speedystats 0.0.0

A simple package to support fast statistics.

speedystats

speedystats is a Python package designed to accelerate NumPy statistical operations using Numba. The package maintains a clean API that mirrors NumPy's interface while providing significant performance improvements through parallel processing.

"How can I use numba to speed up my median computation?"

Yep. Just use speedystats.

"Is it possible to use numba to make np.std go faster?

Yep. Just use speedystats.

Features

• Drop-in replacement for common NumPy statistical functions
• Significant performance improvements through Numba optimization
• Maintains NumPy-like API for easy integration

Installation

pip install speedystats

Quick Start

import numpy as np
import speedystats as fs

# Create some test data
data = np.random.randn(1000, 1000)

# Use just like numpy
mean = fs.mean(data, axis=0)
std = fs.std(data, axis=1)
median = fs.median(data)

# Works with nan values too
data_with_nans = data.copy()
data_with_nans[0, 0] = np.nan
nanmean = fs.nanmean(data_with_nans, axis=0)

Available Functions

• Basic Statistics: mean, median, std, var, sum
• Range Statistics: ptp (peak-to-peak)
• Percentile Functions: percentile, quantile
• NaN-aware Variants: nanmean, nanmedian, nanstd, nanvar, nansum
• Additional Functions: average, zscore

Performance Note

While speedystats is designed for performance, the actual speedup depends on your specific use case, data size, and hardware. The package is most effective with:

• Large arrays (typically > 100,000 elements)
• Multi-core processors (for parallel execution)
• Certain methods are sped up much more than numpy
• Certain axis / dimension combinations get huge speedups, others are usually comparable to numpy

Note: Benchmarking tools for automatic routing to speedystats implementation vs numpy default methods isn't finished --- so you are responsible for determining whether speedystats is faster. Here's an example of how to test it quickly:

from time import time
import numpy as mp
import speedystats as fs

# Suppose you want to test if median is faster for arrays of a certain shape and size
array = np.random.randn(1000, 10000)
axis = 1

# Set repeats to get a better estimate
num_repeats = 20

t = time()
for _ in range(num_repeats):
   _ = np.median(array, axis=axis)
numpy_time = time() - t

t = time()
for _ in range(num_repeats):
   _ = fs.median(array, axis=axis)
speedystat_time = time() - t

print("Speedup: ", numpy_time / speedystat_time)

Comprehensive benchmarking tools are under development.

Development Status

This package is in beta. While the core functionality is stable, we're actively working on:

• Comprehensive benchmarking suite
• Performance optimization guides
• Additional statistical functions
• Advanced documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the GNU General Public License v3 (GPLv3) - see the LICENSE file for details.