MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

MetPy follows semantic versioning in its version number. This means that any MetPy 1.x release will be backwards compatible with an earlier 1.y release. By "backward compatible", we mean that correct code that works on a 1.y version will work on a future 1.x version.

For additional MetPy examples not included in this repository, please see the Unidata Python Gallery.

We support Python >= 3.7.


Other required packages:

  • Numpy
  • Scipy
  • Matplotlib
  • Pandas
  • Pint
  • Xarray

There is also an optional dependency on the pyproj library for geographic
projections (used with cross sections, grid spacing calculation, and the GiniFile interface).

See the installation guide
for more information.


The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs
easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you
take the average GEMPAK script for a weather map, you need to:

  • read data
  • calculate a derived field
  • show on a map/skew-T

One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for
any meteorological Python application; this means making it easy to pull out the LCL
calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides
itself on being well-documented and well-tested, so that on-going maintenance is easily

The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script
up weather analysis. It doesn't even have to be scripting; all python meteorology tools are
hoped to be able to benefit from MetPy. Conversely, it's hoped to be the meteorological
equivalent of the audience of scipy/scikit-learn/skimage.