matplotlib
diff --git a/‎galleries/examples/lines_bars_and_markers/grouped_bar_chart.py
Lines changed: 16 additions & 0 deletions b/‎galleries/examples/lines_bars_and_markers/grouped_bar_chart.py
Lines changed: 16 additions & 0 deletions
diff --git a/‎lib/matplotlib/axes/_axes.py
Lines changed: 26 additions & 9 deletions b/‎lib/matplotlib/axes/_axes.py
Lines changed: 26 additions & 9 deletions
@@ -102,3 +102,19 @@
 #
 #     df = pd.DataFrame(data, index=x, columns=columns)
 #     df.plot.bar()
+
+# %%
+# Numeric x values
+# ----------------
+# In the most common case, one will want to pass categorical labels as *x*.
+# Additionally, we allow numeric values for *x*, as with `~.Axes.bar()`.
+# But for simplicity and clarity, we require that these are equidistant.
+
+x = [0, 2, 4]
+data = {
+    'data1': [1, 2, 3],
+    'data2': [1.2, 2.2, 3.2],
+}
+
+fig, ax = plt.subplots()
+ax.grouped_bar(x, data)
@@ -3017,8 +3017,12 @@ def grouped_bar(self, x, heights, dataset_labels=None):
         """
         Parameters
         -----------
-        x : array-like of str
-            The labels.
+        x : array-like or list of str
+            The center positions of the bar groups. If these are numeric values,
+            they have to be equidistant. As with `~.Axes.bar`, you can provide
+            categorical labels, which will be used at integer numeric positions
+            ``range(x)``.
+
         heights : list of array-like or dict of array-like or 2D array
             The heights for all x and groups. One of:
 
@@ -3077,27 +3081,40 @@ def grouped_bar(self, x, heights, dataset_labels=None):
         elif hasattr(heights, 'shape'):
             heights = heights.T
 
-        num_labels = len(x)
+        num_groups = len(x)
         num_datasets = len(heights)
 
-        for dataset in heights:
-            assert len(dataset) == num_labels
+        if isinstance(x[0], str):
+            tick_labels = x
+            group_centers = np.arange(num_groups)
+        else:
+            if num_groups > 1:
+                d = np.diff(x)
+                if not np.allclose(d, d.mean()):
+                    raise ValueError("'x' must be equidistant")
+            group_centers = np.asarray(x)
+            tick_labels = None
+
+        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"
+                )
 
         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)
+            lefts = group_centers - 0.5 + margin + i * bar_width
             self.bar(lefts, hs, width=bar_width, align="edge", label=dataset_label)
 
-        self.xaxis.set_ticks(block_centers, labels=x)
+        self.xaxis.set_ticks(group_centers, labels=tick_labels)
 
         # TODO: does not return anything for now