM_VERS TODAYS_DATE  \\\n",
       "46308      latex    none    none       NaN      NaN          2  12/15/2020   \n",
       "46327        NaN     NaN     NaN       NaN      NaN          2  12/15/2020   \n",
       "\n",
       "      BIRTH_DEFECT OFC_VISIT ER_ED_VISIT ALLERGIES VAX_TYPE         VAX_MANU  \\\n",
       "46308          NaN       NaN         NaN      none  COVID19  PFIZER\\BIONTECH   \n",
       "46327          NaN       NaN         NaN       NaN  COVID19  PFIZER\\BIONTECH   \n",
       "\n",
       "       VAX_LOT VAX_DOSE_SERIES VAX_ROUTE VAX_SITE  \\\n",
       "46308   EH9899               1        IM       LA   \n",
       "46327  EH 9899               1       SYR       LA   \n",
       "\n",
       "                                  VAX_NAME  \\\n",
       "46308  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "46327  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "\n",
       "                                            SYMPTOMS  COVID_VAX  \n",
       "46308  [Hypoaesthesia, Injection site hypoaesthesia]          1  \n",
       "46327                                     [Headache]          1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_data():\n",
    "    \"\"\"\n",
    "    gets the data and returns a dataframe\n",
    "    \"\"\"\n",
    "\n",
    "    all_data = os.path.join(DATAPATH,'all_data.json')\n",
    "\n",
    "    if os.path.isfile(all_data):\n",
    "        print('loading all_data.json (15sec-30sec)')\n",
    "        df = load_json(all_data)\n",
    "        df = pd.DataFrame(df)\n",
    "    else:\n",
    "        print('processing the 2020-202X files')\n",
    "        print(\"\"\"\n",
    "        .../VAERS/AllVAERSDataCSVS/  \n",
    "            20??VAERSVAX.csv  \n",
    "            20??VAERSSYMPTOMS.csv  \n",
    "            20??VAERSDATA.csv  \n",
    "        \"\"\")\n",
    "        df = process_to_one_file()\n",
    "\n",
    "    return df\n",
    "\n",
    "df = get_data() # this will get all the data\n",
    "# df = get_data().sample(int(946527 * 0.25)) # only 25% of the data\n",
    "\n",
    "# assuming null for these is No\n",
    "df.loc[df['DIED'].isna(),'DIED'] = 'N'\n",
    "df.loc[df['L_THREAT'].isna(),'L_THREAT'] = 'N'\n",
    "df.loc[df['ER_VISIT'].isna(),'ER_VISIT'] = 'N'\n",
    "df.loc[df['HOSPITAL'].isna(),'HOSPITAL'] = 'N'\n",
    "\n",
    "print('\\nloaded {:,} records/rows\\n'.format(len(df)))\n",
    "print('columns:\\n',df.columns.to_list())\n",
    "print('\\n\\ndf.head(10):\\n')\n",
    "display(df.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_row(items,column_lengths=[]):\n",
    "    \"\"\"\n",
    "    comes in handly :)\n",
    "    \"\"\"\n",
    "    row = ''\n",
    "    for index,i in enumerate(items):\n",
    "        try:\n",
    "            cl = column_lengths[index]\n",
    "        except IndexError:\n",
    "            cl = 20\n",
    "        row += str(i)[0:cl].ljust(cl)\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gets a list of symptoms\n",
    "\n",
    "df_symptoms (DataFrame) : a list of all the symtoms and the counts of each \n",
    "all_symptoms (list): a list of all the symtoms and the counts of each \n",
    "\n",
    "**note:**   \n",
    "symptoms might be medical jargon or plain english  \n",
    "i.e. \"RASH\",\"ERYTHEMA\", and \"ITCHY RED SKIN\"   \n",
    "would be reported as different items (for now)\n",
    "\n",
    "**note:**  \n",
    "the counts/percentages below are of the symptoms.  \n",
    "and one adverse reaction can have multiple symptoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved:  .\\AllVAERSDataCSVS\\symptoms.csv\n",
      "below are the top 50 symptoms\n"
     ]
    },
    {
     "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>SYMPTOM</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>PERCENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HEADACHE</td>\n",
       "      <td>158297</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PYREXIA</td>\n",
       "      <td>138507</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FATIGUE</td>\n",
       "      <td>138074</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAIN</td>\n",
       "      <td>114083</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COVID-19</td>\n",
       "      <td>112270</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHILLS</td>\n",
       "      <td>110798</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NAUSEA</td>\n",
       "      <td>86364</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PAIN IN EXTREMITY</td>\n",
       "      <td>84990</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DIZZINESS</td>\n",
       "      <td>83903</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SARS-COV-2 TEST POSITIVE</td>\n",
       "      <td>76103</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MYALGIA</td>\n",
       "      <td>60407</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DYSPNOEA</td>\n",
       "      <td>60203</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ARTHRALGIA</td>\n",
       "      <td>55263</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INJECTION SITE PAIN</td>\n",
       "      <td>47810</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RASH</td>\n",
       "      <td>45363</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>COUGH</td>\n",
       "      <td>41818</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ASTHENIA</td>\n",
       "      <td>41146</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PRURITUS</td>\n",
       "      <td>40539</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EXPIRED PRODUCT ADMINISTERED</td>\n",
       "      <td>39683</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>VOMITING</td>\n",
       "      <td>39199</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PRODUCT STORAGE ERROR</td>\n",
       "      <td>38551</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NO ADVERSE EVENT</td>\n",
       "      <td>37138</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SARS-COV-2 TEST</td>\n",
       "      <td>34800</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>DIARRHOEA</td>\n",
       "      <td>31957</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MALAISE</td>\n",
       "      <td>31379</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>INJECTION SITE ERYTHEMA</td>\n",
       "      <td>30502</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>FEELING ABNORMAL</td>\n",
       "      <td>30168</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CHEST PAIN</td>\n",
       "      <td>29750</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>URTICARIA</td>\n",
       "      <td>27563</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PARAESTHESIA</td>\n",
       "      <td>27341</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ERYTHEMA</td>\n",
       "      <td>26654</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>LYMPHADENOPATHY</td>\n",
       "      <td>26583</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>HYPOAESTHESIA</td>\n",
       "      <td>26398</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>HYPERHIDROSIS</td>\n",
       "      <td>26036</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>INJECTION SITE SWELLING</td>\n",
       "      <td>25964</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CONDITION AGGRAVATED</td>\n",
       "      <td>22796</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BLOOD TEST</td>\n",
       "      <td>22088</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PERIPHERAL SWELLING</td>\n",
       "      <td>21852</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>OROPHARYNGEAL PAIN</td>\n",
       "      <td>20739</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>INJECTION SITE PRURITUS</td>\n",
       "      <td>20443</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>DRUG INEFFECTIVE</td>\n",
       "      <td>19413</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CHEST DISCOMFORT</td>\n",
       "      <td>18729</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>VACCINATION SITE PAIN</td>\n",
       "      <td>18719</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TINNITUS</td>\n",
       "      <td>18541</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>PALPITATIONS</td>\n",
       "      <td>18429</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>DEATH</td>\n",
       "      <td>17825</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>SYNCOPE</td>\n",
       "      <td>17676</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>BACK PAIN</td>\n",
       "      <td>17570</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>HEART RATE INCREASED</td>\n",
       "      <td>16854</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>TREMOR</td>\n",
       "      <td>16377</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         SYMPTOM   COUNT  PERCENT\n",
       "0                       HEADACHE  158297     3.48\n",
       "1                        PYREXIA  138507     3.04\n",
       "2                        FATIGUE  138074     3.03\n",
       "3                           PAIN  114083     2.51\n",
       "4                       COVID-19  112270     2.47\n",
       "5                         CHILLS  110798     2.43\n",
       "6                         NAUSEA   86364     1.90\n",
       "7              PAIN IN EXTREMITY   84990     1.87\n",
       "8                      DIZZINESS   83903     1.84\n",
       "9       SARS-COV-2 TEST POSITIVE   76103     1.67\n",
       "10                       MYALGIA   60407     1.33\n",
       "11                      DYSPNOEA   60203     1.32\n",
       "12                    ARTHRALGIA   55263     1.21\n",
       "13           INJECTION SITE PAIN   47810     1.05\n",
       "14                          RASH   45363     1.00\n",
       "15                         COUGH   41818     0.92\n",
       "16                      ASTHENIA   41146     0.90\n",
       "17                      PRURITUS   40539     0.89\n",
       "18  EXPIRED PRODUCT ADMINISTERED   39683     0.87\n",
       "19                      VOMITING   39199     0.86\n",
       "20         PRODUCT STORAGE ERROR   38551     0.85\n",
       "21              NO ADVERSE EVENT   37138     0.82\n",
       "22               SARS-COV-2 TEST   34800     0.76\n",
       "23                     DIARRHOEA   31957     0.70\n",
       "24                       MALAISE   31379     0.69\n",
       "25       INJECTION SITE ERYTHEMA   30502     0.67\n",
       "26              FEELING ABNORMAL   30168     0.66\n",
       "27                    CHEST PAIN   29750     0.65\n",
       "28                     URTICARIA   27563     0.61\n",
       "29                  PARAESTHESIA   27341     0.60\n",
       "30                      ERYTHEMA   26654     0.59\n",
       "31               LYMPHADENOPATHY   26583     0.58\n",
       "32                 HYPOAESTHESIA   26398     0.58\n",
       "33                 HYPERHIDROSIS   26036     0.57\n",
       "34       INJECTION SITE SWELLING   25964     0.57\n",
       "35          CONDITION AGGRAVATED   22796     0.50\n",
       "36                    BLOOD TEST   22088     0.49\n",
       "37           PERIPHERAL SWELLING   21852     0.48\n",
       "38            OROPHARYNGEAL PAIN   20739     0.46\n",
       "39       INJECTION SITE PRURITUS   20443     0.45\n",
       "40              DRUG INEFFECTIVE   19413     0.43\n",
       "41              CHEST DISCOMFORT   18729     0.41\n",
       "42         VACCINATION SITE PAIN   18719     0.41\n",
       "43                      TINNITUS   18541     0.41\n",
       "44                  PALPITATIONS   18429     0.40\n",
       "45                         DEATH   17825     0.39\n",
       "46                       SYNCOPE   17676     0.39\n",
       "47                     BACK PAIN   17570     0.39\n",
       "48          HEART RATE INCREASED   16854     0.37\n",
       "49                        TREMOR   16377     0.36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_symptom_list(df,column='SYMPTOMS'):\n",
    "    \"\"\"\n",
    "    returns a list of symptoms for the dataframe\n",
    "    \"\"\"\n",
    "    s = df[column].to_list()\n",
    "    l = []\n",
    "    for i in s:\n",
    "        try:\n",
    "            for j in i:\n",
    "                if str(j) == 'nan':\n",
    "                    pass\n",
    "                else:\n",
    "                    l.append(str(j).upper())\n",
    "        except:\n",
    "            pass\n",
    "    return l\n",
    "\n",
    "all_symptoms =  get_symptom_list(df,'SYMPTOMS')\n",
    "symptoms_count = len(all_symptoms)\n",
