matplotlib
diff --git a/‎galleries/examples/lines_bars_and_markers/grouped_bar_chart.py
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diff --git a/‎lib/matplotlib/axes/_axes.py
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@@ -0,0 +1,104 @@
+"""
+=================
+Grouped bar chart
+=================
+
+This example serves to develop and discuss the API. It's geared towards illustating
+API usage and design decisions only through the development phase. It's not intended
+to go into the final PR in this form.
+
+Case 1: multiple separate datasets
+----------------------------------
+
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = ['A', 'B']
+data1 = [1, 1.2]
+data2 = [2, 2.4]
+data3 = [3, 3.6]
+
+
+fig, axs = plt.subplots(1, 2)
+
+# current solution: manual positioning with multiple bar)= calls
+label_pos = np.array([0, 1])
+bar_width = 0.8 / 3
+data_shift = -1*bar_width + np.array([0, bar_width, 2*bar_width])
+axs[0].bar(label_pos + data_shift[0], data1, width=bar_width, label="data1")
+axs[0].bar(label_pos + data_shift[1], data2, width=bar_width, label="data2")
+axs[0].bar(label_pos + data_shift[2], data3, width=bar_width, label="data3")
+axs[0].set_xticks(label_pos, x)
+axs[0].legend()
+
+# grouped_bar() with list of datasets
+# note also that this is a straight-forward generalization of the single-dataset case:
+#   bar(x, data1, label="data1")
+axs[1].grouped_bar(x, [data1, data2, data3], dataset_labels=["data1", "data2", "data3"])
+
+
+# %%
+# Case 1b: multiple datasets as dict
+# ----------------------------------
+# instead of carrying a list of datasets and a list of dataset labels, users may
+# want to organized their datasets in a dict.
+
+datasets = {
+    'data1': data1,
+    'data2': data2,
+    'data3': data3,
+}
+
+# %%
+# While you can feed keys and values into the above API, it may be convenient to pass
+# the whole dict as "data" and automatically extract the labels from the keys:
+
+fig, axs = plt.subplots(1, 2)
+
+# explicitly extract values and labels from a dict and feed to grouped_bar():
+axs[0].grouped_bar(x, datasets.values(), dataset_labels=datasets.keys())
+# accepting a dict as input
+axs[1].grouped_bar(x, datasets)
+
+# %%
+# Case 2: 2D array data
+# ---------------------
+# When receiving a 2D array, we interpret the data as
+#
+# .. code-block:: none
+#
+#             dataset_0 dataset_1 dataset_2
+#    x[0]='A'  ds0_a     ds1_a     ds2_a
+#    x[1]='B'  ds0_b     ds1_b     ds2_b
+#
+# This is consistent with the standard data science interpretation of instances
+# on the vertical and features on the horizontal. And also matches how pandas is
+# interpreting rows and columns.
+#
+# Note that a list of individual datasets and a 2D array behave structurally different,
+# i.e. hen turning a list into a numpy array, you have to transpose that array to get
+# the correct representation. Those two behave the same::
+#
+#     grouped_bar(x, [data1, data2])
+#     grouped_bar(x, np.array([data1, data2]).T)
+#
+# This is a conscious decision, because the commonly understood dimension ordering
+# semantics of "list of datasets" and 2D array of datasets is different.
+
+x = ['A', 'B']
+data = np.array([
+    [1, 2, 3],
+    [1.2, 2.4, 3.6],
+])
+columns = ["data1", "data2", "data3"]
+
+fig, ax = plt.subplots()
+ax.grouped_bar(x, data, dataset_labels=columns)
+
+# %%
+# This creates the same plot as pandas (code cannot be executed because pandas
+# os not a doc dependency)::
+#
+#     df = pd.DataFrame(data, index=x, columns=columns)
+#     df.plot.bar()
@@ -3013,6 +3013,94 @@ def broken_barh(self, xranges, yrange, **kwargs):
 
         return col
 
+    def grouped_bar(self, x, heights, dataset_labels=None):
+        """
+        Parameters
+        -----------
+        x : array-like of str
+            The labels.
+        heights : list of array-like or dict of array-like or 2D array
+            The heights for all x and groups. One of:
+
+            - list of array-like: A list of datasets, each dataset must have
+              ``len(x)`` elements.
+
+              .. code-block:: none
+
+                 x = ['a', 'b']
+                 group_labels = ['ds0', 'ds1', 'ds2']
+
+                 # group_labels: ds0   ds1        dw2
+                 heights = [dataset_0, dataset_1, dataset_2]
+
+                 #            x[0]   x[1]
+                 dataset_0 = [ds0_a, ds0_b]
+
+                 #           x[0]   x[1]
+                 heights = [[ds0_a, ds0_b],  # dataset_0
+                            [ds1_a, ds1_b],  # dataset_1
+                            [ds2_a, ds2_b],  # dataset_2
+                            ]
+
+            - dict of array-like: A names to datasets, each dataset (dict value)
+              must have ``len(x)`` elements.
+
+              group_labels = heights.keys()
+              heights = heights.values()
+
+            - a 2D array: columns map to *x*, columns are the different datasets.
+
+              .. code-block:: none
+
+                          dataset_0 dataset_1 dataset_2
+                 x[0]='a'  ds0_a     ds1_a     ds2_a
+                 x[1]='b'  ds0_b     ds1_b     ds2_b
+
+              Note that this is consistent with pandas. These two calls produce
+              the same bar plot structure::
+
+                  grouped_bar(x, array, group_labels=group_labels)
+                  pd.DataFrame(array, index=x, columns=group_labels).plot.bar()
+
+
+            An iterable of array-like: The iteration runs over the groups.
+            Each individual array-like is the list of label values for that group.
+        dataset_labels : array-like of str, optional
+            The labels of the datasets.
+        """
+        if hasattr(heights, 'keys'):
+            if dataset_labels is not None:
+                raise ValueError(
+                    "'dataset_labels' cannot be used if 'heights' are a mapping")
+            dataset_labels = heights.keys()
+            heights = heights.values()
+        elif hasattr(heights, 'shape'):
+            heights = heights.T
+
+        num_labels = len(x)
+        num_datasets = len(heights)
+
+        for dataset in heights:
+            assert len(dataset) == num_labels
+
+        margin = 0.1
+        bar_width = (1 - 2 * margin) / num_datasets
+        block_centers = np.arange(num_labels)
+
+        if dataset_labels is None:
+            dataset_labels = [None] * num_datasets
+        else:
+            assert len(dataset_labels) == num_datasets
+
+        for i, (hs, dataset_label) in enumerate(zip(heights, dataset_labels)):
+            lefts = block_centers - 0.5 + margin + i * bar_width
+            print(i, x, lefts, hs, dataset_label)
+            self.bar(lefts, hs, width=bar_width, align="edge", label=dataset_label)
+
+        self.xaxis.set_ticks(block_centers, labels=x)
+
+        # TODO: does not return anything for now
+
     @_preprocess_data()
     def stem(self, *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0,
              label=None, orientation='vertical'):