dowhy
DoWhy adds one estimation method a year and keeps its identification edge.
A side-by-side editorial comparison of geopandas and silx — release velocity, themes, recent moves, and the top alternatives to consider.
GeoPandas bet everything on shapely 2 and Pyogrio, and is now paying down pandas 3
The library is in patch mode on the 1.1 line, split between pandas 3.0 compatibility work - Copy-on-Write, the new string dtype - and a run of bug fixes that includes two separate SQL-injection hardenings in to_postgis. The visible history reaches back to the 1.0 pre-releases, where GeoPandas dropped shapely<2 and PyGEOS entirely and switched its default I/O engine from Fiona to Pyogrio.
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
silx releases a couple of times a year and reached 3.0.0 in April 2026, which raised the Python floor to 3.10 and switched the default Qt binding to PySide6. The same release reworked the viewer's data views: 3D scatter support, dedicated RGB(A) image views, the composite ImageView split into Plot2dView and ComplexImageView, and NXdata stacks displayed as images. 3.1.0 has since added asinh axis scaling, the twilight colormaps, and dark-theme icons.
The library is in patch mode on the 1.1 line, split between pandas 3.0 compatibility work - Copy-on-Write, the new string dtype - and a run of bug fixes that includes two separate SQL-injection hardenings in to_postgis. The visible history reaches back to the 1.0 pre-releases, where GeoPandas dropped shapely<2 and PyGEOS entirely and switched its default I/O engine from Fiona to Pyogrio.
The 1.0 cycle collapsed a pile of optional backends into one geometry engine and one I/O engine, and the releases since have been about surviving what moves underneath: pandas 3.0 changing copy semantics and string storage. Expect the compatibility burden, not new spatial capability, to set the release cadence for now.
Further 1.1.x patches tracking pandas 3.x behaviour changes are the most likely next move, with the repeated to_postgis fixes suggesting more scrutiny of SQL construction there. Nothing in these entries points to a 1.2 feature line.
silx releases a couple of times a year and reached 3.0.0 in April 2026, which raised the Python floor to 3.10 and switched the default Qt binding to PySide6. The same release reworked the viewer's data views: 3D scatter support, dedicated RGB(A) image views, the composite ImageView split into Plot2dView and ComplexImageView, and NXdata stacks displayed as images. 3.1.0 has since added asinh axis scaling, the twilight colormaps, and dark-theme icons.
The project is doing a generational refresh of its GUI layer: modern Qt binding, modules broken out of the composite widgets that had accumulated responsibilities, and the theming work that a desktop application needs to look current. Underneath, the recurring fixes are about HDF5 behavior in real facility environments — file locking, NFS refresh, Windows display paths — which is where a synchrotron toolkit actually gets stressed. Feature growth is concentrated in silx view rather than the library API.
Expect the 3.1.x line to keep filling in plotting options and theming, with the PySide6 default flushing out binding-specific bugs from downstream applications over the next few releases.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either geopandas or silx.
DoWhy adds one estimation method a year and keeps its identification edge.
OpenHouse is hardening the seams where table policies and jobs quietly fail.
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
StatsBase.jl is in caretaker mode — correctness fixes in, dependency bumps out.
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
Shiny made reactive apps observable, then gave them a way to tear themselves down
See all geopandas alternatives → · See all silx alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. silx is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. silx is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top geopandas alternatives in Analytics are ranked by recent ship velocity. Browse the "geopandas alternatives" section above for the current picks, or visit /alternatives/geopandas for the full list with editorial commentary on each.
Top silx alternatives in Analytics are ranked by recent ship velocity. Browse the "silx alternatives" section above for the current picks, or visit /alternatives/silx for the full list with editorial commentary on each.