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WIP: revise top-level package description #2430

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Jan 6, 2019
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WIP: revise top-level package description #2430

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revise main package description
rabernat Sep 22, 2018
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Update doc/index.rst
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rabernat Jan 4, 2019
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16 changes: 10 additions & 6 deletions doc/index.rst
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Expand Up @@ -2,12 +2,16 @@ xarray: N-D labeled arrays and datasets in Python
=================================================

**xarray** (formerly **xray**) is an open source project and Python package
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@shoyer can we drop the reference to xray? The set of people that know the old xray and don't know the new xarray name is probably next to empty.

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Sadly, just today in the twitter thread under discussion, someone referenced xray and linked to the v0.2 documentation. 🤦‍♂️

that aims to bring the labeled data power of pandas_ to the physical sciences,
by providing N-dimensional variants of the core pandas data structures.

Our goal is to provide a pandas-like and pandas-compatible toolkit for
analytics on multi-dimensional arrays, rather than the tabular data for which
pandas excels. Our approach adopts the `Common Data Model`_ for self-
that aims to make working with labelled multi-dimensional arrays simple,
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efficient, and fun!

Labelled multi-dimensional (a.k.a. N-dimensional) arrays are encountered in
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many fields, especially physical sciences, engineering, and finance.
But multi-dimensional data doesn't fit neatly into pandas_, python's most
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If we are going to contrast directly with Pandas, I think we need to say what Pandas is first. Maybe also provide an example of what Pandas does (tabular data structures).

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@TomNicholas TomNicholas Oct 31, 2018 •
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I agree that we can't assume that readers know what Pandas is - I certainly didn't. I think that users coming from a more data science background will have used Pandas but those coming from a more low-level array-based numpy/MATLAB/Fortran/C++ point-of-view won't have (e.g. all the physicists I work with).

I also think including an explicit example of a labelled data structure in this explanation would go a long way, the printable representation of an xarray Dataset gives a good idea of how it labels the data it contains.

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popular data analysis package focused on label tabular data.
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Xarray provides a pandas-like and pandas-compatible toolkit for
analytics on multi-dimensional arrays.
Our approach adopts the `Common Data Model`_ for self-
describing scientific data in widespread use in the Earth sciences:
``xarray.Dataset`` is an in-memory representation of a netCDF file.
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This is not completely accurate, an xarray.Dataset represents a netCDF-3 or netCDF-4 classic file, but only one of the Groups in a netCDF-4 file with the new netCDF-4 Data Model https://www.unidata.ucar.edu/software/netcdf/workshops/2011/datamodels/Nc4-uml.html (compatible but not identical with the cited Common Data Model). This may sound pedantic at this level, but I found the subtleties of the netCDF 3/4 data models very hard to grasp once I had the mental map between an xarray.Dataset and a netCDF-4 File.

IMHO the best is to keep the reference to the Unidata Common Data Model as xarray uses the extended type system and add a quick reference to the CDM concept of a Group.


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