How to plot and annotate grouped bars in seaborn / matplotlib

I have a dataframe that looks like this:

I have used a barplot to represent the subscribers for each row. This is what I did:

data = channels.sort_values('subscribers', ascending=False).head(5)
chart = sns.barplot(x = 'name', y='subscribers',data=data)
chart.set_xticklabels(chart.get_xticklabels(), rotation=90)
for p in chart.patches:
    chart.annotate("{:,.2f}".format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points')

Now I want to show the ‘video_count’ for each user on this same plot. The goal is to compare how the number of subscribers relate to the number of videos. How can I depict this on the chart?

Answers:

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Method 1

Data

• The data needs to be converted to a long format using .melt
• Because of the scale of values, 'log' is used for the yscale
• All of the categories in 'cats' are included for the example.
  • Select only the desired columns before melting, or use dfl = dfl[dfl.cats.isin(['sub', 'vc']) to filter for the desired 'cats'.

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

# setup dataframe
data = {'vc': [76, 47, 140, 106, 246],
        'tv': [29645400, 28770702, 50234486, 30704017, 272551386],
        'sub': [66100, 15900, 44500, 37000, 76700],
        'name': ['a', 'b', 'c', 'd', 'e']}
df = pd.DataFrame(data)

    vc        tv    sub name
0   76  29645400  66100    a
1   47  28770702  15900    b
2  140  50234486  44500    c

# convert to long form
dfl = (df.melt(id_vars='name', var_name='cats', value_name='values')
       .sort_values('values', ascending=False).reset_index(drop=True))

  name cats     values
0    e   tv  272551386
1    c   tv   50234486
2    d   tv   30704017

Updated as of matplotlib v3.4.2

• Use matplotlib.pyplot.bar_label
• .bar_label works for matplotlib, seaborn, and pandas plots.
• See How to add value labels on a bar chart for additional details and examples with .bar_label.
• Tested with seaborn v0.11.1, which is using matplotlib as the plot engine.

# plot
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(x='name', y='values', data=dfl, hue='cats', ax=ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
ax.set_yscale('log')

for c in ax.containers:
    # set the bar label
    ax.bar_label(c, fmt='%.0f', label_type='edge', padding=1)
    
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

Plot with seaborn v0.11.1

• Using matplotlib before version 3.4.2
• Note that using .annotate and .patches is much more verbose than with .bar_label.

# plot
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(x='name', y='values', data=dfl, hue='cats', ax=ax)
ax.set_xticklabels(chart.get_xticklabels(), rotation=0)
ax.set_yscale('log')

for p in ax.patches:
    ax.annotate(f"{p.get_height():.0f}", (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', xytext =(0, 7), textcoords='offset points')

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0