matplotlib · ilakkmanoharan · Nov 4, 2025 · Nov 4, 2025 · Nov 4, 2025 · Nov 5, 2025
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -3050,7 +3050,7 @@ def broken_barh(self, xranges, yrange, align="bottom", **kwargs):
     @_docstring.interpd
     def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing=0,
                     tick_labels=None, labels=None, orientation="vertical", colors=None,
-                    **kwargs):
+                    hatch=None, **kwargs):
         """
         Make a grouped bar plot.
 
@@ -3190,6 +3190,15 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
 
             If not specified, the colors from the Axes property cycle will be used.
 
+        hatch : sequence of :mpltype:`hatch` or None, optional
+            Hatch pattern(s) to apply per dataset.
+
+            - If ``None`` (default), no hatching is applied.
+            - If a sequence of strings is provided (e.g., ``['//', 'xx', '..']``),
+              the patterns are cycled across datasets.
+            - If the sequence contains a single element (e.g., ``['//']``),
+              the same pattern is repeated for all datasets.
+
         **kwargs : `.Rectangle` properties
 
             %(Rectangle:kwdoc)s
@@ -3318,6 +3327,38 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
             # TODO: do we want to be more restrictive and check lengths?
             colors = itertools.cycle(colors)
 
+        if hatch is None:
+            # No hatch specified: disable hatching entirely by cycling [None].
+            hatches = itertools.cycle([None])
+
+        elif isinstance(hatch, str):
+            raise ValueError("'hatch' must be a sequence of strings "
+                             "(e.g., ['//']) or None; "
+                             "a single string like '//' is not allowed."
+            )
+
+        else:
+            try:
+                hatch_list = list(hatch)
+            except TypeError:
+                raise ValueError("'hatch' must be a sequence of strings"
+                                 "(e.g., ['//']) or None") from None
+
+            if not hatch_list:
+               # Empty sequence is invalid → raise instead of treating as no hatch.
+               raise ValueError(
+                    "'hatch' must be a non-empty sequence of strings or None; "
+                     "use hatch=None for no hatching."
+                )
+
+            elif not all(h is None or isinstance(h, str) for h in hatch_list):
+                raise TypeError("All entries in 'hatch' must be strings or None")
+
+            else:
+                # Sequence of hatch patterns: cycle through them as needed.
+                # Example: hatch=['//', 'xx', '..'] → patterns repeat across datasets.
+                hatches = itertools.cycle(hatch_list)
+
         bar_width = (group_distance /
                      (num_datasets + (num_datasets - 1) * bar_spacing + group_spacing))
         bar_spacing_abs = bar_spacing * bar_width
@@ -3331,15 +3372,24 @@ 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)):
+
+        # Both colors and hatches are cycled indefinitely using itertools.cycle.
+        # heights and labels, however, are finite (length = num_datasets).
+        # Because zip() stops at the shortest iterable, this loop executes exactly
+        # num_datasets times even though colors and hatches are infinite.
+        # This ensures one (color, hatch) pair per dataset
+        # without explicit length checks.
+        for i, (hs, label, color, hatch_pattern) in enumerate(
+                zip(heights, labels, colors, hatches)
+        ):
             lefts = (group_centers - 0.5 * group_distance + margin_abs
                      + i * (bar_width + bar_spacing_abs))
             if orientation == "vertical":
                 bc = self.bar(lefts, hs, width=bar_width, align="edge",
-                              label=label, color=color, **kwargs)
+                              label=label, color=color, hatch=hatch_pattern, **kwargs)
             else:
                 bc = self.barh(lefts, hs, height=bar_width, align="edge",
-                               label=label, color=color, **kwargs)
+                               label=label, color=color, hatch=hatch_pattern,**kwargs)
             bar_containers.append(bc)
 
         if tick_labels is not None:

diff --git a/lib/matplotlib/axes/_axes.pyi b/lib/matplotlib/axes/_axes.pyi
@@ -287,6 +287,7 @@ class Axes(_AxesBase):
         bar_spacing: float | None = ...,
         orientation: Literal["vertical", "horizontal"] = ...,
         colors: Iterable[ColorType] | None = ...,
+        hatch: Iterable[str] | None = ...,
         **kwargs
     ) -> list[BarContainer]: ...
     def stem(

diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
@@ -3536,6 +3536,7 @@ def grouped_bar(
     labels: Sequence[str] | None = None,
     orientation: Literal["vertical", "horizontal"] = "vertical",
     colors: Iterable[ColorType] | None = None,
+    hatch: Iterable[str] | None = None,
     **kwargs,
 ) -> list[BarContainer]:
     return gca().grouped_bar(
@@ -3547,6 +3548,7 @@ def grouped_bar(
         labels=labels,
         orientation=orientation,
         colors=colors,
+        hatch=hatch,
         **kwargs,
     )
 

diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -2267,6 +2267,121 @@ def test_grouped_bar_return_value():
         assert bc not in ax.containers
 