    "all_symptoms = Counter(all_symptoms).most_common()\n",
    "\n",
    "df_symptoms = pd.DataFrame(all_symptoms,columns=['SYMPTOM','COUNT'])\n",
    "df_symptoms['PERCENT'] = (df_symptoms['COUNT']/symptoms_count)*100\n",
    "\n",
    "file_name = os.path.join(DATAPATH,'symptoms.csv')\n",
    "df_symptoms.to_csv(file_name)\n",
    "print('saved: ',file_name)\n",
    "\n",
    "topX = 50\n",
    "print('below are the top {} symptoms'.format(topX))\n",
    "display(df_symptoms.head(topX))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'the top 50 Symptoms (by most frequent)')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# lets put it in a histogram!\n",
    "\n",
    "# fig = px.histogram(\n",
    "#     df_symptoms.head(topX), \n",
    "#     x=\"SYMPTOM\", \n",
    "#     y=\"PERCENT\", \n",
    "#     # marginal=\"rug\", # can be `box`, `violin`\n",
    "#      hover_data=df_symptoms.head(topX).columns\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=df_symptoms.head(topX),\n",
    "    x=\"PERCENT\", \n",
    "    y=\"SYMPTOM\",\n",
    "    # hue=\"PERCENT\"\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title=f'the top {topX} Symptoms (by most frequent)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## break down functions...\n",
    "these functions will help me breakdown the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### break_down_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def break_down_columns(idf,column):\n",
    "    \"\"\"\n",
    "    shows what values there are for a given column (with counts and percent)\n",
    "    \"\"\"\n",
    "    print('\\nbreak down of {0}'.format(column))\n",
    "    idf = pd.DataFrame(idf[column])\n",
    "    idf = idf.fillna('nan')\n",
    "\n",
    "    result = []\n",
    "\n",
    "    l = list(idf[column].unique())\n",
    "    for i in l:\n",
    "        df0 = idf[idf[column]==i]\n",
    "        result.append({'column':column,'value':i,'count':len(df0),'percent':(len(df0)/len(idf))*100})\n",
    "    \n",
    "    result = pd.DataFrame(result)\n",
    "    display(result)\n",
    "    return result\n",
    "\n",
    "# test\n",
    "# break_down_columns(df,'DIED')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### break_down_buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def break_down_buckets(idf,column,buckets,message='',nan_value=-1):\n",
    "    \"\"\"\n",
    "    breaks a column down into buckets/bins\n",
    "    \"\"\"\n",
    "    idf = idf.fillna(nan_value)\n",
    "\n",
    "    print('',message,'\\ncolumn: ',column, '\\nbuckets: ', buckets)\n",
    "    \n",
    "    idf = pd.DataFrame(idf[column])\n",
    "    idf['bucket'] = pd.cut(idf[column], bins=buckets)\n",
    "    idf = idf.groupby(by='bucket').count()\n",
    "    idf['percent'] = (idf[column]/idf[column].sum())*100\n",
    "    idf['percent'] = idf['percent'].round(2)\n",
    "    display(idf)\n",
    "\n",
    "    return idf\n",
    "\n",
    "#test\n",
    "# break_down_buckets(df,'AGE_YRS',[-1,0,15,25,35,45,55,65,75,85,500])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Numbers\n",
    "\n",
    "~~these numbers are **not** part of the vaers data,  \n",
    "however they are important to analyzing the data~~\n",
    "\n",
    "~~vaxx (int) : the number of vaccinated (1 or more shots) US citizens according to a quick google search (on 8/3/2021)~~\n",
    "\n",
    "~~**google no longer shows partially vaxxed**  so we are using the **Total doses given** number  \n",
    "\"Total doses given\" shows the number of vaccine doses given to people. Since some vaccines require more than 1 dose, the number of fully vaccinated people is likely lower. \"People fully vaccinated\" shows how many people have received the full amount of doses for the COVID-19 vaccine.  ~~\n",
    "\n",
    "~~This number was updated on 06/28/2022~~\n",
    "\n",
    "~~full_vaxx (int) : the number of vaccinated (2 or more shots) US citizens according to a quick google search (on 06/28/2022)~~\n",
    "\n",
    "~~the numbers were taken from https://usafacts.org/visualizations/covid-vaccine-tracker-states\n",
    "on 11/18/2022~~\n",
    "\n",
    "the numbers were taken from https://usafacts.org/visualizations/covid-vaccine-tracker-states\n",
    "on 9/10/2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaxx = 270_227_181 + 230_637_348\n",
    "full_vaxx = 230_637_348"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only death vaers\n",
    "df_death = df[df['DIED']=='Y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the df (all_data.json)\n",
    "this is just a generic break down of the data\n",
    "\n",
    "df_death (DataFrame) :  adverse reactions that resulted in a death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DIED column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "break down of DIED\n"
     ]
    },
    {
     "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>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DIED</td>\n",
       "      <td>N</td>\n",
       "      <td>1038550</td>\n",
       "      <td>97.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DIED</td>\n",
       "      <td>Y</td>\n",
       "      <td>22110</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  column value    count  percent\n",
       "0   DIED     N  1038550    97.92\n",
       "1   DIED     Y    22110     2.08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Died vs not-Died')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "temp = break_down_columns(df,'DIED')\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n",
    "\n",
    "# fig = px.pie(\n",
    "#     temp, \n",
    "#     values='count', \n",
    "#     names='value', \n",
    "#     title='Death as Symptoms from Vaccine',\n",
    "#     # hover_data = temp.columns\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"count\", \n",
    "    y=\"value\",\n",
    "    # hue=\"PERCENT\"\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='Died vs not-Died')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ER_VISIT column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did the adverse reaction result in an ER Visit\n",
      "\n",
      "break down of ER_VISIT\n"
     ]
    },
    {
     "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>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ER_VISIT</td>\n",
       "      <td>N</td>\n",
       "      <td>1060514</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ER_VISIT</td>\n",
       "      <td>Y</td>\n",
       "      <td>146</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     column value    count  percent\n",
       "0  ER_VISIT     N  1060514    99.99\n",
       "1  ER_VISIT     Y      146     0.01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Did the they need the ER?')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('did the adverse reaction result in an ER Visit')\n",
    "temp = break_down_columns(df,'ER_VISIT')\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n",
    "# temp.plot.bar(title='ER_VISIT',x='value',y='count',rot=0,figsize=(5,5),width=0.25,fontsize=18)\n",
    "# temp = temp.set_index('value')\n",
    "# temp.plot.pie(title='ER_VISIT',y='percent',figsize=(5, 5),autopct='%.2f',fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#      x='count', \n",
    "#      y='value',\n",
    "#     title='Did the they need the ER?',\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"count\", \n",
    "    y=\"value\",\n",
    "    # hue=\"PERCENT\"\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='Did the they need the ER?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L_THREAT column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Life Threatening/Leathal Threat\n",
      "\n",
      "break down of L_THREAT\n"
     ]
    },
    {
     "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>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L_THREAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1043787</td>\n",
       "      <td>98.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L_THREAT</td>\n",
       "      <td>Y</td>\n",
       "      <td>16873</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     column value    count  percent\n",
       "0  L_THREAT     N  1043787    98.41\n",
       "1  L_THREAT     Y    16873     1.59"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'was this a lethal threat?')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('Life Threatening/Leathal Threat')\n",
    "temp = break_down_columns(df,'L_THREAT')\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n",
    "# temp.plot.bar(title='L_THREAT',x='value',y='count',rot=0,figsize=(5,5),width=0.25,fontsize=18)\n",
    "# temp = temp.set_index('value')\n",
    "# temp.plot.pie(title='L_THREAT',y='percent',figsize=(5, 5),autopct='%.2f',fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='count', \n",
    "#     y='value',\n",
    "#     title=\"was this a lethal threat?\"\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"count\", \n",
    "    y=\"value\",\n",
    "    # hue=\"PERCENT\"\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='was this a lethal threat?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RECOVD column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did the patient recover?\n",
      "\n",
      "break down of RECOVD\n"
     ]
    },
    {
     "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>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RECOVD</td>\n",
       "      <td>Y</td>\n",
       "      <td>320796</td>\n",
       "      <td>30.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RECOVD</td>\n",
       "      <td>N</td>\n",
       "      <td>339448</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RECOVD</td>\n",
       "      <td>U</td>\n",
       "      <td>259390</td>\n",
       "      <td>24.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RECOVD</td>\n",
       "      <td>nan</td>\n",
       "      <td>141026</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   column value   count  percent\n",
       "0  RECOVD     Y  320796    30.24\n",
       "1  RECOVD     N  339448    32.00\n",
       "2  RECOVD     U  259390    24.46\n",
       "3  RECOVD   nan  141026    13.30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'RECOVD'}, ylabel='percent'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d1EPA0eXA2f8BtrrPwuuh3QmACpQ7yNOasHRnMr7clYzbW4ZiRv843NE6jHWsSq5fv172dVRUVLXHlX/u+vXrVKCcjAoUcS1BAJL+AnZ/DFw/3qBTKAuTMDw0H1vzQhwcjjiLIAB7LuVhz6U8dIwKwKyB8bizfQQkIlnctrS0tOxrjab6nl7558q/hjgHFSjiGoIAnPsN2L0AyD7d6NM9GpSIrXk0i8odnc4oxswfjiMuzAdPDIjHvV2j3Hr2H3Ee+qkgzsXzwOlfgC/6AOumOqQ4AUDXkh0OOQ9h50quDs//cgoDPtqBFXuvwuDkLUtq4ufnV/a1Xq+v9rjyz5V/DXEOKlDEOWxWIPFHYEkvYP10IPe8Q08vL76Ce5rk1H4gEb3rxUa89cc59PtwO5btTobJ6vpC1bRp07KvMzIyqj2u/HPlX0Ocg4b4iOOd/gXY/g5QeNWpzTzsfxy/Zd/l1DaI6xTozHjvzwv4dv81PHdna4ztEgWOc801qnbt2kEikYDneZw5c6baqeZnzpwBAERERDh0gkTHbzs67Fx1cXpq/UYy3n//fWzYsAEXLlyAWq1G37598eGHH6JNmzZOSmhHPSjiOOnHgG+G2XtMTi5OANChqPLNlMT9ZRQZ8PTakxjz+V7su5znkjY1Gg369bOvULJ58+YqjxEEAVu2bAEADB8+3CW5xGLXrl2YPXs2Dh48iL///hsWiwXDhw+HTufcnQKoQJHGK84A1s8AvhkCpB92WbOy0nQ8FJnpsvaIa53JKMHEbw5h6orDuJBV4vT2pk6dCgDYsWMHDh06VOn5devW4cqVKwCAKVOmOD2PmGzevBnTpk1D+/bt0blzZ6xatQqpqak4duyYU9ulAkUazmIAdn4AfN7DvlYeXL9q1mTfoy5vk7jWrou5GPnpHsxfdxI5pUantTN16lR07NgRgiDg/vvvx7Zt2wCgwmKxgH2lCW9fh6+42L6ai7PvA6NrUKT+BAE4vQ74502gJJ1plLYF2yHlRsEm0GctT8YLwLpj6dh8Ngsv3NUWE29r5vDrUzKZDBs3bsSgQYOQkpKCoUOHQqPRgOd5GI32wti1a1f88MMPDm3X3fA8j6eeegr9+vWrcdUNR6D/1aR+8i4BK0cAG2YwL04AINVlY0pk9bOuiGcpNVrx6v/O4D9fHsDFbMffKNuiRQucOnUKr732Gjp06ACO4yCXy9G9e3csWLAABw8ebPBeUJ5i9uzZOHPmDH766Sent0WrmZO64W3A/s/sQ3pW5w2zNMTFmHEYfule1jGIi8mlHB67Iw5zB7eCSu4eGyhWt8K32Gfx3TRnzhz89ttv2L17N2JjY2s8llYzJ66Rfc4+AeKfN0RXnACgZd4OKCXusWo2cRyLTcCSHcm4a/Ful83281aCIGDOnDn49ddfsX379lqLk6NQgSLVs1nsPaZlA4DrJ1inqZbEkIdHolJZxyCMpOTrMfGbQ3hmbSKK9RbWcTzS7NmzsXr1aqxZswZ+fn7IyspCVlYWDAaDU9ulIT5SteuJwG+zgewzrJPUydXosRh0+QHWMQhjEf4qLHygM/q1DGUdpUruumFhdRNSVq5ciWnTplX5HG35ThyPtwE737dvFshbWaepsxa5O+Aj/Q90NhoU8GZZJUZMWn4ID/eNxfN3tXGba1Nix6ofQ/+byS0lmcCq0fatMNyoOAEAZyrG41HOX72CiJ8gACv2XcU9n+9zykw/4jpUoIjd5X+AL28HUvezTtJg9yoOso5ARCQpuxR3f74XPxyqfZdmIk5UoLwdb7PfcLv6P4DevWdCRefsRIDcvXp+xLmMFh6v/HoGs344hmIDTaBwN1SgvNnNIb29i8BimSJH48w6zI5KZh2DiNCfp7Nw9+d7acjPzVCB8lYeMKRXlTFSGuYjVbuWr8e9S/Zhy9ks1lFIHVGB8ka7PvaIIb2qRGTvRpiChnJI1XRmG55YfQyf/H2R2cw0UndUoLyJ1QxseBzY8Q48YUivKpzVgLlRl1jHICImCMCn2y7h8e+PQWuia5ZiRgXKW+gLgO/uAU45f4FH1kZwnjVsSZxj67ls3LtkH1LynLvpHmk4KlDeIO+yfS09D7veVJ3Q7L2IUplYxyBu4FKOFvcs2Yc9l3JZRyFVoALl6VL2AsuHAgVXWCdxGc5mxpNNk1jHIG6i2GDBI6uO4LdE2rZFbGipI0+WuAb4fR5gM7NO4nLD+L14AZ1YxyBuwmIT8NTaRORrzXjkdtes1F3e+bbtXNpeuwvn63X80qVLsXTpUqSkpAAA2rdvj9deew0jRoxwQrpbqAflqXa8B/xvplcWJwAIyjmIOI34tgYh4iUIwFt/nMNHmy+wjiI60dHR+OCDD3Ds2DEcPXoUgwcPxj333IOzZ886tV0qUJ5o88vArg9Zp2CK4614MvIc6xjEDX2xMxkvrj8FG++ZM10bYsyYMRg5ciRatWqF1q1b491334Wvry8OHnTufYdUoDyJIACbngUOLmGdRBQGWfeyjkDc1E9H0jBz9TEYLTbWUUTHZrPhp59+gk6nQ58+fZzaFhUoT8HzwMY5wJFvWCcRDf+cw2jnq2cdg7ipreeyMWXFYZQa6cZvADh9+jR8fX2hVCrxxBNP4Ndff0VCQoJT26QC5Ql4G/Dr48CJ1ayTiAon8Hgywj02XCTidPhqAaauOEw39AJo06YNEhMTcejQIcycORNTp07FuXPOHUanAuXubBbgl4eB0z+zTiJK/U27WUcgbu54ahEeWXkEerN3FymFQoGWLVuie/fueP/999G5c2d8+umnTm2TCpQ7s5qAtZOBc7+xTiJaPrkn0C1AyzoGcXOHUwrw6LdH6ZpUOTzPw2Ry7g3xVKDclc1iL04X/2KdRNQ4CJgdfop1DOIB9ifn47Hvj8Fk9b4i9dJLL2H37t1ISUnB6dOn8dJLL2Hnzp2YOHGiU9ulG3XdEc/brzld2sI6iVvoY9gFoC/rGMQD7L6Yi1mrj+PLyd0hlzru8319b5x1tZycHEyZMgWZmZkICAhAp06dsGXLFgwbNsyp7VKBckd/PgucWc86hdvQ5J1Gv6Bi7CsMYB2FeIBtF3IwZ81xLJnQDTIHFikxW758OZN2veNv15Nsexs4uoJ1CrczMzSRdQTiQbaczcaz607SnlJORgXKnRz6CtizgHUKt9RTt5N1BOJhfku8jgVbaVFiZ6IC5S7O/QZsfpF1CrelLEjCsNAC1jGIh1myIxlrj6SyjuGxqEC5g2v7gQ2PAQLPOolbezQokXUE4oFe+fUM9l7KYx3DI1GBEru8y8CP4wErrczdWF1Lt7OOQDyQlRcw84djSMoqrfNrvOHalSO+RypQYmYsAX4aDxiLWCfxCIqiKxgTTjunEscrNVrxyKojyCmp+YOkXC4HAOj1nr9G5M3v8eb33BA0zVysBMF+r1PeRdZJPMojAcfxe86drGMQD5RRZMD0b49i7eO9oVFU/dYqlUoRGBiInJwcAIBGowHHca6M6XSCIECv1yMnJweBgYGQSqUNPhcneENf0x3t/ADY+T7rFB7H6h+DljnevVcWca672kfgy8ndq31eEARkZWWhqKjIdaEYCAwMRERERKMKMBUoMbrwJ/DTBAD0T+MMzwcuws9ZEaxjEA/2ysh2mHFHXI3H2Gw2WCyeuZWHXC5vVM/pJipQYpN7EfhmCGAqYZ3EY52JmYDRl0azjkE8mEzC4afHeqNHi2DWUdwaTZIQE2OJvedExcmp2hVsh5SjKfvEeay8gNlrjiNP69zVvj0dFSixEAT7vU75l1gn8XhSXRYmRV5nHYN4uOwSE+b9dAI8T4NUDUUFSiz2/5e2znChCT5HWUcgXmDf5Xws+ptm4jYUFSgxyDoNbH+bdQqv0ipvO+QS+mRLnG/JzsvYcSGHdQy3RAWKNYsRWD8DsJlZJ/EqEkMeHo2iNdSI8wkC8PTPicgsNrCO4naoQLH2zxtArrg3K/NU45SHWUcgXqJIb8EL60+zjuF2qECxlLwdOPQl6xReq0XeDqil3rd9N2Fj98VcrDlEvfb6oALFir4A+N8s0M247EiMRXgi6hrrGMSLvLvpHNIKPH8dPkehAsXK7/OA0kzWKbzevYqDrCMQL6Iz2/D8L6e8YjVzR6CVJFhIXAP8bybrFASAoPBFJ/0XKLW6/7rJpqzLMFw+DHP2ZVgKroPXF4M36yFRaCAPiYY6rgd8u46EVO1X7TlsukIUH1oPQ/IR2EpywckUkIc2g0+HIfDtNLzB66pZCq/DcOkgjKmnYc5NgU1XCE4ihdQ3BMro9vDrNgrKiJb1Pm/2z6/DePUYAEAZ0wEREz5oUD5Xe/Pu9pjatwXrGKJHBcrVtDnA5z0AYzHrJOSGr5q8gfevtWYdo9EK/l6K0uObyv7MyRSARArBfGv2mETtj/D7/w/KqHaVXm/Kuoycn18Db7CvZMIp1BCsZoC3X6dTxXZD+P3/B05av+0TjOnnkP3D8xUe4xRqCDYLYLPeeECCgD4PILD/pDqfV3v6H+T/ubjsz+5UoNRyKf6a1x8tQn1YRxE19//Y6G62vkrFSWTGyA7ifbh/gVJEtkbgwCZQRSdAHhINicoXAMCbDdBf3I/CHSvA64uRs+FdRD32FSTKW2+OvEmH3F/eBG8ogSw4GqGjn4UyshUEmwXak1tQsO0bGK8eR8G2rxEyfFb9gvFWgJNA3bIXfBIGQtW8E6Rqfwi8DebsZBRu/wam9HMo3v8TpP7h8Os8vNZT2rSFKNz+DSRKH0h9g2HJT6tfJsYMFhueW3cSPz/eBxKJZ2234Uh0DcqVru4GTq1lnYL8S2TOboQp3H9Vad8OQxBw231QRrUtK04AIFGo4dthCEJHPwsA4PVF0F+uOMW+5NAG+7CbTInwcW9AGdkKAMBJ5fDrNhqBt08AAGgTN8NSkFGvXLLApmj66FKE3/cqfNreDqna335uiRTKyNZo8tC7kIe1sOc4uK5O58z/+wvwRi0CBz0CiSagXnnE4ui1Qqw+RJN0akIFylWsZmDTs6xTkCpwFj1mR11mHcPplE3bln1tK82v8Jz27HYAgKZdf8gDK29F4td9DDiFGhB46M7urFe7Mv9QyIOjqn2ek8rh034QAMBalAmbUVvj+XTn98Bw8QCUMR3g26n23paYLdx6EQU6ukm/OlSgXGX/Z7Q7roiN5PaxjuB0xvSzZV/LyhUhS346bCW5AAB1XI8qXytRqKGMTgAAGFKOOzwbJ1Pc+gNf/b1pNkMJCv75CpDKEXLXXLffjbbYYMHHWy6wjiFaVKBcoTAF2L2AdQpSg7DsvYhUed4nWcFqgbU4GyXHfkf+HwsBALKgSGha3lZ2jDnv1jCTIrR5tee6+Zwlz/HXe0yp9lUWpL7BkNwYAqxKwT9fgdcXIbDvQzX2ytzJ2iNpOJVexDqGKNEkCVf483nASutwiRlnM2Ne0yS8eKUj6ygOcW3BvYCt8nU1ZVQCQu9+Dpzs1kw8m7ag7GupX0i157z5nGDWgzcbIFGoHZLVlHEe+kv2+9Fqmsquv3wI+nO7IA9tDv/b7ndI22LAC8Brv53Fr7P6un2P0NGoB+Vs538HLm1hnYLUwXB+L+sIDiP1CYLEJxCcXFX2mLJZJwQNmQGZf3iFY8tPQ+fkymrPycluPcebHfOBy6YvRu7GjwGBhyyoabWFhzfpULBlCcBJ7EN7Us/6bJ2YVoR1x9JZxxAdz/pXFhurGdjyMusUpI6Ccg4gTvMwruhVtR8sctEzV5R9bdMVQXd2O4oP/Iys755BQN8H63W/kbPwZgNy1r8FW0kOOIUaYWNfrLZXVrh9OWzaAvh1Gw1lVNsqj3F3H22+gLs6RMBfVb/7zDwZ9aCc6egKoIgWh3QXHG/F3MhzrGM4nNQnEP697kP4uDcBjkPx/p8qTDPnyhUFwVL9FuWC9dZzjR3e481G5PzyJszXk8Ap1Agf9wYU4XFVHmtISYT21FZI/UIReMeURrUrZnlaMz6hzQ0roALlLCYtsIcmRribwVbPGeb7N2XTNmUz8bQnN5c9LvUNLvv639PPy7v5HKfQNKpA2YvTGzClnQEnVyH8P69DFd2+2uPzN/8XABA08GGA48CbDRV+QeDtBwp82WNCDTMBxez7A9eQkqdjHUM0aIjPWQ4sAXS5rFOQevLPOYy2vnpc0GpYR3EKqa99ooO18NZCxeVn7pnzrkEeGlPla2/O9qvu+bqoWJyU9uIU06HG19iKswEAeb9/XONxpvRzSPtkHAAg7N5XoGndp8E5WbHyAhb9fRGfje/KOoooUA/KGfQFwIHPWacgDcAJPJ6MOFv7gW7KWpQFoOKwniw4ClL/MACA4cqxKl/Hm40wpduHP9UtujWo7co9pzegauYZsyYd6fdT13HuegnrGKJAPShn2LMQMNEPmLu6w7wbQE/WMepF4G0AJ6lxmrIhJRHmTPs1jvKFgeM4+LYfjOIDa6G/sBvWfg9BFtCkwmtLT/xhn+3HSeDTfmC981UqTuPeqLXndFPzF/6o8fmsNS/ClHbGrRaLrYkgAAu2JmHFNPf6GXQG6kE5WnE6cOQb1ilII/jkHEcX/5qX2xEbW2keMlc9idLEv2Apyqqw35C1JBfFB9chd8M7AARIVH7w7zG2wuv9b7sPUp8gCBYTcta9CVOWfeknwWZB6Yk/UbRnNQDAt8tdVd4gm7fpE1z7cDSufTi60nO8xYic9W/ai9ONCRF1LU7eavuFHBxPLWQdgznqQTnazg8Aq5F1CtIIHATMCT+FR0v6so5SL5acq/Z7hQBAKoNEoYFgNUOw3Pp5lAU0Qdi9L0PqG1ThtRKlD8L+8zpyfn4NlvxUZH371I3tNiz21cgBqFp0RfDgGfXOpU/aV7ZSBHgbcn+ruZcTNvYVqKIrbwfibRb/cwnfPdKLdQymqEA5Ut5l+2aExO31Ne4C4D4FSuobjNB7XoQp7TRM1y/Cps2HzVACjpNC6h8GRXgs1C17wydhACTV3IyrjGiJptOXoPjgL/YNC0vzIJErIQ9rfWPDwmHguAYMupTrzQlWs32PqZrw7r+yvCPsvpiLY9cK0b15UO0HeyjasNCRNs4Fjn/HOgVxkPHqpThQ6J5bORDP0L9VKL6fflvtB3oougblKNoc4CTt9eRJZoWeZB2BeLk9l/K8eiFZKlCOcngZYKv+LnzifnrqdrKOQAiW773KOgIzVKAcwawHjixnnYI4mKrgAoaEFNR+ICFO9OfpTGQVe+fEKypQjpD4A2CgNzJP9FgwDfMRtiw2Ad8eSGEdgwkqUI3F88DBL1inIE7StXQ76wiEYM2hVBjM7rm+YGNQgWqspE1AwRXWKYiTKIqSMTosj3UM4uWKDRb8ctz79ouiAtVY+//LOgFxskcCq16fjhBXWrn3KrztriAqUI2RfgxIO8Q6BXGyTsU0zEfYu5Knw46kHNYxXIoKVGOcoJtyvYGsJA3jIrJYxyAEK/elsI7gUlSgGspiAM5sYJ2CuMgUPxrmI+ztvZyHjCID6xguQwWqoc7/TltqeJGEwu3gOO8a/yfiIwjAhmPeM1mCClRDnVjNOgFxIak2E1Mir7OOQQg2nMhgHcFlqEA1RFEqcHU36xTExSb4HGUdgRBczdPh2DXvWBiAClRDJP4IgIZ7vE2r/G2QS+jfnbD3yzHv6EVRgaovQQBO0p5P3kiiz8MjTdNYxyAEf5y6DqPF81eWoAJVX9f2AYUprFMQRh5QHWYdgRCUGq3Yei6bdQync7sCpdfr8ddff+Gdd97Bfffdh+bNm4PjOHAchzfeeMP5ARJ/dH4bRLRi87ZDLfX8T65E/NZ7wWw+t9vy/fDhwxg5ciSbxnkbkPQnm7aJKEiMRXg86hoWp8axjkK83N7LeSjUmRHko2AdxWncrgcFAEFBQRgyZAjmz5+PH3/8EREREa5pOPUAbatBcJ+Clrci7Nl4AdsvePbSR27Xg+rfvz8KCioWiRdffNE1jV+g3hMBYnJ2wE82DqVWt/vvQzzMP+ezcX/3aNYxnMbtelBSqZRd40mb2LVNRIMzazE7irZYIeztvpgLk9Vzr4m6XYFiJuc8zd4jZe6WHWQdgRDozDbsT85nHcNpqEDV1QXqPZFbInN2I0RhYR2DEPzjwdPNqUDVFc3eI+VwFj3mRF1mHYMQbDuf47EbGVKBqouSTCDjOOsURGRGcvtZRyAEWSVGnM4oZh3DKahA1cXFzaC198i/hWfvRaTKzDoGIR47zEcFqi6SactvUhlnM+HJpkmsYxCCfR46UYIKVG0Ewb7+HiFVGC7QzwZh73R6sUcuHksFqjY55wC9Z346IY0XnL0fLdRG1jGIlzPbeBxPLWQdw+GoQNXm6h7WCYiIcbwVTzY9zzoGITh0xfOWYaMCVZtre1knICI32EY/I4S9w1c9r0C55WJihYWFsNlujbfyPA/AvhVHXl5e2eMqlQq+vr6NayyVFgYlNQvIPoS2vtNxQathHYV4sRNphbDYeMilntPvcMvvpGvXrggLCyv7lZZm3+X0448/rvD4nDlzGtdQwRVA59mrBZPG4wQecyPOso5BvJzRwuNUehHrGA7llgXKZaj3ROroDjNdqyTsHfKwYT63HOJLSUlxTUNprl0Q9HimDb8nWXEs04aL+Txy9QJKTAL8lRzahkowsqUMM3sqEKzmKr02o4THb0lW7Eix4kSmDRml9huLI3w59I6WYkY3BQbHNv6fO1vL46N9ZvxxyYrUYh5qGdA+XIqpneWY3lUOjqucrbzkAh4f7TNh6xUrMksF+Ck5dIuU4LFuCtyfIG90PlZ8c46hi78WiSWNHFImpBGOXC0ABrJO4Tic4KmLODnCl7cDWadd1tycPw1YcuTWAqQqGSCXAKXlFisI1XDY+JAafWJuFZu0Yh7NF2srrHWhkdtv4TJYbz32SBc5lo1RQSqpuYhU59h1G+5crUe+wd6SrwIwWgGr/RIg7oyXYuN4DRTSqs//5yULxq0zQH/jW/RXAlozwN8I/nAXOZbfraq1yInVPzFz8eilPqxjEC8W5qfEkVeGso7hMG7Zg3IJmxXIvejSJntFSdEiUILbm0nRNlSKQJX9jVprFrDhvAXPbTUhVy9g7FoDLs7xRcCN522CfSGmIbFSTOksx9A4GZr6ScALAi7k8Xh5mwm/JVmxItGCpn4c3h6sqne2YqOA0T/ai1PbUAm+v1eNHk2lMNsEfH3Mgqe3GLEl2YanNhvxxSh1pddfLeTxwI3i1C9GihX3qNA6RAqtWcDH+0x4a7cZKxMtaBsqwfP9lI36e2Slr2EXACpQhJ3cUhPytSaE+Lrn/6F/ox5UdXKTgCW9WKeoYGuyFXeu1gMAVt+rxsRO9iGxYqOA5EIe3SKr3sxREASMXKPH5ss2+CqA3Pl+UMnq10v5v+1GvLPHDLUMODvLF7FBFS9fvr/HhJe3myDlgHOzfdA6pGKWyb8asPqUBRG+HM7P9i0rvjc9/rsBy45b4K8EUub5IaiKYUx3MF69FAcKA1jHIF7sh0dvQ7+WoaxjOARNkqhOtvhmZfWOvvWmn17Cl30doOKqLU4AwHEcHumiAGAfUjufy1d7bHW+O2Ufl3uog7xScQKAubcp4Kuw9+Z+OFVxnySdWcD6c/bHZvZQVCpOAPBSf/snvhIT8L8L7rvP0szQU6wjEC93PrOEdQSHoQJVnRzxrQ6w59qtC0rxwfX7p1OVG8y11bPPnJRnQ2qx/UUjWlY9Kuyr4NC/mf25rVcqrgm2N9VWdi2sute3CJSgXajkxuutVR7jDnrpdrKOQLzchaxS1hEchgpUdXLOsU4AADBZBaQU8fj8sBmTfzUAAFoGSzCmdf0uH+5MsRcNhRRoHVK/f/YzObd6XB3Cq3/tzefO5VYsUGdybJWOqen1Z3Pq38MTC1XBeQwO8bw10Yj7uJDlOT0omiRRHcZDfKp3SmCqYnHifjFSrLlfDWU9riFdLeTx5TH7VMAH28vhr6zf9Z3rpbe6XFH+1ReYKD/7eUtM9okdvgquwuuDVIBaXn3bUX6SSu25o8eCE7E9fxDrGMRLXcrWwsYLDZ6tKybUg6qKWQ8UXWMaIcKXQxMfDj7lbg0a1EKKxXep0Cyg7v9sBouAcev00FvsU9Q/GFr/2T2l5lsFQ1PDrUqacsWn1HTrNTdfr6mhOJU/d/n23FG30h2sIxAvZrLyuJKrZR3DIRpUoOLi4pCfX3kLiqKiIsTFxTU6FHO55wGB7TBTylN+yHrOD9qX/ZH9nC8WDFMiMcuGXl/r8NqOum3vYOUFTNhgwLFMHnIJ8MN9ajT1o88kzqYouoxRYXm1H0iIk3jKdagGvVulpKRUWKz1JpPJhIyMjEaHYk5kEyTCfSR4tq8Smyf5gOOAt3eb8cfFmme62XgBEzcY8L8LVsgkwJr71Rge37ARXT/FrZ6PvoZm9ZZbPR+/csOIN19f/vmqX1+5PXc1Peg46wjEi6UW6FlHcIh6vWNt3Lix7OstW7YgIODW/R42mw3btm1DixYtHBaOmcIU1gmq1CtKitubSbH7mg3LjlkwunXV4202XsCkXw34+awVUs5+z9R/GrGMUFO/WwUjo4SHf1jVU9pvLq/kr0TZ9afyry802occq7sOlVHKV2rPXXUq2g5gOOsYxEtlFBlYR3CIehWosWPHArDfVzN16tQKz8nlcrRo0QILFy50WDhmSq6zTlCtmxMRLhdUPQR5s+e09mZxuk+NBzs0bo278jPvzuTwaFdNgbo52y/hX893CJdWOKZnVM2vb1/DTD93IStJxX8isvFLVhPWUYgXuu4hBape7wQ8z4PneTRr1gw5OTllf+Z5HiaTCUlJSRg9erSzsrpOcTrrBNW6UmjvpfhVMdfBduOaU/ni9FAjixNgn5beLMBeGDdfrvoeJZ1ZwJ5U+3PD4yoWoNubSaG+8VGoutdfK+JxPo+/8XrPmFw61e8o6wjES2UUemGBuunq1asIDfWMpTSqxKAHZeMF1Lbq1LYrVhzOsF/7G9i84pv4zZ7Tz2ft15x+cFBxAuw95ik3llX66awFKUWVe29LjpihNQNSDmVLMN3ko+DKVipfetSMYmPl7/PDfSYAgJ8CGNvWfVc1Ly+hcAc4zr1nJBL35Ck9qAZ/VN22bRu2bdtW1pMqb8WKFY0OxlRppsubTCsRMPYnPWb2UGBYvAyxgVzZqt5pxTx+OG3BO7tNEAAEqzk83UdR9tqb15zW3ihOa+5TY1z7+r3Jv7HTiDd32e+VujrPFy0CK352ea6vEt+csCBLK2DUGj2+G6tG9xuLxS4/bsH/7bAXmMe6yyutwwcAbw1U4tfzFmRqBYz5UY/ld6vQKkQKnVnAwgNmfHnUPkPi1TuUbrsO379JtdcxKfI6vr8exToK8TI6sw1FejMCNYraDxaxBhWoN998E2+99RZ69OiByMhIt90eoUqGIsDM5h6Ck9k8nthkn0KukAL+Sg4GiwBduZlzsYEc1j+gQYTvrQKyL82Gn87Yh844AHP/MmLuX9VPRf/0LlW9r0sFqDj8MV6DO1frcS6XR4+vdfC7sd2G5cbnk+HxUnxyZ9UrpccGSfDzODXGrTNgT6oNrT/XIeDGdhu2ctttzO/r3v+h/m2iz1F8DypQxPXSCw3eWaC+/PJLrFq1CpMnT3Z0HvZK2EyTb+rHYd04NXamWHEow4brpQLy9AKkHNAsgEPnJlLc00aGCR3llWbB8eVGkSw8kK2reVjJYG3YsFP3plKcneWDD/eZ8cdFK9JKePgo7JMgpnaW45Guckhq+LAyspUcp56Q4sN9Jvx9Y8PCIDWHrhESPN7dvTcsrE7r/O2QS+6GhfegD3HELVwvMqBDlHuvrN+g7TZCQkJw+PBhxMfHOyMTW5f+Bn74D+sUxIO8F/o+lqU3Zx2DeJnXxyTg4X6xrGM0SoMmSTz66KNYs2aNo7OIA6MeFPFcD6ppNh9xvcKa7qp3Ew0a4jMajVi2bBn++ecfdOrUCXJ5xaGZRYsWOSQcE6XZrBMQDxObux1q6VgYbNXv2UWIo5UYvLRAnTp1Cl26dAEAnDlzpsJzbj9hwuQ5S9UTcZAYC/FY1DV8muoB61QSt1FqdN991W5qUIHascODV2tmNIOPeLb7FIfxKahAEdcpNbp/D6pRa8pcvnwZW7ZsgcFgvymsAfMtxMesY52AeKBmuTvgI6tigy9CnKTEWwtUfn4+hgwZgtatW2PkyJHIzLTf2Dp9+nQ8++yzDg3oclSgiBNwplLMjrrCOgbxIp4wxNegAvX0009DLpcjNTUVGo2m7PEHH3wQmzdvdlg4JmiIjzjJPbKDrCMQL+IJBapB16C2bt2KLVu2IDo6usLjrVq1wrVrbHeibTTqQREnaZqzC8Hyh1Bg8bwbkon4eO0Qn06nq9BzuqmgoABKZf23FBcVKlDESTiLHnOjLrOOQbyEJ/SgGlSg+vfvj++++67szxzHged5fPTRRxg0aJDDwjFBBYo40UjJAdYRiJew8QKstqr3jXMXDRri++ijjzBkyBAcPXoUZrMZzz//PM6ePYuCggLs27fP0Rldi65BEScKz96DCOUkZJncexFP4h54N59Y3aAeVIcOHXDx4kXcfvvtuOeee6DT6XDffffhxIkT7r8+H09TgYnzcDYTnoxKYh2DeAnezW/9afB+UAEBAXjllVccmUUcOPffbpyI253CfryMjqxjEC/glQVq5cqV8PX1xbhx4yo8vm7dOuj1ekydOtUh4ZiQeMZ240S8rIYreNPnLIILPGPXUyJeUtsQNKIfwlyDttto3bo1vvrqq0oTInbt2oXHHnsMSUluPISxoA2gzWKdgniozKAYTG8aiaAiAf+3ygAhv4B1JOLB2hw/BkkVM67dRYPGs1JTUxEbW3mfkebNmyM1NbXRoZiiHhRxkrSQ5pgW2QRp+iycUmTji8nB4Hx8WMcinkzi3pcsGpQ+PDwcp06dqvT4yZMnERIS0uhQTEloSwTieClh8ZgWHoLrhpyyx3aoU7B2WgtATjfuEifxxgI1fvx4PPnkk9ixYwdsNhtsNhu2b9+OefPm4aGHHnJ0RteiHhRxsMtN2uDhED/kGPMqPfeLfxK2Tevg9m8kRJw4qXt/4G7Qu/Hbb7+NlJQUDBkyBDKZ/RQ8z2PKlCl47733HBrQ5ahAEQe6EJmAx/ylKDRVf63pq9DTCJ3QHZ1XH3FhMuLpOKXS7QtUvSdJCIKAtLQ0hIWFIT09HYmJiVCr1ejYsSOaN2/urJyu80VfIOcs6xTEA5yJ6ojHfXmUmEvrdPyCC93Q7NfDTk5FvIU0OBit97v3wgn17i4IgoCWLVvi7NmzaNWqFVq1auWMXOxIqQdFGi8xpitmqo3Q1mPprOfaHseyYd0R+PcxJyYj3kLi68s6QqPVe+BbIpGgVatWyM/Pd0Ye9pT+rBMQN3ekRU88rjJAa6n/uo6zup+GqTfdxEsaT+IBM0QbdGX2gw8+wPz583HmzBlH52FP4+azEAlT++N6Y5asCHqrvkGvt3I8Zg24Ar5jawcnI95G6gEFqkHjWVOmTIFer0fnzp2hUCigVqsrPF9Q4MY3H1KBIg20q2U/PCNkw2wzN+o8pRITnhqRg890zYArbn5fIWHGE3pQDSpQixcvdnAMEaECRRrgn9b9Md+WASvvmD14sqRa/N99KrzzfTiE7JzaX0DIv3htgXLrtfZqQwWK1NOfbQfhFfM1WAXHbhCXJM/DgolReG65P4TiEoeem3g+iZ8f6wiN1uC7A5OTk/Hqq69i/PjxyMmxf8L766+/cPasm0/RpgJF6uF/CUPxkvmqw4vTTYeUGVg5NRKcWuWU8xPPJQsLYx2h0RpUoHbt2oWOHTvi0KFD2LBhA7Ra+yZ/J0+exOuvv+7QgC7nQwWK1M3P7YfhNcMl8IJzdy390ycZv09tA8joFghSd7JwLy1QL774It555x38/fffUChu7Qw6ePBgHDx40GHhmKAeFKmD1R3vwtv6JAhwzX473wWdxYHJnV3SFvEM8iZNWEdotAYVqNOnT+Pee++t9Hh4eDjy8iqvN+ZWqECRWqzoNAIfas+5vN1PIk7i4gO9XN4ucU8yby1QgYGByMzMrPT4iRMnEBUV1ehQTPmEAeBYpyAitbTLKHxSyu4666vxx5Ezqiez9on78NoC9dBDD+GFF15AVlYWOI4Dz/PYt28fnnvuOUyZMsXRGV1LpgT83bzIEqf4tOsofFF8mnUMzO14Aro7urKOQUSMUyggCwpiHaPRGlSg3nvvPbRt2xYxMTHQarVISEhA//790bdvX7z66quOzuh6wZU3YyTe7aOuo/FNEfviBAACB8zsex6W7gmsoxCR8oQZfEADt3y/KS0tDadPn4ZOp0PXrl3RsmVLR2ZjZ+OTwPFvWacgIiCAw7vdRmJtoTiKU3khvAZf/C8UXNIV1lGIyKi7d0eLH1azjtFoDb4Pavny5RgxYgTuvfdeTJo0CWPHjsU333zjyGzshMSzTkBEgOckeEOkxQkA8iV6PH93CbiYpqyjEJFRxLZgHcEhGlSgXnvtNcybNw9jxozBunXrsG7dOowZMwZPP/00XnvtNUdndL3gONYJCGM2TopXut6FDSItTjddkxXh7Qc4cCHBrKMQEVHGesZ7WIOG+MLCwvDZZ59h/PjxFR7/8ccfMXfuXPefap59Fljal3UKwohFIseLXYZia6H7rIoyyNACs77OhKCr/xYfxPNEL/0CfoMGsY7RaA3qQVksFvTo0aPS4927d4fV6pwlX1wqOA401dw7WaQKPNN5iFsVJwDYoU7B2mktALmcdRQiAsp4z7hM0aACNXnyZCxdurTS48uWLcPEiRMbHYo5uRrwi2SdgriYSabC3E4DsbPI9TfhOsIv/knYPq0DIGnwpWXiATiFAvLoaNYxHKLBi3stX74cW7duRe/evQEAhw4dQmpqKqZMmYJnnnmm7LhFixY1PiULIfFA6XXWKYiLGBQazE3oi0NFF1hHaZQvQ08jZEJ3dF59hHUUwoiiRQtwHvIhpUEF6syZM+jWrRsA+6rmABAaGorQ0NAKu+xynBsPkzVpD6TsYZ2CuIBO6YdZ7XriePFF1lEc4t2YE1h4by/E/HrYZW0aeB5H9HqcMxlxzmjEWaMRmTeG+2eFhGBOaPX35RzR67FPp8NZoxFpFjOKbDboeR7+UilaKpQY4ueLcQGBUDXwTXeHthRH9AacMxqRabWgwGaDiecRJJWhjUqJu/z8cLd/AGS1vF+dMxqxqqAARwx6FNhsCJBI0FmtxsTAIPQW0d5LijjPmCABNPI+KI92ci3w62OsUxAnK1EHYGbrrjhV4nn3Ei072hmBfx9zSVuH9TpMS0ur8rnaCtTM9DTsKje5Q32jUBjKvTVFy+VYFh2DFuUWp66ru69ewWXzrV2OfSQSWAUBpnLnT1Aq8WV0DEKrWTH+l6IivJWdhZtX2P0kEmh5vmyp4Nq+R1cKnTMHYXNms47hELR+f3Wa0lIynq5IE4zHWnbAeQ8sTgAwq/tprCztCOVB10yV95dIkKBSIUGpQoJKhQ9yspFns9X6uj4aH/Tz8UE3tQbNFXL4SKQAgCKbDX+UFGNRbi7SLRbMzUjHby1iIannyMxwPz9MlsnRVa1GtFxe1hPLsVrwS1ExvsjPwzmTCS9nZmJZTEyl1ycaDHgzOws2AEN8ffFKeBNEyOUostmwODcXPxcX4Yv8fMQrlBjh71+vbM6g6tCedQSHoR5UdQQB+KAZYKKdTD1Rvm8YZsS2wSVtKusoTuXHK/H1pihIzjh3+NImCJD+q3AMTb6M61Zro3sXPxcV4Y3sLADA6phm6KbRNCrrv32Sm4uvC/IBANvj4hHxr5mQk1Kv4bjBgNYKJda1aAH5v77Px9LSsFevQ5Rcjs2xcZX+Hlyt1f59kAV7xn1xnnElzRk4Doik/Xc8UU5AJB5u0dLjixMAlEpMeGpkDrjYZk5tx5lvyp1Vt3YTznbCbSyd1dWfP81sxnGDAQDwcHBwpeIEADNC7Fv0ZFgsOGrQOzxffcijoz2mOAFUoGoW1Y11AuJgmUExeDi6Ga7qMlhHcZksqRav3m8G1yScdZQGOXajQABAjMLx93kd09vPzwGI+Vfvab/+1rWx26uZCNFNrYbPjWHDfYxvlFZ36si0fUejAlWTplSgPElaSHNMi2yCVH3lvcw8XZI8D4smaMAFsL9GUhdGnkeK2Yyv8vPwUW4OAKCHWo0OKrVDzq/jeVwymbAgJwerCgsAAHf7+yP4X5MkLplMAIAQqRQh1UygkHIc4m5M3kg2mas8xlVUHTsxbd/RaJJETagH5TFSwuLxaFgAsg05rKMwc0CVjpVT4/Hw12YIBiPrOJXkWq0YkHy5yucG+fji3cjG3Tx/0mDA+NRrlR6XArgnIACvhlfe4C/3xpBfeDXF6aabz+cwXkmHelDeJLAZoAllnYI0UnJ4azwc4odsg5uvEekAf/ok4/epbYBa3nBZkOJGT0UqhbLctZ47/fzwbFgYAqXSRp1fznFl5y9/LemBwEDMCgmt8j4rHc8DANS13IOl4iQVjmdCJoOqvefM4AOoB1W76B7Axc2sU5AGSopIwGOBMhSYClhHEY3vgs4idHIX9F55lHWUCoJlMuxp2QoAIAgCsq1WrC0qwqrCAmwrLcWrTSLwQGBgg8+foFKVnZ8XBKRZLPi+sAA/FRXht+ISfNQ0EoN9/RzxrTChatMGknITSjwB9aBqE3sH6wSkgc5GdcQjARwKTEWso4jOoohEXHqwF+sY1eI4DhFyOeaFheGjyKawAngrOwsXjI4ZmpRwHJorFHi1SQSeCwuHXuDx/PXMsiG9m25OfjDU0jMyCnyF41nw6duHWdvOQgWqNrEDWCcgDZAY0xWP+lhRYi5lHUW0Xok7jpxRPVnHqNUwPz9EymTgAawvLnb4+ccHBkLBcdALPDaVVLzvMayO15Zy6nitypl8+lCB8j5N2gM+4ljChNTNkeY98LjKAK2F9kaqzdyOJ6C7Q/yrpjSR2ad/p1ocP0tOKZEg4MbqFf8+fyulEgCQb7OhoJoiZRMEXLmxlFK8sv5LMTkCp1RC3b07k7adiQpUbTiOhvncyP7Y2zBLXgy9le0Nk+5C4ICZfc/D0j2BdZRqCYKA9BuFwxlDaDrehkKbtcrz99XcuvdpbzX3OJ0wGMomR/RjtGispns3SG4UU09CBaou4tx/Z0pvsDu+L+ZK8mG0mVhHcStGzoonh6ZDaOP6VbCtdVhpbUNJcdmafr3U9VvmqC7nX1FQULYIbM9/nT9GoUA3tf3eq5WFBbBUcb6byyQ1lcnQo575HEXjgcN7ABWoumk5lHUCUot/WvfHPGTBzLO9UdJd5Uv0eP7uEnDRTRt8jmKbDYVWa9mvm9MKjLxQ4fHyU7GPG/SYnHoNG4uLkWWxVDhfitmMRbk5eDPLvg5fjFyOsQEBldp9OfM6EpIuICGp8l5ef5SUYHZ6Ov4uLUV+uSE6XhCQZDTi9axMLM23F5huajX6V9EDejYsDFIASSYTnruegewbOYtsNryVnYU9N3pWz4aFM1uHz6dvXybtOhtNM68L/0igSUcg2zWrQpP6+avtQLxsToVVYHuTpLu7JivCuw9G4JVVwRDy6z8t//6Uq7hexXWaFYUFWFF463xj/f3xXuStQnjMYChbzkjJcdBIJDDwPIzleittlEp8HhVV7z2hBAjYodNih04LwL6Vh+rGVhnle0O3aTT4pGlUlXvYdVVr8HqTCLyVnYW/tVr8rdXCXyJB6b+222C1krk0MBCqBPEO0TYGFai6ajWUCpQI/dZuCF4zJYMXGN4g6UESFVn4YnILzPraBMEF68q1V6nwQUQkjhj0OGM0Is9qRbHNBjnHIUYuR4JKheG+fhju59eg3skAH1+81SQCh/V6XDAZkW+zocRmg5KTIEYhRweVCiP8/DHA17fG8/wnMBAJKhVWFhTg6I0NC4OlUnQRwYaFPnf0d+/NYWtA223U1bX9wMoRrFOQcta1H4a39RchgH6EHW1cSRuMW3YR+NewGxGfqP9+Bv9hw1jHcAq6BlVXMb0B38prdRE2fuh4F97SJ1FxcpJ1/knYPq0DwPDGU1I7TqOBb//+rGM4Df301ZVEAiTcwzoFAbCy0wh8oD3HOobH+zL0NE5N8Lx7azyJb//+Hre8UXlUoOqj/X2sE3i9LzuPxKLSs6xjeI13Yk4gbax4l0Tydn7DPXNo7yYqUPXRrDfgH806hdf6rMsoLCk5wzqG13m23XEUDaOelNhwCgV8BwxkHcOpqEDVB8cB7ceyTuGVPu46Gl8X0yxKVmZ1Pw1Tb8/aa8jd+fTtC6kvu9mDrkAFqr460DCfKwng8E63Ufiu6BTrKF7NyvGYNeAK+A6tWUchN/gNH846gtNRgaqvqO5AUCzrFF6B5yR4s9tIrC2knpMYlEpMeGpkDrjYZqyjeD1Oo6ECRapBvSins3FSvNp1BNZTcRKVLKkWr95vAteEVvhnyX/4cI8f3gOoQDVMh/tZJ/BoVokML3Qdjt+pOIlSkjwfiyb4gGO0tA8BAu/3jg/JVKAaokl7oKn499BxRxapAs90GYothTSVXMwOqNKxampTcB58D45YKZo3h6an+DeadAQqUA3VYzrrBB7HJFPhyU4DsaOQbsJ1B5t8L+P3aW0AhrvIeqOAe+9lHcFlqEA1VMf/AKpA1ik8hkGhweyOt2NvUeUtE4h4fRd0FocmdWEdw3tIpQi4dyzrFC5DBaqh5Gqgy0TWKTyCTumHJxJ641DRRdZRSAMsjEzEpQdotQlX8OnXF/Im3rMmKBWoxug5HYBnLnPvKqWqADzWtgeOF19mHYU0wivxx5Ezyjuui7AUOG5co15//PhxvPnmm7j77rvRtm1bhISEQC6XIyQkBP369cO7776LgoL67wXmLLTdRmN9Nxa4soN1CrdUrAnCjJYdcb40hXUU4gBScPhmXwf47D7BOopHkkdFIf7vreAascL8nDlzsGTJkrI/q1QqyOVylJaWlj0WGhqKjRs3oo8ItpGnHlRj9XyUdQK3lO8bhkfi21Nx8iA2CJjZ9zys3Txzd1fWgiZObFRxAoBevXrh448/xoEDB1BYWAiDwYCSkhKUlpbi22+/RVhYGPLy8jB27FgUFxc7KHnDUQ+qsXgbsLgTUJLOOonbyPWPwKPN43BFS39nniiE1+CL/4WCS7rCOorH4DQatNq5A1In33u2detW3HnnnQCA1atXY+JEttfZqQfVWBIp0GMa6xRuIyswGtNimlNx8mD5Ej1euLsEXHRT1lE8RuC99zq9OAFA7969y75OT2f/f5QKlCP0mA4ofFmnEL304GaYFhWJVH0m6yjEyVJkRXj3QQm4kGDWUdyfVIrgh6e5pKk9e/aUfR0fH++SNmtCBcoRNME3ZvSR6lwLjcO0iFBk6LNZRyEukqjIwtJJweB8PH/NOGfyGzYMimjn7UNnMpmQkpKCzz//HJMnTwYAtGzZEmPGjHFam3VF16AcRZcHLO4IWPSsk4hOcnhrPBqsQZ5JPNNXieuMK2mDccsuAhYL6yhuqcW6n6Hu6Pi9uFQqFUwmU6XH+/XrhzVr1qBZM/ar1lMPylF8QoHuD7NOITpJEe3wSLCKipMXW+efhB1TO9o3/CT14jPgDqcUJwCIiIhAkyZN4FOuhzto0CAsXrxYFMUJoALlWP3mATJaPPOms1EdMT1AigJTEesohLGlYadwamIP1jHcTtjcJ5127pSUFGRlZUGr1SI7OxsLFixAYmIievXqhddee81p7dYHDfE52p/zgcPLWKdgLjGmC2apzSi1aFlHISKy8EI3xPx6mHUMt+A7eDBivlhS+4EOdPjwYfTp0wc8z+P333/H6NGjXdr+v1EPytH6PQVIFaxTMHW0eXc8rjJScSKVPNv2OIqGdWcdQ/w4DmFz57i82V69euH2228HACxbxv6DNhUoRwuI8upFZA/E9sIseSn0VposQqo2q/tpmHo757qKp/AbOgSqdu2YtB0VFQUAuHyZ/fqYVKCcof8zgFTJOoXL7Y7vi7nSQhhsRtZRiIhZOR6zBlwB36E16yjixHEInTOXWfNXrthXAPHz82OW4SYqUM4Q2Ay47XHWKVxqW6v+eArZMNkqT1sl5N9KJSY8PTIXXKw4ZouJid9dd0LVxvHF22azobYpB9u2bcPhw/ZrhAMHDnR4hvqiAuUsd8wHfMJYp3CJzW0G4jlbBiw83edC6i5TWopX7zeBa+Id/0/qglMoEP700045d1paGrp27YqvvvoKV65cqVCs0tLS8MEHH+Cee+6BIAgIDg7G007KUR80i8+Zjq4E/niKdQqn2thuCF4zXYFNsLGOQtxUH2M0nlleAKGkhHUU5kJmPIrwZ591yrlTUlIQGxtb9meFQgF/f38YDAbodLqyx2NjY7F+/Xp07drVKTnqgwqUM/E88NUdQPZp1kmc4pf2w/C24RJ4gWcdhbi5UdqWmPZ1CgSj916/lIaFIv6vzZD6OmdpKLPZjI0bN2Lnzp04dOgQrl+/jry8PEilUoSFhaFz58645557MGHCBKjVaqdkqC8qUM52dTfwLfs1rRxtTcc78YH2AgTQjw9xjKmF7THq6zOAzTt745HvvoPA++9nHUNU6BqUs8XeAbQZxTqFQ63qNALva89TcSIO9W3QWRyazH5YiQVV+/YIuO8+1jFEhwqUKwx/22Nu3v2q80gsLD3LOgbxUAsjE3HpgV6sY7hck5dfAkdrFVZCBcoVQuI9Ytr5Z11G4fOSM6xjEA/3Svxx5IzqyTqGy/iPHAFNd1pdoyp0DcpVTFrgiz5AcSrrJA2yoOtofFt0inUM4iWk4PDNvg7w2X2CdRSnkvj7I37TH5CF0VT7qlCBcqXL24DV7jXOLIDDe91G4adC9y5OVq0VpSdKoT2nhfGaEeY8M8ADUj8p1C3UCLo9CP7d67eldu4fucj+5dYGjB1WdXBo5oac32awoWB7AUpOlMCcbQZv4CH1k0LZRAlNGw1Ch4dC6iN1aE5nUQkyrNgaB9nxc6yjOA1NjKiZjHUAr9JyCNBlEpC4mnWSOuE5Cd7qOgLr3bw4AcCFeReAcpPDODkHTsrBWmhFaWEpSk+UwreTL5rNbgaJsvaRb1OmCTm/5Tgtb0POrz2vRfrSdFhLrAAATsaBU9i/R2uhFboLOvh384faRxxTiGtj5KyYOywdS7WxwMWrrOM4nE/fPlScakEFytXufBdI3gaUZrJOUiMbJ8VrXe/CxkIPuYfLBqjj1Ai8PRB+HfygCLdPWjHnmpH7ey4KdxdCe0qLjFUZiHk8psZTCbyAjOUZECwC1C3VMFw2ODRqQ86vu6TDtU+uQTAL8O/uj7DRYVC1UIHjOPAmHsYMI0pPlEKqcY/e0035Ej2ev0eBj9dEQsgQ9/+Z+uA0GkS89RbrGKJHkyRcTR0IjP6EdYoaWSUyvNh1uOcUJwAtXmiB+NfiETI4pKw4AYAiTIGoR6IQNDAIAFB8oBjmfHON58r/Jx/6y3oE9AmAb3tfh2et7/l5E4/0ZekQzAKChwaj2dxmUMeqy2aFSZQSaOI0aHJ/EyjC3G82aYqsCO8+JAUXHMQ6isOEz3sSiuho1jFEjwoUC21GAB3+wzpFlSxSBZ7tMhSbCz1rKrlvu5rf6IPuuPXmZ0ypfjUDc64ZOetzIPWVInJ8pMPyNeb8RfuLYMm1QBYgQ8QDEQ7PJAaJiiwsnRwCzsc5qyy4kqpzJwRNnsw6hlugAsXKyI9Ft5isSabCvE6DsL3Qcy9KV0civ/VfQeCrnzeUsTIDvIlHxPgIyPwdP0LekPMX7SsCAPj39IdE4bn/pbdrUvDztBaAXM46SoNxSiWavvMOOInn/js5El2DYkUTbC9S66axTgIAMCg0eLJ9PxwsOs86ChO6C7cWy1RFq6o8pmBnAXTndPBp74Ogfo4fbmrI+XkLD0OK/RqVuoUa5nwzcjfmovR0KWzFNkh8JNDEahA8KBh+Xdjv79NY6/yTEDq1EwYtPwG44QTk8PnzoWzVinUMt0FlnKX29wKdx7NOAb3SFzMT+uBgURLrKEzYdDbkbsoFAGhaa6CMrLzZpKXQgqy1WeAUHKKmRjk8Q0PPb8mzQLDa36jNuWZcfuUyCncVwlZiA6fkYCuxofRkKa4tvoaMFRm17gfkDpaGncLpie53I6/voEEInuS9u203BBUo1kYtBELbMGu+VBWAx9r2xLHiS8wysCTwAtKXpcNaZAUn59B0ctMqj7u+6jp4A4/wseEVJlk4SkPPb9PdmjufuzEXnJRDzOwYJHyZgIQvEtB6YWv497Tf31W4uxD5W/Idnp2Ft2OOI32s+yyJJAsPR+R777KO4XaoQLGm8AEe+BaQa1zedLEmCI+27oyTJckub1ssMn/IROnJUgBA08lNoYqpPLxXtL8IpSdLoWqmQuidoQ7P0KjzCxW/jnokCgE9A8DJ7DP4FCEKxMyMKfu+cv/IhWBz/14UADzT7jiKhrnBEkESCZp+9CFkQZ4zC9FVqECJQXg7+/UoFyrwCcUj8e1xrjTFpe2KSeZPmSjYVgAAiBgfUWEm303WYisy12QCEiDq4ShwUscu6NnY80tUt/4LK5ooqlwNg5NwCB1hL3w2ra3smpUnmNX9NEy9O7KOUaOQ6dPh07s36xhuiQqUWHSdBHSe4JKmcv0j8HBca1zUuue6gI6QtTYL+Zvtw10RD0ZU23PJWpcFm9aG4IHBUEQqYDPaKvwq3xu5+RhvrfsGjo09vyzo1jynqq6dlT3X9NZzlnxLnfOJnZXjMWvAFfDtxTnxQNW5E8LmPck6htuiWXxiMmohcP04kHvBaU1kBUbh0egYXNOmO60Nscv6KQt5m/MAAE0eaFLWu6iKOdd+027B9gIUbC+o8bznn7DPgAwZFoLIiXW7h6mx55f5yiALksFaaK1Te56oVGLC06Py8Jm+GYSr4vnQJQ0KQvSiReBk9DbbUNSDEhOFBhi3ymnXo9KDm2FaVFNc0113yvndQeZPmRWKU9hIcd2L1hC+Hew3IZsyTdUeY8y4dfOxO64mUZtMaSn+734zuCYi+feUyRC1eDHkUY6f8elNaDVzMTrxA/DbLIee8lpoHB4ND0KWIdeh53UnmT9lVhzWq6HnVFfZv2Yj9zf736mjVzOv6/l1STpcfd++mGqzuc0qXYcSeAHJryfDmGaELEiGNgvbgJN45uZ4fYzReGZ5AYSSEqY5mrz8MoKn0GoRjUU9KDHqOhHoM8dhp7sS3goPh/l7dXGqcM1pvGOKU10V7inEmWlncGbaGWjPax1+fp82PvDvYS9KGSszUHykuOzalTnfjLQv02BMs/egmtzfxGOLEwAcUKVj1dSm4FRV32ztCgH33kvFyUFocFSshr0NFFwFkjY16jRJEe3wWKAcBcaar294MnO+GXl/2Yf1wAF5m/KQtymv2uNDR4S6tIA5QvSMaKSUpkCfpEfakjRwMg4SpaTCfVJh94Qh6HbPn+q8yfcyQqe1x6ivzwA2W+0vcCBVp06IePMNl7bpyagHJVYSCXD/10Bk5waf4mzTDpgeIEWBqchxudxR+Ul1AmAtsdb4y2Z07ZuaI0iUEsS+EIumDzeFpo0GEqUEvJGHLEiGgNsCEPdKHJrc24R1TJf5NugsDk/u6tI2pWGhiP7vZ5AoPO8aHyt0DUrsSjKBb4YAJRn1etnJmM6Yqbag1OL4ISVC3MV7yd3Q8ufDTm+HU6vRfOUKqLt0cXpb3oR6UGLnHwlMWAso6r7v0NHm3fGYykTFiXi9l+OPI2eUk9ftk0oRtXAhFScnoALlDiI6Av9ZAXC174Z6MLYXZslLobfqXRCMEPGb1ykRuju6OO38EW+8Dr/Bg5x2fm9GBcpdtL7Tvl18DfbE98EcaSEMtuo33CPE29ggYGbfC7B2S3D4uUPnzkHQuHEOPy+xowLlTnrPBPpWvWzKtlb9MQ85MNmqv1mTEG9l5KyYOywdaB3rsHMGPvggwmbPdtj5SGVUoNzN8LeBHo9UeGhzmwF4zpYBC+85a6wR4mj5Ej2ev6cUXFTdlqGqie/QIYh47f8ckIrUhAqUOxq1COj0IADg93aD8aIlFVbBe9diI6SuUmRFePchKbjght8PpundG1ELFoCT1n5NmDQOTTN3VzYr9u9+CzPTfgMv1H31bEIIMFjfAjO/yYSg09XrdZrbbkPMl0shUaudlIyURz0odyWVoeeA/8PA6IGskxDidrZrUrBuWiwgl9f5NZpevag4uRgVKDcml8ixYOACDI4ZzDoKIW7nZ/8L2DG1I8DVvjahpmdPxHz1JRUnF6MC5eaoSBHScEvDTuH0xJpv5NX06EHFiREqUB6AihQhDfd2zHGkj+1V5XPqHt0Rs+wrSDTO2aON1IwKlIe4WaSGNBvCOgohbueZdsdRPLR7hcd8+vdHs6+/puLEEBUoDyKXyLFgwAI80PoB1lEIcTuze5yBqXdHAID/yJGI+WIJDesxRtPMPdT3577HgqMLaAo6IfXgxyvxvXkS4mY8CU5Cn99ZowLlwXal7cLzu5+nhWMJqQMOHJ7q/hQe6fBI7QcTl6AC5eGSCpIwe9tsZOuzWUchRLQUEgXeuf0djIgdwToKKYcKlBfI1edizvY5OJd/jnUUQkTHX+GPzwZ/hu5Nutd+MHEpKlBewmA14KU9L2Fb6jbWUQgRjSjfKHwx9AvEBcSxjkKqQAXKiwiCgE+Of4KVZ1ayjkIIc53COuHTQZ8iVB3KOgqpBhUoL7Th0ga8ffBtWHlaAZ14Hw4cpiRMwbzu8yCX1H0tPuJ6VKC81KHMQ3h659MoNZeyjkKIywQqA/Hu7e/ijug7WEchdUAFyotdLb6Kp3c8jeTiZNZRCHG6buHd8OEdHyLCJ4J1FFJHVKC8nMlmwuJji/HD+R8ggH4UiOeRcBJM7zAds7vMhlRCmwy6EypQBABw4PoBvLrvVeToc1hHIcRhQlQheL//++jTtA/rKKQBqECRMsWmYrx78F38lfIX6yiENNptkbfhg/4f0Cw9N0YFilTy55U/8c6hd2gCBXFLUk6KmZ1nYkanGZBwtJ6eO6MCRaqUpcvCq/texaHMQ6yjEFJn4ZpwfNj/Q/SI6ME6CnEAKlCkWoIg4IfzP2Dx8cUw2Uys4xBSo+HNh+PV3q8iSBXEOgpxECpQpFbJRcl4ac9LOF9wnnUUQipp4d8CL932Evo27cs6CnEwKlCkTiy8BV8kfoGVZ1bCJthYxyEEapkaMzrOwLT20yCX0ooQnogKFKmXpIIkfHzkYxzKomtThJ0hzYbghZ4vINI3knUU4kRUoEiD7EzbiYVHFyKlJIV1FOJFmvk1w0u3vYTbo25nHYW4ABUo0mBW3oq1SWvx5ckvUWQqYh2HeDCVVIXpHafjkQ6PQCFVsI5DXIQKFGm0EnMJvjr5FX688CMsvIV1HOJhBkYPxAu9XkC0XzTrKMTFqEARh0krScOiY4vwT+o/rKMQDxDtG40Xe72IATEDWEchjFCBIg53LPsYPj7yMc7mn2Udhbihpj5NMa3DNNzX6j4opUrWcQhDVKCIUwiCgD+u/IFPj3+KbH026zjEDbTwb4HpHadjVNwo2kiQAKACRZzMYDVgw6UNWH1uNdK16azjEBFqG9wWj3Z8FMOaD6O180gFVKCIS/ACjx2pO/Ddue9wPOc46zhEBLqEdcGMTjNod1tSLSpQxOXO5p3Fd+e+w9aUrbAKVtZxiIv1juyNxzo9hp4RPVlHISJHBYowk6XLwo8XfsQvF39BibmEdRziRBw4DIwZiBkdZ6BjWEfWcYiboAJFmNNb9Pgt+Tf8cP4HXCu5xjoOcSA/hR+GNx+Oie0molVQK9ZxiJuhAkVEgxd47Erbhe/Pf48jWUdYxyENJONk6BfVD2Pix2BgzECaKk4ajAoUEaWLhRex+epm/H3tb1rvz00khCRgTNwYjIgdgRB1COs4xANQgSKil1SQhC0pW6hYiVATTROMihuFu+PvRnxgPOs4xMNQgSJuhYoVe2qZGkObDcWY+DG4LfI2uneJOA0VKOK2qFi5TqAyED0jemJQzCAMaTYEGrmGdSTiBahAEY9ws1jtSNuB5KJkCKAf68ZQy9To3qQ7ekf2xm2Rt6FNUBtwHMc6FvEyVKCIxykyFuFk7kmcyDmBEzkncDb/LEw2E+tYoiaTyNAptFNZQeoY1pHWwyPMUYEiHs9is+BcwTkk5iQiMScRJ3JOIN+YzzoWUxw4tA1ui9sib8NtkbehW3g3GrYjokMFiniltJI0nMi197AScxI9eljQT+6H2MBYxAXEIT4gHnGBcegU2gmBqkDW0QipERUoQmDfFfhq8VVklGYgQ5uBdG06Mkrtv2fpsmATbKwj1ipEFYK4wDjEBdz4dePrcE0462iENAgVKEJqYeWtyNRlIkObUVa0yn7XZqDAWODU9lVSFfwUfvBX+Nt/V9p/D1GFIDYgFvGB8YgLiEOAMsCpOQhxNSpQhDSSwWqA1qyF3qqHzqKD3qKH3qqH3mL/s9FmhI23wSbYf1l4S9mfeYGHn8IPfvJbhadCMVL4QyFVsP4WCWGCChQhhBBRolvACSGEiBIVKEIIIaJEBYoQQogoUYEipI7y8/OxcuVKTJo0CQkJCfDx8YFSqUR0dDTGjh2LX3/9lXVEQjwKTZIgpI7kcjmsVmvZn1UqFaRSKXQ6XdljI0aMwC+//AKNhlZlIKSxqAdFSB1ZrVb06tULX3zxBZKTk2EwGKDVanH16lVMnz4dAPDXX3/h8ccfZ5yUEM9APShC6mjHjh0YNGhQtc8/8cQT+OqrrwAAqampiImJcVU0QjwS9aAIqaOaihOAsl4UABw9etTZcQjxeFSgCHEQlUpV9rXNJv61+wgROypQhDjIzp07y77u2LEjuyCEeAi6BkWIAxQVFSEhIQGZmZno378/du/ezToSIW6PelCENBLP85g8eTIyMzOhUqnw+eefs45EiEegAkVII82bNw9//PEHAGDJkiXo1KkT40SEeAYqUIQ0wnPPPVfWY/rkk0/wyCOPME5EiOegAkVIAz3//PNYuHAhAGDBggV46qmn2AYixMPIWAcgxB3Nnz8fCxYsAAB89NFHePbZZxknIsTzUIEipJ6ee+65sp7TRx99hPnz5zNORIhnogJFSD2UL04LFiygnhMhTkQFipA6Kn/NadGiRXj66acZJyLEs9GNuoTUQWpqKpo3bw4AkEgkCAsLq/H45557Ds8995wrohHisagHRUgd8Dxf4evs7Owaj9dqtc6ORIjHox4UIYQQUaL7oAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIgSFShCCCGiRAWKEEKIKFGBIoQQIkpUoAghhIjS/wOj7k1TPI4nKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print('did the patient recover?')\n",
    "temp = break_down_columns(df,'RECOVD')\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n",
    "# temp.plot.bar(title='RECOVD',x='value',y='count',rot=0,figsize=(5,5),width=0.25,fontsize=18)\n",
    "# temp = temp.set_index('value')\n",
