556 lines
24 KiB
Plaintext
556 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9de5907f-18f5-4cb1-903e-26028ff1fa03",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"pd.set_option('display.max_rows', 100)\n",
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"pd.set_option('display.max_columns', None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a271254b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_csv(file, usecols, dtype = {}):\n",
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" return pd.read_csv(\n",
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" file,\n",
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" index_col = 'VAERS_ID',\n",
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" encoding = 'latin1',\n",
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" low_memory = False,\n",
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" usecols = usecols,\n",
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" dtype = dtype)\n",
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"\n",
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"def readVaersDescr(dataDir, year):\n",
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" folder = dataDir + \"/\" + year + \"VAERSData/\"\n",
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" return {\n",
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" 'VAERSDATA':\n",
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" read_csv(\n",
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" folder + year + \"VAERSDATA.csv\",\n",
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" ['VAERS_ID', 'DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT']),\n",
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" 'VAERSVAX':\n",
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" read_csv(\n",
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" folder + year + \"VAERSVAX.csv\",\n",
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" ['VAERS_ID', 'VAX_DOSE_SERIES', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT'],\n",
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" dtype = {\"VAX_DOSE_SERIES\": \"string\"})\n",
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" }\n",
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"\n",
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"def readVaersDescrs(dataDir, years):\n",
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" return [readVaersDescr(dataDir, year) for year in years]\n",
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"\n",
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"def readAllVaersDescrs(dataDir):\n",
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" return readVaersDescrs(dataDir, [\"2021\", \"2022\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b5d6df0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def createDataFrameFromDescr(vaersDescr):\n",
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" return pd.merge(\n",
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" vaersDescr['VAERSDATA'],\n",
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" vaersDescr['VAERSVAX'],\n",
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" how = 'left',\n",
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" left_index = True,\n",
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" right_index = True,\n",
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" validate = 'one_to_many')\n",
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"\n",
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"def createDataFrameFromDescrs(vaersDescrs):\n",
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" dataFrames = [createDataFrameFromDescr(vaersDescr) for vaersDescr in vaersDescrs]\n",
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" return pd.concat(dataFrames)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ebcba86",
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"metadata": {},
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"outputs": [],
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"source": [
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"def filterDataFrame(df, manufacturer = None, dose = None):\n",
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" isCovid19 = df[\"VAX_TYPE\"] == \"COVID19\"\n",
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" isManufacturer = df[\"VAX_MANU\"] == manufacturer if manufacturer is not None else True\n",
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" isDose = df[\"VAX_DOSE_SERIES\"].str.contains(dose) if dose is not None else True\n",
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" return df[isCovid19 & isManufacturer & isDose]\n",
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"\n",
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"def filterDataFrameForSevereEffects(df, dose):\n",
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" return filterDataFrame(df, dose = dose)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "99945ca8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def createBatchCodeTable(df : pd.DataFrame):\n",
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" def filterDataFrame(df, col):\n",
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" return df[df[col] == 'Y'][['VAX_LOT']]\n",
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"\n",
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" batchCodeTableDict = {\n",
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" 'ADRs': df[['VAX_LOT']].value_counts(),\n",
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" 'DEATHS': filterDataFrame(df, 'DIED').value_counts(),\n",
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" 'DISABILITIES': filterDataFrame(df, 'DISABLE').value_counts(),\n",
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" 'LIFE THREATENING ILLNESSES': filterDataFrame(df, 'L_THREAT').value_counts()\n",
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" }\n",
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" return pd.concat(batchCodeTableDict, axis = 'columns').replace(to_replace = np.nan, value = 0)\n",
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"\n",
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"def createManufacturerByBatchCodeTable(df):\n",
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" manufacturerByBatchCodeTable = df[['VAX_LOT', 'VAX_MANU']]\n",
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" manufacturerByBatchCodeTable = manufacturerByBatchCodeTable.drop_duplicates(subset = ['VAX_LOT'])\n",
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" return manufacturerByBatchCodeTable.set_index('VAX_LOT')\n",
