matplotlib
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/grouped_bar.png
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8.84 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/grouped_bar.png
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8.84 KB
diff --git a/‎lib/matplotlib/tests/test_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_axes.py
+84Lines changed: 84 additions & 0 deletions b/‎lib/matplotlib/tests/test_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_axes.py
+84Lines changed: 84 additions & 0 deletions
@@ -2144,6 +2144,90 @@ def test_bar_datetime_start():
     assert isinstance(ax.xaxis.get_major_formatter(), mdates.AutoDateFormatter)
 
 
+@image_comparison(["grouped_bar.png"], style="mpl20")
+def test_grouped_bar():
+    data = {
+        'data1': [1, 2, 3],
+        'data2': [1.2, 2.2, 3.2],
+        'data3': [1.4, 2.4, 3.4],
+    }
+
+    fig, ax = plt.subplots()
+    ax.grouped_bar(data, tick_labels=['A', 'B', 'C'],
+                   group_spacing=0.5, bar_spacing=0.1,
+                   colors=['#1f77b4', '#58a1cf', '#abd0e6'])
+
+
+@check_figures_equal(extensions=["png"])
+def test_grouped_bar_list_of_datasets(fig_test, fig_ref):
+    categories = ['A', 'B']
+    data1 = [1, 1.2]
+    data2 = [2, 2.4]
+    data3 = [3, 3.6]
+
+    ax = fig_test.subplots()
+    ax.grouped_bar([data1, data2, data3], tick_labels=categories,
+                   labels=["data1", "data2", "data3"])
+    ax.legend()
+
+    ax = fig_ref.subplots()
+    label_pos = np.array([0, 1])
+    bar_width = 1 / (3 + 1.5)  # 3 bars + 1.5 group_spacing
+    data_shift = -1 * bar_width + np.array([0, bar_width, 2 * bar_width])
+    ax.bar(label_pos + data_shift[0], data1, width=bar_width, label="data1")
+    ax.bar(label_pos + data_shift[1], data2, width=bar_width, label="data2")
+    ax.bar(label_pos + data_shift[2], data3, width=bar_width, label="data3")
+    ax.set_xticks(label_pos, categories)
+    ax.legend()
+
+
+@check_figures_equal(extensions=["png"])
+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])
+
+    ax = fig_test.subplots()
+    ax.grouped_bar(data_dict, tick_labels=categories)
+    ax.legend()
+
+    ax = fig_ref.subplots()
+    ax.grouped_bar(data_dict.values(), tick_labels=categories, labels=data_dict.keys())
+    ax.legend()
+
+
+@check_figures_equal(extensions=["png"])
+def test_grouped_bar_array(fig_test, fig_ref):
+    categories = ['A', 'B']
+    array = np.array([[1, 2, 3], [1.2, 2.4, 3.6]])
+    labels = ['data1', 'data2', 'data3']
+
+    ax = fig_test.subplots()
+    ax.grouped_bar(array, tick_labels=categories, labels=labels)
+    ax.legend()
+
+    ax = fig_ref.subplots()
+    list_of_datasets = [column for column in array.T]
+    ax.grouped_bar(list_of_datasets, tick_labels=categories, labels=labels)
+    ax.legend()
+
+
+@check_figures_equal(extensions=["png"])
+def test_grouped_bar_dataframe(fig_test, fig_ref, pd):
+    categories = ['A', 'B']
+    labels = ['data1', 'data2', 'data3']
+    df = pd.DataFrame([[1, 2, 3], [1.2, 2.4, 3.6]],
+                      index=categories, columns=labels)
+
+    ax = fig_test.subplots()
+    ax.grouped_bar(df)
+    ax.legend()
+
+    ax = fig_ref.subplots()
+    list_of_datasets = [df[col].to_numpy() for col in df.columns]
+    ax.grouped_bar(list_of_datasets, tick_labels=categories, labels=labels)
+    ax.legend()
+
+
 def test_boxplot_dates_pandas(pd):
     # smoke test for boxplot and dates in pandas
     data = np.random.rand(5, 2)