    "temp.plot.pie(title='RECOVD',y='percent',figsize=(5, 5),autopct='%.2f',fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='count', \n",
    "#     y='value',\n",
    "#     title=\"Did the patient recover? (Y=yes, N=no, U=unknown, nan=N/A )\"\n",
    "#     )\n",
    "# fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the Age of the patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Age of the patient\n",
      "  \n",
      "column:  AGE_YRS \n",
      "buckets:  [0, 15, 25, 35, 45, 55, 65, 75, 85, 500]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JGarza\\AppData\\Local\\Temp\\ipykernel_9980\\4231830367.py:11: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  idf = idf.groupby(by='bucket').count()\n"
     ]
    },
    {
     "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>AGE_YRS</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 15]</th>\n",
       "      <td>45824</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(15, 25]</th>\n",
       "      <td>74452</td>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 35]</th>\n",
       "      <td>121851</td>\n",
       "      <td>12.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35, 45]</th>\n",
       "      <td>144225</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45, 55]</th>\n",
       "      <td>150044</td>\n",
       "      <td>15.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 65]</th>\n",
       "      <td>167417</td>\n",
       "      <td>17.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65, 75]</th>\n",
       "      <td>151579</td>\n",
       "      <td>15.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(75, 85]</th>\n",
       "      <td>74810</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(85, 500]</th>\n",
       "      <td>26849</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AGE_YRS  percent\n",
       "bucket                     \n",
       "(0, 15]      45824     4.79\n",
       "(15, 25]     74452     7.78\n",
       "(25, 35]    121851    12.73\n",
       "(35, 45]    144225    15.07\n",
       "(45, 55]    150044    15.68\n",
       "(55, 65]    167417    17.49\n",
       "(65, 75]    151579    15.84\n",
       "(75, 85]     74810     7.82\n",
       "(85, 500]    26849     2.81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "print('the Age of the patient')\n",
    "temp = break_down_buckets(df,'AGE_YRS',[0,15,25,35,45,55,65,75,85,500])\n",
    "temp = temp.reset_index()\n",
    "temp[\"bucket\"] = temp[\"bucket\"].astype(str)\n",
    "\n",
    "# temp = temp.reset_index()\n",
    "# temp.plot.bar(title='AGE_YRS',x='bucket',y='AGE_YRS',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "# # temp.plot.bar(title='AGE_YRS',x='bucket',y='percent',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='percent', \n",
    "#     y='bucket',\n",
    "#     title=\"The Age of the Patient\"\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"percent\", \n",
    "    y=\"bucket\",\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='The Age of the Patient')\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ***deaths only*** \n",
      "column:  AGE_YRS \n",
      "buckets:  [0, 15, 25, 35, 45, 55, 65, 75, 85, 500]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JGarza\\AppData\\Local\\Temp\\ipykernel_9980\\4231830367.py:11: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  idf = idf.groupby(by='bucket').count()\n"
     ]
    },
    {
     "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>AGE_YRS</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 15]</th>\n",
       "      <td>65</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(15, 25]</th>\n",
       "      <td>155</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25, 35]</th>\n",
       "      <td>265</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35, 45]</th>\n",
       "      <td>491</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45, 55]</th>\n",
       "      <td>1047</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55, 65]</th>\n",
       "      <td>2772</td>\n",
       "      <td>13.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65, 75]</th>\n",
       "      <td>5265</td>\n",
       "      <td>25.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(75, 85]</th>\n",
       "      <td>5904</td>\n",
       "      <td>28.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(85, 500]</th>\n",
       "      <td>4456</td>\n",
       "      <td>21.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AGE_YRS  percent\n",
       "bucket                     \n",
       "(0, 15]         65     0.32\n",
       "(15, 25]       155     0.76\n",
       "(25, 35]       265     1.30\n",
       "(35, 45]       491     2.40\n",
       "(45, 55]      1047     5.13\n",
       "(55, 65]      2772    13.57\n",
       "(65, 75]      5265    25.78\n",
       "(75, 85]      5904    28.91\n",
       "(85, 500]     4456    21.82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = break_down_buckets(df_death,'AGE_YRS',[0,15,25,35,45,55,65,75,85,500],message='***deaths only***')\n",
    "temp = temp.reset_index()\n",
    "temp[\"bucket\"] = temp[\"bucket\"].astype(str)\n",
    "\n",
    "# temp = temp.reset_index()\n",
    "# temp.plot.bar(title='AGE_YRS (death only)',x='bucket',y='AGE_YRS',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "# # temp.plot.bar(title='AGE_YRS (death only)',x='bucket',y='percent',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='percent', \n",
    "#     y='bucket',\n",
    "#     title=\"The Age of the Patient\"\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"percent\", \n",
    "    y=\"bucket\",\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='The Age of the Patient (Deaths Only)')\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the number of days between the vaccine and the adverse rection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of days between the vaccine and the adverse rection\n",
      "  \n",
      "column:  NUMDAYS \n",
      "buckets:  [0, 1, 5, 10, 20, 30, 40, 50, 60]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JGarza\\AppData\\Local\\Temp\\ipykernel_9980\\4231830367.py:11: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  idf = idf.groupby(by='bucket').count()\n"
     ]
    },
    {
     "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>NUMDAYS</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>150580</td>\n",
       "      <td>37.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 5]</th>\n",
       "      <td>90093</td>\n",
       "      <td>22.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, 10]</th>\n",
       "      <td>63874</td>\n",
       "      <td>15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 20]</th>\n",
       "      <td>40664</td>\n",
       "      <td>10.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 30]</th>\n",
       "      <td>30280</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 40]</th>\n",
       "      <td>15302</td>\n",
       "      <td>3.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 50]</th>\n",
       "      <td>8336</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 60]</th>\n",
       "      <td>6835</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NUMDAYS  percent\n",
       "bucket                    \n",
       "(0, 1]     150580    37.09\n",
       "(1, 5]      90093    22.19\n",
       "(5, 10]     63874    15.73\n",
       "(10, 20]    40664    10.02\n",
       "(20, 30]    30280     7.46\n",
       "(30, 40]    15302     3.77\n",
       "(40, 50]     8336     2.05\n",
       "(50, 60]     6835     1.68"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "print('the number of days between the vaccine and the adverse rection')\n",
    "temp = break_down_buckets(df,'NUMDAYS',[0,1,5,10,20,30,40,50,60])\n",
    "temp = temp.reset_index()\n",
    "temp[\"bucket\"] = temp[\"bucket\"].astype(str)\n",
    "\n",
    "# temp.plot.barh(title='NUMDAYS',x='bucket',y='NUMDAYS',rot=0,figsize=(10,5),width=0.25,fontsize=18)\n",
    "# # temp.plot.bar(title='AGE_YRS',x='bucket',y='percent',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='percent', \n",
    "#     y='bucket',\n",
    "#     title=\"the number of days between the vaccine and the adverse rection\"\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"percent\", \n",
    "    y=\"bucket\",\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='the number of days between the vaccine and the adverse rection')\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ***deaths only*** \n",
      "column:  NUMDAYS \n",
      "buckets:  [0, 1, 5, 10, 20, 30, 40, 50, 60]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JGarza\\AppData\\Local\\Temp\\ipykernel_9980\\4231830367.py:11: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  idf = idf.groupby(by='bucket').count()\n"
     ]
    },
    {
     "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>NUMDAYS</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>1322</td>\n",
       "      <td>16.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 5]</th>\n",
       "      <td>1663</td>\n",
       "      <td>21.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, 10]</th>\n",
       "      <td>1049</td>\n",
       "      <td>13.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 20]</th>\n",
       "      <td>1380</td>\n",
       "      <td>17.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 30]</th>\n",
       "      <td>1034</td>\n",
       "      <td>13.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 40]</th>\n",
       "      <td>626</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 50]</th>\n",
       "      <td>430</td>\n",
       "      <td>5.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 60]</th>\n",
       "      <td>412</td>\n",
       "      <td>5.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NUMDAYS  percent\n",
       "bucket                    \n",
       "(0, 1]       1322    16.70\n",
       "(1, 5]       1663    21.01\n",
       "(5, 10]      1049    13.25\n",
       "(10, 20]     1380    17.43\n",
       "(20, 30]     1034    13.06\n",
       "(30, 40]      626     7.91\n",
       "(40, 50]      430     5.43\n",
       "(50, 60]      412     5.20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = break_down_buckets(df_death,'NUMDAYS',[0,1,5,10,20,30,40,50,60],message='***deaths only***')\n",
    "temp = temp.reset_index()\n",
    "temp[\"bucket\"] = temp[\"bucket\"].astype(str)\n",
    "\n",
    "# temp.plot.barh(title='NUMDAYS (deaths only)',x='bucket',y='NUMDAYS',rot=0,figsize=(10,5),width=0.25,fontsize=18)\n",
    "# temp.plot.bar(title='AGE_YRS',x='bucket',y='percent',rot=90,figsize=(5,5),width=0.25,fontsize=18)\n",
    "\n",
    "# fig = px.bar(\n",
    "#     temp, \n",
    "#     x='percent', \n",
    "#     y='bucket',\n",
    "#     title=\"the number of days between the vaccine and death\"\n",
    "#     )\n",
    "# fig.show()\n",
    "\n",
    "sns.barplot(\n",
    "    data=temp,\n",
    "    x=\"percent\", \n",
    "    y=\"bucket\",\n",
    "    palette = \"Spectral\"\n",
    "    ).set(title='the number of days between the vaccine and death')\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\JGarza\\pythons\\Python310\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'showing number of days between vaccine and adverse reaction ( only 10 days or less, broke out death into other color)')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df[df['NUMDAYS'] <= 10]\n",
    "\n",
    "sns.histplot(\n",
    "    data=temp, \n",
    "    x=\"NUMDAYS\",\n",
    "    hue='DIED', \n",
    "    kde=True\n",
    "    ).set(title='showing number of days between vaccine and adverse reaction ( only 10 days or less, broke out death into other color)')\n",
    "\n",
    "# fig = px.histogram(\n",
    "#     temp, \n",
    "#     x=\"NUMDAYS\",\n",
    "#     color='DIED',\n",
    "#     marginal=\"violin\",\n",
    "#     hover_data=temp.columns,\n",
    "#     title=\"showing number of days between vaccine and adverse reaction ( only 25 days or less, 25% sample,broke out death into other color)\"\n",
    "#     )\n",
    "# fig.update_layout(barmode='overlay')\n",
    "# fig.update_traces(opacity=0.75)\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### break down of the VAX_NAME column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "break down of the VAX_NAME column\n",
      "\n",
      "break down of VAX_NAME\n"
     ]
    },
    {
     "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>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>474706</td>\n",
       "      <td>44.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (UNKNOWN))</td>\n",