 
+def test_grouped_bar_single_hatch_str_raises():
+    """Passing a single string for hatch should raise a ValueError."""
+    fig, ax = plt.subplots()
+    x = np.arange(3)
+    heights = [np.array([1, 2, 3]), np.array([2, 1, 2])]
+    with pytest.raises(ValueError, match="must be a sequence of strings"):
+        ax.grouped_bar(heights, positions=x, hatch='//')
+
+
+def test_grouped_bar_hatch_non_iterable_raises():
+    """Non-iterable hatch values should raise a ValueError."""
+    fig, ax = plt.subplots()
+    heights = [np.array([1, 2]), np.array([2, 3])]
+    with pytest.raises(ValueError, match="must be a sequence of strings"):
+        ax.grouped_bar(heights, hatch=123)  # invalid non-iterable
+
+
+def test_grouped_bar_hatch_sequence():
+    """Each dataset should receive its own hatch pattern when a sequence is passed."""
+    fig, ax = plt.subplots()
+    x = np.arange(2)
+    heights = [np.array([1, 2]), np.array([2, 3]), np.array([3, 4])]
+    hatches = ['//', 'xx', '..']
+    containers = ax.grouped_bar(heights, positions=x, hatch=hatches)
+
+    # Verify each dataset gets the corresponding hatch
+    for hatch, c in zip(hatches, containers.bar_containers):
+        for rect in c:
+            assert rect.get_hatch() == hatch
+
+
+def test_grouped_bar_hatch_cycles_when_shorter_than_datasets():
+    """When the hatch list is shorter than the number of datasets,
+    patterns should cycle.
+    """
+
+    fig, ax = plt.subplots()
+    x = np.arange(2)
+    heights = [
+        np.array([1, 2]),
+        np.array([2, 3]),
+        np.array([3, 4]),
+    ]
+    hatches = ['//', 'xx']  # shorter than number of datasets → should cycle
+    containers = ax.grouped_bar(heights, positions=x, hatch=hatches)
+
+    expected_hatches = ['//', 'xx', '//']  # cycle repeats
+    for gi, c in enumerate(containers.bar_containers):
+        for rect in c:
+            assert rect.get_hatch() == expected_hatches[gi]
+
+
+def test_grouped_bar_hatch_none():
+    """Passing hatch=None should result in bars with no hatch."""
+    fig, ax = plt.subplots()
+    x = np.arange(2)
+    heights = [np.array([1, 2]), np.array([2, 3])]
+    containers = ax.grouped_bar(heights, positions=x, hatch=None)
+
+    # All bars should have no hatch applied
+    for c in containers.bar_containers:
+        for rect in c:
+            assert rect.get_hatch() in (None, ''), \
+                f"Expected no hatch, got {rect.get_hatch()!r}"
+
+
+def test_grouped_bar_empty_string_disables_hatch():
+    """
+    Empty strings or None in the hatch list should result in no hatch
+    for the corresponding dataset, while valid strings should apply
+    the hatch pattern normally.
+    """
+    fig, ax = plt.subplots()
+    x = np.arange(3)
+    heights = [np.array([1, 2, 3]), np.array([2, 1, 2]), np.array([3, 2, 1])]
+    hatches = ["", "xx", None]
+    containers = ax.grouped_bar(heights, positions=x, hatch=hatches)
+    # Collect the hatch pattern for each bar in each dataset
+    counts = [[rect.get_hatch() for rect in bc] for bc in containers.bar_containers]
+    # First dataset: empty string disables hatch
+    assert all(h in ("", None) for h in counts[0])
+    # Second dataset: hatch pattern applied
+    assert all(h == "xx" for h in counts[1])
+    # Third dataset: None disables hatch
+    assert all(h in ("", None) for h in counts[2])
+
+
+def test_grouped_bar_empty_hatch_sequence_raises():
+    """An empty hatch sequence should raise a ValueError."""
+    fig, ax = plt.subplots()
+    heights = [np.array([1, 2]), np.array([2, 3])]
+    with pytest.raises(
+        ValueError,
+        match="must be a non-empty sequence of strings or None"
+    ):
+        ax.grouped_bar(heights, hatch=[])
+
+
+def test_grouped_bar_dict_with_labels_forbidden():
+    """Passing labels along with dict input should raise an error."""
+    fig, ax = plt.subplots()
+    data = {"a": [1, 2], "b": [2, 1]}
+    with pytest.raises(ValueError, match="cannot be used if 'heights' is a mapping"):
+        ax.grouped_bar(data, labels=["x", "y"])
+
+
+def test_grouped_bar_positions_not_equidistant():
+    """Passing non-equidistant positions should raise an error."""
+    fig, ax = plt.subplots()
+    x = np.array([0, 1, 3])
+    heights = [np.array([1, 2, 3]), np.array([2, 1, 2])]
+    with pytest.raises(ValueError, match="must be equidistant"):
+        ax.grouped_bar(heights, positions=x)
+
+
 def test_boxplot_dates_pandas(pd):
     # smoke test for boxplot and dates in pandas
     data = np.random.rand(5, 2)