Extreme Value Analysis (EVA) in Python



License: MIT

E-Mail: [email protected]

pyextremes is a Python library aimed at performing univariate Extreme Value Analysis (EVA). It provides tools necessary to perform a wide range of tasks required to perform EVA, such as:

  • extraction of extreme events from time series using methods such as Block Maxima (BM) or Peaks Over Threshold (POT)
  • fitting continuous distributions, such as GEVD, GPD, or user-specified continous distributions to the extracted extreme events
  • visualization of model inputs, results, and goodness-of-fit statistics
  • estimation of extreme events of given probability or return period (e.g. 100-year event) and of corresponding confidence intervals
  • tools assisting with model selection and tuning, such as selection of block size in BM and threshold in POT


Get latest version from PyPI:

pip install pyextremes

Get latest experimental build from GitHub:

pip install "git+"

Get pyextremes for the Anaconda Python distribution:

conda install -c conda-forge pyextremes


This section will be removed in the future in favor of the official documentation which can be found at


Model diagnostic

Diagnostic plot

Extreme value extraction

Diagnostic plot

Trace plot

Diagnostic plot

Corner plot

Diagnostic plot
GitHub - georgebv/pyextremes: Extreme Value Analysis (EVA) in Python
Extreme Value Analysis (EVA) in Python. Contribute to georgebv/pyextremes development by creating an account on GitHub.