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"\n",
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"def createCompanyByBatchCodeTable(df):\n",
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" return createManufacturerByBatchCodeTable(df).rename(columns = {\"VAX_MANU\": \"COMPANY\"})\n",
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"\n",
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"# create table from https://www.howbadismybatch.com/combined.html\n",
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"# FK-TODO: DRY with createBatchCodeTable()\n",
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"def createSevereEffectsBatchCodeTable(df):\n",
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" def filterDataFrame(df, col):\n",
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" return df[df[col] == 'Y']['VAX_LOT']\n",
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"\n",
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" batchCodeTableDict = {\n",
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" 'ADRs': df['VAX_LOT'].value_counts(),\n",
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" 'DEATHS': filterDataFrame(df, 'DIED').value_counts(),\n",
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" 'DISABILITIES': filterDataFrame(df, 'DISABLE').value_counts(),\n",
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" 'LIFE THREATENING ILLNESSES': filterDataFrame(df, 'L_THREAT').value_counts(),\n",
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" 'HOSPITALISATIONS': filterDataFrame(df, 'HOSPITAL').value_counts(),\n",
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" 'EMERGENCY ROOM OR DOCTOR VISITS': filterDataFrame(df, 'ER_VISIT').value_counts()\n",
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" }\n",
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" batchCodeTable = pd.concat(batchCodeTableDict, axis = 'columns')\n",
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" batchCodeTable.index.name = 'VAX_LOT'\n",
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" # add Company column:\n",
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" batchCodeTable = pd.merge(\n",
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" batchCodeTable,\n",
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" createCompanyByBatchCodeTable(df),\n",
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" how = 'left',\n",
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" left_index = True,\n",
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" right_index = True,\n",
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" validate = 'one_to_one')\n",
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" return batchCodeTable.replace(to_replace = np.nan, value = 0)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3dacedfd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import unittest"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e59a1825",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pandas.testing import assert_frame_equal\n",
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"\n",
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"class CreateAndFilterDataFrameTest(unittest.TestCase):\n",
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"\n",
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" def test_createAndFilterDataFrameFromDescrs(self):\n",
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" # Given\n",
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" vaersDescrs = [\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ ['Y', np.NaN, np.NaN],\n",
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" [np.NaN, np.NaN, 'Y']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['COVID19', 'MODERNA', '037K20A', '1'],\n",
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" ['COVID19', 'MODERNA', '025L20A', '1']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" },\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ [np.NaN, np.NaN, np.NaN],\n",
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" [np.NaN, np.NaN, 'Y']],\n",
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" index = [\n",
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" \"1996873\",\n",
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" \"1996874\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['HPV9', 'MERCK & CO. INC.', 'R017624', 'UNK'],\n",
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" ['COVID19', 'MODERNA', '025L20A', '1']],\n",
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" index = [\n",
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" \"1996873\",\n",
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" \"1996874\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" }\n",
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" ]\n",
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" dataFrame = createDataFrameFromDescrs(vaersDescrs)\n",
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" \n",
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" # When\n",
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" dataFrame = filterDataFrame(dataFrame, manufacturer = \"MODERNA\", dose = '1')\n",
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" \n",
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" # Then\n",
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" dataFrameExpected = self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['Y', np.NaN, np.NaN, 'COVID19', 'MODERNA', '037K20A', '1'],\n",
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" [np.NaN, np.NaN, 'Y', 'COVID19', 'MODERNA', '025L20A', '1'],\n",
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" [np.NaN, np.NaN, 'Y', 'COVID19', 'MODERNA', '025L20A', '1']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\",\n",
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" \"1996874\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n",
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"\n",
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" def test_createDataFrameFromForSevereEffects(self):\n",
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" # Given\n",
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" vaersDescrs = [\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT'],\n",
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" data = [ ['Y', 'Y', np.NaN, 'Y', 'Y'],\n",
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" [np.NaN, np.NaN, 'Y', np.NaN, 'Y']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['COVID19', 'MODERNA', '037K20A', '1'],\n",
