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__init__.py	0 B	-rw-r--r--	2024-04-12 10:40:17	Edit Del
__pycache__	-	drwxr-xr-x	2024-04-12 10:40:22	Del
test_corrwith.py	615 B	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_describe.py	9.45 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_groupby_shift_diff.py	7.74 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_is_monotonic.py	2.51 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_nlargest_nsmallest.py	3.32 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_nth.py	27.53 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_quantile.py	15.97 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_rank.py	23.69 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_sample.py	5.03 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_size.py	4.15 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_skew.py	841 B	-rw-r--r--	2024-04-12 10:40:17	Edit Del
test_value_counts.py	38.94 KB	-rw-r--r--	2024-04-12 10:40:17	Edit Del

Editing: test_skew.py (841 B)

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)