def get_table()

in functions/source/job-creation/job_creation.py [0:0]

• 43 lines of code
• 10 McCabe index (conditional complexity)

def get_table(dataframe):
    """
    Generate the tabular data for the input manitest file.
    Parameters
    ----------
    dataframe : pd.DataFrame
        One patient data from the csv.
    Returns
    -------
    table : dictionary
        key: encounter_date, value: {field_name: value}
    Format
    ------
    'table': {
        'encounter_date': {
            'field': 'value',
            },
        }
    Note
    ----
    Because of the payload limit of the input manifest file,
    the table size is restricted (max_record_number)
    """
    dataframe['collection_dt_tm'] = pd.to_datetime(
        dataframe['collection_dt_tm'])
    max_record_number = 11
    # Sort by collection dates
    dataframe.sort_values(['collection_dt_tm'], inplace=True)
    if len(dataframe) > max_record_number:
        dataframe = dataframe.loc[:max_record_number]
        dataframe.loc[max_record_number, :] = ""
        dataframe.loc[max_record_number, 'name_first'] = \
            'Patient has too many collection dates,\
            please check Cerner to for more details!'
    # Generate a label for collection count, if there are multiple collections
    # on the same day
    count = 0
    collection_date = ''
    for index in dataframe.index:
        if index < max_record_number - 1:
            print(f'index :{index}')
            print(dataframe['collection_dt_tm'])
            if collection_date == dataframe.loc[index, 'collection_dt_tm']:
                count += 1
            else:
                count = 1
            collection_date = dataframe.loc[index, 'collection_dt_tm']
            dataframe.loc[index, 'collection_count'] = \
                '{collection_date}_{number}'.format(
                    collection_date=pd.to_datetime(dataframe.loc[
                        index, 'collection_dt_tm']).strftime('%Y-%m-%d'),
                    number=count,
            )
        else:
            dataframe.loc[index, 'collection_count'] = "DateNotAvailable"

    dataframe = dataframe.set_index('collection_count')
    # Transform into a dictionary
    table_dict = dataframe.astype(str).T.to_dict()

    table = {}
    for collection_date in table_dict:
        table[collection_date] = []
        for fieldname, humanname in get_table_fields():
            try:
                table[collection_date].append(
                    [humanname, table_dict[collection_date][fieldname]]
                    )
            except KeyError:
                pass
    return table