How To Annotate A Seaborn Barplot With The Aggregated Value

How can the following code be modified to show the mean as well as the different error bars on each bar of the bar plot? import numpy as np import pandas as pd import matplotlib.py

Solution 1:

• Given the example data, for a seaborn.barplot with capped error bars, data_df must be converted from a wide format, to a tidy (long) format, which can be accomplished with pandas.DataFrame.stack or pandas.DataFrame.melt
  • It is also important to keep in mind that a bar plot shows only the mean (or other estimator) value

Sample Data and DataFrame

• .iloc[:, 1:] is used to skip the 'stages' column at column index 0.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# given data_df from the OP, select the columns except stage and reshape to long format
df = data_df.iloc[:, 1:].melt(var_name='set', value_name='val')

# display(df.head())set        val
0  S1  43.3404401  S1  43.7198982  S1  46.0159583  S1  54.3405974  S2  61.609735

Updated as of matplotlib v3.4.2

• Use matplotlib.pyplot.bar_label
• See the matplotlib: Bar Label Demo page for additional formatting options.
  • Some formatting can be done with the fmt parameter, but more sophisticated formatting should be done with the labels parameter, as show in the bottom Demo example, and in How to add multiple annotations to a barplot.
• Tested with seaborn v0.11.1, which is using matplotlib as the plot engine.

fig, ax = plt.subplots(figsize=(8, 6))

# add the plot
sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax)

# add the annotation
ax.bar_label(ax.containers[-1], fmt='Mean:\n%.2f', label_type='center')

ax.set(ylabel='Mean Time')
plt.show()

Annotation Resources - from matplotlib v3.4.2

• Adding value labels on a matplotlib bar chart
• How to annotate each segment of a stacked bar chart
• Stacked Bar Chart with Centered Labels
• How to plot and annotate multiple data columns in a seaborn barplot
• stack bar plot in matplotlib and add label to each section
• How to add multiple annotations to a barplot
• How to plot and annotate a grouped bar chart

plot with seaborn.barplot

• Using matplotlib before version 3.4.2
• The default for the estimator parameter is mean, so the height of the bar is the mean of the group.
• The bar height is extracted from p with .get_height, which can be used to annotate the bar.

fig, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax)

# show the meanfor p in ax.patches:
    h, w, x = p.get_height(), p.get_width(), p.get_x()
    xy = (x + w / 2., h / 2)
    text = f'Mean:\n{h:0.2f}'
    ax.annotate(text=text, xy=xy, ha='center', va='center')

ax.set(xlabel='Delay', ylabel='Time')
plt.show()

Solution 2:

Seaborn is most powerfull with long form data. So you might want to transform your data, something like this:

sns.barplot(data=data_df.melt('stages', value_name='Delay', var_name='Time'), 
            x='Time', y='Delay',
            capsize=0.1, edgecolor='k')

Output: