arkouda
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Arkouda (αρκούδα): Interactive Data Analytics at Supercomputing Scale :bear:
should we add `mink` and `maxk` aggregations to groupby, which return a segarray. Most of the logic needed for this was implemented for `find` to return all occurrences in issue...
This PR fixes #3001 by using transmute to interpret the floats as `uint64`s before broadcasting and transmuting back afterwards
Parent ticket for items related to the pandas alignment. - [ ] https://github.com/Bears-R-Us/arkouda/issues/3171 - [ ] https://github.com/Bears-R-Us/arkouda/issues/3176 - [ ] https://github.com/Bears-R-Us/arkouda/issues/3177 - [ ] https://github.com/Bears-R-Us/arkouda/issues/3178 - [ ] https://github.com/Bears-R-Us/arkouda/issues/3179 -...
Add a assert_indexing_slices_equivalent to match pandas: https://pandas.pydata.org/docs/reference/api/pandas.testing.assert_index_equal.html
This PR (closes #3009) refactors `indexof1d` to use `find` since they have similar functionality and `find` is fairly optimized and correctly handles null values (once we `dropna=False` to the `Groupby`)....
This does several things. Fixes all the numeric tests that were failing. Revamps trigonometric and hyperbolic tests, in the same fashion as the _trig_test_helper did (which is now _trig_and_hyp_test_helper). Adds...
This PR fixes #3156, `Categorical.sort` bug and adds testing
This PR adds support for reading and writing potentially multi-dimensional arrays from and to zarr stores. Current support only allows for whole-array IO but future development may add support for...
Categorical.sort has a bug and no unit tests: ```python In [67]: c1 = Categorical(ak.array(["a", "a", "b"])) ...: c2 = Categorical(ak.array(["a", "b", "a"])) In [68]: c2.sort() Out[68]: array(['a', 'b', 'a']) ```
Add `normal` to our generators, we already have a `fillRandom` helper in `Random.chpl` so this shouldn't be a big lift