eofs

At a glance

• Editor score
  3.0 / 10
• Pricing
  Free plan
• Best for
  Climate and geoscience teams analyzing spatial-temporal data
• Free plan
  Yes
• Paid from
  Free
• Deployment
  Desktop
• Facts checked
  24 Sep 2026

• Where it wins

  • Efficient EOF processing for large datasets, with missing-value handling
  • Interfaces for NumPy and dask arrays, plus metadata-aware Iris and xarray support
  • Multivariate solvers return EOF patterns and principal component time series

• Where it doesn't

  • Focused on EOF analysis rather than broad statistical workflows
  • Does not provide statistical tests, regression models, or Bayesian analysis
  • Self-hosted package with documentation as its listed support channel

Our verdict on eofs

eofs is an open-source Python package for empirical orthogonal function analysis of spatial-temporal data, including climate-model and other geophysical fields. It is suited to climate and geoscience teams that need EOF solutions, spatial patterns, or principal component time series rather than a general-purpose statistical suite. The package runs wherever Python and NumPy are available, and the listed platforms are Windows, macOS, and Linux. It can be installed with pip or conda.

Its interface breadth is central to the package's fit. A standard interface works with NumPy and dask arrays, while metadata-aware interfaces support Iris and xarray and can preserve input metadata. These options let teams work with familiar array and scientific-data structures, including NetCDF through the Iris example. Computationally efficient processing targets large datasets, and automatic missing-value handling addresses an important feature of spatial-temporal fields. Multivariate EOF solvers support combined analysis of multiple fields. This is a focused scientific workflow, not a replacement for software centered on statistical tests, regression models, or Bayesian analysis.

The package is open source and has a free plan, so there are no paid tiers to compare here. Deployment is self-hosted, with documentation as the listed support channel; teams should be comfortable installing and working with a Python package. Choose eofs when EOF analysis and its outputs are the core need, especially if the workflow already uses NumPy, dask, Iris, or xarray. Teams seeking a broad statistical suite, built-in statistical tests, regression, Bayesian analysis, or a different support model should look elsewhere.

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eofs pricing

eofs fact sheet

eofs integrations

eofs lists 4 integrations on its own site.

• NumPy
• dask
• Iris
• xarray

Alternatives to eofs

• RA free, programmable environment for statistical analysis, modeling, visualization, and data import.9.4
• SStataA broad, scriptable desktop environment for professional and reproducible applied analysis.9.3
• IBM SPSS StatisticsA broad desktop statistics suite with testing, modeling, forecasting, reporting, and R/Python integration.9.2

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Featured on iTechGuides

eofs is listed in our Statistical Analysis Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update