Jun 5, 2025 · Jul 13, 2024 · Jan 23, 2025 · Jan 22, 2025 · Jan 23, 2025 · Feb 1, 2025
diff --git a/doc/_embedded_plots/grouped_bar.py b/doc/_embedded_plots/grouped_bar.py
 import matplotlib.pyplot as plt

 categories = ['A', 'B']
 data0 = [1.0, 3.0]
 data1 = [1.4, 3.4]
 data2 = [1.8, 3.8]

 fig, ax = plt.subplots(figsize=(4, 2.2))
 ax.grouped_bar(
    [data0, data1, data2],
    tick_labels=categories,
    labels=['dataset 0', 'dataset 1', 'dataset 2'],
    colors=['#1f77b4', '#58a1cf', '#abd0e6'],
 )
 ax.legend()
diff --git a/doc/api/axes_api.rst b/doc/api/axes_api.rst
   Axes.bar
   Axes.barh
   Axes.bar_label
   Axes.grouped_bar

   Axes.stem
   Axes.eventplot
diff --git a/doc/api/pyplot_summary.rst b/doc/api/pyplot_summary.rst
   bar
   barh
   bar_label
   grouped_bar
   stem
   eventplot
   pie
diff --git a/doc/users/next_whats_new/grouped_bar.rst b/doc/users/next_whats_new/grouped_bar.rst
 Grouped bar charts
 ------------------

 The new method `~.Axes.grouped_bar()` simplifies the creation of grouped bar charts
 significantly. It supports different input data types (lists of datasets, dicts of
 datasets, data in 2D arrays, pandas DataFrames), and allows for easy customization
 of placement via controllable distances between bars and between bar groups.

 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, 11],
        'dataset 1': [3, 13],
        'dataset 2': [5, 15],
    }

    fig, ax = plt.subplots()
    ax.grouped_bar(datasets, tick_labels=categories)
    ax.legend()
diff --git a/galleries/examples/lines_bars_and_markers/barchart.py b/galleries/examples/lines_bars_and_markers/barchart.py
 # data from https://allisonhorst.github.io/palmerpenguins/

 import matplotlib.pyplot as plt
 import numpy as np

 species = ("Adelie", "Chinstrap", "Gentoo")
 penguin_means = {
    'Flipper Length': (189.95, 195.82, 217.19),
 }

 x = np.arange(len(species))  # the label locations
 width = 0.25  # the width of the bars
 multiplier = 0

 fig, ax = plt.subplots(layout='constrained')

 for attribute, measurement in penguin_means.items():
    offset = width * multiplier
    rects = ax.bar(x + offset, measurement, width, label=attribute)
    ax.bar_label(rects, padding=3)
    multiplier += 1
 res = ax.grouped_bar(penguin_means, tick_labels=species, group_spacing=1)
 for container in res.bar_containers:
    ax.bar_label(container, padding=3)

 # Add some text for labels, title and custom x-axis tick labels, etc.
 # Add some text for labels, title, etc.
 ax.set_ylabel('Length (mm)')
 ax.set_title('Penguin attributes by species')
 ax.set_xticks(x + width, species)
 ax.legend(loc='upper left', ncols=3)
 ax.set_ylim(0, 250)
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,15 @@
		import matplotlib.pyplot as plt

		categories = ['A', 'B']
		data0 = [1.0, 3.0]
		data1 = [1.4, 3.4]
		data2 = [1.8, 3.8]
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		fig, ax = plt.subplots(figsize=(4, 2.2))
		ax.grouped_bar(
		[data0, data1, data2],
		tick_labels=categories,
		labels=['dataset 0', 'dataset 1', 'dataset 2'],
		colors=['#1f77b4', '#58a1cf', '#abd0e6'],
		)
		ax.legend()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -67,6 +67,7 @@ Basic
		Axes.bar
		Axes.barh
		Axes.bar_label
		Axes.grouped_bar

		Axes.stem
		Axes.eventplot
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -60,6 +60,7 @@ Basic
		bar
		barh
		bar_label
		grouped_bar
		stem
		eventplot
		pie
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,26 @@
		Grouped bar charts
		------------------

		The new method `~.Axes.grouped_bar()` simplifies the creation of grouped bar charts
		significantly. It supports different input data types (lists of datasets, dicts of
		datasets, data in 2D arrays, pandas DataFrames), and allows for easy customization
		of placement via controllable distances between bars and between bar groups.

		Example:

		.. plot::
		:include-source: true
		:alt: Diagram of a grouped bar chart of 3 datasets with 2 categories.

timhoffm marked this conversation as resolved. Show resolvedHide resolved
		import matplotlib.pyplot as plt

		categories = ['A', 'B']
		datasets = {
		'dataset 0': [1, 11],
		'dataset 1': [3, 13],
		'dataset 2': [5, 15],
		}

		fig, ax = plt.subplots()
		ax.grouped_bar(datasets, tick_labels=categories)
		ax.legend()
Original file line number	Diff line number	Diff line change
Expand Up		@@ -10,7 +10,6 @@
		# data from https://allisonhorst.github.io/palmerpenguins/

		import matplotlib.pyplot as plt
		import numpy as np

		species = ("Adelie", "Chinstrap", "Gentoo")
		penguin_means = {
Expand All		@@ -19,22 +18,15 @@
		'Flipper Length': (189.95, 195.82, 217.19),
		}

		x = np.arange(len(species)) # the label locations
		width = 0.25 # the width of the bars
		multiplier = 0

		fig, ax = plt.subplots(layout='constrained')

		for attribute, measurement in penguin_means.items():
		offset = width * multiplier
		rects = ax.bar(x + offset, measurement, width, label=attribute)
		ax.bar_label(rects, padding=3)
		multiplier += 1
		res = ax.grouped_bar(penguin_means, tick_labels=species, group_spacing=1)
		for container in res.bar_containers:
		ax.bar_label(container, padding=3)

		# Add some text for labels, title and custom x-axis tick labels, etc.
		# Add some text for labels, title, etc.
		ax.set_ylabel('Length (mm)')
		ax.set_title('Penguin attributes by species')
		ax.set_xticks(x + width, species)
		ax.legend(loc='upper left', ncols=3)
		ax.set_ylim(0, 250)

Expand Down