1498 lines (1497 with data), 264.2 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8700b4d6-ba03-437b-8732-b3ea4963d458",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import pymongo\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af22a4b5-1c13-4019-a25d-1caace781768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51d4c987-4017-406a-81f4-0a793caae194",
   "metadata": {},
   "outputs": [],
   "source": [
    "username = os.environ.get(\"USERNAME\")\n",
    "password = os.environ.get(\"PASSWORD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5d0618a-985d-49a0-8328-f28002914d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = pymongo.MongoClient(f\"mongodb+srv://{username}:{password}@cluster0.lxsbb.mongodb.net/?retryWrites=true&w=majority\")\n",
    "db = client.medical_record\n",
    "collection = db[\"report\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bd8131e-0f2d-4bc1-8baa-a34d1b871524",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4091c4d8-11b7-40e5-9400-22cbe47ac02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('./data/output/data.json', 'r') as fp:\n",
    "#     data = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c80b19ca-7d4e-4622-b457-60748ff90e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# collection.insert_many(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5431a953-10ab-4e95-a58e-5dc2f6313381",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = collection.find({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "531db59d-d54e-41e2-8803-cb0cc97eafd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymongo.cursor.Cursor"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d815c06-dc27-4183-b29a-d477d306c38c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "b = list(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a7a50ad-51af-45bc-9d04-5539ab401001",
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = {\n",
    "    k:[] for k in b[0][\"demographics\"].keys()\n",
    "}\n",
    "patient[\"patient_id\"] = []\n",
    "for d in b:\n",
    "    patient[\"patient_id\"].append(d[\"_id\"]),\n",
    "    for k, v in d[\"demographics\"].items():\n",
    "        patient[k].append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2e47ed70-bde8-4f12-80d3-931467a749be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_patient = pd.DataFrame(patient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2aef9ac-2c8a-41fa-b439-b11b053c6ba8",
   "metadata": {
    "tags": []
   },
   "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>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>birth date</th>\n",
       "      <th>marital status</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>1994-01-08</td>\n",
       "      <td>M</td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>M</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>50</td>\n",
       "      <td>1972-02-22</td>\n",
       "      <td>M</td>\n",
       "      <td>6395c34e7585b36740092fcb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "      <td>1992-04-27</td>\n",
       "      <td>M</td>\n",
       "      <td>6395c34e7585b36740092fcc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>F</td>\n",
       "      <td>33</td>\n",
       "      <td>1989-07-16</td>\n",
       "      <td>M</td>\n",
       "      <td>6395c34e7585b36740092fcd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    race     ethnicity gender age  birth date marital status  \\\n",
       "0  White  Non-Hispanic      M  28  1994-01-08              M   \n",
       "1  White  Non-Hispanic      F  55  1967-01-12              M   \n",
       "2  White  Non-Hispanic      F  50  1972-02-22              M   \n",
       "3  White  Non-Hispanic      F  30  1992-04-27              M   \n",
       "4  White  Non-Hispanic      F  33  1989-07-16              M   \n",
       "\n",
       "                 patient_id  \n",
       "0  6395c34e7585b36740092fc9  \n",
       "1  6395c34e7585b36740092fca  \n",
       "2  6395c34e7585b36740092fcb  \n",
       "3  6395c34e7585b36740092fcc  \n",
       "4  6395c34e7585b36740092fcd  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_patient.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12ea72c0-2fde-4b09-b17c-877dc992b1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GG9StWzeNHz9eHTt2VFFRkfLy8rR//35t2rSpUTXUPhcmIyNDw4cP19ChQ7VhwwZ98sknCggIcOj7+OOP64MPPtDw4cM1duxY9erVSxUVFfrmm2/03nvvac+ePXW2+bnBgwcrODhYffv2VVBQkP79739r9uzZGjZsWL1zXJqSv7+/nn766bP2mz59ulavXq24uDiNHz9e0dHROnz4sNavX6+VK1fq8OHDkqTx48dr9uzZSkxM1Lp16xQSEqI33nhDvr6+Zz3GwIEDdf/99+ull17Sjh07NGTIENXU1OiLL77QwIEDlZKSosGDB8vLy0u33HKLJkyYoPLyci1YsEAdOnSoN/yei5tvvlldu3ZVZmamHnnkEfXv318TJkxQRkaGNm7cqMGDB6t58+basWOHlixZohdffNFhInXnzp01btw4/etf/1JQUJCys7NVVFTkEMSefPJJvfPOO7r55ps1adIktWvXTq+99pp2796tpUuXNuhhljExMfL09NSMGTNUWloqb29v+3OAAJdxyz1awEXm+++/t375y19aUVFRlo+Pj9WiRQurS5cu1sMPP2xt3LixTv+dO3daiYmJVnBwsNW8eXMrNDTUGj58uPXee+/Z+9TeCv6vf/3LYdva22h/eqtsdXW19cwzz1ghISFWixYtrAEDBlhbtmyxwsPDHW4Ft6xTt+ampaVZUVFRlpeXlxUQEGBdf/311gsvvGBVVVVZlvXf27ZnzpxZp/ZXXnnF+sUvfmG1b9/e8vb2tiIjI63HH3/cKi0tPeM5Ot2t4H5+fnX6pqenWw356+ent4KfTn23gluWZRUVFVmPPPKIFRYWZjVv3twKDg62Bg0aZM2fP9+h3969e61bb73V8vX1tQICAqzJkyfbb5c+063glmVZJ0+etGbOnGl16dLF8vLysgIDA62bb77ZWrdunb3PBx98YHXv3t3y8fGxIiIirBkzZljZ2dmWJGv37t0O77Wht4IPGzas3nULFy6s8xnMnz/f6tWrl9WiRQurVatWVrdu3awnnnjCOnjwYJ19fvrpp1b37t0tb29vq0uXLnXOqWWd+tm+8847rTZt2lg+Pj5W7969reXLlzv0Od1nUmvBggVWx44dLU9PT24Lx3lhsywnZi0CAIwRERGhrl27avny5e4uBXAJ5twAAACjEG4AAIBRCDcAAMAozLkBAABGYeQGAAAYhXADAACMcsk9xK+mpkYHDx5Uq1ateAw4AAAXCcuydOzYMV122WVnfWDkJRduDh48qLCwMHeXAQAAnLBv3z6Hp8LX55ILN7WPj9+3b59at27t5moAAEBDlJWVKSwsrEFfA3PJhZvaS1GtW7cm3AAAcJFpyJQSJhQDAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjNLM3QWg6UQ8+ZG7S0AT2jN9mLtLAAC3YOQGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxyQYSbOXPmKCIiQj4+PoqLi9PatWtP23fhwoWy2WwOLx8fnyasFgAAXMjcHm4WL16s1NRUpaena/369erRo4cSEhJUXFx82m1at26tgoIC+2vv3r1NWDEAALiQuT3cZGZmavz48UpOTlZ0dLTmzZsnX19fZWdnn3Ybm82m4OBg+ysoKKgJKwYAABcyt4abqqoqrVu3TvHx8fY2Dw8PxcfHKy8v77TblZeXKzw8XGFhYbrtttu0devWpigXAABcBNwabg4dOqTq6uo6Iy9BQUEqLCysd5urrrpK2dnZ+utf/6o333xTNTU1uv7667V///56+1dWVqqsrMzhBQAAzOX2y1KN1adPHyUmJiomJkb9+/fXsmXLFBgYqFdeeaXe/hkZGfL397e/wsLCmrhiAADQlNwabgICAuTp6amioiKH9qKiIgUHBzdoH82bN1fPnj31/fff17s+LS1NpaWl9te+ffvOuW4AAHDhcmu48fLyUq9evZSbm2tvq6mpUW5urvr06dOgfVRXV+ubb75RSEhIveu9vb3VunVrhxcAADBXM3cXkJqaqqSkJMXGxqp3797KyspSRUWFkpOTJUmJiYkKDQ1VRkaGJOkPf/iDrrvuOkVFReno0aOaOXOm9u7dqwcffNCdbwMAAFwg3B5uRo0apZKSEk2dOlWFhYWKiYnRihUr7JOM8/Pz5eHx3wGmI0eOaPz48SosLFTbtm3Vq1cvffXVV4qOjnbXWwAAABcQm2VZlruLaEplZWXy9/dXaWnpJXeJKuLJj9xdAprQnunD3F0CALhMY/79vujulgIAADgTwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMIrbv1sKAHDu+HqVSwtfr3JmjNwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAY5YIIN3PmzFFERIR8fHwUFxentWvXNmi7RYsWyWazacSIEee3QAAAcNFwe7hZvHixUlNTlZ6ervXr16tHjx5KSEhQcXHxGbfbs2ePpkyZon79+jVRpQAA4GLg9nCTmZmp8ePHKzk5WdHR0Zo3b558fX2VnZ192m2qq6t133336ZlnnlHHjh2bsFoAAHChc2u4qaqq0rp16xQfH29v8/DwUHx8vPLy8k673R/+8Ad16NBB48aNO+sxKisrVVZW5vACAADmcmu4OXTokKqrqxUUFOTQHhQUpMLCwnq3+fvf/64///nPWrBgQYOOkZGRIX9/f/srLCzsnOsGAAAXLrdflmqMY8eO6f7779eCBQsUEBDQoG3S0tJUWlpqf+3bt+88VwkAANypmTsPHhAQIE9PTxUVFTm0FxUVKTg4uE7/nTt3as+ePbrlllvsbTU1NZKkZs2aafv27YqMjHTYxtvbW97e3uehegAAcCFy68iNl5eXevXqpdzcXHtbTU2NcnNz1adPnzr9u3Tpom+++UYbN260v2699VYNHDhQGzdu5JITAABw78iNJKWmpiopKUmxsbHq3bu3srKyVFFRoeTkZElSYmKiQkNDlZGRIR8fH3Xt2tVh+zZt2khSnXYAAHBpcnu4GTVqlEpKSjR16lQVFhYqJiZGK1assE8yzs/Pl4fHRTU1CAAAuJHbw40kpaSkKCUlpd51a9asOeO2CxcudH1BAADgosWQCAAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEZxKtzs2rXL1XUAAAC4hFPhJioqSgMHDtSbb76pEydOuLomAAAApzkVbtavX6/u3bsrNTVVwcHBmjBhgtauXevq2gAAABrNqXATExOjF198UQcPHlR2drYKCgp0ww03qGvXrsrMzFRJSYmr6wQAAGiQc5pQ3KxZM91+++1asmSJZsyYoe+//15TpkxRWFiYEhMTVVBQ4Ko6AQAAGuScws3XX3+tiRMnKiQkRJmZmZoyZYp27typzz//XAcPHtRtt93mqjoBAAAapJkzG2VmZionJ0fbt2/X0KFD9frrr2vo0KHy8DiVla688kotXLhQERERrqwVAADgrJwKN3PnztUDDzygsWPHKiQkpN4+HTp00J///OdzKg4AAKCxnAo3O3bsOGsfLy8vJSUlObN7AAAApzk15yYnJ0dLliyp075kyRK99tpr51wUAACAs5wKNxkZGQoICKjT3qFDBz333HPnXBQAAICznAo3+fn5uvLKK+u0h4eHKz8//5yLAgAAcJZT4aZDhw7avHlznfZNmzapffv2jd7fnDlzFBERIR8fH8XFxZ3xacfLli1TbGys2rRpIz8/P8XExOiNN95o9DEBAICZnAo39957ryZNmqTVq1erurpa1dXVWrVqlSZPnqx77rmnUftavHixUlNTlZ6ervXr16tHjx5KSEhQcXFxvf3btWunp556Snl5edq8ebOSk5OVnJysTz/91Jm3AgAADONUuJk2bZri4uI0aNAgtWjRQi1atNDgwYN14403NnrOTWZmpsaPH6/k5GRFR0dr3rx58vX1VXZ2dr39BwwYoJEjR+rqq69WZGSkJk+erO7du+vvf/+7M28FAAAYxqlbwb28vLR48WJNmzZNmzZtUosWLdStWzeFh4c3aj9VVVVat26d0tLS7G0eHh6Kj49XXl7eWbe3LEurVq3S9u3bNWPGjHr7VFZWqrKy0r5cVlbWqBoBAMDFxalwU6tz587q3Lmz09sfOnRI1dXVCgoKcmgPCgrStm3bTrtdaWmpQkNDVVlZKU9PT7388su66aab6u2bkZGhZ555xukaAQDAxcWpcFNdXa2FCxcqNzdXxcXFqqmpcVi/atUqlxR3Oq1atdLGjRtVXl6u3NxcpaamqmPHjhowYECdvmlpaUpNTbUvl5WVKSws7LzWBwAA3MepcDN58mQtXLhQw4YNU9euXWWz2Zw6eEBAgDw9PVVUVOTQXlRUpODg4NNu5+HhoaioKElSTEyM/v3vfysjI6PecOPt7S1vb2+n6gMAABcfp8LNokWL9O6772ro0KHndHAvLy/16tVLubm5GjFihCSppqZGubm5SklJafB+ampqHObVAACAS5fTE4prR07OVWpqqpKSkhQbG6vevXsrKytLFRUVSk5OliQlJiYqNDRUGRkZkk7NoYmNjVVkZKQqKyv18ccf64033tDcuXNdUg8AALi4ORVuHnvsMb344ouaPXu205ekao0aNUolJSWaOnWqCgsLFRMToxUrVtgnGefn58vD4793rFdUVGjixInav3+/WrRooS5duujNN9/UqFGjzqkOAABgBptlWVZjNxo5cqRWr16tdu3a6ZprrlHz5s0d1i9btsxlBbpaWVmZ/P39VVpaqtatW7u7nCYV8eRH7i4BTWjP9GHuLgFNiN/vS8ul+PvdmH+/nRq5adOmjUaOHOlUcQAAAOeTU+EmJyfH1XUAAAC4hFNfvyBJJ0+e1MqVK/XKK6/o2LFjkqSDBw+qvLzcZcUBAAA0llMjN3v37tWQIUOUn5+vyspK3XTTTWrVqpVmzJihyspKzZs3z9V1AgAANIhTIzeTJ09WbGysjhw5ohYtWtjbR44cqdzcXJcVBwAA0FhOjdx88cUX+uqrr+Tl5eXQHhERoQMHDrikMAAAAGc4NXJTU1Oj6urqOu379+9Xq1atzrkoAAAAZzkVbgYPHqysrCz7ss1mU3l5udLT08/5KxkAAADOhVOXpWbNmqWEhARFR0frxIkTGj16tHbs2KGAgAC98847rq4RAACgwZwKN5dffrk2bdqkRYsWafPmzSovL9e4ceN03333OUwwBgAAaGpOhRtJatasmcaMGePKWgAAAM6ZU+Hm9ddfP+P6xMREp4oBAAA4V06Fm8mTJzss//jjjzp+/Li8vLzk6+tLuAEAAG7j1N1SR44ccXiVl5dr+/btuuGGG5hQDAAA3Mrp75b6uU6dOmn69Ol1RnUAAACaksvCjXRqkvHBgwdduUsAAIBGcWrOzQcffOCwbFmWCgoKNHv2bPXt29clhQEAADjDqXAzYsQIh2WbzabAwEDdeOONmjVrlivqAgAAcIpT4aampsbVdQAAALiES+fcAAAAuJtTIzepqakN7puZmenMIQAAAJziVLjZsGGDNmzYoB9//FFXXXWVJOm7776Tp6enrr32Wns/m83mmioBAAAayKlwc8stt6hVq1Z67bXX1LZtW0mnHuyXnJysfv366bHHHnNpkQAAAA3l1JybWbNmKSMjwx5sJKlt27Z69tlnuVsKAAC4lVPhpqysTCUlJXXaS0pKdOzYsXMuCgAAwFlOhZuRI0cqOTlZy5Yt0/79+7V//34tXbpU48aN0+233+7qGgEAABrMqTk38+bN05QpUzR69Gj9+OOPp3bUrJnGjRunmTNnurRAAACAxnAq3Pj6+urll1/WzJkztXPnTklSZGSk/Pz8XFocAABAY53TQ/wKCgpUUFCgTp06yc/PT5ZluaouAAAApzgVbn744QcNGjRInTt31tChQ1VQUCBJGjduHLeBAwAAt3Iq3Pz6179W8+bNlZ+fL19fX3v7qFGjtGLFCpcVBwAA0FhOzbn57LPP9Omnn+ryyy93aO/UqZP27t3rksIAAACc4dTITUVFhcOITa3Dhw/L29v7nIsCAABwllPhpl+/fnr99dftyzabTTU1NXr++ec1cOBAlxUHAADQWE5dlnr++ec1aNAgff3116qqqtITTzyhrVu36vDhw/ryyy9dXSMAAECDOTVy07VrV3333Xe64YYbdNttt6miokK33367NmzYoMjISFfXCAAA0GCNHrn58ccfNWTIEM2bN09PPfXU+agJAADAaY0euWnevLk2b958PmoBAAA4Z05dlhozZoz+/Oc/u7oWAACAc+bUhOKTJ08qOztbK1euVK9evep8p1RmZqZLigMAAGisRoWbXbt2KSIiQlu2bNG1114rSfruu+8c+thsNtdVBwAA0EiNCjedOnVSQUGBVq9eLenU1y289NJLCgoKOi/FAQAANFaj5tz8/Fu/P/nkE1VUVLi0IAAAgHPh1ITiWj8POwAAAO7WqHBjs9nqzKlhjg0AALiQNGrOjWVZGjt2rP3LMU+cOKGHH364zt1Sy5Ytc12FAAAAjdCocJOUlOSwPGbMGJcWAwAAcK4aFW5ycnLOVx0AAAAucU4TigEAAC40hBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEuiHAzZ84cRUREyMfHR3FxcVq7du1p+y5YsED9+vVT27Zt1bZtW8XHx5+xPwAAuLS4PdwsXrxYqampSk9P1/r169WjRw8lJCSouLi43v5r1qzRvffeq9WrVysvL09hYWEaPHiwDhw40MSVAwCAC5Hbw01mZqbGjx+v5ORkRUdHa968efL19VV2dna9/d966y1NnDhRMTEx6tKli1599VXV1NQoNze3iSsHAAAXIreGm6qqKq1bt07x8fH2Ng8PD8XHxysvL69B+zh+/Lh+/PFHtWvXrt71lZWVKisrc3gBAABzuTXcHDp0SNXV1QoKCnJoDwoKUmFhYYP28Zvf/EaXXXaZQ0D6qYyMDPn7+9tfYWFh51w3AAC4cLn9stS5mD59uhYtWqT3339fPj4+9fZJS0tTaWmp/bVv374mrhIAADSlZu48eEBAgDw9PVVUVOTQXlRUpODg4DNu+8ILL2j69OlauXKlunfvftp+3t7e8vb2dkm9AADgwufWkRsvLy/16tXLYTJw7eTgPn36nHa7559/XtOmTdOKFSsUGxvbFKUCAICLhFtHbiQpNTVVSUlJio2NVe/evZWVlaWKigolJydLkhITExUaGqqMjAxJ0owZMzR16lS9/fbbioiIsM/NadmypVq2bOm29wEAAC4Mbg83o0aNUklJiaZOnarCwkLFxMRoxYoV9knG+fn58vD47wDT3LlzVVVVpTvvvNNhP+np6Xr66aebsnQAAHABcnu4kaSUlBSlpKTUu27NmjUOy3v27Dn/BQEAgIvWRX23FAAAwM8RbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARnF7uJkzZ44iIiLk4+OjuLg4rV279rR9t27dqjvuuEMRERGy2WzKyspqukIBAMBFwa3hZvHixUpNTVV6errWr1+vHj16KCEhQcXFxfX2P378uDp27Kjp06crODi4iasFAAAXA7eGm8zMTI0fP17JycmKjo7WvHnz5Ovrq+zs7Hr7/8///I9mzpype+65R97e3k1cLQAAuBi4LdxUVVVp3bp1io+P/28xHh6Kj49XXl6eu8oCAAAXuWbuOvChQ4dUXV2toKAgh/agoCBt27bNZceprKxUZWWlfbmsrMxl+wYAABcet08oPt8yMjLk7+9vf4WFhbm7JAAAcB65LdwEBATI09NTRUVFDu1FRUUunSyclpam0tJS+2vfvn0u2zcAALjwuC3ceHl5qVevXsrNzbW31dTUKDc3V3369HHZcby9vdW6dWuHFwAAMJfb5txIUmpqqpKSkhQbG6vevXsrKytLFRUVSk5OliQlJiYqNDRUGRkZkk5NQv7222/tfz5w4IA2btyoli1bKioqym3vAwAAXDjcGm5GjRqlkpISTZ06VYWFhYqJidGKFSvsk4zz8/Pl4fHfwaWDBw+qZ8+e9uUXXnhBL7zwgvr37681a9Y0dfkAAOAC5NZwI0kpKSlKSUmpd93PA0tERIQsy2qCqgAAwMXK+LulAADApYVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwygURbubMmaOIiAj5+PgoLi5Oa9euPWP/JUuWqEuXLvLx8VG3bt308ccfN1GlAADgQuf2cLN48WKlpqYqPT1d69evV48ePZSQkKDi4uJ6+3/11Ve69957NW7cOG3YsEEjRozQiBEjtGXLliauHAAAXIjcHm4yMzM1fvx4JScnKzo6WvPmzZOvr6+ys7Pr7f/iiy9qyJAhevzxx3X11Vdr2rRpuvbaazV79uwmrhwAAFyI3BpuqqqqtG7dOsXHx9vbPDw8FB8fr7y8vHq3ycvLc+gvSQkJCaftDwAALi3N3HnwQ4cOqbq6WkFBQQ7tQUFB2rZtW73bFBYW1tu/sLCw3v6VlZWqrKy0L5eWlkqSysrKzqX0i1JN5XF3l4AmdCn+jF/K+P2+tFyKv9+179myrLP2dWu4aQoZGRl65pln6rSHhYW5oRqg6fhnubsCAOfLpfz7fezYMfn7+5+xj1vDTUBAgDw9PVVUVOTQXlRUpODg4Hq3CQ4OblT/tLQ0paam2pdramp0+PBhtW/fXjab7RzfAS50ZWVlCgsL0759+9S6dWt3lwPAhfj9vrRYlqVjx47psssuO2tft4YbLy8v9erVS7m5uRoxYoSkU+EjNzdXKSkp9W7Tp08f5ebm6tFHH7W3ff755+rTp0+9/b29veXt7e3Q1qZNG1eUj4tI69at+csPMBS/35eOs43Y1HL7ZanU1FQlJSUpNjZWvXv3VlZWlioqKpScnCxJSkxMVGhoqDIyMiRJkydPVv/+/TVr1iwNGzZMixYt0tdff6358+e7820AAIALhNvDzahRo1RSUqKpU6eqsLBQMTExWrFihX3ScH5+vjw8/ntT1/XXX6+3335bv/vd7/Tb3/5WnTp10l/+8hd17drVXW8BAABcQGxWQ6YdAxepyspKZWRkKC0trc7lSQAXN36/cTqEGwAAYBS3P6EYAADAlQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADALgo7Nq1q0FfmghwKziM8sADDzSoX3Z29nmuBICreXp6qqCgQB06dJB06iGwL730kv2hr0Atwg2M4uHhofDwcPXs2fOM/4f3/vvvN2FVAFzBw8NDhYWF9nDTqlUrbdq0SR07dnRzZbjQuP3rFwBX+uUvf6l33nlHu3fvVnJyssaMGaN27dq5uywAQBNizg2MMmfOHBUUFOiJJ57Qhx9+qLCwMN1999369NNPuVYPXORsNptsNludNuDnuCwFo+3du1cLFy7U66+/rpMnT2rr1q1q2bKlu8sC4AQPDw/dfPPN9u+R+vDDD3XjjTfKz8/Pod+yZcvcUR4uIFyWgtE8PDxks9lkWZaqq6vdXQ6Ac5CUlOSwPGbMGDdVggsdIzcwTmVlpZYtW6bs7Gz9/e9/1/Dhw5WcnKwhQ4bIw4MrsQBgOkZuYJSJEydq0aJFCgsL0wMPPKB33nlHAQEB7i4LANCEGLmBUTw8PHTFFVeoZ8+eZ5xoyDV5ADAXIzcwSmJiIndPAMAljpEbAABgFGZXAgAAoxBuAACAUQg3AADAKIQbAJeUAQMG6NFHH3V3GQDOI8INgCZXWFioyZMnKyoqSj4+PgoKClLfvn01d+5cHT9+3N3lAbjIcSs4gCa1a9cu9e3bV23atNFzzz2nbt26ydvbW998843mz5+v0NBQ3Xrrre4u87Sqq6tls9l42jVwAeO3E0CTmjhxopo1a6avv/5ad999t66++mp17NhRt912mz766CPdcsstkqSjR4/qwQcfVGBgoFq3bq0bb7xRmzZtsu/n6aefVkxMjN544w1FRETI399f99xzj44dO2bvU1FRocTERLVs2VIhISGaNWtWnXoqKys1ZcoUhYaGys/PT3FxcVqzZo19/cKFC9WmTRt98MEHio6Olre3t/Lz88/fCQJwzgg3AJrMDz/8oM8++0yPPPJInW9yrlX7EMa77rpLxcXF+uSTT7Ru3Tpde+21GjRokA4fPmzvu3PnTv3lL3/R8uXLtXz5cv3tb3/T9OnT7esff/xx/e1vf9Nf//pXffbZZ1qzZo3Wr1/vcLyUlBTl5eVp0aJF2rx5s+666y4NGTJEO3bssPc5fvy4ZsyYoVdffVVbt25Vhw4dXHlaALiaBQBN5B//+IclyVq2bJlDe/v27S0/Pz/Lz8/PeuKJJ6wvvvjCat26tXXixAmHfpGRkdYrr7xiWZZlpaenW76+vlZZWZl9/eOPP27FxcVZlmVZx44ds7y8vKx3333Xvv6HH36wWrRoYU2ePNmyLMvau3ev5enpaR04cMDhOIMGDbLS0tIsy7KsnJwcS5K1ceNG15wEAOcdc24AuN3atWtVU1Oj++67T5WVldq0aZPKy8vVvn17h37/+c9/tHPnTvtyRESEWrVqZV8OCQlRcXGxpFOjOlVVVYqLi7Ovb9euna666ir78jfffKPq6mp17tzZ4TiVlZUOx/by8lL37t1d82YBnHeEGwBNJioqSjabTdu3b3do79ixoySpRYsWkqTy8nKFhIQ4zH2p1aZNG/ufmzdv7rDOZrOppqamwfWUl5fL09NT69atk6enp8O6li1b2v/cokULvrMMuIgQbgA0mfbt2+umm27S7Nmz9atf/eq0826uvfZaFRYWqlmzZoqIiHDqWJGRkWrevLn++c9/6oorrpAkHTlyRN9995369+8vSerZs6eqq6tVXFysfv36OXUcABceJhQDaFIvv/yyTp48qdjYWC1evFj//ve/tX37dr355pvatm2bPD09FR8frz59+mjEiBH67LPPtGfPHn311Vd66qmn9PXXXzfoOC1bttS4ceP0+OOPa9WqVdqyZYvGjh3rcAt3586ddd999ykxMVHLli3T7t27tXbtWmVkZOijjz46X6cAwHnGyA2AJhUZGakNGzboueeeU1pamvbv3y9vb29FR0drypQpmjhxomw2mz7++GM99dRTSk5OVklJiYKDg/WLX/xCQUFBDT7WzJkzVV5erltuuUWtWrXSY489ptLSUoc+OTk5evbZZ/XYY4/pwIEDCggI0HXXXafhw4e7+q0DaCI2y7IsdxcBAADgKlyWAgAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAo/w9oZt+aRKVLyQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient[\"gender\"].value_counts(normalize=True).plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Gender\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Genders in Medical Report\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "621f4492-4c1d-407e-b23d-ba55bd2bc6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient[\"ethnicity\"].value_counts(normalize=True).plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Ethnicity\",\n",
    "    ylabel=\"Frequency\",\n",
    "    title=\"Ethnicity Distribution\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "569cade4-f61b-463e-beb5-47dda7616c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "allergies = {\n",
    "    k:[] for k in b[0][\"allergies\"][0].keys()\n",
    "}\n",
    "allergies[\"patient_id\"] = []\n",
    "for d in b:\n",
    "    if d[\"allergies\"]:\n",
    "        for allergy in d[\"allergies\"]:\n",
    "            allergies[\"patient_id\"].append(d[\"_id\"])\n",
    "            for k, v in allergy.items():\n",
    "                allergies[k].append(v)\n",
    "    else:\n",
    "        allergies[\"patient_id\"].append(d[\"_id\"])\n",
    "        keys = list(allergies.keys())[:-1]\n",
    "        for k in keys:\n",
    "            allergies[k].append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c811820-b075-4010-bd33-9dc13e32bbc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_id',\n",
       " 'patient_name',\n",
       " 'demographics',\n",
       " 'allergies',\n",
       " 'medications',\n",
       " 'conditions',\n",
       " 'care_plans',\n",
       " 'vitals',\n",
       " 'labs',\n",
       " 'immunization',\n",
       " 'imaging_studies']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(b[1].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "322f2726-c053-4aab-8909-bb532f8bedb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_allergy = pd.DataFrame(allergies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8bf2393-9ccb-4ecc-94ca-7c0f8d83f177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 325 entries, 0 to 324\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   to          164 non-null    object\n",
      " 1   type        164 non-null    object\n",
      " 2   patient_id  325 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 7.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_allergy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09b3a748-4870-4bcf-8be7-0256cd82b9db",
   "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>to</th>\n",
       "      <th>type</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wheat</td>\n",
       "      <td>substance</td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree nut</td>\n",
       "      <td>substance</td>\n",
       "      <td>6395c34e7585b36740092fcb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tree pollen</td>\n",
       "      <td>substance</td>\n",
       "      <td>6395c34e7585b36740092fcb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grass pollen</td>\n",
       "      <td>substance</td>\n",
       "      <td>6395c34e7585b36740092fcb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             to       type                patient_id\n",
       "0         Wheat  substance  6395c34e7585b36740092fc9\n",
       "1          None       None  6395c34e7585b36740092fca\n",
       "2      Tree nut  substance  6395c34e7585b36740092fcb\n",
       "3   Tree pollen  substance  6395c34e7585b36740092fcb\n",
       "4  Grass pollen  substance  6395c34e7585b36740092fcb"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_allergy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "282937e7-82e5-4a39-a80c-4392c2d7e872",
