notebooks/synthea.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import fhiry.parallel as fp\n",
    "import matplotlib.pyplot as plt\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 527432 entries, 0 to 328\nColumns: 117 entries, fullUrl to resource.context.reference\ndtypes: float64(6), object(111)\nmemory usage: 474.8+ MB\nNone\n"
     ]
    }
   ],
   "source": [
    "df = fp.process(os.getenv(\"SYNTHEA\"))  #fp.process('path/to/fhir/resources')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Index(['fullUrl', 'resource.resourceType', 'resource.id',\n       'resource.text.status', 'resource.extension', 'resource.identifier',\n       'resource.name', 'resource.telecom', 'resource.gender',\n       'resource.birthDate',\n       ...\n       'resource.multipleBirthInteger', 'resource.udiCarrier',\n       'resource.distinctIdentifier', 'resource.manufactureDate',\n       'resource.expirationDate', 'resource.lotNumber',\n       'resource.serialNumber', 'resource.deviceName', 'resource.type.text',\n       'resource.context.reference'],\n      dtype='object', length=117)\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0      9dca5e7a-73b9-465c-9f28-d9507b12244d\n1      9dca5e7a-73b9-465c-9f28-d9507b12244d\n2      9dca5e7a-73b9-465c-9f28-d9507b12244d\n3      9dca5e7a-73b9-465c-9f28-d9507b12244d\n4      9dca5e7a-73b9-465c-9f28-d9507b12244d\n                       ...                 \n324    83f01277-56cc-4de6-b4f4-c4d209031606\n325    83f01277-56cc-4de6-b4f4-c4d209031606\n326    83f01277-56cc-4de6-b4f4-c4d209031606\n327    83f01277-56cc-4de6-b4f4-c4d209031606\n328    83f01277-56cc-4de6-b4f4-c4d209031606\nName: patientId, Length: 527432, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['patientId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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      "text/plain": [
       "<AxesSubplot:>"
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     "metadata": {},
     "execution_count": 5
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['resource.gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}