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[MNT]: Discussion : Normalize hatch patterns per dataset instead of per patch in grouped_bar #30789

Closed as not planned
@ilakkmanoharan

Description

@ilakkmanoharan

Summary

The current implementation cycles hatch patterns per patch, which breaks grouped bar semantics because each dataset produces multiple patches. This causes hatch patterns to repeat incorrectly within a dataset.

Proposed fix

Replace the patch-level hatch cycling with dataset-level normalization.
Specifically:
Determine the number of datasets (num_datasets = len(heights)),
Normalize hatch to one pattern per dataset,
Apply that hatch consistently to all patches belonging to the same dataset.
This aligns hatch behavior with existing color/label broadcasting in grouped_bar and resolves the test failures caused by per-patch cycling.

# Normalize hatches: one hatch per dataset (no per-patch cycling)num_datasets = len(heights)user_hatch = kwargs.get("hatch", None)if user_hatch is None:    # No hatch specified → all datasets get None    hatches = [None] * num_datasetselse:    # Ensure list-like    if isinstance(user_hatch, str):        raise TypeError("hatch must be a list-like of strings, not a single string")    try:        hatch_list = list(user_hatch)    except TypeError:        raise TypeError("hatch must be list-like")    if len(hatch_list) < num_datasets:        raise ValueError(            f"Expected at least {num_datasets} hatch patterns for {num_datasets} datasets"        )    # Use exactly one hatch per dataset    hatches = hatch_list[:num_datasets]# Iterator used by the bar-creation loophatches = iter(hatches)

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