This script interacts with the WikiRate API to retrieve metrics and answers based on specific keywords and corporate identifiers, and then saves the gathered data to a CSV file.

wikirate_answer_export.py

from itertools import islice

from wikirate4py import API, Cursor, to_dataframe


# splits a list into chunks of a given size
def chunked_iterable(iterable, size):
    iterable = iter(iterable)
    return iter(lambda: list(islice(iterable, size)), [])


# gathers metrics based on keywords
def gather_metrics(api, keywords, per_page=100):
    metrics = []
    for keyword in keywords:
        cursor = Cursor(api.get_metrics, metric_keyword=keyword, per_page=per_page)
        while cursor.has_next():
            metrics.extend(item.id for item in cursor.next())
    return metrics


# gathers answers based on metric and corporate identifier chunks
def gather_answers(api, metrics, corporate_identifiers, chunk_size=100):
    answers = []
    metric_chunks = chunked_iterable(metrics, chunk_size)
    corporate_chunks = chunked_iterable(corporate_identifiers, chunk_size)

    for metric_chunk in metric_chunks:
        for corporate_chunk in corporate_chunks:
            cursor = Cursor(api.get_answers, metric=metric_chunk, company_identifier=corporate_chunk, per_page=100)
            while cursor.has_next():
                answers.extend(cursor.next())
    return answers


api = API('your_wikirate_api_key')

keywords = ['climate', 'deforestation']
corporate_identifiers = ['AEDFXA0M6V00', 'AEDFXA14NUL7', 'AEE000301011', 'AEE000401019',
                         'AEN000101016', 'ARCTIO010019', 'ARENOR010020', 'ARP432631215',
                         'ARP689251337', 'ARP9897X1319', 'ARSAMA010012', 'AT0000743059',
                         'AU0000002339', 'AU000000AAC9', 'AU000000AMI1', 'AU000000ANZ3',
                         'AU000000ARG4', 'AU000000AUB9', 'AU000000BGA8', 'AU000000BLD2',
                         'AU000000CHC0', 'AU000000CMW8', 'AU000000CWY3', 'AU000000EVN4',
                         'AU000000GMG2', 'AU000000GNC9', 'AU000000GOR5', 'AU000000IMD5',
                         'AU000000ING6', 'AU000000JHX1', 'AU000000LLC3', 'AU000000MWY2',
                         'AU000000NGI6', 'AU000000NUF3', 'AU000000ORG5', 'AU000000PRU3',
                         'AU000000QUB5', 'AU000000RRL8', 'AU000000SEQ3', 'AU000000VG15',
                         'AU000000WOW2', 'AU000000WPL2', 'AU0000016875', 'AU0000018236',
                         'AU0000109662', 'AU0000186678', 'AU0000273229', 'AU0000336679',
                         'AU0000340770', 'AU3CB0305118', 'AU3FN0016077', 'AU3FN0076360',
                         'BE0003008019', 'BE0003604155', 'BE0021271284', 'BE0029825461',
                         'BE0055924527', 'BE0974338700', 'BE0974413453', 'BMG0R24BAA92',
                         'BMG169621056', 'BMG210901242', 'BMG276311039', 'BMG3922B1072',
                         'BMG491BT1088', 'BMG540501027', 'BMG5485F1692', 'BMG667211046',
                         'BMG7000R1088', 'BMG812761002', 'BMG9349W1038', 'BRBBASACNOR3',
                         'BRBBDCACNPR8', 'BRBEEFACNOR6', 'BRBRFSACNOR8', 'BRBRPRACNOR9',
                         'BRCCROACNOR2', 'BRCYREACNOR7', 'BRELETACNPB7', 'BREVENACNOR8',
                         'BRGOAUACNPR8', 'BRITUBACNPR1', 'BRJBSSACNOR8', 'BRKLBNACNPR9',
                         'BRLRENACNOR1', 'BRMDIAACNOR7', 'BRMRFGACNOR0', 'BRMRVEACNOR2',
                         'BRMULTACNOR5', 'BRNTCOACNOR5', 'BRPETRACNOR9', 'BRSANBACNPR5',
                         'BRSAPRACNPR6', 'BRSBSPACNOR5', 'BRSUZBACNOR0', 'BRUGPAACNOR8',
                         'BRVALEACNOR0', 'BRVEBMCDAM15', 'CA01626P4033', 'CA05534B7604',
                         'AIMCCA61XXX', 'AIMCCA66XXX', 'AU000000AEF4', 'AU000000ASX7',
                         'AU000000COH5', 'AU000000DOW2', 'AU000000IGO4', 'AU000000JRV4',
                         'AU000000MPL3', 'AU000000WOR2', 'AU000000WTC3', 'AU0000064966',
                         'AU0000250532', 'AU3CB0292977', 'AU3CB0298487', 'AU3CB0310100',
                         'AU3CB0310118', 'AU3CB0310373', 'AU3CB0310381', 'BE0003764785',
                         'BE0003765790', 'BE0024914823', 'BE0050811802', 'BE0944264663',
                         'BE6221795212', 'BE6227255492', 'BE6229290471', 'BE6349293611',
                         'BMG0585R1060', 'BMG0585R1144', 'BMG668971101', 'BMG677491539',
                         'CH0005345571', 'CH0011037469', 'CH0011404735', 'CH0012032048',
                         'CH0012032113', 'CH0025238863', 'CH0025751329', 'CH0126881561',
                         'CH0286491805', 'CH0479514314', 'CH1282125983', 'DE0005089031',
                         'DE0005200000', 'DE0005470405', 'DE0006202005', 'DE0006231004',
                         'DE0006766504', 'DE0007297004', 'DE0007500001', 'DE0007664005',
                         'DE0007664039', 'DE000A14J587', 'DE000A1HKQE8', 'DE000A1PHFF7',
                         'DE000A2TSMN4', 'DE000SL0E6T6', 'DE000SL0E7R8', 'DE000SL0FME0',
                         'DE000SLA2VW9', 'DE000SYM7720', 'DE000SYM9999', 'DK0010213628',
                         'DK0010253921', 'DK0010272632', 'DK0060050474', 'DK0060050557',
                         'DK0060094928', 'DK0060448595', 'DK0060542181', 'ES0112501012',
                         'ES0116870314', 'ES0116920333', 'ES0118594417', 'ES0127797019',
                         'ES0127797035', 'ES0130960018', 'ES0143416115', 'ES0148396007',
                         'ES0177542018', 'ES0305199004', 'ES05051138V2', 'ES05051138W0',
                         'ES0505199721', 'ES0S00001123', 'ES0SDIV00052', 'FR0000039109',
                         'FR0000130403', 'FR0004048007', 'FR0004053478', 'FR0004268563',
                         'FR0010452433', 'FR0010533075', 'FR0011129832', 'FR0011742329',
                         'FR0011950732', 'FR0013154002', 'FR0013420072', 'FR0014004L86'
                         ]

metrics = gather_metrics(api, keywords)
# Gather answers and convert to dataframe
answers = gather_answers(api, metrics, corporate_identifiers)
df = to_dataframe(answers)
df.to_csv("./answers.csv")