Window Operations

0.0.15 · active · verified Sun Apr 12

The `window-ops` library provides optimized implementations of window operations such as rolling and expanding functions. It aims to offer a significant speedup over `pandas.Series.rolling` and `pandas.Series.expanding` by utilizing Numba-optimized functions that operate on NumPy arrays. The library also includes 'online' classes for efficiently updating window statistics as new data arrives. It is currently in an alpha development stage (0.0.15), with releases as features are added or improved.

Warnings

gotcha The library primarily operates on NumPy arrays. If you are working with pandas Series or DataFrames, you will need to convert your data to NumPy arrays before using `window-ops` functions and potentially convert back to pandas if needed. This is different from `pd.Series.rolling` and `pd.Series.expanding` which work directly on Series.
Fix: Ensure your input data is a NumPy array (e.g., `series.to_numpy()`) before calling `window-ops` functions.
breaking This library is currently in 'Development Status :: 3 - Alpha'. This means the API is not yet stable and might introduce breaking changes between minor or patch versions (e.g., 0.0.x to 0.0.y) without following strict semantic versioning, although recent changes have been additive.
Fix: Pin exact versions in your `requirements.txt` (e.g., `window-ops==0.0.15`) and review changelogs carefully when upgrading.
gotcha Functions like `rolling_mean` and `expanding_mean` (and other statistics) are imported directly from submodules like `window_ops.rolling` and `window_ops.expanding`, not from the top-level `window_ops` package.
Fix: Use specific imports like `from window_ops.rolling import rolling_mean`.

Install

pip install window-ops Install with pip

Imports

rolling_mean

from window_ops.rolling import rolling_mean

expanding_mean

from window_ops.expanding import expanding_mean

online
```
from window_ops import online
```
For classes that allow online updates to window statistics.

Quickstart

This quickstart demonstrates how to use `window-ops` to compute rolling and expanding means on a NumPy array. The functions directly operate on NumPy arrays for performance benefits.

import numpy as np
from window_ops.rolling import rolling_mean
from window_ops.expanding import expanding_mean

data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])

# Calculate rolling mean with a window size of 3
rolling_result = rolling_mean(data, window_size=3)
print(f"Original data: {data}")
print(f"Rolling mean (window=3): {rolling_result}")

# Calculate expanding mean
expanding_result = expanding_mean(data)
print(f"Expanding mean: {expanding_result}")

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