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" ['COVID19', 'PFIZER\\BIONTECH', '025L20A', '1']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" }\n",
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" ]\n",
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" dataFrame = createDataFrameFromDescrs(vaersDescrs)\n",
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" \n",
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" # When\n",
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" dataFrame = filterDataFrameForSevereEffects(dataFrame, dose = '1')\n",
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" \n",
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" # Then\n",
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" dataFrameExpected = self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['Y', 'Y', np.NaN, 'Y', 'Y', 'COVID19', 'MODERNA', '037K20A', '1'],\n",
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" [np.NaN, np.NaN, 'Y', np.NaN, 'Y', 'COVID19', 'PFIZER\\BIONTECH', '025L20A', '1']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n",
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"\n",
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" def test_createAndFilterDataFrameFromDescrsWithFirstDose(self):\n",
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" # Given\n",
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" vaersDescrs = [\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ ['Y', np.NaN, np.NaN]],\n",
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" index = [\n",
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" \"1048786\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['COVID19', 'MODERNA', '016M20A', '2'],\n",
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" ['COVID19', 'MODERNA', '030L20A', '1']],\n",
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" index = [\n",
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" \"1048786\",\n",
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" \"1048786\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" }\n",
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" ]\n",
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" dataFrame = createDataFrameFromDescrs(vaersDescrs)\n",
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" \n",
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" # When\n",
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" dataFrame = filterDataFrame(dataFrame, manufacturer = \"MODERNA\", dose = '1')\n",
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" \n",
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" # Then\n",
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" dataFrameExpected = self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['Y', np.NaN, np.NaN, 'COVID19', 'MODERNA', '030L20A', '1']],\n",
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" index = [\n",
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" \"1048786\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n",
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"\n",
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" def test_createAndFilterDataFrameFromDescrsWithSecondDose(self):\n",
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" # Given\n",
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" vaersDescrs = [\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ ['Y', np.NaN, np.NaN]],\n",
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" index = [\n",
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" \"1048786\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['COVID19', 'MODERNA', '016M20A', '2'],\n",
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" ['COVID19', 'MODERNA', '030L20A', '1']],\n",
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" index = [\n",
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" \"1048786\",\n",
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" \"1048786\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" }\n",
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" ]\n",
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" dataFrame = createDataFrameFromDescrs(vaersDescrs)\n",
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" \n",
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" # When\n",
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" dataFrame = filterDataFrame(dataFrame, manufacturer = \"MODERNA\", dose = '2')\n",
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" \n",
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" # Then\n",
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" dataFrameExpected = self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE', 'VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['Y', np.NaN, np.NaN, 'COVID19', 'MODERNA', '016M20A', '2']],\n",
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" index = [\n",
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" \"1048786\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" assert_frame_equal(dataFrame, dataFrameExpected, check_dtype = False)\n",
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"\n",
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" def createDataFrame(self, index, columns, data, dtypes = {}):\n",
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" return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e14465d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pandas.testing import assert_frame_equal\n",
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"\n",
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"class BatchCodeTableTest(unittest.TestCase):\n",
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"\n",
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" def test_createBatchCodeTable2(self):\n",
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" dataFrame = createDataFrameFromDescrs(\n",
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" [\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ ['Y', np.NaN, np.NaN],\n",
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" [np.NaN, np.NaN, 'Y']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['COVID19', 'MODERNA', '037K20A', '1'],\n",
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" ['COVID19', 'MODERNA', '025L20A', '1']],\n",
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" index = [\n",
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" \"0916600\",\n",
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" \"0916601\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" },\n",
