I'm trying to "robustly" center the data labels in a stacked bar chart. A simple code example and the result are given below. As you can see, the data labels aren't really centered in all rectangles. What am I missing?
import numpy as np
import matplotlib.pyplot as plt
A = [45, 17, 47]
B = [91, 70, 72]
fig = plt.figure(facecolor="white")
ax = fig.add_subplot(1, 1, 1)
bar_width = 0.5
bar_l = np.arange(1, 4)
tick_pos = [i + (bar_width / 2) for i in bar_l]
ax1 = ax.bar(bar_l, A, width=bar_width, label="A", color="green")
ax2 = ax.bar(bar_l, B, bottom=A, width=bar_width, label="B", color="blue")
ax.set_ylabel("Count", fontsize=18)
ax.set_xlabel("Class", fontsize=18)
ax.legend(loc="best")
plt.xticks(tick_pos, ["C1", "C2", "C3"], fontsize=16)
plt.yticks(fontsize=16)
for r1, r2 in zip(ax1, ax2):
h1 = r1.get_height()
h2 = r2.get_height()
plt.text(r1.get_x() + r1.get_width() / 2., h1 / 2., "%d" % h1, ha="center", va="bottom", color="white", fontsize=16, fontweight="bold")
plt.text(r2.get_x() + r2.get_width() / 2., h1 + h2 / 2., "%d" % h2, ha="center", va="bottom", color="white", fontsize=16, fontweight="bold")
plt.show()
pandas.DataFrame
is the easiest way to plot a stacked bar plot.pandas.DataFrame.plot.bar(stacked=True)
, or pandas.DataFrame.plot(kind='bar', stacked=True)
, is the easiest way to plot a stacked bar plot.
matplotlib.axes.Axes
or a numpy.ndarray
of them.seaborn
is just a high-level API for matplotlib
, these solutions also work with seaborn
plots, as shown in How to annotate a seaborn barplot with the aggregated value .python 3.10
, pandas 1.4.2
, matplotlib 3.5.1
, seaborn 0.11.2
import pandas as pd
import matplotlib.pyplot as plt
A = [45, 17, 47]
B = [91, 70, 72]
C = [68, 43, 13]
# pandas dataframe
df = pd.DataFrame(data={'A': A, 'B': B, 'C': C})
df.index = ['C1', 'C2', 'C3']
A B C
C1 45 91 68
C2 17 70 43
C3 47 72 13
matplotlib v3.4.2
matplotlib.pyplot.bar_label
, which will automatically center the values in the bar..bar_label
.pandas v1.2.4
, which is using matplotlib
as the plot engine.labels
for .bar_label()
.ax.bar_label(c, fmt='%0.0f', label_type='center')
will change the number format to show no decimal places, if needed. ax = df.plot(kind='bar', stacked=True, figsize=(8, 6), rot=0, xlabel='Class', ylabel='Count')
for c in ax.containers:
# Optional: if the segment is small or 0, customize the labels
labels = [v.get_height() if v.get_height() > 0 else '' for v in c]
# remove the labels parameter if it's not needed for customized labels
ax.bar_label(c, labels=labels, label_type='center')
seaborn
is a high-level api for matplotlib
seaborn.barplot
api doesn't have an option for stacking, but it "can" be implemented with sns.histplot
, orsns.displot
. # create the data frame
df = pd.DataFrame(data={'A': A, 'B': B, 'C': C, 'cat': ['C1', 'C2', 'C3']})
A B C cat
0 45 91 68 C1
1 17 70 43 C2
2 47 72 13 C3
# convert the dataframe to a long form
df = df.melt(id_vars='cat')
cat variable value
0 C1 A 45
1 C2 A 17
2 C3 A 47
3 C1 B 91
4 C2 B 70
5 C3 B 72
6 C1 C 68
7 C2 C 43
8 C3 C 13
# plot
ax = sns.histplot(data=df, x='cat', hue='variable', weights='value', discrete=True, multiple='stack')
# iterate through each container
for c in ax.containers:
# Optional: if the segment is small or 0, customize the labels
labels = [v.get_height() if v.get_height() > 0 else '' for v in c]
# remove the labels parameter if it's not needed for customized labels
ax.bar_label(c, labels=labels, label_type='center')
# plot
g = sns.displot(data=df, x='cat', hue='variable', weights='value', discrete=True, multiple='stack')
# iterate through each axes
for ax in g.axes.flat:
# iterate through each container
for c in ax.containers:
# Optional: if the segment is small or 0, customize the labels
labels = [v.get_height() if v.get_height() > 0 else '' for v in c]
# remove the labels parameter if it's not needed for customized labels
ax.bar_label(c, labels=labels, label_type='center')
.patches
method unpacks a list of matplotlib.patches.Rectangle
objects, one for each of the sections of the stacked bar.
.Rectangle
has methods for extracting the various values that define the rectangle..Rectangle
is in order from left to right, and bottom to top, so all the .Rectangle
objects, for each level, appear in order, when iterating through .patches
.label_text = f'{height}'
, so any additional text can be added as needed, such as label_text = f'{height}%'
label_text = f'{height:0.0f}'
will display numbers with no decimal places. plt.style.use('ggplot')
ax = df.plot(stacked=True, kind='bar', figsize=(12, 8), rot='horizontal')
# .patches is everything inside of the chart
for rect in ax.patches:
# Find where everything is located
height = rect.get_height()
width = rect.get_width()
x = rect.get_x()
y = rect.get_y()
# The height of the bar is the data value and can be used as the label
label_text = f'{height}' # f'{height:.2f}' to format decimal values
# ax.text(x, y, text)
label_x = x + width / 2
label_y = y + height / 2
# plot only when height is greater than specified value
if height > 0:
ax.text(label_x, label_y, label_text, ha='center', va='center', fontsize=8)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
ax.set_ylabel("Count", fontsize=18)
ax.set_xlabel("Class", fontsize=18)
plt.show()
kind='barh'
label_text = f'{width}'
if width > 0:
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