   "metadata": {},
   "outputs": [],
   "source": [
    "allergic = df_allergy.groupby(\"patient_id\")[\"to\"].count()\n",
    "allergic = allergic.gt(0).replace({True:\"allergic\", False:\"not allergic\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a54dd27-3e8a-4246-89fb-caaca15b71ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mCdZvg5Bbvj7wFB0beIuOgrJEMuNhKnUG/Dl71fxQVQs8ko4kwjVTY8QD/xjYAu09ncRHYVkhOVGQuy7fBNv7riI+AzOJkLGMbStL14d3BJevCoBgeVGZhZ3swBvbL+EAzwCkkzAyd4G8x5pjsndgnmOnMqx3MgscovL8f6vsfjqj6uo5H41MrHWfi5YPKI1N1WqGMuNTG7X+RuYv+UcsorKRUchFbHSAJO7BePvA0M5b6UKsdzIZPJLK7Bw2wX8cCpVdBRSMX83Hd4e2QYPh/AKBGrCciOTOBKXib9vPovU3BLRUYgAAOM6BWD+4JZw0tqKjkJmwHIjoyqt0GPJrstYeySJl6Ah2fFx0WLxiNbo08JLdBQyMZYbGc25a3mY82004m4Wio5CdF9THwrGPwe1hC3nqlQslhvVW6XegJX74rFibyyPhCSLERHoipVPtoevq050FDIBlhvVS3xGIeZ+ewZnUnJFRyGqNbcGtnhvbDv0DuVmSqVhuVGd/XwuDfO+PYOSCr3oKER1ptEAM3o3w5wBzWHNE78Vg+VGtSZJEt7bE4MV++J40AgpRrcm7vhwfAQ8nexFRyEjYLlRrRSXV2LON9HYfSFddBQio/NysseK8RHowisNWDyWG9VYSnYxnv7yBC7fKBAdhchkrK00mPdIczzbqykvimrBWG5UI38kZOG5DaeQzSm0SCX6t/TGivER0NlZi45CdcByowfacPQqFm67gAo9f1VIXdoFuGLNlE5wc7ATHYVqieVG91SpN2DhTxfw1R/JoqMQCdPU0wHr/tYZ/m4NREehWmC50V3lFJXjuQ2n8HtClugoRMJ5O9tj3d86o0UjZ9FRqIZYbnSHG3mlmPj5UU6jRfQnzlobfDa5I4+ktBAsN6omOasYEz7/AynZnM2f6K/sbazwwbgIPBreSHQUegCWG1WJu1mACf85ivT8MtFRiGTLSgO8MTwcE7oEiY5C98FyIwDA+dQ8TP7iGA/1J6qhF/qFYM6A5qJj0D2w3Aink3Mw+YtjKCitFB2FyKI82SUQbw0P58neMsSLGakci42o7r4+moxFP10UHYPuguWmYqeTczD5cxYbUX2sPZKEZb9cER2D/oLlplJVxVbGYiOqrxV74/DZbwmiY9CfsNxU6Oy1XBYbkZG99fMlbDrG2XzkguWmMinZxfjb2hMsNiIT+OeWc9h+9rroGASWm6rkFVdgyppjyCzkeWxEpmCQgDnfRGPflZuio6gey00lyisNmLb+BOIzikRHIVK0Cr2EZ786iWOJ2aKjqBrLTQUkScI/Np/BUf5nIzKL0goD/m/tcZxPzRMdRbVYbiqw7JcYbI3mfgAicyooq0TkF8eQkMEJyEVguSncpmPJ+GhfnOgYRKqUVVSOp9adQH5phegoqsNyU7ADMRn419bzomMQqVpCZhFmbTwNg4EzHZoTy02hLl7Px4wNp1DJ/1BEwu2/koEluy+LjqEqLDcFupFXir+tPY5CnstGJBufHEjAj9GpomOoBstNYfQGCTO/PoUb+aWioxDRX7z0/VkeQWkmLDeFeW9PDE5czREdg4juorTCgGc3nEReCQ8wMTWWm4Icic/Eqv08MpJIzlKySzDv22jwUpqmxXJTiKzCMszeFA0eP0Ikf79euonVB+JFx1A0lpsCSJKEF787g5sFnDOSyFIs+yUGv8dniY6hWCw3Bfj8UCL2XckQHYOIakFvkPD8xtPI4kTmJsFys3Bnr+XinV28CjCRJcosLMNr2y6IjqFILDcLVlhWiec3nka53iA6ChHV0Y6zadh1Pk10DMVhuVmw+VvO4WpWsegYRFRP/9p6ATlF5aJjKArLzUJ9dyIFP3KmfyJFyCwsw8KfuHnSmFhuFiijoAyvb78oOgYRGdGP0dfxy4UbomMoBsvNAr214yIKSjlvJJHSzN96HnnFnL3EGFhuFuZIfCYvPEqkUBkFZVjEzZNGYdHlptFosHXrVgBAUlISNBoNoqOjTb7etWvXwtXV1eTr+avySgNe5fXZiBTth9OpiLqULjqGxTN7uS1cuBDt2rUz92qNauzYsYiJiTH7ej87mID4jCKzr5eIzOufW85xcuV6suiRm7GVl9fsUFydTgcvLy8Tp6kuJbsYK/bGmnWdRCRGen4Z3uRBY/VSq3Lr3bs3Zs2ahX/84x9o2LAhGjVqhIULF1a7T3JyMoYNGwZHR0c4OztjzJgxSE+/NcReu3YtFi1ahDNnzkCj0UCj0WDt2rV3Xdfx48cxYMAAeHh4wMXFBb169cKpU6dq9eTOnz+Pxx57DI6OjvD29sakSZOQmZlZ7fnMnDkTs2fPhoeHBwYOHAgA2LZtG0JCQqDVatGnTx+sW7cOGo0Gubm5Vc/jr5slf/rpJ3Tq1AlarRYeHh4YMWJErbI+yMJtF1BawZO1idRi86lrOHeN136rq1qP3NatWwcHBwccPXoU77zzDl5//XXs2bMHAGAwGDBs2DBkZ2fjwIED2LNnDxISEjB27FgAtzbnzZs3D2FhYUhLS0NaWlrVz/6qoKAAkZGROHToEP744w+EhIRg0KBBKCgoqFHO3Nxc9O3bFxEREThx4gR27dqF9PR0jBkz5o7nY2dnh8OHD+Pjjz9GYmIiRo8ejeHDh+PMmTOYPn065s+ff9917dixAyNGjMCgQYNw+vRpREVFoXPnzjXKWRO7L9xA1OWbRlseEcmfJAGLf74kOobFsqntA9q0aYMFCxYAAEJCQvDRRx8hKioKAwYMQFRUFM6dO4fExEQEBAQAAL788kuEhYXh+PHj6NSpExwdHWFjY4NGjRrddz19+/at9v2nn34KV1dXHDhwAEOGDHlgzo8++ggRERFYvHhx1W1ffPEFAgICEBMTg+bNm1c9h3feeafqPi+//DJCQ0Px7rvvAgBCQ0Nx/vx5vPXWW/dc11tvvYVx48Zh0aJFVbe1bdv2gRlrori8Eq//xM0TRGr0e0IW9l5OR98W3qKjWJxaj9zatGlT7XsfHx/cvHlrVHHp0iUEBARUFRsAtGrVCq6urrh0qXafQNLT0/H0008jJCQELi4ucHZ2RmFhIZKTk2v0+DNnzmDfvn1wdHSs+mrRogUAID7+f9dR6tChQ7XHXblyBZ06dap224NGYdHR0ejXr1+NctXWB1GxSM0tMcmyiUj+3t55GXpeqLHWaj1ys7W1rfa9RqOBwWD8fUGRkZHIysrCBx98gKCgINjb26Nbt241PuijsLAQQ4cOxZIlS+74mY+PT9XfHRwc6p1Vp9PVexl3k5BRiC8OJZpk2URkGWLSC/HdiRSM6xwoOopFMerRki1btkRKSgpSUlKqbrt48SJyc3PRqlUrAICdnR30ev0Dl3X48GHMmjULgwYNQlhYGOzt7asdDPIg7du3x4ULFxAcHIxmzZpV+7pfoYWGhuLEiRPVbjt+/Ph919WmTRtERUXVOFtNvfdrLCr0/MRGpHbv/RqDkvIHv2/S/xi13Pr374/WrVtjwoQJOHXqFI4dO4bJkyejV69e6NixIwAgODgYiYmJiI6ORmZmJsrK7n6hvpCQEKxfvx6XLl3C0aNHMWHChFqNkGbMmIHs7GyMHz8ex48fR3x8PHbv3o2pU6fet1ynT5+Oy5cv46WXXkJMTAy+/fbbqiM6NRrNXR+zYMECbNy4EQsWLMClS5dw7ty5u44Ya+NSWj62n+VMJER069SAzw4miI5hUYxabhqNBj/++CPc3NzQs2dP9O/fH02aNME333xTdZ9Ro0bh0UcfRZ8+feDp6YmNGzfedVmff/45cnJy0L59e0yaNAmzZs2q1bllvr6+OHz4MPR6PR555BG0bt0as2fPhqurK6ys7v20GzdujM2bN+OHH35AmzZtsHr16qqjJe3t7e/6mN69e+O7777Dtm3b0K5dO/Tt2xfHjh2rcda7WfbLFUgctBHRf336WwIyedXuGtNIEt9CH+Stt97Cxx9/XG1zqymdTs7BiFVHzLIuIrIcE7sG4s3hrUXHsAicoeQuVq1ahePHjyMhIQHr16/Hu+++i8jISLOtf9kv5p/ai4jkb9OxFMRnFIqOYRFYbncRGxuLYcOGoVWrVnjjjTcwb968O2ZiMZUTSdk4FFfzA2eISD0qDRKW7r4iOoZF4GZJmZn8xTH8FpMhOgYRyZSVBtg7rzeCPep/GpOSceQmI2ev5bLYiOi+DBLwxWGe//ogLDcZWbE3TnQEIrIAm09e4xW7H4DlJhOXb+TjV16gkIhqoLhcj6+P1WwqQrViucnEx/vjeV4bEdXYuiNJqNDzMlj3wnKTgeyicvx87oboGERkQW7kl2LH2TTRMWSL5SYDm0+moJyfwIiolj7nxOr3xHKTgU3HzDPzCREpy7nUPBxNyBIdQ5ZYboIdic9EQmaR6BhEZKH+w9HbXbHcBPv6KI94IqK6i7qUjiR+QL4Dy02grMIy/HKBh/8TUd0ZJGANT+q+A8tNoM0nr/FAEiKqty2nU1FawYuZ/hnLTRBJkrDpOA8kIaL6yy+tRNSlm6JjyArLTZDf47OQyO3kRGQkW05fEx1BVlhugmzg1DlEZEQHYjKQXVQuOoZssNwEyCoswx4eSEJERlShl/DTmeuiY8gGy02Anedv8EASIjK6H06nio4gGyw3ATj7PxGZwpmUXKRkF4uOIQssNzMrKqvEkXhOl0NEprHzPCdTBlhuZncgJgPlldwkSUSmsYNXGAHAcjO7PRe5SZKITOdMSi6u5XDTJMvNjCr1Buy7whMtici0dnL0xnIzp+NJOcgtrhAdg4gUjvvdWG5mxU2SRGQOZ67loaBU3R+kWW5mxFMAiMgc9AYJxxKzRccQiuVmJlduFCCZ558QkZn8rvJTjlhuZrLnInfwEpH5qP18WpabmfzKy1EQkRldupGP3GL1TqTMcjODknI9zqfmiY5BRCoiScAfCeodvbHczODstVxUGiTRMYhIZdS8aZLlZganU3JFRyAiFVLzQSUsNzM4nZwjOgIRqVDszUJkFJSJjiEEy80MojlyIyJBflfpfjeWm4ldzy1Ber46PzkRkXi/x2eKjiAEy83ETifnio5ARCp2NEGdM5Ww3EyM+9uISKSkrCKUVuhFxzA7lpuJ8UhJIhLJIAFxNwtFxzA7lpsJVegNPHmbiISLSS8QHcHsWG4mdCktH2WVBtExiEjlrrDcyJh4CgARyUFsOjdLkhGpcVMAEcmPGt+LWG4mlJhZJDoCERFSc0tQVFYpOoZZsdxMKCmTFyclIvEk6dZUXGrCcjOR0go9rueViI5BRARAfZsmWW4mcjWrGBKvckNEMhHLciNj4P42IpKTKyo7YpLlZiLXcri/jYjkI44jNzKGaznc30ZE8nGzoAySivaVsNxM5Houy42I5KPSICGvpEJ0DLNhuZkIj5QkIrnJKioXHcFsWG4mcj23VHQEIqJqsgpZblQPpRV6ZKvoExIRWYbsojLREcyG5WYCahr6E5HlyOTIjepDbXO4EZFlUNMWJZabCbDciEiOsgq5WZLqobhcLzoCEdEd1LTLhOVmAhy5EZEccbMk1QtHbkQkRzwVgOqlqJwjNyKSH26WpHopLuPIjYjkR027TFhuJsCRGxHJkd7AiZOpHrjPjYjkqNJgEB3BbFhuJlCooqE/EVkOFQ3cWG6mUMxyIyKZqtSrY/TGcjOBcpX88hCR5alUyfDNRnQAJbKz5mcGMp73m53CwJJdomOQQthJ/QBYi45hciw3E9DaKv8Xh8xnflJbDPTeBV3WedFRSAmsNKITmAWHGCbAciNjKqq0xlPFMyDZOYqOQkqgUcf7E8vNBFhuZGyHc1yw1n2O6BikBBp1vO2r41mamdaWLysZ36LElogLGCU6Blk0DWCljvcndTxLM9Nx5EYmMippGEobthAdgyyVvbPoBGbDcjMBbpYkU8mrsMEzpc9DsnUQHYUskZblRvXAzZJkSvuz3fC15wuiY5Al4siN6oMjNzK1+QnhSPQfLjoGWRqO3Kg+WG5kDiOvjkS5W4joGGRJOHKj+mC5kTnkVNhgZsULkGx0oqOQpeDIjeqjgR3Ljczjl8yG+N5rlugYZCk4cqP68HS0Fx2BVOTFhLZI8R8iOgZZAo7cqD58XLXQqGP