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" {\n",
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" 'VAERSDATA': self.createDataFrame(\n",
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" columns = ['DIED', 'L_THREAT', 'DISABLE'],\n",
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" data = [ [np.NaN, np.NaN, np.NaN],\n",
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" [np.NaN, np.NaN, 'Y']],\n",
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" index = [\n",
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" \"1996873\",\n",
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" \"1996874\"]),\n",
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" 'VAERSVAX': self.createDataFrame(\n",
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" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
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" data = [ ['HPV9', 'MERCK & CO. INC.', 'R017624', 'UNK'],\n",
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" ['COVID19', 'MODERNA', '025L20A', '1']],\n",
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" index = [\n",
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" \"1996873\",\n",
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" \"1996874\"],\n",
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" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
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" }\n",
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" ])\n",
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" dataFrame = filterDataFrame(dataFrame, manufacturer = \"MODERNA\", dose = '1')\n",
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" self._test_createBatchCodeTable(dataFrame);\n",
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"\n",
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" def test_createBatchCodeTable(self):\n",
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" self._test_createBatchCodeTable(\n",
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" filterDataFrame(\n",
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" createDataFrameFromDescrs(\n",
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" readAllVaersDescrs(\"test/VAERS\")),\n",
|
|
" manufacturer = \"MODERNA\",\n",
|
|
" dose = '1'))\n",
|
|
"\n",
|
|
" def _test_createBatchCodeTable(self, dataFrame):\n",
|
|
" # When\n",
|
|
" batchCodeTable = createBatchCodeTable(dataFrame)\n",
|
|
"\n",
|
|
" # Then\n",
|
|
" batchCodeTableExpected = pd.DataFrame(\n",
|
|
" data = {\n",
|
|
" 'ADRs': [2, 1],\n",
|
|
" 'DEATHS': [0, 1],\n",
|
|
" 'DISABILITIES': [2, 0],\n",
|
|
" 'LIFE THREATENING ILLNESSES': [0, 0]\n",
|
|
" },\n",
|
|
" index = pd.MultiIndex.from_arrays([['025L20A', '037K20A']], names = ('VAX_LOT',)))\n",
|
|
" assert_frame_equal(batchCodeTable, batchCodeTableExpected, check_dtype = False)\n",
|
|
"\n",
|
|
" def createDataFrame(self, index, columns, data, dtypes = {}):\n",
|
|
" return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ded70c87",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from pandas.testing import assert_frame_equal\n",
|
|
"\n",
|
|
"class SevereEffectsBatchCodeTableTest(unittest.TestCase):\n",
|
|
"\n",
|
|
" def test_createSevereEffectsBatchCodeTable(self):\n",
|
|
" # Given\n",
|
|
" dataFrame = createDataFrameFromDescrs(\n",
|
|
" [\n",
|
|
" {\n",
|
|
" 'VAERSDATA': self.createDataFrame(\n",
|
|
" columns = ['DIED', 'L_THREAT', 'DISABLE', 'HOSPITAL', 'ER_VISIT'],\n",
|
|
" data = [ ['Y', 'Y', np.NaN, 'Y', 'Y'],\n",
|
|
" [np.NaN, np.NaN, 'Y', np.NaN, 'Y']],\n",
|
|
" index = [\n",
|
|
" \"0916600\",\n",
|
|
" \"0916601\"]),\n",
|
|
" 'VAERSVAX': self.createDataFrame(\n",
|
|
" columns = ['VAX_TYPE', 'VAX_MANU', 'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
|
|
" data = [ ['COVID19', 'MODERNA', '037K20A', '1'],\n",
|
|
" ['COVID19', 'PFIZER\\BIONTECH', '025L20A', '1']],\n",
|
|
" index = [\n",
|
|
" \"0916600\",\n",
|
|
" \"0916601\"],\n",
|
|
" dtypes = {'VAX_DOSE_SERIES': \"string\"})\n",
|
|
" }\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" dataFrame = filterDataFrameForSevereEffects(dataFrame, dose = '1')\n",
|
|
"\n",
|
|
" # When\n",
|
|
" batchCodeTable = createSevereEffectsBatchCodeTable(dataFrame)\n",
|
|
"\n",
|
|
" # Then\n",
|
|
" batchCodeTableExpected = pd.DataFrame(\n",
|
|
" data = {\n",
|
|
" 'ADRs': [1, 1],\n",
|
|
" 'DEATHS': [1, 0],\n",
|
|
" 'DISABILITIES': [0, 1],\n",
|
|
" 'LIFE THREATENING ILLNESSES': [1, 0],\n",
|
|
" 'HOSPITALISATIONS': [1, 0],\n",
|
|
" 'EMERGENCY ROOM OR DOCTOR VISITS': [1, 1],\n",
|
|
" 'COMPANY': ['MODERNA', 'PFIZER\\BIONTECH']\n",
|
|
" },\n",
|
|
" index = pd.Index(['037K20A', '025L20A'], name='VAX_LOT'))\n",
|
|
" assert_frame_equal(batchCodeTable, batchCodeTableExpected, check_dtype = False)\n",
|
|
"\n",
|
|
" def createDataFrame(self, index, columns, data, dtypes = {}):\n",
|
|
" return pd.DataFrame(index = index, columns = columns, data = data).astype(dtypes)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5a8bff1b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"unittest.main(argv = [''], verbosity = 2, exit = False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "86e0e4f2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def saveBatchCodeTable(manufacturer, excelFile):\n",
|
|
" batchCodeTable = createBatchCodeTable(\n",
|
|
" filterDataFrame(\n",
|
|
" createDataFrameFromDescrs(\n",
|
|
" readAllVaersDescrs(\"VAERS\")),\n",
|
|
" manufacturer = manufacturer,\n",
|
|
" dose = '1'))\n",
|
|
" display(manufacturer, batchCodeTable)\n",
|
|
" batchCodeTable.to_excel(excelFile)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ab170c16",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"saveBatchCodeTable(\"MODERNA\", \"results/moderna.xlsx\")\n",
|
|
"saveBatchCodeTable(\"PFIZER\\BIONTECH\", \"results/pfizer.xlsx\")\n",
|
|
"saveBatchCodeTable(\"JANSSEN\", \"results/janssen.xlsx\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "bc56831d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def saveSevereEffectsBatchCodeTable(excelFile):\n",
|
|
" severeEffectsBatchCodeTable = createSevereEffectsBatchCodeTable(\n",
|
|
" filterDataFrameForSevereEffects(\n",
|
|
" createDataFrameFromDescrs(\n",
|
|
" readAllVaersDescrs(\"VAERS\")),\n",
|
|
" dose = '1'))\n",
|
|
" display('severeEffectsBatchCodeTable', severeEffectsBatchCodeTable)\n",
|
|
" severeEffectsBatchCodeTable.to_excel(excelFile)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ace3fed9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"saveSevereEffectsBatchCodeTable('results/severeEffects.xlsx')"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|