tidyr
tidyr replaced separate() with a family that says what it does.
Comparison · Analytics
iris vs statsmodels
A side-by-side editorial comparison of iris and statsmodels — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:python
iris vs statsmodels: at a glance
What is iris?
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
What is statsmodels?
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.
iris vs statsmodels: editorial side-by-side
iris
2.5ANALYTICS
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
◆ Current state
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
◆ Where it's heading
The version numbers say a mature Met Office library is being maintained on a predictable schedule; nothing in the published entries says what is being maintained. Until the project puts release content in the tag body, its public trail will read as cadence without substance, and readers have to leave the feed to learn anything. The pattern has been identical across four consecutive releases, so it is a deliberate publishing choice rather than an oversight.
◆ Prediction
Expect v3.17.0rc0 around late 2026 on the same schedule, carrying the same boilerplate — the notes will again live on the documentation site rather than in the release entry.
statsmodels
0.0ANALYTICS
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
◆ Current state
Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.
◆ Where it's heading
The library is being kept alive rather than developed: each release answers a break introduced upstream, and the interval between them is set by the NumPy, SciPy and pandas release calendars rather than by anything statsmodels is building. Two consecutive releases whose stated purpose was restoring the ability to import the package is the sharpest available signal about maintainer bandwidth. The 0.15 line remains a dev tag with no visible progress toward a release.
◆ Prediction
The next release is most likely another compatibility patch triggered by a NumPy, SciPy or pandas major, and nothing in these entries indicates 0.15 is close.
Alternatives to iris and statsmodels
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 iris or statsmodels.
modeltime
modeltime built conformal intervals in, then went quiet on features.
performance
performance keeps adding ways to check a model you have already fitted.
cmdstanpy
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
dowhy
DoWhy adds one estimation method a year and keeps its identification edge.
OpenHouse
OpenHouse is hardening the seams where table policies and jobs quietly fail.
See all iris alternatives → · See all statsmodels alternatives →
Recent activity from iris and statsmodels
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 13d agoirisv3.16.0rc0
- 4mo agoirisv3.15.0rc0
- 8mo agostatsmodels0.14.6: restores importing under pandas 3.0
- 9mo agoirisv3.14.0rc0
- 1y agoirisv3.13.0rc0
- 1y agostatsmodels0.14.5: restores importing under SciPy 1.16
- 1y agostatsmodels0.14.4: Pyodide support
- 1y agostatsmodels0.14.3: NumPy 2 environments and corrected macOS builds
- 2y agostatsmodels0.14.2: full NumPy 2 compatibility
- 2y agostatsmodelsRelease 0.14.1
Frequently asked questions
What is the difference between iris and statsmodels?
Both compete on the same themes — python — within Analytics. iris 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.
Is iris better than statsmodels?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. iris 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.
What are the best alternatives to iris?
Top iris alternatives in Analytics are ranked by recent ship velocity. Browse the "iris alternatives" section above for the current picks, or visit /alternatives/scitools-iris for the full list with editorial commentary on each.
What are the best alternatives to statsmodels?
Top statsmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "statsmodels alternatives" section above for the current picks, or visit /alternatives/statsmodels for the full list with editorial commentary on each.