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?
.melt
'log'
is used for the yscale
'cats'
are included for the example.
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
matplotlib v3.4.2
matplotlib.pyplot.bar_label
.bar_label
works for matplotlib
, seaborn
, and pandas
plots. .bar_label
.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)
seaborn v0.11.1
matplotlib
before version 3.4.2.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')
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