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Commitdfcd52f

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timhoffmstory645
andcommitted
Apply suggestions from code review
Co-authored-by: hannah <story645@gmail.com>
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‎doc/users/next_whats_new/grouped_bar.rst

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Original file line numberDiff line numberDiff line change
@@ -10,16 +10,17 @@ Example:
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..plot::
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:include-source: true
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:alt: Diagram of a grouped bar chart of 3 datasets with 2 categories.
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import matplotlib.pyplot as plt
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categories = ['A', 'B']
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datasets = {
18-
'dataset 0': [1.0, 3.0],
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'dataset 1': [1.4, 3.4],
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'dataset 2': [1.8, 3.8],
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'dataset 0': [1, 11],
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'dataset 1': [3, 13],
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'dataset 2': [5, 15],
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}
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fig, ax = plt.subplots(figsize=(4, 2.2))
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fig, ax = plt.subplots()
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ax.grouped_bar(datasets, tick_labels=categories)
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ax.legend()

‎lib/matplotlib/axes/_axes.py

Lines changed: 43 additions & 47 deletions
Original file line numberDiff line numberDiff line change
@@ -3073,22 +3073,20 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
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"""
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Make a grouped bar plot.
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.. note::
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.. versionadded:: 3.11
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This function is new in v3.11, and the API is still provisional.
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We may still fine-tune some aspects based on user-feedback.
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This is a convenience function to plot bars for multiple datasets.
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In particular, it simplifies positioning of the bars compared to individual
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`~.Axes.bar` plots.
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Bar plots present categorical data as a sequence of bars, one bar per category.
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We call one set of such values a *dataset* and it's bars all share the same
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color. Grouped bar plots show multiple such datasets, where the values per
3087-
category are grouped together. The category names are drawn as tick labels
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below the bar groups. Each dataset has a distinct bar color, and can optionally
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get a label that is used for the legend.
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Grouped bar charts visualize a collection of multiple categorical datasets.
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A categorical dataset is a mapping *name* -> *value*. The values of the
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dataset are represented by a sequence of bars of the same color.
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In a grouped bar chart, the bars of all datasets are grouped together by
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category. The category names are drawn as tick labels next to the bar group.
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Each dataset has a distinct bar color, and can optionally get a label that
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is used for the legend.
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Here is an example call structure and the corresponding plot:
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Example:
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.. code-block:: python
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@@ -3121,25 +3119,20 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
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- dict of array-like: A mapping from names to datasets. Each dataset
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(dict value) must have the same number of elements.
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This is similar to passing a list of array-like, with the addition that
3125-
each dataset gets a name.
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Example call:
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.. code-block:: python
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3131-
grouped_bar({'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2]})
3126+
data_dict = {'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2}
3127+
grouped_bar(data_dict)
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3133-
The names are used as *labels*, i.e. the following two calls are
3134-
equivalent:
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The names are used as *labels*, i.e. this is equivalent to
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.. code-block:: python
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data_dict = {'ds0': dataset_0, 'ds1': dataset_1, 'ds2': dataset_2]}
3139-
grouped_bar(data_dict)
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grouped_bar(data_dict.values(), labels=data_dict.keys())
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When using a dict-like input, you must not pass *labels* explicitly.
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When using a dict input, you must not pass *labels* explicitly.
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- a 2D array: The rows are the categories, the columns are the different
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datasets.
@@ -3154,30 +3147,31 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
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.. code-block:: python
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3157-
group_labels = ["group_A", "group_B"]
3150+
categories = ["A", "B"]
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dataset_labels = ["dataset_0", "dataset_1", "dataset_2"]
31593152
array = np.random.random((2, 3))
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Note that this is consistent with pandas. These two calls produce
3162-
the same bar plot structure:
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.. code-block:: python
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grouped_bar(array, tick_labels=categories, labels=dataset_labels)
3167-
df = pd.DataFrame(array, index=categories, columns=dataset_labels)
3168-
df.plot.bar()
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- a `pandas.DataFrame`.
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The index is used for the categories, the columns are used for the
3158+
datasets.
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.. code-block:: python
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df = pd.DataFrame(
3175-
np.random.random((2, 3))
3176-
index=["group_A", "group_B"],
3163+
np.random.random((2, 3)),
3164+
index=["A", "B"],
31773165
columns=["dataset_0", "dataset_1", "dataset_2"]
31783166
)
31793167
grouped_bar(df)
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i.e. this is equivalent to
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.. code-block::
3172+
3173+
grouped_bar(df.to_numpy(), tick_labels=df.index, labels=df.columns)
3174+
31813175
Note that ``grouped_bar(df)`` produces a structurally equivalent plot like
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``df.plot.bar()``.
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@@ -3187,22 +3181,21 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
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tick_labels : list of str, optional
31893183
The category labels, which are placed on ticks at the center *positions*
3190-
of the bar groups.
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If not set, the axis ticks (positions and labels) are left unchanged.
3184+
of the bar groups. If not set, the axis ticks (positions and labels) are
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left unchanged.
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labels : list of str, optional
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The labels of the datasets, i.e. the bars within one group.
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These will show up in the legend.
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group_spacing : float, default: 1.5
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The space between two bar groupsin units of bar width.
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The space between two bar groupsas multiples of bar width.
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The default value of 1.5 thus means that there's a gap of
32023195
1.5 bar widths between bar groups.
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bar_spacing : float, default: 0
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The space between barsin units of bar width.
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The space between barsas multiples of bar width.
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orientation : {"vertical", "horizontal"}, default: "vertical"
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The direction of the bars.
@@ -3249,7 +3242,7 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
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aspects. ``bar(x, y)`` is a lower-level API and places bars with height *y*
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at explicit positions *x*. It also allows to specify individual bar widths
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and colors. This kind of detailed control and flexibility is difficult to
3252-
manage and often not needed when plotting multiple datasets as grouped bar
3245+
manage and often not needed when plotting multiple datasets asagrouped bar
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plot. Therefore, ``grouped_bar`` focusses on the abstraction of bar plots
32543247
as visualization of categorical data.
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@@ -3309,8 +3302,18 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
33093302
heights=heights.T
33103303

33113304
num_datasets=len(heights)
3312-
dataset_0=next(iter(heights))
3313-
num_groups=len(dataset_0)
3305+
num_groups=len(next(iter(heights)))# inferred from first dataset
3306+
3307+
# validate that all datasets have the same length, i.e. num_groups
3308+
# - can be skipped if heights is an array
3309+
ifnothasattr(heights,'shape'):
3310+
fori,datasetinenumerate(heights):
3311+
iflen(dataset)!=num_groups:
3312+
raiseValueError(
3313+
"'heights' contains datasets with different number of "
3314+
f"elements. dataset 0 has{num_groups} elements but "
3315+
f"dataset{i} has{len(dataset)} elements."
3316+
)
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33153318
ifpositionsisNone:
33163319
group_centers=np.arange(num_groups)
@@ -3325,13 +3328,6 @@ def grouped_bar(self, heights, *, positions=None, group_spacing=1.5, bar_spacing
33253328
else:
33263329
group_distance=1
33273330

3328-
fori,datasetinenumerate(heights):
3329-
iflen(dataset)!=num_groups:
3330-
raiseValueError(
3331-
f"'x' indicates{num_groups} groups, but dataset{i} "
3332-
f"has{len(dataset)} groups"
3333-
)
3334-
33353331
_api.check_in_list(["vertical","horizontal"],orientation=orientation)
33363332

33373333
ifcolorsisNone:

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