Flexible indexing blog post #795

scottyhq · 2025-08-11T19:55:35Z

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keewis

thanks, Scott, that was a nice read.

I didn't check how this looks, but from the markdown at least this looks good to me in general. I do have a few comments on the details, though.

keewis · 2025-08-13T18:57:40Z

src/components/layout.js

+  // const bannerChildren = (
+  //   <Link
+  //     href='https://docs.google.com/forms/d/e/1FAIpQLSeGvTLONF-24V7z2HoACm4MhEr82c2V-VIzA9eqM9-jt-Xh8g/viewform?usp=sharing&ouid=111570313164368772519'
+  //     fontSize='sm'
+  //   >
+  //     {' '}
+  //     {/* Add your second link here, smaller font */}
+  //     <b>SciPy 2025</b> Click here for info about an Xarray for Bio Sprint!
+  //   </Link>
+  //)


remove? Or did you want to leave that as an example?

I could go either way, I commented instead of deleting to more easily go back to the existing multi-line banner

keewis · 2025-08-13T18:58:07Z

src/posts/flexible-indexing/index.md

+  </figcaption>
+</figure>
+
+{/* This is a comment that won't be rendered! */}


Suggested change

{/* This is a comment that won't be rendered! */}

Yeah, I'll add a small section to the readme on authoring tips. I was commenting some things out while testing the build locally and had to search around to figure out this syntax! Eventually it would be nice for a more streamlined blogging interface, just using mystmd or jupyterbook, I'll open an issue with a couple ideas.

keewis · 2025-08-13T19:00:30Z

src/posts/flexible-indexing/index.md

+> In brief, an _index_ makes repeated selection of data more efficient. Xarray Indexes connect coordinate labels to associated data values and encode important contextual information about the coordinate space.
+
+Examples of indexes are all around you and are a fundamental way to organize and simplify access to information.
+If you want a book about Natural Sciences, you can go to your local library branch and head straight to section `500`, or if you're in the mood for a good novel go to section `800`. Connecting thematic labels with numbers is a classic indexing system that's been around for hundreds of years [(Dewey Decimal System, 1876)](https://en.wikipedia.org/wiki/Dewey_Decimal_Classification).


this might be a US thing, I've never seen this kind of numbering scheme (although admittedly I've not been to a lot of libraries with science sections). Still, the example is interesting and I would keep it, I'd just qualify it with something like "In the US ..." (or whatever fits)

Good call, definitely Dewey Decimal is a US thing, and there are plenty of alternatives :)

keewis · 2025-08-13T19:04:17Z

src/posts/flexible-indexing/index.md

+
+## Pandas.Index
+
+Xarray's [label-based selection](https://docs.xarray.dev/en/latest/user-guide/indexing.html#indexing-with-dimension-names) allows a more expressive and simple syntax in which you don't have to think about the index (`da.sel(x=8) = 40`). Up until now, Xarray has relied exclusively on [Pandas.Index](https://pandas.pydata.org/docs/user_guide/indexing.html), which is still used by default:


to be closer to the import

Suggested change

Xarray's [label-based selection](https://docs.xarray.dev/en/latest/user-guide/indexing.html#indexing-with-dimension-names) allows a more expressive and simple syntax in which you don't have to think about the index (`da.sel(x=8) = 40`). Up until now, Xarray has relied exclusively on [Pandas.Index](https://pandas.pydata.org/docs/user_guide/indexing.html), which is still used by default:

Xarray's [label-based selection](https://docs.xarray.dev/en/latest/user-guide/indexing.html#indexing-with-dimension-names) allows a more expressive and simple syntax in which you don't have to think about the index (`da.sel(x=8) = 40`). Up until now, Xarray has relied exclusively on [pandas.Index](https://pandas.pydata.org/docs/user_guide/indexing.html), which is still used by default:

also, should this mention databases and hash-tables? It doesn't have to, just a thought.

should this mention databases and hash-tables? It doesn't have to, just a thought.

I'm actually not sure what you mean here? I thought pandas.Index is a hash-table? But my understanding of this space is admittedly not that great 😅 . If you have a sentence or two please suggest!

that's correct. I think I remember hearing that hash tables were created for databases, so that's why I mentioned both (but either way, I don't think it's necessary to follow this, and I don't have any specific sentences in mind).

keewis · 2025-08-13T19:34:35Z

src/posts/flexible-indexing/index.md

+
+There are currently over 7000 commonly used [Coordinate Reference Systems (CRS)](https://spatialreference.org/ref/epsg/) for geospatial data in the authoritative EPSG database!
+And of course an infinite number of custom-defined CRSs.
+[xproj.CRSIndex](https://xproj.readthedocs.io/en/latest/) gives Xarray objects an automatic awareness of the coordinate reference system operations like `xr.align()`, which can raise an an informative error when there is a CRS mismatch:


Suggested change

[xproj.CRSIndex](https://xproj.readthedocs.io/en/latest/) gives Xarray objects an automatic awareness of the coordinate reference system operations like `xr.align()`, which can raise an an informative error when there is a CRS mismatch:

[xproj.CRSIndex](https://xproj.readthedocs.io/en/latest/) gives Xarray objects an automatic awareness of the coordinate reference system operations like `xr.align()`, which can raise an informative error when there is a CRS mismatch:

keewis · 2025-08-13T19:40:18Z

src/posts/flexible-indexing/index.md

+summary: 'An introduction to customizable coordinate-based data selection and alignment for more efficient handling of both traditional and more exotic data structures'
+---
+
+\_TLDR: Xarray>2025.6 has been through a major refactoring of its internals that makes coordinate-based data selection and alignment customizable, enabling more efficient handling of both traditional and more exotic data structures. In this post we highlight a few examples that take advantage of this new superpower!


The TLDR looks a bit weird with the leading underscore after rendering, and I'd probably write the version comparison as xarray>2025.06.0. Is it truly > or rather >=, though?

xarray>2025.06.0. Is it truly > or rather >= ?

While some pieces of this have been around longer, I know there have been a lot of incremental improvements, so I thought I'd play it safe and go very recent. @benbovy do you have a preference?

the version itself is fine (but I didn't check for anything specific that we'd need), I just found the > a bit unusual, I would have used >= instead (and adjusted the version as necessary)

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scottyhq mentioned this pull request Aug 13, 2025

Possible alternatives for authoring blog posts #796

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		## Pandas.Index

		Xarray's [label-based selection](https://docs.xarray.dev/en/latest/user-guide/indexing.html#indexing-with-dimension-names) allows a more expressive and simple syntax in which you don't have to think about the index (`da.sel(x=8) = 40`). Up until now, Xarray has relied exclusively on [Pandas.Index](https://pandas.pydata.org/docs/user_guide/indexing.html), which is still used by default:

	[xproj.CRSIndex](https://xproj.readthedocs.io/en/latest/) gives Xarray objects an automatic awareness of the coordinate reference system operations like `xr.align()`, which can raise an an informative error when there is a CRS mismatch:
	[xproj.CRSIndex](https://xproj.readthedocs.io/en/latest/) gives Xarray objects an automatic awareness of the coordinate reference system operations like `xr.align()`, which can raise an informative error when there is a CRS mismatch:

Flexible indexing blog post #795

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