Speed Improvement: polars backends

[mdancho84]

Running checklist of backends: #77 (comment)

[iamjakkie]

I will check what's possible there

[JustinKurland]

Adding this as a running checklist for tracking what has been completed and by whom. Should you wish to contribute to this issue, and there are plenty of functions to work on please just @JustinKurland here and I will add you to the respective function you are working on and when completed make sure it is listed here for ongoing efforts and to get some credit for helping out!

Polars Backend Functions

Wrangling Pandas Time Series DataFrames

• summarize_by_time @JustinKurland
• apply_by_time
• pad_by_time @JustinKurland
• future_frame

Anomaly Detection

• anomalize

Adding Features to Time Series DataFrames (Augmenting)

• augment_timeseries_signature @JustinKurland
• augment_holiday_signature @JustinKurland
• augment_lags @JustinKurland
• augment_leads @JustinKurland
• augment_rolling @alexriggio
• augment_rolling_apply
• augment_expanding @alexriggio
• augment_expanding_apply
• augment_fourier @GTimothee
• augment_hilbert @tackes
• augment_wavelet

TS Features

• ts_features
• ts_summary @GTimothee

Finance Module

• augment_ewn

Time Series for Pandas Series

• make_future_timeseries
• make_weekday_sequence @JustinKurland
• make_weekend_sequence @JustinKurland
• get_date_summary @JustinKurland
• get_frequency_summary
• get_diff_summary
• get_frequency
• get_seasonal_frequency
• get_trend_frequency
• get_timeseries_signature @JustinKurland
• get_holiday_signature @JustinKurland

Date Utilities

• floor_date @JustinKurland
• ceil_date @seyf97
• is_holiday @JustinKurland
• week_of_month @JustinKurland
• time_series_unit_frequency_table @JustinKurland
• time_scale_template @JustinKurland

Extra Pandas Helpers

• glimpse @JustinKurland
• parallel_apply
• progress_apply
• flatten_multiindex_column_names

13 Datasets

• get_available_datasets @JustinKurland
• load_dataset @JustinKurland

[GTimothee]

Will do augment_fourier (discussed with Justin Kurland) :)

[mdancho84]

Awesome that's much appreciated!

[GTimothee]

I will do ts_summary at the same time because I need it.

I updated checks.py like this (not yet pushed):

def check_data_type(data, authorized_dtypes: list, error_str=None):
    if not error_str:
        error_str = f'Input type must be one of {authorized_dtypes}'
    if not sum(map(lambda dtype: isinstance(data, dtype), authorized_dtypes)) > 0:
        raise TypeError(error_str)


def check_dataframe_or_groupby(data: Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]) -> None:
    check_data_type(
        data, authorized_dtypes = [
        pd.DataFrame,
        pd.core.groupby.generic.DataFrameGroupBy
    ], error_str='`data` is not a Pandas DataFrame or GroupBy object.')

def check_dataframe_or_groupby_polar(data: Union[pl.DataFrame, pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]) -> None:
    check_data_type(data, authorized_dtypes = [
        pl.DataFrame,
        pd.DataFrame,
        pd.core.groupby.generic.DataFrameGroupBy
    ])

It seems more Pythonic to me, if you agree with it. I ran the tests/ it is working :)

I am doing a polars version of augment_fourier, then if possible I plan to merge the polar version with augment_fourier_v2, converting pandas dtypes to polars dtypes, then doing the computations, then converting back. Is that what you intended to do ?

[mdancho84]

As long as it works as intended I'm Ok. Thanks!