Apply suggestions from code review

Author: timhoffm

lib/matplotlib/axes/_axes.py

@@ -3057,9 +3057,9 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
 
         .. code-block:: python
 
-           grouped_bar([dataset_1, dataset_2, dataset_3],
+           grouped_bar([dataset_0, dataset_1, dataset_2],
                        tick_labels=['A', 'B'],
-                       labels=['dataset 1', 'dataset 2', 'dataset 3'])
+                       labels=['dataset 0', 'dataset 1', 'dataset 2'])
 
         .. plot:: _embedded_plots/grouped_bar.py
 
@@ -3156,13 +3156,13 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             These will show up in the legend.
 
         group_spacing : float, default: 1.5
-            The space between two bar groups as multiples of bar width.
+            The space between two bar groups as a multiple of bar width.
 
             The default value of 1.5 thus means that there's a gap of
             1.5 bar widths between bar groups.
 
         bar_spacing : float, default: 0
-            The space between bars as multiples of bar width.
+            The space between bars as a multiple of bar width.
 
         orientation : {"vertical", "horizontal"}, default: "vertical"
             The direction of the bars.
@@ -3181,17 +3181,17 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
 
         Returns
         -------
-            _GroupedBarReturn
+        _GroupedBarReturn
 
-                A provisional result object. This will be refined in the future.
-                For now, the guaranteed API on the returned object is limited to
+            A provisional result object. This will be refined in the future.
+            For now, the guaranteed API on the returned object is limited to
 
-                - the attribute ``bar_containers``, which is a list of
-                  `.BarContainer`, i.e. the results of the individual `~.Axes.bar`
-                  calls for each dataset.
+            - the attribute ``bar_containers``, which is a list of
+              `.BarContainer`, i.e. the results of the individual `~.Axes.bar`
+              calls for each dataset.
 
-                - a ``remove()`` method, that remove all bars from the Axes.
-                  See also `.Artist.remove()`.
+            - a ``remove()`` method, that remove all bars from the Axes.
+              See also `.Artist.remove()`.
 
         See Also
         --------
@@ -3261,8 +3261,7 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             heights = heights.to_numpy().T
         elif hasattr(heights, 'keys'):  # dict
             if labels is not None:
-                raise ValueError(
-                    "'labels' cannot be used if 'heights' are a mapping")
+                raise ValueError("'labels' cannot be used if 'heights' is a mapping")
             labels = heights.keys()
             heights = list(heights.values())
         elif hasattr(heights, 'shape'):  # numpy array
@@ -3317,8 +3316,7 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
         # place the bars, but only use numerical positions, categorical tick labels
         # are handled separately below
         bar_containers = []
-        for i, (hs, label, color) in enumerate(
-                zip(heights, labels, colors)):
+        for i, (hs, label, color) in enumerate(zip(heights, labels, colors)):
             lefts = (group_centers - 0.5 * group_distance + margin_abs
                      + i * (bar_width + bar_spacing_abs))
             if orientation == "vertical":

lib/matplotlib/axes/_axes.pyi

@@ -41,6 +41,7 @@ import pandas as pd
 
 
 class _GroupedBarReturn:
+    bar_containers: list[BarContainer]
     def __init__(self, bar_containers: list[BarContainer]) -> None: ...
     def remove(self) -> None: ...
 
@@ -273,13 +274,13 @@ class Axes(_AxesBase):
     ) -> PolyCollection: ...
     def grouped_bar(
         self,
-        heights : Sequence[ArrayLike] | dict[str, ArrayLike] | np.ndarray | pd.DataFrame,
+        heights: Sequence[ArrayLike] | dict[str, ArrayLike] | np.ndarray | pd.DataFrame,
         *,
-        positions : ArrayLike | None = ...,
-        tick_labels : Sequence[str] | None = ...,
-        labels : Sequence[str] | None = ...,
-        group_spacing : float | None = ...,
-        bar_spacing : float | None = ...,
+        positions: ArrayLike | None = ...,
+        tick_labels: Sequence[str] | None = ...,
+        labels: Sequence[str] | None = ...,
+        group_spacing: float | None = ...,
+        bar_spacing: float | None = ...,
         orientation: Literal["vertical", "horizontal"] = ...,
         colors: Iterable[ColorType] | None = ...,
         **kwargs

lib/matplotlib/tests/test_axes.py

@@ -2182,7 +2182,7 @@ def test_grouped_bar():
     ax.set_yticks([])
 
 
-@check_figures_equal(extensions=["png"])
+@check_figures_equal()
 def test_grouped_bar_list_of_datasets(fig_test, fig_ref):
     categories = ['A', 'B']
     data1 = [1, 1.2]
@@ -2205,7 +2205,7 @@ def test_grouped_bar_list_of_datasets(fig_test, fig_ref):
     ax.legend()
 
 
-@check_figures_equal(extensions=["png"])
+@check_figures_equal()
 def test_grouped_bar_dict_of_datasets(fig_test, fig_ref):
     categories = ['A', 'B']
     data_dict = dict(data1=[1, 1.2], data2=[2, 2.4], data3=[3, 3.6])
@@ -2219,7 +2219,7 @@ def test_grouped_bar_dict_of_datasets(fig_test, fig_ref):
     ax.legend()
 
 
-@check_figures_equal(extensions=["png"])
+@check_figures_equal()
 def test_grouped_bar_array(fig_test, fig_ref):
     categories = ['A', 'B']
     array = np.array([[1, 2, 3], [1.2, 2.4, 3.6]])
@@ -2235,7 +2235,7 @@ def test_grouped_bar_array(fig_test, fig_ref):
     ax.legend()
 
 
-@check_figures_equal(extensions=["png"])
+@check_figures_equal()
 def test_grouped_bar_dataframe(fig_test, fig_ref, pd):
     categories = ['A', 'B']
     labels = ['data1', 'data2', 'data3']