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BoC Valet API Wrapper

Project description

pyvalet

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Simple, pandas integrated API wrapper for the Bank of Canada Valet API.

Their documentation page can be found here

Installation:

To install this package

Getting Started:

To get started using pyvalet, simply open up a new python file and type:

from pyvalet import ValetInterpreter

vi = ValetInterpreter()

This will be your interface with all the features of pyvalet.

To see what sort of data is available, try running one of the following commands:

vi.list_series()

vi.list_groups()

These two commands will provide you with a pandas DataFrame containing all possible series, or groups to explore using the Valet API. The three fields output are 'name', 'label' and 'link'.

The first time you run these commands, the ValetInterpreter will cache them so there is no need to assign the output, unless you plan to filter these lists.

They can be accessed through:

vi.series_list

vi.groups_list

To get more details about these series or groups, the get_series_detail() or get_group_detail() methods are available

df = vi.get_series_detail("FXUSDCAD", response_format='csv')

df_group, df_series = vi.get_group_detail("FX_RATES_DAILY", response_format='csv')

The output of .get_series_detail() is a pandas DataFrame containing, among other things, the name and description of a given series.

The output of .get_group_detail() is one pandas Series, and one DataFrame. The Series containing details about the group itself, and the DataFrame containing the same information about all series in the group.

Diving even deeper, you can pull observations from these series or groups using the get_series_observations() and get_groups_observations() methods.

df_series, df = vi.get_series_observations("FXUSDCAD", response_format='csv')
df = vi.get_group_observations("FX_RATES_DAILY", response_format='csv')

Additional keyword arguments can be passed to alter the query. See the docstrings for more information.

Like the methods for group details, the output of get_series_observations() is one pandas Series, and one DataFrame. The Series contains the details for the series queries, and the DataFrame contains the observations themselves.

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0.1

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