window-ops 0.0.6

Naive implementations of window operations such as rolling and expanding.

Window ops

Naive and fast implementations of common window operations.

This library is intended to be used as an alternative to pd.Series.rolling and pd.Series.expanding to gain a speedup by using numba optimized functions operating on numpy arrays and avoiding input checks. There are also online classes for more efficient updates of window statistics.

Install

pip install window-ops

How to use

For a transformations n_samples -> n_samples you can use {[seasonal_](rolling|expanding)}_{(mean|max|min|std)} on an array.

Benchmarks

n_samples = 1_000  # array size
window_size = 8  # for rolling operations
season_length = 7  # for seasonal operations
execute_times = 1_000 # number of times each function will be executed

Average times in milliseconds.

display_dataframe(times)

	window_ops	pandas
rolling_mean	0	0.33
rolling_max	0.01	0.37
rolling_min	0.01	0.32
rolling_std	0.01	0.33
expanding_mean	0	0.31
expanding_max	0.01	0.32
expanding_min	0.01	0.32
expanding_std	0.01	0.33
seasonal_rolling_mean	0.01	3.78
seasonal_rolling_max	0.03	3.85
seasonal_rolling_min	0.02	3.8
seasonal_rolling_std	0.02	3.93
seasonal_expanding_mean	0.01	3.73
seasonal_expanding_max	0.24	3.86
seasonal_expanding_min	0.22	3.83
seasonal_expanding_std	0.02	3.86

display_dataframe(speedups)

	times faster
rolling_mean	122.05
rolling_max	29.85
rolling_min	23.45
rolling_std	64.31
expanding_mean	104.72
expanding_max	22.17
expanding_min	23.35
expanding_std	40.69
seasonal_rolling_mean	275.68
seasonal_rolling_max	148.73
seasonal_rolling_min	154.22
seasonal_rolling_std	229.91
seasonal_expanding_mean	267.48
seasonal_expanding_max	16.25
seasonal_expanding_min	17.34
seasonal_expanding_std	212.16