6NpKJkclPoNy1iegYJHcN3EUnMBuWmwnY21hz9EZmlVFui9n62ZBstKKjkJy5+ItOYDYsNxPxc+NOfjKvnzM88FOjGaJjkJy5BIhOYDYsNxPxdWW5kfnNiuuA636Pio5BcsWRG9WXP8uNBBl5bRwqnINExyC5sbYDHL1FpzAblpuJcLMkiXKjzA5/18yBZG0nOgrJibMv1HSkG8vNRPw4ciOBtqZ7YZfPs6JjkJyoaH8bwHIzGY7cSLRn47og3be/6BgkFyra3waw3EyGIzeSg1HXJ6DSSV1vanQPHLmRMThpbeGktREdg1TuWqk95tvMhWRlKzoKieYZKjqBWbHcTIijN5KDb9IaYa/fM6JjkGjeYaITmBXLzYSaejmKjkAEAHgqrisyfXuLjkGiWNsB7uq6PBLLzYTCfNUzSSnJmyRpMDptMvSOvqKjkAgeoYC1unaTsNxMKMzXRXQEoipJJVostJsDScNLMqmOdyvRCcyO5WZCHLmR3Ky/7oeD/tNExyBz82K5kRF5ONrD25lXByB5iYx7GNmNHhYdg8xJZQeTACw3kwvnpkmSGUnSYGzGVOgd1DPPoOpx5EbG1tqf5UbyE1ukw2LtXEgavgUonoMX4OInOoXZ8TfbxCIC3URHILqrz1MD8If//4mOQaYW2FV0AiFYbibWLsBVTRNxk4WZGNcLed7qfPNTjaDuohMIwXIzMRedLZp58mRukie9ZIVxWU/B0MBDdBQyFY7cyFTac9MkydilwgZ412EeJHATg+LYOQGN2ohOIQTLzQwiAl1FRyC6r9UpQTgZOFV0DDI2/46AlTpP2me5mUHXJu6iIxA90Pi4fijw6ig6BhmTSve3ASw3swj2cEATDwfRMYjuq8KgwYTcaTDoGoqOQsYS2E10AmFYbmbSt4WX6AhED3Q23xEfOM3l/jclsLa/tVlSpVhuZsJyI0vxQXITnA2cKDoG1VfjHoCteq8pyXIzk06NG/LK3GQxxsQNRKFnhOgYVB/NHxWdQCiWm5nYWluhZ4in6BhENVJmsMLk/Gdg0LqKjkJ1xXIjc+nDTZNkQU7lOWGVy1zRMaguvMMB1wDRKYRiuZlRn1BPWHE/PVmQpVeb4ULAeNExqLZUPmoDWG5m5e5ojzb+rqJjENXKmITBKPZQ5ywXFiv0MdEJhGO5mVk/bpokC1Okt8LUwucg2fPK8hbBwRPw6yA6hXAsNzPr25LlRpbnaK4zPnebIzoG1UTIQPBSJCw3swvzdYGPi1Z0DKJaezMpFFcCxoiOQQ/SerToBLLAchNgcGsf0RGI6mRk4lCUurcSHYPuxckXaNxLdApZYLkJMLaTug/RJctVVGmNaSUzIdlxrlRZaj0asOLbOsByEyLE2wnteRkcslC/ZbtivTv3v8lSW562cRvLTZBxnQJFRyCqs9cSWyEhYKToGPRnjVoD3txkfBvLTZAhbX3gaM+5JslyjUgajrKGoaJj0G1txolOICssN0Ea2NlgSBseWEKWK6/CBs+WzYJk20B0FNJYA62fEJ1CVlhuAo3hgSVk4fZmueEbzxdEx6CmfQAnb9EpZIXlJlD7QDc093YUHYOoXl5OaI2r/o+LjqFuHf9PdALZYbkJNqYjR29k+UYmj0K5azPRMdTJLZgTJd8Fy02wUe39YWfNfwaybFnltpilfwGSjXqv/CxM52k8t+0u+IoI5uZghwGtuK2cLN+uDHds8Z4pOoa62DkCERNFp5AllpsMTOjCc95IGebGR+Ca/yDRMdSj7XhA6yI6hSyx3GSgezMPtA1wFR2DyChGpoxFhUtj0TFUQAN0mS46hGyx3GRiVl/ujCdluFlmi7nSbEjW9qKjKFvTvoBHiOgUssVyk4l+Lb0R5suLQZIy/HTTEzt8ZoiOoWxdnxOdQNZYbjLyPEdvpCAz4zriht8jomMok09bIKS/6BSyxnKTkYFhjRDq7SQ6BpHRjEh9EpXOPGDK6Hr+XXQC2WO5yYhGo8EMjt5IQdJK7fCSZg4kK1vRUZTDKwxoMUR0CtljucnMkNY+aOrJC0GScnyf7o09vs+KjqEcPV8ENBrRKWSP5SYzVlYazOjD0Rspy7S4rsjw7Ss6huXzDgfCRphk0UlJSdBoNIiOjgYA7N+/HxqNBrm5uSZZ358tXLgQ7dq1M+oyWW4yNKydH4LceRkRUpZRaRNR6eQnOoZl6/2KIkdtL774IqKiooy6TJabDFlbafBc76aiYxAZVXKJFv+ymQvJihfprRPfCKClZe1rKy8vr9H9HB0d4e7ubtR1s9xkamR7f/i7cRJaUpZNaT7Y7zdNdAzL1O+1ei9i165dePjhh+Hq6gp3d3cMGTIE8fHxNX78oUOH0KNHD+h0OgQEBGDWrFkoKiqq+nlwcDDeeOMNTJ48Gc7Ozpg27da/9WeffYaAgAA0aNAAI0aMwPLly+Hq6lr1uLttlvziiy8QFhYGe3t7+Pj4YObM2s1bynKTKVtrK7zyWEvRMYiM7m9xDyHLp5foGJal+WO3ZiSpp6KiIsydOxcnTpxAVFQUrKysMGLECBgMhgc+Nj4+Ho8++ihGjRqFs2fP4ptvvsGhQ4fuKJ2lS5eibdu2OH36NF599VUcPnwYzzzzDF544QVER0djwIABeOutt+67rtWrV2PGjBmYNm0azp07h23btqFZs9odi6CRJEmq1SPIrMZ+8juOJmaLjkFkVE0alGJPg/mwLkwTHUX+rO2A5/4A3I2/qyIzMxOenp44d+4cHB0d0bhxY5w+fRrt2rXD/v370adPH+Tk5MDV1RVPPfUUrK2t8cknn1Q9/tChQ+jVqxeKioqg1WoRHByMiIgIbNmypeo+48aNQ2FhIbZv315128SJE7F9+/aqg1UWLlyIrVu3Vh3M4ufnh6lTp+LNN9+s83PjyE3mFgwNg5Xy9h+TyiUUa/G63VxIGmvRUeSvy3SjFVtsbCzGjx+PJk2awNnZGcHBwQCA5OTkBz72zJkzWLt2LRwdHau+Bg4cCIPBgMTExKr7dezYsdrjrly5gs6dO1e77a/f/9nNmzdx/fp19OvXrxbP7E7csytzrXydMbZTIDYee/AvH5ElWXfdDwNCnsLDKZ88+M5q5eAF9PyH0RY3dOhQBAUF4bPPPoOvry8MBgPCw8NrdOBHYWEhpk+fjlmzZt3xs8DA/81C4+BQv/N0dTrjHGvAcrMAfx8Yih1nryO/tFJ0FCKjiozrgRNB5+F247DoKPLU71VAa5wJ1bOysnDlyhV89tln6NGjB4BbmxVrqn379rh48WKt932Fhobi+PHj1W776/d/5uTkhODgYERFRaFPnz61WtefcbOkBWjoYIcXB4aKjkFkdHrJCmMz/ga9g5foKPLj0w5oZ7yrbLu5ucHd3R2ffvop4uLisHfvXsydO7fGj3/ppZdw5MgRzJw5E9HR0YiNjcWPP/74wKMYn3/+efz8889Yvnw5YmNj8cknn2Dnzp3Q3Od8vYULF2LZsmX48MMPERsbi1OnTmHFihU1zgqw3CzGxC5BaOPPK+6S8sQU6bBENxeShm9H1Ty2BLAy3mtiZWWFTZs24eTJkwgPD8ecOXPw7rvv1vjxbdq0wYEDBxATE4MePXogIiICr732Gnx9fe/7uIceeggff/wxli9fjrZt22LXrl2YM2cOtFrtPR8TGRmJ999/H6tWrUJYWBiGDBmC2NjYGmcFeLSkRTmfmodhKw9Db+A/GSnPNyF70SXlP6JjyEP7SODxD0WnMJmnn34aly9fxsGDB022Dn5UsiDhfi6Y1DVIdAwik5gY3wf53l1ExxDP2R94pO6HwMvR0qVLcebMGcTFxWHFihVYt24dIiMjTbpOjtwsTEFpBfotO4CbBWWioxAZXZhTEX6yeQVWJZmio4gzYTMQMkB0CqMaM2YM9u/fj4KCAjRp0gTPP/88nnnmGZOuk+VmgX65cAPT1p8UHYPIJGYEJOHFjPnQQIVvTW3HAyM+Fp1CEbhZ0gI9EtYI4zvz6sakTCtTgnE6YLLoGObn2Ah49N+iUygGy81CvTakFS9qSoo1Pn4ACrw6PviOSjJkOaBzE51CMVhuFkpnZ40Px0fAzpr/hKQ8ZQYrTMqdBoNWJW/2YSOBFoNFp1AUvjNasDBfF/ydJ3eTQkXnO2KlS81PMrZYzv7A4GWiUygOy83CPdWjMXqEeIiOQWQSy642xblA483SITtWNsDoL4AGDUUnURyWm4XTaDRYNqYt3B3sREchMokx8QNR5NlOdAzT6PsvIJDn9pkCy00BvJy0eGd0G9ExiEyiRG+NyPxnIdkrbPq5ZgOAh2aLTqFYLDeF6NfSG5O7cfYSUqYTeU74xHWO6BjG4+QLjPgEuM/kwVQ/LDcF+eeglmju7Sg6BpFJvH21OS4FjBMdo/401sCo/wAO7qKTKBrLTUG0ttZY+WR7ONnzMn2kTKMTB6PEI1x0jPrp/QoQ/JDoFIrHclOYEG8nfDShPaytuLmDlKeo0hp/K5oByd5JdJS6aTkU6Pmi6BSqwHJToF7NPbFwaCvRMYhM4vccF6xpOFt0jNrzaQuM+JT72cyE5aZQk7oFY0r3YNExiEzi9cSWiA0YLTpGzTk2AsZvAuwaiE6iGiw3BXttSCv0a+ElOgaRSYxMfBylDVuKjvFgNjpg/EbA+f5XrCbjYrkpmJWVBh+Oj0BLH2fRUYiMrqDSBtPLZkKyk/ME4hpgxGrAr73oIKrDclM4B3sbfDGlI7yc7EVHITK6A1lu2OAxW3SMe+vzTyBshOgUqsRyUwEfFx0+j+wEna216ChERvevhDAkBgwXHeNObcYBvf4hOoVqsdxUorW/C94f1w48Q4CUaGTSSJS5NRcd439CBwPDVopOoWosNxUZGNYI/xxkATvgiWopp8IGMytmQbKVwdGIjXsBT6wBrDmZgkgsN5V5qkcTvPiIjD7hEhnJnsyG2Ow5U2wI/863joy04T5u0TSSJEmiQ5D5rdwXh3d3XxEdg8joDjb7GgHXtpt/xd6tgSnbAZ2r+ddNd+DITaVm9GmGlx5tIToGkdGNTH4C5a5NzbtS9xBg0hYWm4yw3FTs2d5N8cpjLDhSloxyW8zWvwDJRmueFboGApO3Ao6e5lkf1QjLTeWm92qKfw5iwZGy/JzhgW3eZtj/5h4CTN0FuPibfl1UK9znRgCA/xxMwJs7LomOQWRUR5p+Cd/UXaZZeKPWwMQtHLHJFEduBODWUZSvDuGVBEhZRl4bhwqXYOMv2L8zELmdxSZjLDeq8n8PN8YCXiqHFORGmR3+jtmQrO2Mt9DGvW7tY+PBI7LGcqNqpj7UGG8OD+fFTkkxtqZ7YZfPc8ZZWOhgYMJ3gKwnayaA+9zoHvZeTsfMr0+juFwvOgqRUfzRZA0aXd9T9wW0GQsMW8WZRywEy43u6XxqHv5v3XGk55eJjkJUb37aMhxwfg02+Sm1fKTm1uz+nATZonCzJN1TuJ8Lts54CC0aOYmOQlRvqaX2eMVqDiQr25o/yEZ3a55IFpvFYbnRffm46LD52e7oE8qjwsjyfXejEaL8nqnZnR0bAVN38HpsFoqbJalGDAYJS3ZfxicHEkRHIaoXjUbCscb/gef1ffe+U6M2wJPfAM6+5gtGRsVyo1r5MToVL31/FqUVBtFRiOosWFeKKId/wbrw+p0/bDEEGPkZYCeDy+dQnXGzJNXKsHZ++G56d/i4mGnePiITSCrR4jXbuZCs/nTko8YK6P1PYOxXLDYF4MiN6iSjoAxzvonGobhM0VGI6mxdyEH0SlkNOHgCo/4DNOktOhIZCcuN6kySJHx+KBHv7L6C8kpupiTLo9FIONLpMHwGvAA4NRIdh4yI5Ub1diktH7M3ReNKeoHoKEQ1Zm2lway+IZjZtxln5FEglhsZRVmlHkt2XsGaI4ngbxTJnZ+rDh+Ma4eOwQ1FRyETYbmRUR2MzcC8b8/gZgFnNSF5GtzGB4tHtIaLrhYnc5PFYbmR0eUUleOVH85h14UboqMQVXFtYIv5g1riiY4BoqOQGbDcyGS+PZ6CRT9dQBEnXybBRkb4Yf7glnB3tBcdhcyE5UYmdTWrCC9/fw6/J2SJjkIq1MTDAW8OD0f3Zh6io5CZsdzILH4+l4a3dlxCam6J6CikAnY2Vniud1M827sp7G2sRcchAVhuZDalFXqs2h+PTw7Eo4znxZGJdG/qjjeHh6OJp6PoKCQQy43MLiW7GG/uuIjdF9JFRyEFcXeww/zBLTGyvb/oKCQDLDcS5lBsJhb+dAFxNwtFRyELptEAYzoE4JVBLeDawE50HJIJlhsJVak3YO2RJHzwaywKyipFxyELM6CVN+b0b45Wvs6io5DMsNxIFjIKyrB09xVsPnUNegN/Jen+eod6Yu6A5mjj7yo6CskUy41kJTmrGKsPxOP7k9dQrudBJ1TdQ83cMXdAKDoEuYmOQjLHciNZupFXis8OJuDro8koqeBJ4GrXuXFDzB3QHF2buIuOQhaC5Uayll1Uji8OJWLd70koKOU