Frequently Asked Questions

Why is pandas not enough?

pandas is a fantastic library for analysis of low-dimensional labelled data - if it can be sensibly described as “rows and columns”, pandas is probably the right choice. However, sometimes we want to use higher dimensional arrays (ndim > 2), or arrays for which the order of dimensions (e.g., columns vs rows) shouldn’t really matter. For example, climate and weather data is often natively expressed in 4 or more dimensions: time, x, y and z.

Pandas has historically supported N-dimensional panels, but deprecated them in version 0.20 in favor of Xarray data structures. There are now built-in methods on both sides to convert between pandas and Xarray, allowing for more focussed development effort. Xarray objects have a much richer model of dimensionality - if you were using Panels:

  • You need to create a new factory type for each dimensionality.
  • You can’t do math between NDPanels with different dimensionality.
  • Each dimension in a NDPanel has a name (e.g., ‘labels’, ‘items’, ‘major_axis’, etc.) but the dimension names refer to order, not their meaning. You can’t specify an operation as to be applied along the “time” axis.
  • You often have to manually convert collections of pandas arrays (Series, DataFrames, etc) to have the same number of dimensions. In contrast, this sort of data structure fits very naturally in an xarray Dataset.

You can read about switching from Panels to Xarray here. Pandas gets a lot of things right, but scientific users need fully multi- dimensional data structures.

How do xarray data structures differ from those found in pandas?

The main distinguishing feature of xarray’s DataArray over labeled arrays in pandas is that dimensions can have names (e.g., “time”, “latitude”, “longitude”). Names are much easier to keep track of than axis numbers, and xarray uses dimension names for indexing, aggregation and broadcasting. Not only can you write x.sel(time='2000-01-01') and x.mean(dim='time'), but operations like x - x.mean(dim='time') always work, no matter the order of the “time” dimension. You never need to reshape arrays (e.g., with np.newaxis) to align them for arithmetic operations in xarray.

Should I use xarray instead of pandas?

It’s not an either/or choice! xarray provides robust support for converting back and forth between the tabular data-structures of pandas and its own multi-dimensional data-structures.

That said, you should only bother with xarray if some aspect of data is fundamentally multi-dimensional. If your data is unstructured or one-dimensional, stick with pandas.

Why don’t aggregations return Python scalars?

xarray tries hard to be self-consistent: operations on a DataArray (resp. Dataset) return another DataArray (resp. Dataset) object. In particular, operations returning scalar values (e.g. indexing or aggregations like mean or sum applied to all axes) will also return xarray objects.

Unfortunately, this means we sometimes have to explicitly cast our results from xarray when using them in other libraries. As an illustration, the following code fragment

In [1]: arr = xr.DataArray([1, 2, 3])

In [2]: pd.Series({'x': arr[0], 'mean': arr.mean(), 'std': arr.std()})
mean          <xarray.DataArray ()>\narray(2.)
std     <xarray.DataArray ()>\narray(0.816497)
x              <xarray.DataArray ()>\narray(1)
dtype: object

does not yield the pandas DataFrame we expected. We need to specify the type conversion ourselves:

In [3]: pd.Series({'x': arr[0], 'mean': arr.mean(), 'std': arr.std()}, dtype=float)
mean    2.000000
std     0.816497
x       1.000000
dtype: float64

Alternatively, we could use the item method or the float constructor to convert values one at a time

In [4]: pd.Series({'x': arr[0].item(), 'mean': float(arr.mean())})
mean    2.0
x       1.0
dtype: float64

What is your approach to metadata?

We are firm believers in the power of labeled data! In addition to dimensions and coordinates, xarray supports arbitrary metadata in the form of global (Dataset) and variable specific (DataArray) attributes (attrs).

Automatic interpretation of labels is powerful but also reduces flexibility. With xarray, we draw a firm line between labels that the library understands (dims and coords) and labels for users and user code (attrs). For example, we do not automatically interpret and enforce units or CF conventions. (An exception is serialization to and from netCDF files.)

