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