+ObWJCHTFvAGheDiEJ2FT7bDcyCIUlFbgy9+v4otDicgqKhcdh0ysc+OGeLZXU/Rp4SU6ClkolhtZlJJyPTYeS8ZnBxOQllcqOg4ZkZO9DUa298OErkFo7u0kOg5ZOJYbWaTySgN2X7iBjceS8XtCFq8hZ8HCfJ0xsWsQhrXzRQM7G9FxSCFYbmTxrmYVYeOxFGw+eQ2ZhbyOnCWwt7HCkDa+mNg1EBGBPPKRjI/lRopRoTcg6lI6Np9MxYGYm6jQ81dbbhp7OGBCl0CM7uDP2UTIpFhupEjZReX46cx1/HA6FWdSckXHUTV3BzsMDG+EIW180K2JOzQajehIpAIsN1K8+IxC/Bh9Hfsu38T563ncP2cGHo72eDTcG4Na+6BLY3dYW7HQyLxYbqQqGQVl2H/lJvbHZOBgTAbyee6c0TTxdED/lt7o18ILnYIbwoqFRgKx3Ei19AYJp5JzsO/yTey/koGLafmiI1kUGysNOgU3RL+WXujf0hvBHg6iIxFVYbkR/Vd6fikOXMnAvis3cTguk6O6v3BrYIv2gW5oH+SG9oFuaBfgCp0dr3JN8sRyI7oLSZJwNasY51LzcD41r+pPtRSeRgOEeDmiQ5AbIgLd0CHIDU15ZWuyICw3ohqSJAnJ2bcK73bZnU/NR15Jheho9WJrrYGfqw7BHg5o4++K9oGuiAh0g4vOVnQ0ojpjuRHVU3JWMS6m5SM1twRpuSVIyyvF9bwSpOWW4mZBKeRweTqtrRUCGzZAkLsDgho2QJCHA4LdGyCooQP83HQ8mpEUh+VGZEKVegPSC8qQlluC63mlVeWXlleC/JJKFFfoUVJeieJyPUrK9Sgu16Ncb7jnBVutNICDvQ2c7G3gqLWBo70NHLW2t77/021OWhu46GyrCs3b2Z7nl5GqsNyIZKpSb4BekqA3SKg0SLDWaOBgz7kXiWqC5UZERIpjJToAERGRsbHciIhIcVhuRESkOCw3IiJSHJYbEREpDsuNiIgUh+VGRESKw3IjIiLFYbkREZHisNyIiEhxWG5ERKQ4LDciIlIclhsRESkOy42IiBSH5UZERIrDciMiIsVhuRERkeKw3IiISHFYbkREpDgsNyIiUhyWGxERKQ7LjYiIFIflRkREisNyIyIixWG5ERGR4rDciIhIcVhuRESkOCw3IiJSHJYbEREpDsuNiIgUh+VGRESKw3IjIiLFYbkREZHisNyIiEhxWG5ERKQ4LDciIlIclhsRESkOy42IiBSH5UZERIrDciMiIsVhuRERkeKw3IiISHFYbkREpDgsNyIiUhyWGxERKc7/AygkpEqS3DZ3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allergic.value_counts(normalize=True).plot(\n",
    "    kind=\"pie\",\n",
    "    title=\"Allergic Patients\",\n",
    "    ylabel=\"\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3f39bb71-38e4-47f2-b6ac-ba5c1a6d5ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = {\n",
    "     k:[] for k in b[0][\"conditions\"][0].keys()\n",
    "}\n",
    "conditions[\"patient_id\"] = []\n",
    "for d in b:\n",
    "    if d[\"conditions\"]:\n",
    "        for condition in d[\"conditions\"]:\n",
    "            conditions[\"patient_id\"].append(d[\"_id\"])\n",
    "            for k, v in condition.items():\n",
    "                conditions[k].append(v)\n",
    "    else:\n",
    "        conditions[\"patient_id\"].append(d[\"_id\"])\n",
    "        keys = list(conditions.keys())[:-1]\n",
    "        for k in keys:\n",
    "            conditions[k].append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e6f11f10-9a14-4a88-826b-cdb580995de5",
   "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>condition</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anemia (disorder)</td>\n",
       "      <td>2022-03-19</td>\n",
       "      <td></td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prediabetes</td>\n",
       "      <td>2022-03-19</td>\n",
       "      <td></td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Severe anxiety (panic) (finding</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td></td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miscarriage in first trimester</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td></td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Severe anxiety (panic) (finding</td>\n",
       "      <td>2008-05-08</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         condition        from          to  \\\n",
       "0                Anemia (disorder)  2022-03-19               \n",
       "1                      Prediabetes  2022-03-19               \n",
       "2  Severe anxiety (panic) (finding  2021-07-22               \n",
       "3   Miscarriage in first trimester  2015-12-03               \n",
       "4  Severe anxiety (panic) (finding  2008-05-08  2009-05-14   \n",
       "\n",
       "                 patient_id  \n",
       "0  6395c34e7585b36740092fc9  \n",
       "1  6395c34e7585b36740092fc9  \n",
       "2  6395c34e7585b36740092fca  \n",
       "3  6395c34e7585b36740092fca  \n",
       "4  6395c34e7585b36740092fca  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conditions = pd.DataFrame(conditions)\n",
    "df_conditions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d62f1624-77c3-4b4e-8835-a0ca9614bf5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_conditions = df_conditions[\"condition\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "09ad47a2-d322-4006-aae8-f2e2430b810b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_10_conditions.plot(\n",
    "    kind=\"bar\",\n",
    "    title=\"Top ten conditions of patients\",\n",
    "    xlabel=\"Conditions\",\n",
    "    ylabel=\"Number of patients\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "50e8f66c-27b4-4e73-86ad-17856a88d9d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demographic_condition = pd.merge(\n",
    "    df_patient, df_conditions, how='inner', on=[\"patient_id\"]\n",
    ")[[\"gender\", \"condition\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2fdd6147-fbbe-47d9-8988-c995a6202228",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_conds = top_10_conditions.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "026d0505-6388-45dd-88ba-1f34fb1327fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demographic_condition[\"gender\"] = df_demographic_condition[\"gender\"].replace({\"M\": \"male\", \"F\": \"female\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6aa49050-4a08-4d4b-bef4-a6b93013e52d",
   "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>gender</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>Anemia (disorder)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>Prediabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>Severe anxiety (panic) (finding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>Miscarriage in first trimester</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>Severe anxiety (panic) (finding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>male</td>\n",
       "      <td>Febrile neutropenia (disorder)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>male</td>\n",
       "      <td>Acute myeloid leukemia, disease (disorder)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>male</td>\n",
       "      <td>Injury of medial collateral ligament of knee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>male</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>male</td>\n",
       "      <td>Perennial allergic rhinitis with seasonal vari...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>706 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender                                          condition\n",
       "0      male                                  Anemia (disorder)\n",