       "      <td>7104</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (MODERNA))</td>\n",
       "      <td>465382</td>\n",
       "      <td>43.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (MODERNA BIVALENT))</td>\n",
       "      <td>16851</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))</td>\n",
       "      <td>23066</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (JANSSEN))</td>\n",
       "      <td>73130</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (NOVAVAX))</td>\n",
       "      <td>421</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     column                                         value   count  percent\n",
       "0  VAX_NAME           COVID19 (COVID19 (PFIZER-BIONTECH))  474706    44.76\n",
       "1  VAX_NAME                   COVID19 (COVID19 (UNKNOWN))    7104     0.67\n",
       "2  VAX_NAME                   COVID19 (COVID19 (MODERNA))  465382    43.88\n",
       "3  VAX_NAME          COVID19 (COVID19 (MODERNA BIVALENT))   16851     1.59\n",
       "4  VAX_NAME  COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))   23066     2.17\n",
       "5  VAX_NAME                   COVID19 (COVID19 (JANSSEN))   73130     6.89\n",
       "6  VAX_NAME                   COVID19 (COVID19 (NOVAVAX))     421     0.04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "***deaths only***\n",
      "\n",
      "break down of VAX_NAME\n"
     ]
    },
    {
     "data": {
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       "      <td>VAX_NAME</td>\n",
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       "      <td>9552</td>\n",
       "      <td>43.20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>10195</td>\n",
       "      <td>46.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (UNKNOWN))</td>\n",
       "      <td>79</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (JANSSEN))</td>\n",
       "      <td>1906</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))</td>\n",
       "      <td>214</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (MODERNA BIVALENT))</td>\n",
       "      <td>160</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (NOVAVAX))</td>\n",
       "      <td>4</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     column                                         value  count  percent\n",
       "0  VAX_NAME                   COVID19 (COVID19 (MODERNA))   9552    43.20\n",
       "1  VAX_NAME           COVID19 (COVID19 (PFIZER-BIONTECH))  10195    46.11\n",
       "2  VAX_NAME                   COVID19 (COVID19 (UNKNOWN))     79     0.36\n",
       "3  VAX_NAME                   COVID19 (COVID19 (JANSSEN))   1906     8.62\n",
       "4  VAX_NAME  COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))    214     0.97\n",
       "5  VAX_NAME          COVID19 (COVID19 (MODERNA BIVALENT))    160     0.72\n",
       "6  VAX_NAME                   COVID19 (COVID19 (NOVAVAX))      4     0.02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculating the death ratio ( death_count / count )\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JGarza\\AppData\\Local\\Temp\\ipykernel_9980\\2074247858.py:12: FutureWarning: The behavior of pd.concat with len(keys) != len(objs) is deprecated. In a future version this will raise instead of truncating to the smaller of the two sequences\n",
      "  df_CVN = pd.concat([df_VN,df_DVN],keys=['value'],join=\"inner\",axis=1)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "      <th>death_count</th>\n",
       "      <th>death_ratio</th>\n",
       "      <th>death_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>474706</td>\n",
       "      <td>10195</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (UNKNOWN))</td>\n",
       "      <td>7104</td>\n",
       "      <td>79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (MODERNA))</td>\n",
       "      <td>465382</td>\n",
       "      <td>9552</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (MODERNA BIVALENT))</td>\n",
       "      <td>16851</td>\n",
       "      <td>160</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))</td>\n",
       "      <td>23066</td>\n",
       "      <td>214</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (JANSSEN))</td>\n",
       "      <td>73130</td>\n",
       "      <td>1906</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VAX_NAME</td>\n",
       "      <td>COVID19 (COVID19 (NOVAVAX))</td>\n",
       "      <td>421</td>\n",
       "      <td>4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     column                                         value   count  \\\n",
       "0  VAX_NAME           COVID19 (COVID19 (PFIZER-BIONTECH))  474706   \n",
       "1  VAX_NAME                   COVID19 (COVID19 (UNKNOWN))    7104   \n",
       "2  VAX_NAME                   COVID19 (COVID19 (MODERNA))  465382   \n",
       "3  VAX_NAME          COVID19 (COVID19 (MODERNA BIVALENT))   16851   \n",
       "4  VAX_NAME  COVID19 (COVID19 (PFIZER-BIONTECH BIVALENT))   23066   \n",
       "5  VAX_NAME                   COVID19 (COVID19 (JANSSEN))   73130   \n",
       "6  VAX_NAME                   COVID19 (COVID19 (NOVAVAX))     421   \n",
       "\n",
       "   death_count  death_ratio  death_percent  \n",
       "0        10195         0.02           2.15  \n",
       "1           79         0.01           1.11  \n",
       "2         9552         0.02           2.05  \n",
       "3          160         0.01           0.95  \n",
       "4          214         0.01           0.93  \n",
       "5         1906         0.03           2.61  \n",
       "6            4         0.01           0.95  "
      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('break down of the VAX_NAME column')\n",
    "df_VN = break_down_columns(df,'VAX_NAME')\n",
    "\n",
    "print('\\n***deaths only***')\n",
    "df_DVN = break_down_columns(df_death,'VAX_NAME')\n",
    "\n",
    "# df_CVN = df_VN.join(df_DVN[['value','count']],on='value')\n",
    "df_DVN = df_DVN.rename(columns={\"count\": \"death_count\"})\n",
    "\n",
    "\n",
    "print('calculating the death ratio ( death_count / count )')\n",
    "df_CVN = pd.concat([df_VN,df_DVN],keys=['value'],join=\"inner\",axis=1)\n",
    "\n",
    "df_CVN = pd.merge(df_VN,df_DVN,on=['value'])\n",
    "df_CVN = df_CVN.drop(columns=['percent_x','percent_y','column_y']) \n",
    "df_CVN = df_CVN.rename(columns={\"column_x\": \"column\"})\n",
    "df_CVN['death_ratio'] = df_CVN['death_count'] / df_CVN['count']\n",
    "df_CVN['death_percent'] = df_CVN['death_ratio']*100\n",
    "\n",
    "display(df_CVN)\n",
    "\n",
    "\n",
    "\n",
    "print('\\n' + '-'*25 + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approximating actual adverse reaction numbers\n",
    "\n",
    "VAERS only contains reported data and  \n",
    "*'...fewer than 1% of vaccine adverse events are reported.'*  \n",
    "Source: \n",
    "https://digital.ahrq.gov/sites/default/files/docs/publication/r18hs017045-lazarus-final-report-2011.pdf (page 6)\n",
    "\n",
    "\n",
    "we will me multiplying the counts by 80 and 120,  \n",
    "in order to get an approzimate min and max of what the numbers might actually be.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "( approx adverse reactions / vaxxed [1 or more shots] ) * 100\n",
      " min: ( 84,852,800 / 500,864,529 ) * 100 \n",
      " max: ( 127,279,200 / 500,864,529 ) * 100 \n",
      " 16.94 % - 25.41 % \n",
      "\n",
      "( approx adverse deaths / vaxxed [1 or more shots] ) * 100\n",
      " min: ( 1,768,800 / 500,864,529 ) * 100 \n",
      " max: ( 2,653,200 / 500,864,529 ) * 100 \n",
      " 0.35 % - 0.53 % \n",
      "\n",
      "( approx no recovery / vaxxed [1 or more shots] ) * 100\n",
      " min: ( 27,155,840 / 500,864,529 ) * 100 \n",
      " max: ( 40,733,760 / 500,864,529 ) * 100 \n",
      " 5.42 % - 8.13 % \n",
      "\n",
      "( approx no recovery + (50% of unknowns) / vaxxed [1 or more shots] ) * 100\n",
      " min: ( 37,531,440.0 / 500,864,529 ) * 100 \n",
      " max: ( 56,297,160.0 / 500,864,529 ) * 100 \n",
      " 7.49 % - 11.24 % \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def print_percent(vmin,vmax,label0,vcount,label1):\n",
    "    \"\"\"\n",
    "    vmin: vaers min\n",
    "    vmax: vaers max\n",
    "    label0: vaers label\n",
    "    vcount: vaxxed count\n",
    "    label1: vaxxed label\n",
    "    \"\"\"\n",
    "    print(\n",
    "        '( {label0} / {lavel1} ) * 100\\n'.format(\n",
    "            label0=label0,\n",
    "            lavel1=label1\n",
    "            ),\n",
    "        'min: ( {0:,} / {1:,} ) * 100 \\n'.format(vmin,vcount),\n",
    "        'max: ( {0:,} / {1:,} ) * 100 \\n'.format(vmax,vcount),\n",
    "        '{:.2f} %'.format((vmin/vcount)*100),\n",
    "        '-',\n",
    "        '{:.2f} %'.format((vmax/vcount)*100),\n",
    "        '\\n'\n",
    "        )   \n",
    "\n",
    "print_percent(\n",
    "    len(df)*80,\n",
    "    len(df)*120,\n",
    "    'approx adverse reactions',\n",
    "    vaxx,\n",
    "    'vaxxed [1 or more shots]'\n",
    "    )\n",
    "\n",
    "print_percent(\n",
    "    len(df_death)*80,\n",
    "    len(df_death)*120,\n",
    "    'approx adverse deaths',\n",
    "    vaxx,\n",
    "    'vaxxed [1 or more shots]'\n",
    "    )\n",
    "\n",
    "df_nrecovd = df[df['RECOVD']=='N']\n",
    "print_percent(\n",
    "    len(df_nrecovd)*80,\n",
    "    len(df_nrecovd)*120,\n",
    "    'approx no recovery',\n",
    "    vaxx,\n",
    "    'vaxxed [1 or more shots]'\n",
    "    )\n",
    "\n",
    "df_urecovd = df[df['RECOVD']=='U']\n",
    "print_percent(\n",
    "    (len(df_nrecovd) + (len(df_urecovd)*0.5))*80,\n",
    "    (len(df_nrecovd) + (len(df_urecovd)*0.5))*120,\n",
    "    'approx no recovery + (50% of unknowns)',\n",
    "    vaxx,\n",
    "    'vaxxed [1 or more shots]'\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Women's Reproductive Symptoms\n",
    "\n",
    "Why?\n",
    "I have women in my life that were curious about this.\n",
    "\n",
    "WRS_list (list) :  a list of symptoms that effect or could cause effects to a women's reproductive system  \n",
    "df_WRS (DataFrame) :  a dataframe that contains VAERS events that have at least 1 of the WRS symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def symptom_filter_search(idf, search_list):\n",
    "    \"\"\"\n",
    "    returns a dataframe pf vaers events where the patient \n",
    "    has had 1 or more of the symptoms on the list\n",
    "    \"\"\"\n",
    "    print('this could take between 20sec-60sec')\n",
    "\n",
    "    data = idf.to_dict(orient='records')\n",
    "\n",
    "    search_list = [i.upper() for i in search_list]\n",
    "    \n",
    "    results = []\n",
    "    for index,d in enumerate(data):\n",
    "        # if index%5000 == 0:\n",
    "        #     print('{:.2f}'.format(index/len(data)))\n",
    "        try:\n",
    "            d['SYMPTOMS'] = [i.upper() for i in d['SYMPTOMS'] if isinstance(i, str)]\n",
    "        \n",
    "            symptom_match = list(set(d['SYMPTOMS']) & set(search_list))\n",
    "            d['SYMPTOMS_MATCH'] = symptom_match\n",
    "            d['SYMPTOMS_MATCH_LENGTH'] = len(symptom_match)\n",
    "            if len(symptom_match) > 0:\n",
    "                results.append(d)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    return pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "WRS_list = [\n",
    "    'Intermenstrual bleeding',\n",
    "    'Menopause',\n",
    "    'Heavy menstrual bleeding',\n",
    "    'dysmenorrhoea',\n",
    "    'ABNORMAL UTERINE BLEEDING',\n",
    "    'MATERNAL EXPOSURE BEFORE PREGNANCY',\n",
    "    'MENSTRUATION IRREGULAR',\n",
    "    'Oligomenorrhea',\n",
    "    'OLIGOMENORRHOEA',\n",
    "    'POLYMENORRHOEA',\n",
    "    'MENSTRUAL DISORDER', \n",
    "    'OLIGOMENORRHOEA',\n",
    "    'ANOVULATORY CYCLE',\n",
    "    'OVULATION DELAYED',\n",
    "    'BACTERIAL VAGINOSIS',\n",
    "    'GYNAECOLOGICAL EXAMINATION ABNORMAL',\n",
    "    'OVARIAN CYST',\n",
    "    'BIOPSY UTERUS',\n",
    "    'UTERINE LEIOMYOMA',\n",
    "    'HOT FLUSH',\n",
    "    'BREAST TENDERNESS',\n",
    "    'BREAST SWELLING',\n",
    "    'BREAST PAIN',\n",
    "    'VAGINAL HAEMORRHAGE'\n",
    "    ]\n",
    "WRS_list = [i.upper() for i in WRS_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this could take between 20sec-60sec\n",
      "df_WRS.head(5)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAERS_ID</th>\n",
       "      <th>RECVDATE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>AGE_YRS</th>\n",
       "      <th>CAGE_YR</th>\n",
       "      <th>CAGE_MO</th>\n",
       "      <th>SEX</th>\n",
