pymannkendall
A python package for non-parametric Mann-Kendall family of trend tests.
Decision gist · record as of 2026-08-14
Yes, if you need robust non-parametric trend analysis for time series data. Install friction is minimal and the package is stable and well-established. However, note that the last release was in January 2023; verify that it supports your target Python version and that no critical bugs have emerged in your use case before production deployment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with only numpy and scipy as runtime dependencies.
- Package is aging (last release 2023-01-14, 1308 days prior) but remains in production-stable status with an active repository and modest community engagement (289 stars).
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with minimal obligations beyond attribution.
last release 2023-01-14 (1308 days) · last repo commit 2025-03-22 · 289 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 633,123 downloads/mo, #5,648 on PyPI
Alternatives
Verify before relying
pip install pymannkendall
import numpy as np
import pymannkendall as mk
data = np.random.rand(360)
result = mk.original_test(data)
print(result.trend, result.p, result.slope)- Whether the package actively supports modern Python versions beyond 3.9 (classifiers list 3.9 as latest)
- Current test coverage and whether pytest suite remains comprehensive
What it is and what it does
pyMannKendall is a pure Python implementation of the Mann-Kendall family of non-parametric trend tests, designed for analyzing time series data without assuming normal distribution. It provides 11 variants of the Mann-Kendall test (original, Hamed-Rao, Yue-Wang, pre-whitening, multivariate, seasonal, regional, correlated multivariate, correlated seasonal, and partial) plus two Sen's slope estimators for quantifying trend magnitude. The package depends on numpy and scipy for numerical computation.
The tests return structured results including trend direction, p-value, test statistics, Kendall's Tau, and slope estimates. It is particularly useful when time series data violates normality assumptions or contains serial correlation, seasonal patterns, or spatial structure. Each test accepts a data vector and optional parameters like significance level and seasonal period, making it flexible for different analytical scenarios.
Use it for
- Detect long-term increasing or decreasing trends in climate or environmental monitoring data with seasonal adjustments
- Analyze regional hydrological trends across multiple monitoring stations using regional MK test
- Estimate trend magnitude and confidence intervals in water quality or air pollution time series
- Test for trends in autocorrelated economic or financial time series using variance-corrected variants
- Identify monotonic patterns in multi-parameter datasets where parameters are correlated with each other
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need robust non-parametric trend analysis for time series data.
Install friction is minimal and the package is stable and well-established. However, note that the last release was in January 2023; verify that it supports your target Python version and that no critical bugs have emerged in your use case before production deployment.
Install
pymannkendall on PyPI
Before you install
Low friction installation with only numpy and scipy as runtime dependencies. Package is aging (last release 2023-01-14, 1308 days prior) but remains in production-stable status with an active repository and modest community engagement (289 stars).
License in practice
MIT license permits unrestricted use, modification, and distribution in both open and proprietary projects with minimal obligations beyond attribution.
Quickstart
pip install pymannkendall
import numpy as np
import pymannkendall as mk
data = np.random.rand(360)
result = mk.original_test(data)
print(result.trend, result.p, result.slope)Verify before relying
- Whether the package actively supports modern Python versions beyond 3.9 (classifiers list 3.9 as latest)
- Current test coverage and whether pytest suite remains comprehensive
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Aging 1,308 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 633,123 / month, #5,648 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 :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 2.7Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: pymannkendall-1.4.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “mann-kendall trend test”
- pymannkendallImplements a suite of Mann-Kendall trend tests and Sen's slope…
- pytrendsProvides an unofficial interface to download and analyze Google…
- django-slowtestsIntegrates with Django's test runner to identify and report the…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also scikit-posthocs · prophet · corner · utide · lifelines · tbats · ruptures · coreforecast · statsmodels · neuralprophet