v0.8.0

What's Changed

Major performance improvements!!!

1. Support numbagg throughengine="numbagg" for many common nan-skipping reductions in #72. Using numbagg appears to be a major speedup (2x-3x in general, 6X for nanmean). Special thanks to @max-sixty for major work on numbagg's grouped aggregations! Here are timings for reducing a 2D array along the last axis with ordered group labels.

   func	engine
   nansum	flox	70.3±0.2ms
   	numpy	122±0.2ms
   	numbagg	18.4±0.04ms
   nanmean	flox	144±0.4ms
   	numpy	196±0.5ms
   	numbagg	23.7±0.2ms
   nanmax	flox	93.4±0.8ms
   	numpy	953±2ms
   	numbagg	20.3±0.2ms
   count	flox	59.8±1ms
   	numpy	114±0.2ms
   	numbagg	29.3±0.1ms

2. Support engine=None in #266. This will

   • Use numbagg if available
   • If not, use flox if the group labels are sorted
   • Fallback to numpy otherwise.
     Thanks to @mathause for kicking off this work.

3. Significant speed to detecting "cohorts" of groups in #272

Other Major Changes

1. Test and support for python 3.12 (note numba does not support 3.12 yet)
2. Bump minimum numpy version to 1.22.
3. New Aggregations : Support quantile, median, mode with method="blockwise". by @dcherian in #269
4. Add multidimensional bins demo notebook by @dcherian in #203 . This is useful for prediction/forecasting problems.

Minor Changes

• Delete resample_reduce by @dcherian in #246
• Fix test failure on i386 by @avalentino in #248
• typing fixes by @Illviljan in #235, #253
• replace the deprecated provision-with-micromamba with setup-micromamba by @keewis in #258
• compatibility with numpy>=2.0 by @keewis in #257
• convert datetime: micro-optimizations by @mathause in #261

New Contributors

• @keewis made their first contribution in #258
• @mathause made their first contribution in #261

Full Changelog: v0.7.2...v0.8.0