An implication of this choice is that we do not propagate attrs through most operations unless explicitly flagged (some methods have a keep_attrs option). Similarly, xarray does not check for conflicts between attrs when combining arrays and datasets, unless explicitly requested with the option compat='identical'. The guiding principle is that metadata should not be allowed to get in the way.

What other projects leverage xarray?

Here are several existing libraries that build functionality upon xarray.


  • aospy: Automated analysis and management of gridded climate data.
  • infinite-diff: xarray-based finite-differencing, focused on gridded climate/meterology data
  • marc_analysis: Analysis package for CESM/MARC experiments and output.
  • MPAS-Analysis: Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Accelerated Climate Model for Energy (ACME).
  • OGGM: Open Global Glacier Model
  • Oocgcm: Analysis of large gridded geophysical datasets
  • Open Data Cube: Analysis toolkit of continental scale Earth Observation data from satellites.
  • Pangaea:: xarray extension for gridded land surface & weather model output).
  • Pangeo: A community effort for big data geoscience in the cloud.
  • PyGDX: Python 3 package for accessing data stored in GAMS Data eXchange (GDX) files. Also uses a custom subclass.
  • Regionmask: plotting and creation of masks of spatial regions
  • salem: Adds geolocalised subsetting, masking, and plotting operations to xarray’s data structures via accessors.
  • Spyfit: FTIR spectroscopy of the atmosphere
  • windspharm: Spherical harmonic wind analysis in Python.
  • wrf-python: A collection of diagnostic and interpolation routines for use with output of the Weather Research and Forecasting (WRF-ARW) Model.
  • xarray-simlab: xarray extension for computer model simulations.
  • xarray-topo: xarray extension for topographic analysis and modelling.
  • xbpch: xarray interface for bpch files.
  • xESMF: Universal Regridder for Geospatial Data.
  • xgcm: Extends the xarray data model to understand finite volume grid cells (common in General Circulation Models) and provides interpolation and difference operations for such grids.
  • xmitgcm: a python package for reading MITgcm binary MDS files into xarray data structures.
  • xshape: Tools for working with shapefiles, topographies, and polygons in xarray.

Machine Learning

Extend xarray capabilities

  • Collocate: Collocate xarray trajectories in arbitrary physical dimensions
  • eofs: EOF analysis in Python.
  • xarray_extras: Advanced algorithms for xarray objects (e.g. intergrations/interpolations).
  • xrft: Fourier transforms for xarray data.
  • xr-scipy: A lightweight scipy wrapper for xarray.
  • X-regression: Multiple linear regression from Statsmodels library coupled with Xarray library.



  • ptsa: EEG Time Series Analysis
  • pycalphad: Computational Thermodynamics in Python

More projects can be found at the “xarray” Github topic.

How should I cite xarray?

If you are using xarray and would like to cite it in academic publication, we would certainly appreciate it. We recommend two citations.

  1. At a minimum, we recommend citing the xarray overview journal article, published in the Journal of Open Research Software.

    • Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI:

      Here’s an example of a BibTeX entry:

        title     = {xarray: {N-D} labeled arrays and datasets in {Python}},
        author    = {Hoyer, S. and J. Hamman},
        journal   = {Journal of Open Research Software},
        volume    = {5},
        number    = {1},
        year      = {2017},
        publisher = {Ubiquity Press},
        doi       = {10.5334/jors.148},
        url       = {}
  2. You may also want to cite a specific version of the xarray package. We provide a Zenodo citation and DOI for this purpose:

    An example BibTeX entry:

          author = {Stephan Hoyer and Clark Fitzgerald and Joe Hamman and others},
          title  = {xarray: v0.8.0},
          month  = aug,
          year   = 2016,
          doi    = {10.5281/zenodo.59499},
          url    = {}