notebooks/ndjson.ipynb

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   "mimetype": "text/x-python",
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   "language": "python"
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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: 839 entries, 0 to 0\nData columns (total 37 columns):\n #   Column                     Non-Null Count  Dtype \n---  ------                     --------------  ----- \n 0   resourceType               839 non-null    object\n 1   id                         839 non-null    object\n 2   status                     800 non-null    object\n 3   reasonReference            437 non-null    object\n 4   code.text                  813 non-null    object\n 5   subject.reference          799 non-null    object\n 6   context.reference          799 non-null    object\n 7   performedPeriod.start      799 non-null    object\n 8   performedPeriod.end        799 non-null    object\n 9   code.codingcodes           813 non-null    object\n 10  patientId                  839 non-null    object\n 11  clinicalStatus             14 non-null     object\n 12  verificationStatus         14 non-null     object\n 13  type                       14 non-null     object\n 14  category                   14 non-null     object\n 15  criticality                14 non-null     object\n 16  assertedDate               14 non-null     object\n 17  patient.reference          15 non-null     object\n 18  lotNumber                  1 non-null      object\n 19  manufactureDate            1 non-null      object\n 20  expirationDate             1 non-null      object\n 21  udi.deviceIdentifier       1 non-null      object\n 22  udi.carrierHRF             1 non-null      object\n 23  type.text                  1 non-null      object\n 24  type.codingcodes           1 non-null      object\n 25  extension                  25 non-null     object\n 26  identifier                 25 non-null     object\n 27  name                       25 non-null     object\n 28  telecom                    25 non-null     object\n 29  gender                     25 non-null     object\n 30  birthDate                  25 non-null     object\n 31  address                    25 non-null     object\n 32  multipleBirthBoolean       25 non-null     object\n 33  communication              25 non-null     object\n 34  text.status                25 non-null     object\n 35  maritalStatus.text         25 non-null     object\n 36  maritalStatus.codingcodes  25 non-null     object\ndtypes: object(37)\nmemory usage: 249.1+ KB\nNone\n"
     ]
    }
   ],
   "source": [
    "df = fp.ndjson(os.getenv(\"NDJSON\"))  #fp.process('path/to/ndjson/files')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Index(['resourceType', 'id', 'status', 'reasonReference', 'code.text',\n       'subject.reference', 'context.reference', 'performedPeriod.start',\n       'performedPeriod.end', 'code.codingcodes', 'patientId',\n       'clinicalStatus', 'verificationStatus', 'type', 'category',\n       'criticality', 'assertedDate', 'patient.reference', 'lotNumber',\n       'manufactureDate', 'expirationDate', 'udi.deviceIdentifier',\n       'udi.carrierHRF', 'type.text', 'type.codingcodes', 'extension',\n       'identifier', 'name', 'telecom', 'gender', 'birthDate', 'address',\n       'multipleBirthBoolean', 'communication', 'text.status',\n       'maritalStatus.text', 'maritalStatus.codingcodes'],\n      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0    fbfec681-d357-4b28-b1d2-5db6434c7846\n0    fbfec681-d357-4b28-b1d2-5db6434c7846\n0    fbfec681-d357-4b28-b1d2-5db6434c7846\n0    fbfec681-d357-4b28-b1d2-5db6434c7846\n0    fbfec681-d357-4b28-b1d2-5db6434c7846\n                     ...                 \n0    ff9d23d8-f3c8-4eee-a5f9-e05e843675b5\n0    8d3e1155-278a-4824-a7e0-fddb24c7c179\n0    8c9fea57-6ded-47b0-88c9-75518430b572\n0    d2524ab6-4db9-440d-b588-6dcfcab89270\n0    f98b23bf-4443-46d0-9eaf-563e767cf948\nName: patientId, Length: 839, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['patientId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     },
     "metadata": {},
     "execution_count": 5
    },
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['gender'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}