       "1      male                                        Prediabetes\n",
       "2    female                    Severe anxiety (panic) (finding\n",
       "3    female                     Miscarriage in first trimester\n",
       "4    female                    Severe anxiety (panic) (finding\n",
       "..      ...                                                ...\n",
       "701    male                     Febrile neutropenia (disorder)\n",
       "702    male         Acute myeloid leukemia, disease (disorder)\n",
       "703    male       Injury of medial collateral ligament of knee\n",
       "704    male                         Viral sinusitis (disorder)\n",
       "705    male  Perennial allergic rhinitis with seasonal vari...\n",
       "\n",
       "[706 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demographic_condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fc19c27e-501a-4e67-b021-ddb9fabdab96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Prediabetes': {'male': 33, 'female': 32},\n",
       " 'Anemia (disorder)': {'male': 30, 'female': 24},\n",
       " 'Hypertension': {'male': 30, 'female': 18},\n",
       " 'Viral sinusitis (disorder)': {'male': 22, 'female': 23},\n",
       " 'Normal pregnancy': {'male': 0, 'female': 34},\n",
       " 'Chronic sinusitis (disorder)': {'male': 15, 'female': 17},\n",
       " 'Miscarriage in first trimester': {'male': 0, 'female': 31},\n",
       " 'Acute viral pharyngitis (disorder)': {'male': 13, 'female': 7},\n",
       " 'Metabolic syndrome X (disorder)': {'male': 9, 'female': 8},\n",
       " 'Acute bronchitis (disorder)': {'male': 5, 'female': 9}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond_gender = {k:{\"male\":0, \"female\":0} for k in top_conds}\n",
    "\n",
    "for index, row in df_demographic_condition.iterrows():\n",
    "    if row[\"condition\"] in top_conds:\n",
    "        cond_gender[row[\"condition\"]][row[\"gender\"]] += 1\n",
    "\n",
    "cond_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a61ae315-344a-4fa1-92cb-0f99ba246ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cond_gender = pd.DataFrame(cond_gender).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "28f6a650-9d0c-4689-b82c-422fa14e7472",
   "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>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prediabetes</th>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anemia (disorder)</th>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hypertension</th>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viral sinusitis (disorder)</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal pregnancy</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chronic sinusitis (disorder)</th>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscarriage in first trimester</th>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acute viral pharyngitis (disorder)</th>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metabolic syndrome X (disorder)</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acute bronchitis (disorder)</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    male  female\n",
       "Prediabetes                           33      32\n",
       "Anemia (disorder)                     30      24\n",
       "Hypertension                          30      18\n",
       "Viral sinusitis (disorder)            22      23\n",
       "Normal pregnancy                       0      34\n",
       "Chronic sinusitis (disorder)          15      17\n",
       "Miscarriage in first trimester         0      31\n",
       "Acute viral pharyngitis (disorder)    13       7\n",
       "Metabolic syndrome X (disorder)        9       8\n",
       "Acute bronchitis (disorder)            5       9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cond_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea73771e-1283-41f9-9caf-d606adf967cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cond_gender.plot(\n",
    "    kind=\"bar\",\n",
    "    title=\"Top ten conditions grouped with gender\",\n",
    "    xlabel=\"Conditions\",\n",
    "    ylabel=\"Number of patients\"\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "269b2a8c-29c5-43e4-ae4c-4e711ded30b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "immunizations = {\n",
    "    k:[] for k in b[0][\"immunization\"][0].keys()\n",
    "}\n",
    "\n",
    "immunizations[\"patient_id\"] = []\n",
    "\n",
    "for d in b:\n",
    "    if d[\"immunization\"]:\n",
    "        for imm in d[\"immunization\"]:\n",
    "            immunizations[\"patient_id\"].append(d[\"_id\"])\n",
    "            for k, v in imm.items():\n",
    "                immunizations[k].append(v)\n",
    "    else:\n",
    "        immunizations[\"patient_id\"].append(d[\"_id\"])\n",
    "        keys = list(immunizations.keys())[:-1]\n",
    "        for k in keys:\n",
    "            immunizations[k].append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4e26980c-b516-455f-90c4-61c67da32ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 646 entries, 0 to 645\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   immunization  642 non-null    object\n",
      " 1   date          642 non-null    object\n",
      " 2   patient_id    646 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 15.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_imm = pd.DataFrame(immunizations)\n",
    "df_imm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f7580f81-df7c-40c8-8ad7-f743df99288b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imm[\"date\"] = pd.to_datetime(df_imm['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "74e994d6-7718-41cb-b501-81620d9014fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Influenza, seasonal, injectable, preservative free                                                                  287\n",
       "SARS-COV-2 (COVID-19) vaccine, mRNA, spike protein, LNP, preservative free, 30 mcg/0.3mL dose                       146\n",
       "SARS-COV-2 (COVID-19) vaccine, mRNA, spike protein, LNP, preservative free, 100 mcg/0.5mL dose                      102\n",
       "Td (adult) preservative free                                                                                         27\n",
       "meningococcal MCV4P                                                                                                  21\n",
