get_series_exp Function

get_series_exp Function#

The get_series_exp function fetches economic data series from a specified API and returns it formatted as a pandas DataFrame, making extensive use of various parameters to fine-tune the data retrieval process.

Parameters#

indexstr or tuple

Identifier(s) for the data series to fetch. This can be a single string for one series or a tuple of strings for multiple series.

start_datestr, optional

The start date for the data retrieval in ‘DD-MM-YYYY’ format. By default, this calls default_start_date_fnc().

end_datestr, optional

The end date for the data retrieval in ‘DD-MM-YYYY’ format. By default, this calls default_end_date_fnc().

frequencystr, optional

The frequency at which data should be retrieved. Options include:

  • daily (1): Data retrieved every day.

  • business (2): Data retrieved only on business days.

  • weekly (3): Data retrieved weekly on Fridays.

  • semimonthly (4): Data retrieved twice a month.

  • monthly (5): Data retrieved once a month.

  • quarterly (6): Data retrieved every quarter.

  • semiannually (7): Data retrieved twice a year.

  • annual/annually (8): Data retrieved once a year.

formulasstr or tuple, optional

Computation methods to apply to the data series. Options include:

  • level (0): Raw data values.

  • percentage change (1): Percent change between consecutive data points.

  • difference (2): Difference between consecutive data points.

  • year to year percent change (3): Percent change from the same date in the previous year.

  • year to year differences (4): Difference from the same date in the previous year.

  • percentage change compared to end-of-previous year (5): Percent change relative to the end of the previous year.

  • difference compared to end-of-previous year (6): Difference relative to the end of the previous year.

  • moving average (7): Moving average of data points.

  • moving sum (8): Moving sum of data points.

aggregationstr or tuple, optional

Aggregation methods to apply to the data, options include:

  • avg: Average value over the specified period.

  • min: Minimum value over the specified period.

  • max: Maximum value over the specified period.

  • first: First value within the specified period.

  • last: Last value within the specified period.

  • sum: Sum of all values within the specified period.

cachebool, optional [when it is False refreshes data on each call ]

If True, uses cached data when available to speed up the data retrieval process. Default is False.

meta_cachebool, optional [when it is False refreshes metadata on each call ]

If True, uses cached data when available to speed up the data retrieval process. Default is False.

proxystr, optional

The URL of the proxy server to use for the requests. Default is None.

proxiesdict, optional

A dictionary of proxies to use for the request. Default is None.

debugbool, optional

If True, runs the function in debug mode, providing additional debug information without making a real API request. Default is False.

api_keystr, optional

The API key required for accessing the data. Initially, it can be saved using the save("APIKEY") function or via command line with $ evdspy save.

Returns#

Result Class data : pd.DataFrame

contains data [ same as what get_series function returns ]

metadatapd.DataFrame

contains metadata if available

writeCallable

creates and Excel file with data and metadadata of two sheets

to_excelCallable

same with write to meet pandas to_excel function

Raises#

ValueError

Raised if an invalid API key is provided or required parameters are missing.

Examples#

Basic usage:

index = "TP.ODEMGZS.BDTTOPLAM"
result = get_series_exp(index, start_date="01-01-2020", end_date="01-01-2021", frequency="monthly")
print(result.data.head())
print(result.metadata)
result.write('output.xlsx')
result.to_excel('output.xlsx')

Using multiple indexes and cache:

indexes = ("TP.ODEMGZS.BDTTOPLAM", "TP.ODEMGZS.ABD")
result = get_series_exp(indexes, start_date="01-01-2020", end_date="01-01-2021", frequency="monthly")
print(result.data.head())
print(result.metadata)
result.write('output.xlsx')
result.to_excel('output.xlsx')

Applying formulas and aggregation:

template = "TP.ODEMGZS.BDTTOPLAM"
df = get_series(template,  formulas="level", aggregation="sum")
result = get_series_exp(indexes,  formulas="level", aggregation="sum"))
print(result.data.head())
print(result.metadata)
result.write('output.xlsx')
result.to_excel('output.xlsx')
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