---
id: pywavelets
version: "1.9.0"
license: MIT AND BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# PyWavelets — PyWavelets, wavelet transform module

License: permissive · Maintenance: active · Downloads: 15.3M/mo

## What it is and what it does

PyWavelets is a Python library for computing wavelet transforms—mathematical operations that decompose signals into time-frequency components using basis functions localized in both time and frequency. Unlike Fourier transforms, which capture only frequency information, wavelet transforms preserve temporal localization, making them useful for analyzing non-stationary signals. The library supports forward and inverse discrete wavelet transforms in 1D, 2D, and nD, multilevel decomposition, stationary (undecimated) transforms, wavelet packet decomposition, continuous wavelet transforms, and custom wavelet definitions. It includes over 100 built-in wavelet filters and handles both real and complex data in single and double precision.

The package depends only on numpy and is available as precompiled wheels for modern Python versions on common platforms. It is actively maintained, marked Production/Stable, and has been in continuous development since 2006. Installation is straightforward on supported platforms via pip, though building from source requires a C compiler and Cython.

Use it for:

- Decompose audio or seismic signals into frequency bands for feature extraction or denoising.
- Analyze non-stationary signals where frequency content changes over time, such as medical data.
- Perform image compression or feature detection using 2D wavelet transforms.
- Implement wavelet-based filtering or signal reconstruction in processing pipelines.
- Conduct time-frequency analysis for research in signal processing or engineering.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

PyWavelets provides discrete, continuous, and stationary wavelet transforms in 1D, 2D, and nD, along with wavelet packet decomposition and over 100 built-in wavelet filters for time-frequency analysis.

Yes. PyWavelets is a mature, actively maintained library with no known vulnerabilities, permissive dual licensing (MIT and BSD-3-Clause), and stable precompiled wheels for modern Python. Install friction is moderate but manageable. It is the standard choice for wavelet transforms in Python and worth installing if your work involves time-frequency analysis, signal decomposition, or wavelet-based feature extraction.

## Install

pip install pywavelets
uv add pywavelets
poetry add pywavelets

## Installing PyWavelets

Before you install: Medium install friction due to compiled C extensions, but prebuilt wheels are available for Python 3.11, 3.12, and 3.13 on Linux, macOS, and Windows. Active maintenance with last commit on 2026-08-13; library has been in development since 2006 and is marked Production/Stable.

License in practice: Dual-licensed under MIT and BSD-3-Clause (permissive). Both are permissive open-source licenses with minimal restrictions on use, modification, and distribution.

Quickstart:

pip install PyWavelets

import numpy

data = numpy.array([1, 2, 3])
# PyWavelets functions operate on numpy arrays

Requires numpy >= 1.23.0; C compiler needed only if building from source.

Verify before relying:

- Specific API functions and their signatures for performing discrete wavelet transforms.
- Performance characteristics and typical use-case scale for common transforms.
- Exact compatibility claims with Matlab Wavelet Toolbox across different wavelet families.

## Package facts

- License: MIT AND BSD-3-Clause (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 15.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

wavelet transform python, discrete wavelet decomposition, time-frequency analysis, signal processing wavelets, continuous wavelet transform, wavelet packet decomposition, DWT IDWT python, signal-processing, time-frequency-analysis, scientific-computing

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