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Commit7241d40

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Add support for numeric x
1 parent1212500 commit7241d40

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2 files changed

+42
-9
lines changed

2 files changed

+42
-9
lines changed

‎galleries/examples/lines_bars_and_markers/grouped_bar_chart.py

Lines changed: 16 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -102,3 +102,19 @@
102102
#
103103
# df = pd.DataFrame(data, index=x, columns=columns)
104104
# df.plot.bar()
105+
106+
# %%
107+
# Numeric x values
108+
# ----------------
109+
# In the most common case, one will want to pass categorical labels as *x*.
110+
# Additionally, we allow numeric values for *x*, as with `~.Axes.bar()`.
111+
# But for simplicity and clarity, we require that these are equidistant.
112+
113+
x= [0,2,4]
114+
data= {
115+
'data1': [1,2,3],
116+
'data2': [1.2,2.2,3.2],
117+
}
118+
119+
fig,ax=plt.subplots()
120+
ax.grouped_bar(x,data)

‎lib/matplotlib/axes/_axes.py

Lines changed: 26 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -3004,8 +3004,12 @@ def grouped_bar(self, x, heights, dataset_labels=None):
30043004
"""
30053005
Parameters
30063006
-----------
3007-
x : array-like of str
3008-
The labels.
3007+
x : array-like or list of str
3008+
The center positions of the bar groups. If these are numeric values,
3009+
they have to be equidistant. As with `~.Axes.bar`, you can provide
3010+
categorical labels, which will be used at integer numeric positions
3011+
``range(x)``.
3012+
30093013
heights : list of array-like or dict of array-like or 2D array
30103014
The heights for all x and groups. One of:
30113015
@@ -3064,27 +3068,40 @@ def grouped_bar(self, x, heights, dataset_labels=None):
30643068
elifhasattr(heights,'shape'):
30653069
heights=heights.T
30663070

3067-
num_labels=len(x)
3071+
num_groups=len(x)
30683072
num_datasets=len(heights)
30693073

3070-
fordatasetinheights:
3071-
assertlen(dataset)==num_labels
3074+
ifisinstance(x[0],str):
3075+
tick_labels=x
3076+
group_centers=np.arange(num_groups)
3077+
else:
3078+
ifnum_groups>1:
3079+
d=np.diff(x)
3080+
ifnotnp.allclose(d,d.mean()):
3081+
raiseValueError("'x' must be equidistant")
3082+
group_centers=np.asarray(x)
3083+
tick_labels=None
3084+
3085+
fori,datasetinenumerate(heights):
3086+
iflen(dataset)!=num_groups:
3087+
raiseValueError(
3088+
f"'x' indicates{num_groups} groups, but dataset{i} "
3089+
f"has{len(dataset)} groups"
3090+
)
30723091

30733092
margin=0.1
30743093
bar_width= (1-2*margin)/num_datasets
3075-
block_centers=np.arange(num_labels)
30763094

30773095
ifdataset_labelsisNone:
30783096
dataset_labels= [None]*num_datasets
30793097
else:
30803098
assertlen(dataset_labels)==num_datasets
30813099

30823100
fori, (hs,dataset_label)inenumerate(zip(heights,dataset_labels)):
3083-
lefts=block_centers-0.5+margin+i*bar_width
3084-
print(i,x,lefts,hs,dataset_label)
3101+
lefts=group_centers-0.5+margin+i*bar_width
30853102
self.bar(lefts,hs,width=bar_width,align="edge",label=dataset_label)
30863103

3087-
self.xaxis.set_ticks(block_centers,labels=x)
3104+
self.xaxis.set_ticks(group_centers,labels=tick_labels)
30883105

30893106
# TODO: does not return anything for now
30903107

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