Models:
Joseph Gordon
joseph-gordon/
prescreen-prediction
0
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[ae9c43]: / prescreen / evaluation / clean_files.py

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"""

loads the validation files, removes useless rows and columns

"""

import pandas as pd







def main():

    base_path = 'data/cohorte_validation'



    # tables to get NIP

    path = base_path + '/POUMON_Etudes_II_III_Patients.xlsx'

    poumon_ref = pd.read_excel(path, sheet_name='POUMON_Etudes_Patients')\

        .loc[:, ['NOIGR', 'LC']]



    path = base_path + '/DITEP_Etudes_II_III_Patients.xlsx'

    ditep_ref = pd.read_excel(path, sheet_name='DITEP_Etudes_Patients')\

        .loc[:, ['NOIGR', 'LC']]







    # DITEP screenfail

    path = base_path + '/ditep_sf.xlsx'



    df = pd.read_excel(path).loc[:, ['NOIGR', 'CR', 'DATE CR', 'DATE_DEBUT']]



    mask_pdf = df['CR'].str.contains('\\NAS', regex=False)

    mask_empty = df['CR'].str.contains('Examen du', regex=False)



    df = df[~mask_pdf][~mask_empty]



    df['DATE_DEBUT'] = df.loc[:, 'DATE_DEBUT'].dt.date

    df['DATE CR'] = df.loc[:, 'DATE CR'].dt.date

    df['DATE SIGN_OK'] = df['DATE_DEBUT'] + pd.Timedelta(1, 'M')



    df.drop('DATE_DEBUT', axis=1, inplace=True)



    df = df.merge(ditep_ref, on='NOIGR')



    df['nip'] = df['NOIGR'].astype(str) + df['LC']

    df['nip'] = df.loc[:, 'nip'] \

        .apply(lambda s: s[:4] + '-' + s[4:-2] + ' ' + s[-2:])



    df.drop(['NOIGR', 'LC'], axis=1, inplace=True)



    df.to_csv(base_path + '/ditep_sf.csv', sep=';', encoding='utf-8')





    ## DITEP inclus

    path = base_path + '/ditep_inclus.xlsx'



    df = pd.read_excel(path).loc[:, ['NOIGR', 'CR', 'DATE CR', 'DATE_DEBUT']]



    mask_pdf = df['CR'].str.contains('\\NAS', regex=False)

    mask_empty = df['CR'].str.contains('Examen du', regex=False)



    df = df[~mask_pdf][~mask_empty]



    df['DATE_DEBUT'] = df.loc[:, 'DATE_DEBUT'].dt.date

    df['DATE CR'] = pd.to_datetime(df.loc[:, 'DATE CR'],

                                   errors='coerce').dropna().dt.date

    df['DATE SIGN_OK'] = df['DATE_DEBUT'] + pd.Timedelta(1, 'M')



    df.drop('DATE_DEBUT', axis=1, inplace=True)



    df = df.merge(ditep_ref, on='NOIGR').dropna()



    df['nip'] = df['NOIGR'].astype(str) + df['LC']

    df['nip'] = df.loc[:, 'nip'] \

        .apply(lambda s: s[:4] + '-' + s[4:-2] + ' ' + s[-2:])



    df.drop(['NOIGR', 'LC'], axis=1, inplace=True)

    df.to_csv(base_path + '/ditep_inclus.csv', sep=';', encoding='utf-8')





    ## poumons 1

    path = base_path + '/poumons_inclusion.xlsx'



    df = pd.read_excel(path).loc[:, ['NOIGR', 'CR', 'DATE CR',

                                     'DATE_SIGN_OK']]



    df.dropna(axis=0, inplace=True)



    mask_pdf = df['CR'].str.contains('\\NAS', regex=False)

    mask_empty = df['CR'].str.contains('Examen du', regex=False)



    df = df[~mask_pdf][~mask_empty]

    df['DATE_SIGN_OK'] = df.loc[:, 'DATE_SIGN_OK'].dt.date

    df['DATE CR'] = pd.to_datetime(df.loc[:, 'DATE CR'],

                                   errors='coerce').dropna().dt.date



    df = df.merge(poumon_ref, on='NOIGR').dropna()



    df['nip'] = df['NOIGR'].astype(str) + df['LC']

    df['nip'] = df.loc[:, 'nip'] \

        .apply(lambda s: s[:4] + '-' + s[4:-2] + ' ' + s[-2:])



    df.drop(['NOIGR', 'LC'], axis=1, inplace=True)



    df.to_csv(base_path + '/poumons_inclusion.csv', sep=';', encoding='utf-8')



    ## poumons2

    path = base_path + '/poumons_inclusion2.xlsx'



    df = pd.read_excel(path).loc[:, ['NOIGR', 'CR', 'DATE CR',

                                     'DATE_SIGN_OK']]



    df.dropna(axis=0, inplace=True)



    mask_pdf = df['CR'].str.contains('\\NAS', regex=False)

    mask_empty = df['CR'].str.contains('Examen du', regex=False)



    df = df[~mask_pdf][~mask_empty]

    df['DATE_SIGN_OK'] = pd.to_datetime(df['DATE_SIGN_OK'],

                                       errors='coerce').dropna().dt.date

    df['DATE CR'] = pd.to_datetime(df['DATE CR'],

                                       errors='coerce').dropna().dt.date



    df = df.merge(poumon_ref, on='NOIGR').dropna()



    df['nip'] = df['NOIGR'].astype(str) + df['LC']

    df['nip'] = df.loc[:, 'nip'] \

        .apply(lambda s: s[:4] + '-' + s[4:-2] + ' ' + s[-2:])



    df.drop(['NOIGR', 'LC'], axis=1, inplace=True)



    df.to_csv(base_path + '/poumons_inclusion2.csv', sep=';', encoding='utf-8')





    ## poumons SF

    path = base_path + '/poumons_sf.xlsx'



    colnames =['NOIGR', 'IDENTIFIANT', 'CR', 'DATE CR', 'DATE_SIGN_OK']

    # first col is scanned report

    df = pd.read_excel(path, header=1, names=colnames)\

        .loc[:, ['NOIGR', 'CR', 'DATE CR', 'DATE_SIGN_OK']]



    df.dropna(axis=0, inplace=True)



    mask_pdf = df['CR'].str.contains('\\NAS', regex=False)

    mask_empty = df['CR'].str.contains('Examen du', regex=False)



    df = df[~mask_pdf][~mask_empty]

    df['DATE_SIGN_OK'] = df.loc[:, 'DATE_SIGN_OK'].dt.date

    df['DATE CR'] = pd.to_datetime(df['DATE CR'],

                                   errors='coerce').dropna().dt.date



    df = df.merge(poumon_ref, on='NOIGR').dropna()



    df['nip'] = df['NOIGR'].astype(str) + df['LC']

    df['nip'] = df.loc[:, 'nip'] \

        .apply(lambda s: s[:4] + '-' + s[4:-2] + ' ' + s[-2:])



    df.drop(['NOIGR', 'LC'], axis=1, inplace=True)



    df.to_csv(base_path + '/poumons_sf.csv', sep=';', encoding='utf-8')



    return 0





if __name__ == "__main__":

    main()