       "      <th>RPT_DATE</th>\n",
       "      <th>SYMPTOM_TEXT</th>\n",
       "      <th>DIED</th>\n",
       "      <th>DATEDIED</th>\n",
       "      <th>L_THREAT</th>\n",
       "      <th>ER_VISIT</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>HOSPDAYS</th>\n",
       "      <th>X_STAY</th>\n",
       "      <th>DISABLE</th>\n",
       "      <th>RECOVD</th>\n",
       "      <th>VAX_DATE</th>\n",
       "      <th>ONSET_DATE</th>\n",
       "      <th>NUMDAYS</th>\n",
       "      <th>LAB_DATA</th>\n",
       "      <th>V_ADMINBY</th>\n",
       "      <th>V_FUNDBY</th>\n",
       "      <th>OTHER_MEDS</th>\n",
       "      <th>CUR_ILL</th>\n",
       "      <th>HISTORY</th>\n",
       "      <th>PRIOR_VAX</th>\n",
       "      <th>SPLTTYPE</th>\n",
       "      <th>FORM_VERS</th>\n",
       "      <th>TODAYS_DATE</th>\n",
       "      <th>BIRTH_DEFECT</th>\n",
       "      <th>OFC_VISIT</th>\n",
       "      <th>ER_ED_VISIT</th>\n",
       "      <th>ALLERGIES</th>\n",
       "      <th>VAX_TYPE</th>\n",
       "      <th>VAX_MANU</th>\n",
       "      <th>VAX_LOT</th>\n",
       "      <th>VAX_DOSE_SERIES</th>\n",
       "      <th>VAX_ROUTE</th>\n",
       "      <th>VAX_SITE</th>\n",
       "      <th>VAX_NAME</th>\n",
       "      <th>SYMPTOMS</th>\n",
       "      <th>COVID_VAX</th>\n",
       "      <th>SYMPTOMS_MATCH</th>\n",
       "      <th>SYMPTOMS_MATCH_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>902796</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>TX</td>\n",
       "      <td>34.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I inserted my NuvaRing birth control on 12/14/2020.  I have the Covid19 vaccine on 12/15/2020 at about 8:30am.  The injection site was mildly sore and that continued into the next day.  I woke up on 12/16/2020 feeling a little off and it progressed throughout the day.  I felt a headache that I knew was turning into a migraine, threw up a few times (this is normal for me when I get migraines), and started having hot flashes.  My temperature never went above 98.  After sleeping a few hours and taking some Excedrin migraine I was feeling much better, though still a little sickly, by 4pm.  I really think this is due to my migraines (which I get roughly once a month) even though it was a day later than normal.</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Methylphenidate Citalopram Buspirone Tylenol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I normally get a migraine a day or two after I insert my NuvaRing.  I inserted it on Monday, 12/14/2020 and I think all my symptoms are related to that rather than the vaccination.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>EH9899</td>\n",
       "      <td>1</td>\n",
       "      <td>SYR</td>\n",
       "      <td>LA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[CONDITION AGGRAVATED, FEELING ABNORMAL, HOT FLUSH, INJECTION SITE PAIN, MALAISE, MIGRAINE, VOMITING]</td>\n",
       "      <td>1</td>\n",
       "      <td>[HOT FLUSH]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>903202</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>NE</td>\n",
       "      <td>41.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Severe dizzy spell about 5-10 following injection, helped to the floor, this lasted approximately 30-40 minutes after start of symptom.  Hot flashes and visual disturbance lasting into following day.</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Buspar, propranolol,  escitalopram, crestor, amitriptyline,  baby aspirin, super B complex, black elderberry, calcium/mag/zinc supplement, CoQ10, vitamin D3, biotin, antarctic Krill oil, women's multivitamin,  tart cherry supplement, ibupr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Migraine,  anxiety, depression, hyperlipidemia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aimovig</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>EH9899</td>\n",
       "      <td>1</td>\n",
       "      <td>IM</td>\n",
       "      <td>RA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[DIZZINESS, HOT FLUSH, VISUAL IMPAIRMENT]</td>\n",
       "      <td>1</td>\n",
       "      <td>[HOT FLUSH]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>903247</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>AR</td>\n",
       "      <td>42.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I had arm pain. Yesterday I had a hot flash with throwing up and diarrhea.</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>12/15/2020</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>1.00</td>\n",
       "      <td>No</td>\n",
       "      <td>UNK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I take Estradiol, Metprolol, Lexapro, Multivitamin, Calcium, Ambien, Benadryl, Melatonin</td>\n",
       "      <td>No</td>\n",
       "      <td>I have SVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vsafe</td>\n",
       "      <td>2</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I am allergic to Sulphur</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNK</td>\n",
       "      <td>IM</td>\n",
       "      <td>LA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[DIARRHOEA, HOT FLUSH, PAIN IN EXTREMITY, VOMITING]</td>\n",
       "      <td>1</td>\n",
       "      <td>[HOT FLUSH]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>903329</td>\n",
       "      <td>12/18/2020</td>\n",
       "      <td>PA</td>\n",
       "      <td>46.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hot flushing feeling, light headed, legs heavy gave patient a chair to sit and candy symptoms resolved by 11:36</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PVT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/18/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>EK5730</td>\n",
       "      <td>1</td>\n",
       "      <td>IM</td>\n",
       "      <td>RA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[DIZZINESS, HOT FLUSH, LIMB DISCOMFORT]</td>\n",
       "      <td>1</td>\n",
       "      <td>[HOT FLUSH]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>903345</td>\n",
       "      <td>12/18/2020</td>\n",
       "      <td>VA</td>\n",
       "      <td>37.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right arm soreness, headache, hot flashes.</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>12/16/2020</td>\n",
       "      <td>12/17/2020</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WRK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12/18/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COVID19</td>\n",
       "      <td>PFIZER\\BIONTECH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RA</td>\n",
       "      <td>COVID19 (COVID19 (PFIZER-BIONTECH))</td>\n",
       "      <td>[HEADACHE, HOT FLUSH, PAIN IN EXTREMITY]</td>\n",
       "      <td>1</td>\n",
       "      <td>[HOT FLUSH]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VAERS_ID    RECVDATE STATE  AGE_YRS  CAGE_YR  CAGE_MO SEX RPT_DATE  \\\n",
       "0    902796  12/16/2020    TX    34.00    34.00      NaN   F      NaN   \n",
       "1    903202  12/17/2020    NE    41.00    41.00      NaN   F      NaN   \n",
       "2    903247  12/17/2020    AR    42.00    42.00      NaN   F      NaN   \n",
       "3    903329  12/18/2020    PA    46.00    46.00      NaN   F      NaN   \n",
       "4    903345  12/18/2020    VA    37.00    37.00      NaN   F      NaN   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 SYMPTOM_TEXT  \\\n",
       "0  I inserted my NuvaRing birth control on 12/14/2020.  I have the Covid19 vaccine on 12/15/2020 at about 8:30am.  The injection site was mildly sore and that continued into the next day.  I woke up on 12/16/2020 feeling a little off and it progressed throughout the day.  I felt a headache that I knew was turning into a migraine, threw up a few times (this is normal for me when I get migraines), and started having hot flashes.  My temperature never went above 98.  After sleeping a few hours and taking some Excedrin migraine I was feeling much better, though still a little sickly, by 4pm.  I really think this is due to my migraines (which I get roughly once a month) even though it was a day later than normal.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Severe dizzy spell about 5-10 following injection, helped to the floor, this lasted approximately 30-40 minutes after start of symptom.  Hot flashes and visual disturbance lasting into following day.   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  I had arm pain. Yesterday I had a hot flash with throwing up and diarrhea.   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             hot flushing feeling, light headed, legs heavy gave patient a chair to sit and candy symptoms resolved by 11:36   \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Right arm soreness, headache, hot flashes.   \n",
       "\n",
       "  DIED DATEDIED L_THREAT ER_VISIT HOSPITAL  HOSPDAYS X_STAY DISABLE RECOVD  \\\n",
       "0    N      NaN        N        N        N       NaN    NaN     NaN      Y   \n",
       "1    N      NaN        N        N        N       NaN    NaN     NaN      Y   \n",
       "2    N      NaN        N        N        N       NaN    NaN     NaN      N   \n",
       "3    N      NaN        N        N        N       NaN    NaN     NaN      Y   \n",
       "4    N      NaN        N        N        N       NaN    NaN     NaN      Y   \n",
       "\n",
       "     VAX_DATE  ONSET_DATE  NUMDAYS LAB_DATA V_ADMINBY V_FUNDBY  \\\n",
       "0  12/15/2020  12/16/2020     1.00      NaN       PVT      NaN   \n",
       "1  12/16/2020  12/16/2020     0.00      NaN       OTH      NaN   \n",
       "2  12/15/2020  12/16/2020     1.00       No       UNK      NaN   \n",
       "3  12/17/2020  12/17/2020     0.00      NaN       PVT      NaN   \n",
       "4  12/16/2020  12/17/2020     1.00      NaN       WRK      NaN   \n",
       "\n",
       "                                                                                                                                                                                                                                        OTHER_MEDS  \\\n",
       "0                                                                                                                                                                                                     Methylphenidate Citalopram Buspirone Tylenol   \n",
       "1  Buspar, propranolol,  escitalopram, crestor, amitriptyline,  baby aspirin, super B complex, black elderberry, calcium/mag/zinc supplement, CoQ10, vitamin D3, biotin, antarctic Krill oil, women's multivitamin,  tart cherry supplement, ibupr   \n",
       "2                                                                                                                                                         I take Estradiol, Metprolol, Lexapro, Multivitamin, Calcium, Ambien, Benadryl, Melatonin   \n",
       "3                                                                                                                                                                                                                                              NaN   \n",
       "4                                                                                                                                                                                                                                              NaN   \n",
       "\n",
       "  CUR_ILL  \\\n",
       "0     NaN   \n",
       "1     NaN   \n",
       "2      No   \n",
       "3     NaN   \n",
       "4     NaN   \n",
       "\n",
       "                                                                                                                                                                                HISTORY  \\\n",
       "0  I normally get a migraine a day or two after I insert my NuvaRing.  I inserted it on Monday, 12/14/2020 and I think all my symptoms are related to that rather than the vaccination.   \n",
       "1                                                                                                                                        Migraine,  anxiety, depression, hyperlipidemia   \n",
       "2                                                                                                                                                                            I have SVT   \n",
       "3                                                                                                                                                                                   NaN   \n",
