pip install sie_banxico

 >>> from api_banxico import SIEBanxico
 >>> api = SIEBanxico(token = token, id_series = ['SF311408' ,'SF311418'], language = 'en')

>>> api.__doc__
'Returns the full class documentation'
>>> api.token
'1b7da065cf574289a2cb511faeXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' # This is an example token
>>> api.series
'SF311408,SF311418'

>>> api.set_token(token = new_token)

>>> api.append_id_series(id_series = ['SF311412'])
>>> api.series
'SF311408,SF311418,SF311412'

>>> api.set_id_series(id_series='SF311408-SF311418')
>>> api.series
'SF311408-SF311418'

>>> api = SIEBanxico(token = token, id_series = ['SF311408' ,'SF311418']

    Allows to consult metadata of the series.
    Returns:
        dict: json response format

>>> api.get_metadata()
{'bmx': {'series': [{'idSerie': 'SF311418', 'titulo': 'Monetary Aggregates M2 = M1 + monetary instruments held by residents', 'fechaInicio': '12/01/2000', 'fechaFin': '11/01/2021', 'periodicidad': 'Monthly', 'cifra': 'Stocks', 'unidad': 'Thousands of Pesos', 'versionada': False}, {'idSerie': 'SF311408', 'titulo': 'Monetary Aggregates M1', 'fechaInicio': '12/01/2000', 'fechaFin': '11/01/2021', 'periodicidad': 'Monthly', 'cifra': 'Stocks', 'unidad': 'Thousands of Pesos', 'versionada': False}]}}

>>> api.get_lastdata()
{'bmx': {'series': [{'idSerie': 'SF311418', 'titulo': 'Monetary Aggregates M2 = M1 + monetary instruments held by residents', 'datos': [{'fecha': '01/11/2021', 'dato': '11,150,071,721.09'}]}, {'idSerie': 'SF311408', 'titulo': 'Monetary Aggregates M1', 'datos': [{'fecha': '01/11/2021', 'dato': '6,105,266,291.65'}]}]}}

    Allows to consult the whole time series data, corresponding to the period defined between the initial date and the final date in the metadata.
    Args:
        pct_change (str, optional): None (default) for levels, "PorcObsAnt" for change rate compared to the previous observation, "PorcAnual" for anual change rate, "PorcAcumAnual" for annual acummulated change rate.
    Returns:
        dict: json response format

>>> api.get_timeseries(pct_change='PorcAnual')
{'bmx': {'series': [{'idSerie': 'SF311418',
    'titulo': 'Monetary Aggregates M2 = M1 + monetary instruments held by residents',
    'datos': [{'fecha': '01/12/2001', 'dato': '12.89'},
     {'fecha': '01/01/2002', 'dato': '13.99'},
     ...
     {'fecha': '01/11/2021', 'dato': '13.38'}],
     'incrementos': 'PorcAnual'}]}}

    Returns the data of the requested series, for the defined period.
    Args:
        init_date (str): The date on which the period of obtained data starts. The date must be sent in the format yyyy-mm-dd. If the given date is out of the metadata time range, the oldest value is returned.
        end_date (str): The date on which the period of obtained data concludes. The date must be sent in the format yyyy-mm-dd. If the given date is out of the metadata time range, the most recent value is returned.
        pct_change (str, optional): None (default) for levels, "PorcObsAnt" for change rate compared to the previous observation, "PorcAnual" for anual change rate, "PorcAcumAnual" for annual acummulated change rate.     
    Returns:
        dict: json response format

>>> api.get_timeseries_range(init_date='2000-12-31', end_date='2004-04-01')
{'bmx': {'series': [{'idSerie': 'SF311408',
    'titulo': 'Monetary Aggregates M1',
    'datos': [{'fecha': '01/01/2001', 'dato': '524,836,129.99'},
     {'fecha': '01/02/2001', 'dato': '517,186,605.97'},
     ...
     {'fecha': '01/04/2004', 'dato': '2,306,755,672.89'}]}]}}

data = api.get_timeseries_range(init_date='2000-12-31', end_date='2004-04-01')

# Extract the Monetary Aggregate M1 data
data['bmx']['series'][0]['datos']
[{'fecha': '01/01/2001', 'dato': '524,836,129.99'},
 ...
 {'fecha': '01/04/2004', 'dato': '799,774,807.43'}]

# Transform the data into a pandas DataDrame
import pandas as pd
df = pd.DataFrame(timeseries_range['bmx']['series'][0]['datos'])
df.head()
        fecha            dato
0  01/01/2001  524,836,129.99
1  01/02/2001  517,186,605.97
2  01/03/2001  509,701,873.04
3  01/04/2001  511,952,430.01
4  01/05/2001  514,845,459.96

df = pd.json_normalize(timeseries_range['bmx']['series'], record_path = 'datos', meta = ['idSerie', 'titulo'])
df['titulo'] = df['titulo'].apply(lambda x: x.replace('Monetary Aggregates M2 = M1 + monetary instruments held by residents', 'Monetary Aggregates M2'))
df.head()
        fecha            dato   idSerie                  titulo
0  01/01/2001  524,836,129.99  SF311408  Monetary Aggregates M1
1  01/02/2001  517,186,605.97  SF311408  Monetary Aggregates M1
2  01/03/2001  509,701,873.04  SF311408  Monetary Aggregates M1
3  01/04/2001  511,952,430.01  SF311408  Monetary Aggregates M1
4  01/05/2001  514,845,459.96  SF311408  Monetary Aggregates M1
df.tail()
         fecha              dato   idSerie                  titulo
75  01/12/2003  2,331,594,974.69  SF311418  Monetary Aggregates M2
76  01/01/2004  2,339,289,328.74  SF311418  Monetary Aggregates M2
77  01/02/2004  2,285,732,239.36  SF311418  Monetary Aggregates M2
78  01/03/2004  2,312,217,167.10  SF311418  Monetary Aggregates M2
79  01/04/2004  2,306,755,672.89  SF311418  Monetary Aggregates M2