import numpy as np
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import pandas as pd
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import pandas._testing as tm
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def test_groupby_skew_equivalence():
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# Test that that groupby skew method (which uses libgroupby.group_skew)
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# matches the results of operating group-by-group (which uses nanops.nanskew)
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nrows = 1000
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ngroups = 3
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ncols = 2
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nan_frac = 0.05
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arr = np.random.default_rng(2).standard_normal((nrows, ncols))
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arr[np.random.default_rng(2).random(nrows) < nan_frac] = np.nan
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df = pd.DataFrame(arr)
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grps = np.random.default_rng(2).integers(0, ngroups, size=nrows)
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gb = df.groupby(grps)
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result = gb.skew()
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grpwise = [grp.skew().to_frame(i).T for i, grp in gb]
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expected = pd.concat(grpwise, axis=0)
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expected.index = expected.index.astype(result.index.dtype) # 32bit builds
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tm.assert_frame_equal(result, expected)
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