feat: Add support for Polars DataFrames as an alternative to Pandas DataFrames

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Is your feature request related to a problem? Please describe.

As a user of the jquants-api-client-python library, I would like to request the addition of support for Polars DataFrames as an alternative to the current implementation that returns Pandas DataFrames. Polars is a fast DataFrame library implemented in Rust and Python and has been gaining popularity among users for its performance benefits.

By providing support for Polars DataFrames, users can choose between Pandas and Polars based on their preferences and use case requirements. This added flexibility will cater to a wider range of user needs and improve the overall user experience of the jquants-api-client-python library.

An example of how Polars support could be implemented is as follows:

import jquantsapi

my_mail_address = "*****"
my_password = "*****"
cli = jquantsapi.Client(mail_address=my_mail_address, password=my_password, dataframe_lib="polars")

pl_df = cli.get_prices_daily_quotes(
    start_dt=datetime(2022, 7, 25, tzinfo=tz.gettz("Asia/Tokyo")),
    end_dt=datetime(2022, 7, 26, tzinfo=tz.gettz("Asia/Tokyo")),
)

In this example, pl_df would contain the data in a Polars DataFrame format instead of a Pandas DataFrame.

Thank you for considering this feature request. Adding support for Polars DataFrames will enhance the jquants-api-client-python library's flexibility, allowing it to cater to a broader range of user needs and preferences.

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J-Quants / jquants-api-client-python