Apply suggestions from code review

Co-authored-by: hannah <story645@gmail.com>

Author: timhoffm

doc/users/next_whats_new/grouped_bar.rst

@@ -10,16 +10,17 @@ Example:
 
 .. plot::
     :include-source: true
+    :alt: Diagram of a grouped bar chart of 3 datasets with 2 categories.
 
     import matplotlib.pyplot as plt
 
     categories = ['A', 'B']
     datasets = {
-        'dataset 0': [1.0, 3.0],
-        'dataset 1': [1.4, 3.4],
-        'dataset 2': [1.8, 3.8],
+        'dataset 0': [1, 11],
+        'dataset 1': [3, 13],
+        'dataset 2': [5, 15],
     }
 
-    fig, ax = plt.subplots(figsize=(4, 2.2))
+    fig, ax = plt.subplots()
     ax.grouped_bar(datasets, tick_labels=categories)
     ax.legend()

lib/matplotlib/axes/_axes.py

@@ -3039,22 +3039,20 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
         """
         Make a grouped bar plot.
 
-        .. note::
+        .. versionadded:: 3.11
+
             This function is new in v3.11, and the API is still provisional.
             We may still fine-tune some aspects based on user-feedback.
 
-        This is a convenience function to plot bars for multiple datasets.
-        In particular, it simplifies positioning of the bars compared to individual
-        `~.Axes.bar` plots.
-
-        Bar plots present categorical data as a sequence of bars, one bar per category.
-        We call one set of such values a *dataset* and it's bars all share the same
-        color. Grouped bar plots show multiple such datasets, where the values per
-        category are grouped together. The category names are drawn as tick labels
-        below the bar groups. Each dataset has a distinct bar color, and can optionally
-        get a label that is used for the legend.
+        Grouped bar charts visualize a collection of multiple categorical datasets.
+        A categorical dataset is a mapping *name* -> *value*. The values of the
+        dataset are represented by a sequence of bars of the same color.
+        In a grouped bar chart, the bars of all datasets are grouped together by
+        category. The category names are drawn as tick labels next to the bar group.
+        Each dataset has a distinct bar color, and can optionally get a label that
+        is used for the legend.
 
-        Here is an example call structure and the corresponding plot:
+        Example:
 
         .. code-block:: python
 
@@ -3087,25 +3085,20 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             - dict of array-like: A mapping from names to datasets. Each dataset
               (dict value) must have the same number of elements.
 
-              This is similar to passing a list of array-like, with the addition that
-              each dataset gets a name.
-
               Example call:
 
               .. code-block:: python
 
-                grouped_bar({'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2]})
+                data_dict = {'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2}
+                grouped_bar(data_dict)
 
-              The names are used as *labels*, i.e. the following two calls are
-              equivalent:
+              The names are used as *labels*, i.e. this is equivalent to
 
               .. code-block:: python
 
-                data_dict = {'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2]}
-                grouped_bar(data_dict)
                 grouped_bar(data_dict.values(), labels=data_dict.keys())
 
-              When using a dict-like input, you must not pass *labels* explicitly.
+              When using a dict input, you must not pass *labels* explicitly.
 
             - a 2D array: The rows are the categories, the columns are the different
               datasets.
@@ -3120,30 +3113,31 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
 
               .. code-block:: python
 
-                  group_labels = ["group_A", "group_B"]
+                  categories = ["A", "B"]
                   dataset_labels = ["dataset_0", "dataset_1", "dataset_2"]
                   array = np.random.random((2, 3))
-
-              Note that this is consistent with pandas. These two calls produce
-              the same bar plot structure:
-
-              .. code-block:: python
-
                   grouped_bar(array, tick_labels=categories, labels=dataset_labels)
-                  df = pd.DataFrame(array, index=categories, columns=dataset_labels)
-                  df.plot.bar()
 
             - a `pandas.DataFrame`.
 
+              The index is used for the categories, the columns are used for the
+              datasets.
+
               .. code-block:: python
 
                   df = pd.DataFrame(
-                      np.random.random((2, 3))
-                      index=["group_A", "group_B"],
+                      np.random.random((2, 3)),
+                      index=["A", "B"],
                       columns=["dataset_0", "dataset_1", "dataset_2"]
                   )
                   grouped_bar(df)
 
+              i.e. this is equivalent to
+
+              .. code-block::
+
+                  grouped_bar(df.to_numpy(), tick_labels=df.index, labels=df.columns)
+
               Note that ``grouped_bar(df)`` produces a structurally equivalent plot like
               ``df.plot.bar()``.
 
@@ -3153,22 +3147,21 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
 
         tick_labels : list of str, optional
             The category labels, which are placed on ticks at the center *positions*
-            of the bar groups.
-
-            If not set, the axis ticks (positions and labels) are left unchanged.
+            of the bar groups. If not set, the axis ticks (positions and labels) are
+            left unchanged.
 
         labels : list of str, optional
             The labels of the datasets, i.e. the bars within one group.
             These will show up in the legend.
 
         group_spacing : float, default: 1.5
-            The space between two bar groups in units of bar width.
+            The space between two bar groups as multiples 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 in units of bar width.
+            The space between bars as multiples of bar width.
 
         orientation : {"vertical", "horizontal"}, default: "vertical"
             The direction of the bars.
@@ -3215,7 +3208,7 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
         aspects. ``bar(x, y)`` is a lower-level API and places bars with height *y*
         at explicit positions *x*. It also allows to specify individual bar widths
         and colors. This kind of detailed control and flexibility is difficult to
-        manage and often not needed when plotting multiple datasets as grouped bar
+        manage and often not needed when plotting multiple datasets as a grouped bar
         plot. Therefore, ``grouped_bar`` focusses on the abstraction of bar plots
         as visualization of categorical data.
 
@@ -3275,8 +3268,18 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             heights = heights.T
 
         num_datasets = len(heights)
-        dataset_0 = next(iter(heights))
-        num_groups = len(dataset_0)
+        num_groups = len(next(iter(heights)))  # inferred from first dataset
+
+        # validate that all datasets have the same length, i.e. num_groups
+        # - can be skipped if heights is an array
+        if not hasattr(heights, 'shape'):
+            for i, dataset in enumerate(heights):
+                if len(dataset) != num_groups:
+                    raise ValueError(
+                        "'heights' contains datasets with different number of "
+                        f"elements. dataset 0 has {num_groups} elements but "
+                        f"dataset {i} has {len(dataset)} elements."
+                    )
 
         if positions is None:
             group_centers = np.arange(num_groups)
@@ -3291,13 +3294,6 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             else:
                 group_distance = 1
 
-        for i, dataset in enumerate(heights):
-            if len(dataset) != num_groups:
-                raise ValueError(
-                    f"'x' indicates {num_groups} groups, but dataset {i} "
-                    f"has {len(dataset)} groups"
-                )
-
         _api.check_in_list(["vertical", "horizontal"], orientation=orientation)
 
         if colors is None: