BUG: groupby raises on non-C-contiguous masks

[rhshadrach]

• closes BUG: ValueError: ndarray is not C-contiguous for cummax on nullable dtypes #61031 (Replace xxxx with the GitHub issue number)
• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments/methods/functions.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.
• If I used AI to develop this pull request, I prompted it to follow AGENTS.md.

I'm seeing a minor slowdown due to not assuming a C-contiguous mask. Alternative could be to prevent the mask from ever not being C-contiguous. Will be posting ASVs in a day or so.

size = 10_000_000 df = pd.DataFrame( { 'a': np.random.randint(0, 100, size), 'b': np.random.randint(0, 100, size), 'c': np.random.randint(0, 100, size), 'd': np.random.randint(0, 100, size), }, dtype='Int64', ) gb = df.groupby('a') gb.cummax() # populate caches %timeit gb.cummax() # 155 ms ± 52.4 μs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- main # 160 ms ± 1.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- PR

ASVs are clean.

asv compare -f 1.05 --only-changed 49f4a94a 19df340e | Change | Before [49f4a94a] <main> | After [19df340e] <bug_gb_c_contiguous> | Ratio | Benchmark (Parameter) | |----------|----------------------------|------------------------------------------|---------|-------------------------------------------------------------------| | - | 24.8±0.1ms | 22.4±0.2ms | 0.91 | groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'min') | 

[jbrockmendel]

thanks @rhshadrach