ENH: Support kurtosis (kurt) in DataFrameGroupBy and SeriesGroupBy #60433
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rhshadrach
requested changes
Dec 3, 2024
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Thanks for the review @rhshadrach.
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rhshadrach
requested changes
Dec 15, 2024
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Added test case for float64[pyarrow] (we already have one for float64)
The request was for Float64, the NumPy-nullable array. Can just parameterize your arrow test I think:
@pytest.mark.parametrize("dtype", [pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")), "Float64")| # GH#40139 | ||
| # Test that that groupby kurt method (which uses libgroupby.group_kurt) | ||
| # matches the results of operating group-by-group (which uses nanops.nankurt) | ||
| nrows = 1000 |
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Was first concerned about runtime, but 10, 100, and 1000 all run in about the same time on my machine, the bottleneck appears to be O(1) overhead. I don't see O(n) behavior until 100_000.
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| arr = np.random.default_rng(2).standard_normal((nrows, ncols)) | ||
| arr[np.random.default_rng(2).random(nrows) < nan_frac] = np.nan |
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@mroeschke - I think you reworked the random data generation a while back, want to make sure this agrees with those patterns.
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doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.DataFrameGroupBy and SeriesGroupBy currently support mean, std and skew (the first 3 moments) but not kurtosis (the 4th moment). This change addresses that. I implemented kurtosis in cython in similar fashion to skewness. I've verified that the output of this function matches that of DataFrame.kurt().