[MNT]: Discussion : Normalize hatch patterns per dataset instead of per patch in grouped_bar #30789
Closed as not planned
Description
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_datasets
else:
# 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 loop
hatches = iter(hatches)