Pangeo is not one specific software package. It’s a community built around open science. Members of the pangeo community have integrated many different open-source software packages, each of which has its own source repository, documentation, and development team.

This page discusses some of the core packages used within Pangeo, and some best practices libraries should follow to work well with other packages in the pangeo ecosystem.

If you’re interested in learning how to use these packages, consult either the project’s documentation or the Project Pythia site.


Many people and organizations were involved in the development of the software described on this page. Most of them have no formal affiliation with Pangeo. By listing software packages on this website, we are in no way claiming credit for their hard work. Full information about the developers responsible for creating these tools can be found by following the links below.

Pangeo Core Packages

Because of the Technical Architecture of Pangeo environments, these core packages are considered essential to the project.


Xarray is an open source project and Python package that provides a toolkit for working with labeled multi-dimensional arrays of data. Xarray adopts the Common Data Model for self-describing scientific data in widespread use in the Earth sciences. An xarray.Dataset is an in-memory representation of a netCDF file. Xarray provides the basic data structures used by many other Pangeo packages, as well as powerful tools for computation and visualization.



Iris seeks to provide a powerful, easy to use, and community-driven Python library for analysing and visualising meteorological and oceanographic data sets.

With Iris you can:

  • Use a single API to work on your data, irrespective of its original format.

  • Read and write (CF-)netCDF, GRIB, and PP files.

  • Easily produce graphs and maps via integration with matplotlib and cartopy.

Iris is an alternative to Xarray. Iris is developed primarily by the UK Met Office Analysis, Visualisation and Data team (AVD).



Dask is a flexible parallel computing library for analytics. Dask is the key to the scalability of the Pangeo platform; its data structures are capable of representing extremely large datasets without actually loading them in memory, and its distributed schedulers permit supercomputers and cloud computing clusters to efficiently parallelize computations across many nodes.



Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Jupyter provides the interactive layer to the Pangeo platform, allowing scientists to interact with remote systems where data and computing resources live.

Pangeo Affiliated Packages

There are many other python packages that can work with the core packages to provide additional functionality. We plan to eventually catalog these packages here on the Pangeo website. For now, please refer to the Xarray list of related projects.

Guidelines for New Packages

Our vision for the Pangeo project is an ecosystem of mutually compatible Geoscience python packages which follow open-source best practices. These practices are well established across the scientific python community.

General Best Practices for Open Source

How to develop and maintain an open-source project is a large topic that extends beyond pangeo. The pyOpenSci Package Development Guide provides a comprehensive guide.

Projects can submit their package for peer review from pyOpenSci.

Best Practices for Pangeo Projects

In addition to general software best practices, there are some additional best-practices packages to work well with the pangeo ecosystem.

  1. Solve a general problem

    Packages should solve a general problem that is encountered by a relatively broad groups of users.

  2. Clearly define a scope

    Packages should have a clear and relatively narrow scope, solving the specific problem[s] identified in the point above (rather than attempting to cover every possible aspect of geoscience research computing). By consuming and producing standard data containers (see below) packages can compose together to solve large problems.

  3. Avoid duplication

    Developers should try to leverage existing packages as much as possible to avoid duplication of effort. (In early-stage development and experimentation, however, some duplication will be inevitable as developers try implementing different solutions to the same general problems.)

  4. Consume and produce standard data container

    Packages should consume and produce standard data containers like xarray.Dataset and geopandas.GeoDataFrame. This facilitate interoperability between packages in the ecosystem.

  5. Avoid data I/O where possible

    Unless the package is specifically focused on reading or writing data, it should not include its own custom code for reading and writing data. Instead, it should produce and consume standard data containers.

    Where data I/O is required, package should use existing libraries (e.g. Zarr via Xarray, geoparquet via geopandas, etc.). This ensures that the data reading and writing works with a large variety of file systems.

  6. Operate Lazily

    Whenever possible, packages should avoid explicitly triggering computation on Dask objects. Instead, they should return standard data containers that can be backed by lazy Dask objects. This allows users to control when computation actually occurs.

Why Xarray and Dask?

The Pangeo project strongly encourages the use of Xarray data structures wherever possible. Xarray Dataset and DataArrays contain multidimensional numeric array data and also the metadata describing the data’s coordinates, labels, units, and other relevant attributes. Xarray makes it easy to keep this important metadata together with the raw data; applications can then take advantage of the metadata to perform calculations or create visualizations in a coordinate-aware fashion. The use of Xarray eliminates many common bugs, reduces the need to write boilerplate code, makes code easier to understand, and generally makes users and developers happier and more productive in their day-to-day scientific computing.

Xarray’s data model is explicitly based on the CF Conventions, a well-established community standard which encompasses many different common scenarios encountered in Earth System science. However, Xarray is flexible and does not require compliance with CF conventions. We encourage Pangeo packages to follow CF conventions wherever it makes sense to do so.

Most geoscientists have encountered the CF data model via the ubiquitous netCDF file format. While Xarray can easily read and write netCDF files, it doesn’t have to. This is a key difference between software built on Xarray and numerous other tools designed to process netCDF data (e.g. nco, cdo, etc. etc.): Xarray data can be passed directly between python libraries (or over a network) without ever touching a disk drive. This “in-memory” capability is a key ingredient to the big data scalability of Pangeo packages. Very frequently the bottleneck in data processing pipelines is reading and writing files.

Another important aspect of scalability is the use of Dask for parallel and out-of-core computations. The raw data underlying Xarray objects can be either standard in-memory numpy arrays or Dask arrays. Dask arrays behave nearly identically to numpy arrays (they support the same API), but instead of storing raw data, they store a symbolic computational graph of operations. An example computational graph would start by reading data from disk or network and then preform transformations or mathematical calculations. No operations are actually executed until actual numerical values are required, such as for making a figure. This is called lazy execution. Dask figures out how to execute these computational graphs efficiently on different computer architectures using sophisticated techniques. By chaining operations on dask arrays together, researchers can symbolically represent large and complex data analysis pipelines and then deploy them effectively on large computer clusters.