       "Hep B, adult                                                                                                         15\n",
       "Hep A, adult                                                                                                         15\n",
       "zoster                                                                                                               12\n",
       "SARS-COV-2 (COVID-19) vaccine, vector non-replicating, recombinant spike protein-Ad26, preservative free, 0.5 mL      7\n",
       "HPV, quadrivalent                                                                                                     5\n",
       "Tdap                                                                                                                  4\n",
       "Hep A, ped/adol, 2 dose                                                                                               1\n",
       "Name: immunization, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imm[\"immunization\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "327e408c-86f8-4971-9dc0-61a459724a7d",
   "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>immunization</th>\n",
       "      <th>date</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
       "      <td>2022-03-19</td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro...</td>\n",
       "      <td>2021-08-28</td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro...</td>\n",
       "      <td>2021-08-07</td>\n",
       "      <td>6395c34e7585b36740092fc9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        immunization       date  \\\n",
       "0  Influenza, seasonal, injectable, preservative ... 2022-03-19   \n",
       "1  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-08-28   \n",
       "2  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-08-07   \n",
       "3  Influenza, seasonal, injectable, preservative ... 2022-07-28   \n",
       "4  Influenza, seasonal, injectable, preservative ... 2021-07-22   \n",
       "\n",
       "                 patient_id  \n",
       "0  6395c34e7585b36740092fc9  \n",
       "1  6395c34e7585b36740092fc9  \n",
       "2  6395c34e7585b36740092fc9  \n",
       "3  6395c34e7585b36740092fca  \n",
       "4  6395c34e7585b36740092fca  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5c09cd9b-06c6-4445-981d-a22ab56f5be9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imm_covid = df_imm[df_imm[\"immunization\"].str.contains(\"COVID\")==True].sort_values(by='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "74a6b2a2-d772-47d4-a272-835b0b3cd47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>immunization</th>\n",
       "      <th>date</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro...</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>6395c34e7585b36740092ff2</td>\n",
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       "      <td>6395c34e7585b36740092fe9</td>\n",
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       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro...</td>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>6395c34e7585b36740093038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro...</td>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>6395c34e7585b36740093035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
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       "      <td>2021-01-20</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "                                          immunization       date  \\\n",
       "137  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-01-11   \n",
       "109  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-01-14   \n",
       "393  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-01-17   \n",
       "380  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-01-18   \n",
       "327  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-01-20   \n",
       "\n",
       "                   patient_id  \n",
       "137  6395c34e7585b36740092ff2  \n",
       "109  6395c34e7585b36740092fe9  \n",
       "393  6395c34e7585b36740093038  \n",
       "380  6395c34e7585b36740093035  \n",
       "327  6395c34e7585b36740093025  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imm_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bf31fd7b-6ef9-4730-aa96-d15bedf4539d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_imm[\"month_year\"] = df_imm_covid[\"date\"].dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a0636f8-8d44-42e5-bdd4-ddd2854e904f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>immunization</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
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       "      <td>NaT</td>\n",
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       "      <td>2021-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Influenza, seasonal, injectable, preservative ...</td>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>6395c34e7585b36740092fca</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                        immunization       date  \\\n",
       "0  Influenza, seasonal, injectable, preservative ... 2022-03-19   \n",
       "1  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-08-28   \n",
       "2  SARS-COV-2 (COVID-19) vaccine, mRNA, spike pro... 2021-08-07   \n",
       "3  Influenza, seasonal, injectable, preservative ... 2022-07-28   \n",
       "4  Influenza, seasonal, injectable, preservative ... 2021-07-22   \n",
       "\n",
       "                 patient_id month_year  \n",
       "0  6395c34e7585b36740092fc9        NaT  \n",
       "1  6395c34e7585b36740092fc9    2021-08  \n",
       "2  6395c34e7585b36740092fc9    2021-08  \n",
       "3  6395c34e7585b36740092fca        NaT  \n",
       "4  6395c34e7585b36740092fca        NaT  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "efcb0770-6aa9-4bce-83dc-1e456de32191",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_21_22 = df_imm.groupby(\"month_year\")[\"immunization\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "aa970004-cbda-42b9-bb60-656e7186aff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_21_22.plot(\n",
    "    title=\"Time plot of COVID vaccination from Jan 2021 to June 2022\",\n",
    "    xlabel=\"Time\",\n",
    "    ylabel=\"Frequency of vaccination\"\n",
    ");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8 (main, Nov  1 2022, 14:18:21) [GCC 12.2.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}