pykalman
An implementation of the Kalman Filter, Kalman Smoother, and EM algorithm in Python
Decision gist · record as of 2026-08-14
Yes. pykalman is worth installing for any project requiring Kalman filtering or state estimation. It has low install friction, active maintenance, no known vulnerabilities, permissive BSD licensing for PyPI users, and a stable API with examples and documentation. The dependency footprint (numpy, scipy, scikit-base, packaging) is standard in scientific Python. Use it unless you need specialized variants (e.g., particle filters, adaptive Kalman filters) not provided here.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with a pure-Python wheel.
- Maintenance is active with recent commits and a stable release history since 2012; the package depends on numpy, scipy, scikit-base, and packaging—all well-established scientific libraries.
License · maintenance · safety
permissive license (permissive) — Licensed under BSD 3-Clause for PyPI distribution (explicitly exempted from commercial terms in Section 2.3 of the license). Free to use, modify, and distribute for end users; no fees apply to PyPI installations.
last release 2026-01-31 (195 days) · last repo commit 2026-04-20 · 1,327 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 264,522 downloads/mo, #8,336 on PyPI
Alternatives
Verify before relying
pip install pykalman
from pykalman import KalmanFilter
import numpy as np
kf = KalmanFilter(transition_matrices=[[1, 1], [0, 1]], observation_matrices=[[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1, 0], [0, 0], [0, 1]])
kf = kf.em(measurements, n_iter=5)
filtered_means, filtered_covs = kf.filter(measurements)
smoothed_means, smoothed_covs = kf.smooth(measurements)- Whether the package handles missing measurements transparently in all filter variants (documented for basic KalmanFilter but not confirmed for UnscentedKalmanFilter).
- Performance characteristics and scalability limits for high-dimensional state spaces or long time series.
- Numerical stability guarantees of the square-root filter variants under ill-conditioned covariance matrices.
What it is and what it does
pykalman is a Python library for Kalman filtering and smoothing that solves the problem of estimating hidden state in linear and nonlinear dynamical systems from noisy observations. It implements the classical Kalman Filter for linear systems, the Unscented Kalman Filter for nonlinear dynamics, and an EM algorithm for automatic parameter learning. The library also supports numerically robust square-root filter variants and can handle missing measurements via masked arrays.
Developers use pykalman for tracking, signal smoothing, and state estimation tasks in time series analysis. It depends on numpy for array operations, scipy for numerical routines, scikit-base for base classes, and packaging for version handling. The package is actively maintained, supports Python 3.10 through 3.14, and has been in development since 2012.
Use it for
- Estimate position and velocity from noisy GPS or sensor measurements in tracking applications.
- Smooth noisy time-series data (e.g., stock prices, sensor readings) using forward-backward filtering.
- Learn optimal filter parameters automatically from observed data using the EM algorithm.
- Handle nonlinear state transitions and observation models with the Unscented Kalman Filter.
- Perform online state estimation by updating filter state incrementally as new measurements arrive.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pykalman is worth installing for any project requiring Kalman filtering or state estimation. It has low install friction, active maintenance, no known vulnerabilities, permissive BSD licensing for PyPI users, and a stable API with examples and documentation. The dependency footprint (numpy, scipy, scikit-base, packaging) is standard in scientific Python. Use it unless you need specialized variants (e.g., particle filters, adaptive Kalman filters) not provided here.
Install
pykalman on PyPI
Before you install
Low friction installation with a pure-Python wheel. Maintenance is active with recent commits and a stable release history since 2012; the package depends on numpy, scipy, scikit-base, and packaging—all well-established scientific libraries.
License in practice
Licensed under BSD 3-Clause for PyPI distribution (explicitly exempted from commercial terms in Section 2.3 of the license). Free to use, modify, and distribute for end users; no fees apply to PyPI installations.
Quickstart
pip install pykalman
from pykalman import KalmanFilter
import numpy as np
kf = KalmanFilter(transition_matrices=[[1, 1], [0, 1]], observation_matrices=[[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1, 0], [0, 0], [0, 1]])
kf = kf.em(measurements, n_iter=5)
filtered_means, filtered_covs = kf.filter(measurements)
smoothed_means, smoothed_covs = kf.smooth(measurements)Verify before relying
- Whether the package handles missing measurements transparently in all filter variants (documented for basic KalmanFilter but not confirmed for UnscentedKalmanFilter).
- Performance characteristics and scalability limits for high-dimensional state spaces or long time series.
- Numerical stability guarantees of the square-root filter variants under ill-conditioned covariance matrices.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpypackagingscikit-basescipy |
| Maintenance | Actively maintained 195 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 264,522 / month, #8,336 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: pykalman-0.11.2-py2.py3-none-any.whl
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