a9ce6d9c8d
* removes the Excel file stored in Git LFS and disable LFS * replaces the local Excel file with an URL to download the file from a static website
56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
import pandas as pd
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from tabulate import tabulate
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'''
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Script file to generate the vaccine effectiveness values.
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To generate the primary vaccine effectiveness values, uncomment lines 16-21.
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To generate the booster effectiveness values, uncomment lines 26-56.
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'''
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# Data from 08 Sep. 2022
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file_loc = "https://caimira-resources.web.cern.ch/WeeklySummary_COVID19_VE_Studies_08Sep2022_adapted.xlsx"
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# ------- PRIMARY VACCINATION ------ #
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# df = pd.read_excel(file_loc, sheet_name="Primary_filtered", usecols="A, B, E")
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# calculate the VE value
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# df = df.drop(df[df['VE'] < 0].index)
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# ve_data = df.groupby(['vaccine'])['VE'].mean().divide(100).reset_index()
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# print(tabulate(ve_data, headers='keys', tablefmt='psql'))
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# ------- BOOSTER VACCINATION ------ #
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# df = pd.read_excel(file_loc, sheet_name="Booster_filtered", usecols="A, B, C, F")
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# # create df without the ' or ' substring in primary vaccines
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# rows_with_or = df[df['primary series vaccine'].str.contains(' or ')]
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# rows_indexes = list(rows_with_or.index)
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# df_without_or = df.drop(labels=rows_indexes, axis=0)
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# # copy of all the rows that contain ' or '
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# new_rows_with_or = rows_with_or.reset_index().copy()
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# # create new dataframe empty
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# rows_to_add = pd.DataFrame(columns=rows_with_or.columns)
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# # duplicate each row and add it into the new dataframe
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# for index, row in new_rows_with_or.iterrows():
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# new_rows_with_or.at[index, 'primary series vaccine'] = row['primary series vaccine'].split(' or ')[0]
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# rows_to_add.loc[index] = new_rows_with_or.loc[index]
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# new_rows_with_or.at[index, 'primary series vaccine'] = row['primary series vaccine'].split(' or ')[1]
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# rows_to_add.loc[len(rows_indexes)+index] = new_rows_with_or.loc[index]
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# # merge the dataframe without the ' or ' with the new dataframe that has the rows divided in two
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# final_df = pd.concat([df_without_or, rows_to_add]).reset_index().drop(columns=['index'])
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# # calculate the VE value
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# final_df = final_df.drop(final_df[final_df['VE'] < 0].index)
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# ve_data = final_df.groupby(['primary series vaccine', 'booster vaccine'])['VE'].mean().divide(100).reset_index()
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# result = ve_data.to_dict('records')
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# print(tabulate(ve_data, headers='keys', tablefmt='psql'))
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