Naive implementations of window operations such as rolling and expanding.
Project description
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 |
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