PyWavelets
PyWavelets, wavelet transform module
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy >= 1.23.0; C compiler needed only if building from source.
- 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 · maintenance · safety
MIT AND BSD-3-Clause (permissive) — Dual-licensed under MIT and BSD-3-Clause (permissive). Both are permissive open-source licenses with minimal restrictions on use, modification, and distribution.
last release 2025-08-04 (375 days) · last repo commit 2026-08-13 · 2,397 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 15,254,016 downloads/mo, #1,191 on PyPI
Alternatives
Verify before relying
pip install PyWavelets
import numpy
data = numpy.array([1, 2, 3])
# PyWavelets functions operate on numpy arrays- 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.
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 on this page. Verify before relying on it.
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
pywavelets on PyPI
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.
Requires numpy >= 1.23.0; C compiler needed only if building from source.
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 arraysVerify 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 the current Python release >=3.11 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 375 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 15,254,016 / month, #1,191 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Software Development :: Libraries :: Python Modules |
Evidence: pywavelets-1.9.0-cp311-cp311-macosx_10_9_x86_64.whl; pywavelets-1.9.0-cp311-cp311-macosx_11_0_arm64.whl; pywavelets-1.9.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; pywavelets-1.9.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; pywavelets-1.9.0-cp311-cp311-musllinux_1_2_aarch64.whl; pywavelets-1.9.0-cp311-cp311-musllinux_1_2_x86_64.whl; pywavelets-1.9.0-cp311-cp311-win32.whl; pywavelets-1.9.0-cp311-cp311-win_amd64.whl; pywavelets-1.9.0-cp312-cp312-macosx_10_13_x86_64.whl; pywavelets-1.9.0-cp312-cp312-macosx_11_0_arm64.whl; pywavelets-1.9.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; pywavelets-1.9.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; pywavelets-1.9.0-cp312-cp312-musllinux_1_2_aarch64.whl; pywavelets-1.9.0-cp312-cp312-musllinux_1_2_x86_64.whl; pywavelets-1.9.0-cp312-cp312-win32.whl; pywavelets-1.9.0-cp312-cp312-win_amd64.whl; pywavelets-1.9.0-cp313-cp313-macosx_10_13_x86_64.whl; pywavelets-1.9.0-cp313-cp313-macosx_11_0_arm64.whl; pywavelets-1.9.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; pywavelets-1.9.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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