       "4                                                                                                                                                                                   NaN   \n",
       "\n",
       "  PRIOR_VAX SPLTTYPE  FORM_VERS TODAYS_DATE BIRTH_DEFECT OFC_VISIT  \\\n",
       "0       NaN      NaN          2  12/16/2020          NaN       NaN   \n",
       "1       NaN      NaN          2  12/17/2020          NaN       NaN   \n",
       "2       NaN    vsafe          2  12/17/2020          NaN       NaN   \n",
       "3       NaN      NaN          2  12/18/2020          NaN       NaN   \n",
       "4       NaN      NaN          2  12/18/2020          NaN       NaN   \n",
       "\n",
       "  ER_ED_VISIT                 ALLERGIES VAX_TYPE         VAX_MANU VAX_LOT  \\\n",
       "0         NaN                       NaN  COVID19  PFIZER\\BIONTECH  EH9899   \n",
       "1         NaN                   Aimovig  COVID19  PFIZER\\BIONTECH  EH9899   \n",
       "2         NaN  I am allergic to Sulphur  COVID19  PFIZER\\BIONTECH     NaN   \n",
       "3         NaN                       NaN  COVID19  PFIZER\\BIONTECH  EK5730   \n",
       "4         NaN                       NaN  COVID19  PFIZER\\BIONTECH     NaN   \n",
       "\n",
       "  VAX_DOSE_SERIES VAX_ROUTE VAX_SITE                             VAX_NAME  \\\n",
       "0               1       SYR       LA  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "1               1        IM       RA  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "2             UNK        IM       LA  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "3               1        IM       RA  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "4             UNK       NaN       RA  COVID19 (COVID19 (PFIZER-BIONTECH))   \n",
       "\n",
       "                                                                                                SYMPTOMS  \\\n",
       "0  [CONDITION AGGRAVATED, FEELING ABNORMAL, HOT FLUSH, INJECTION SITE PAIN, MALAISE, MIGRAINE, VOMITING]   \n",
       "1                                                              [DIZZINESS, HOT FLUSH, VISUAL IMPAIRMENT]   \n",
       "2                                                    [DIARRHOEA, HOT FLUSH, PAIN IN EXTREMITY, VOMITING]   \n",
       "3                                                                [DIZZINESS, HOT FLUSH, LIMB DISCOMFORT]   \n",
       "4                                                               [HEADACHE, HOT FLUSH, PAIN IN EXTREMITY]   \n",
       "\n",
       "   COVID_VAX SYMPTOMS_MATCH  SYMPTOMS_MATCH_LENGTH  \n",
       "0          1    [HOT FLUSH]                      1  \n",
       "1          1    [HOT FLUSH]                      1  \n",
       "2          1    [HOT FLUSH]                      1  \n",
       "3          1    [HOT FLUSH]                      1  \n",
       "4          1    [HOT FLUSH]                      1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_WRS = symptom_filter_search(df,WRS_list)\n",
    "\n",
    "print('df_WRS.head(5)')\n",
    "display(df_WRS.head(5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What percent of women experienced WRS during their adverse reactions?\n",
    "\n",
    "(women experiencing reproductive symptoms ➗ number of women in VAERS data) * 100\n",
    "\n",
    "WRS_ratio (float) : count WRS / count VAERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.22 %\n",
      "VAERS records of women experiencing reproductive symptoms have been saved.\n",
      "saved:  .\\AllVAERSDataCSVS\\WRS.csv\n"
     ]
    }
   ],
   "source": [
    "#get count of women in VAERS data \n",
    "\n",
    "w_df = df[df['SEX']=='F']\n",
    "u_df = df[df['SEX']=='U']\n",
    "w_count = len(w_df) + (len(u_df)/2) #half of unknown\n",
    "\n",
    "WRS_ratio = (len(df_WRS)/w_count)\n",
    "\n",
    "print( '{:.2f} %'.format(WRS_ratio*100))\n",
    "\n",
    "file_name = os.path.join(DATAPATH,'WRS.csv')\n",
    "df_WRS.to_csv(file_name)\n",
    "\n",
    "print('VAERS records of women experiencing reproductive symptoms have been saved.')\n",
    "print('saved: ',file_name)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approximate the number of WRS in reality\n",
    "\n",
    "since '~63% of the people who are vaccinated are women'\n",
    "source: https://www.statista.com/statistics/1212103/share-of-persons-initiating-covid-vaccinations-by-gender-us-first-month/\n",
    "\n",
    "we will be multiplying the total number of people vaccinated (1 or more shots) by 0.63 to get the count of women vaccinated.\n",
    "\n",
    "women_vaxx (float) : an approximate number of women who have had 1 or more vaccine shot.\n",
    "\n",
    ">note: 0.63 is an estimate, and the actual could be somewhere between 0.50 and 0.70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315544653.27\n"
     ]
    }
   ],
   "source": [
    "women_vaxx = vaxx * 0.63\n",
    "print(women_vaxx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### approximate real WRS\n",
    "if we assume that the VAERS data is a random sample (or close to it)  \n",
    "then the ratio of WRS systems should be the same...\n",
    "\n",
    "and thus we can get an approximate number of women that would be experiencing reproductive symptoms by multiplying the number of vaxxed women by the ratio\n",
    "\n",
    "WRS (float) : the approximate number of actual women experiencing reproductive symptoms\n",
    "min_WRS (float) : WRS * 0.80\n",
    "max_WRS (float) : WRS * 1.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "WRS = women_vaxx * WRS_ratio\n",
    "min_WRS = WRS*0.80\n",
    "max_WRS = WRS*1.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total vaxxed (1 or more)           ---  500,864,529.00 \n",
      "women vaxxed ~0.63%                ---  315,544,653.27 \n",
      "repro sympt / women count          ---  0.0322         \n",
      "(repro sympt / women count) * 100  ---  3.22 %         \n",
      "women w/ repro symptoms            ---  10,147,648.30  \n",
      "min women w/ repro symptoms        ---  8,118,118.64   \n",
      "max women w/ repro symptoms        ---  12,177,177.96  \n"
     ]
    }
   ],
   "source": [
    "cl = [35,5,15]\n",
    "print_row(['total vaxxed (1 or more)','---','{:,.2f}'.format(vaxx)],column_lengths=cl)\n",
    "print_row(['women vaxxed ~0.63%','---','{:,.2f}'.format(women_vaxx)],column_lengths=cl)\n",
    "print_row(['repro sympt / women count','---','{:,.4f}'.format(WRS_ratio)],column_lengths=cl)\n",
    "print_row(['(repro sympt / women count) * 100','---','{:,.2f} %'.format(WRS_ratio*100)],column_lengths=cl)\n",
    "print_row(['women w/ repro symptoms','---','{:,.2f}'.format(WRS)],column_lengths=cl)\n",
    "print_row(['min women w/ repro symptoms','---','{:,.2f}'.format(min_WRS)],column_lengths=cl)\n",
    "print_row(['max women w/ repro symptoms','---','{:,.2f}'.format(max_WRS)],column_lengths=cl)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where do the WRS (Women reproductive symptoms) rank against with the other symptoms?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>index</th>\n",
       "      <th>symptoms</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141</td>\n",
       "      <td>HEAVY MENSTRUAL BLEEDING</td>\n",
       "      <td>5,821.00</td>\n",
       "      <td>45.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>175</td>\n",
       "      <td>MENSTRUATION IRREGULAR</td>\n",
       "      <td>4,451.00</td>\n",
       "      <td>34.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176</td>\n",
       "      <td>HOT FLUSH</td>\n",
       "      <td>4,425.00</td>\n",
       "      <td>34.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>204</td>\n",
       "      <td>MENSTRUAL DISORDER</td>\n",
       "      <td>3,368.00</td>\n",
       "      <td>26.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>259</td>\n",
       "      <td>DYSMENORRHOEA</td>\n",
       "      <td>2,449.00</td>\n",
       "      <td>19.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>264</td>\n",
       "      <td>BREAST PAIN</td>\n",
       "      <td>2,379.00</td>\n",
       "      <td>18.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>289</td>\n",
       "      <td>VAGINAL HAEMORRHAGE</td>\n",
       "      <td>2,049.00</td>\n",
       "      <td>16.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>327</td>\n",
       "      <td>INTERMENSTRUAL BLEEDING</td>\n",
       "      <td>1,727.00</td>\n",
       "      <td>13.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>503</td>\n",
       "      <td>BREAST SWELLING</td>\n",
       "      <td>929.00</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>523</td>\n",
       "      <td>POLYMENORRHOEA</td>\n",
       "      <td>871.00</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>569</td>\n",
       "      <td>OLIGOMENORRHOEA</td>\n",
       "      <td>780.00</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>637</td>\n",
       "      <td>BREAST TENDERNESS</td>\n",
       "      <td>655.00</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>783</td>\n",
       "      <td>MATERNAL EXPOSURE BEFORE PREGNANCY</td>\n",
       "      <td>478.00</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1305</td>\n",
       "      <td>OVARIAN CYST</td>\n",
       "      <td>208.00</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1308</td>\n",
       "      <td>MENOPAUSE</td>\n",
       "      <td>207.00</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1446</td>\n",
       "      <td>UTERINE LEIOMYOMA</td>\n",
       "      <td>172.00</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1719</td>\n",
       "      <td>ABNORMAL UTERINE BLEEDING</td>\n",
       "      <td>126.00</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3167</td>\n",
       "      <td>BACTERIAL VAGINOSIS</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3305</td>\n",
       "      <td>ANOVULATORY CYCLE</td>\n",
       "      <td>33.00</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3400</td>\n",
       "      <td>BIOPSY UTERUS</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3887</td>\n",
       "      <td>OVULATION DELAYED</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5537</td>\n",
       "      <td>GYNAECOLOGICAL EXAMINATION ABNORMAL</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                             symptoms     count percent\n",
       "0     141             HEAVY MENSTRUAL BLEEDING  5,821.00   45.72\n",
       "1     175               MENSTRUATION IRREGULAR  4,451.00   34.96\n",
       "2     176                            HOT FLUSH  4,425.00   34.76\n",
       "3     204                   MENSTRUAL DISORDER  3,368.00   26.46\n",
       "4     259                        DYSMENORRHOEA  2,449.00   19.24\n",
       "5     264                          BREAST PAIN  2,379.00   18.69\n",
       "6     289                  VAGINAL HAEMORRHAGE  2,049.00   16.09\n",
       "7     327              INTERMENSTRUAL BLEEDING  1,727.00   13.57\n",
       "8     503                      BREAST SWELLING    929.00    7.30\n",
       "9     523                       POLYMENORRHOEA    871.00    6.84\n",
       "10    569                      OLIGOMENORRHOEA    780.00    6.13\n",
       "11    637                    BREAST TENDERNESS    655.00    5.14\n",
       "12    783   MATERNAL EXPOSURE BEFORE PREGNANCY    478.00    3.75\n",
       "13   1305                         OVARIAN CYST    208.00    1.63\n",
       "14   1308                            MENOPAUSE    207.00    1.63\n",
       "15   1446                    UTERINE LEIOMYOMA    172.00    1.35\n",
       "16   1719            ABNORMAL UTERINE BLEEDING    126.00    0.99\n",
       "17   3167                  BACTERIAL VAGINOSIS     36.00    0.28\n",
       "18   3305                    ANOVULATORY CYCLE     33.00    0.26\n",
       "19   3400                        BIOPSY UTERUS     30.00    0.24\n",
       "20   3887                    OVULATION DELAYED     22.00    0.17\n",
       "21   5537  GYNAECOLOGICAL EXAMINATION ABNORMAL      9.00    0.07"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = []\n",
    "\n",
    "for index,i in enumerate(all_symptoms):\n",
    "    if i[0].upper() in WRS_list:\n",
    "        temp.append(\n",
    "            {\n",
    "                'index':index,\n",
    "                'symptoms':i[0],\n",
    "                'count': '{:,.2f}'.format(i[1]),\n",
    "                'percent': '{:.2f}'.format((i[1]/len(all_symptoms))*100) \n",
    "            }\n",
    "        )\n",
    "\n",
    "temp = pd.DataFrame(temp)\n",
    "display(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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