How to plot a grouped bar plot of count from pandas

Question

I have a dataframe with the following columns:

gender     class

male       A
female     A
male       B
female     B
male       B
female     A

I want to plot a double bar graph with the columns as each gender and the values as the count of how many of each gender are in class A vs B respectively.

So the bars should be grouped by gender and there should be 2 bars - one for each class.

How do I visualize this? I see this example but I'm really confused

speed = [0.1, 17.5, 40, 48, 52, 69, 88]
lifespan = [2, 8, 70, 1.5, 25, 12, 28]
index = ['snail', 'pig', 'elephant',
         'rabbit', 'giraffe', 'coyote', 'horse']
df = pd.DataFrame({'speed': speed,
                   'lifespan': lifespan}, index=index)

          speed  lifespan
snail       0.1       2.0
pig        17.5       8.0
elephant   40.0      70.0
rabbit     48.0       1.5
giraffe    52.0      25.0
coyote     69.0      12.0
horse      88.0      28.0

ax = df.plot.bar(rot=0)

My index is just row 0 to the # of rows, so I'm confused how I can configure df.plot.bar to work with my use case. Any help would be appreciated!

Trenton McKinney · Accepted Answer · 2021-08-24 01:19:31Z

Use pandas.DataFrame.pivot_table to reshape the dataframe from a long to wide format. The index will be the x-axis, and the columns will be the groups when plotted with pandas.DataFrame.plot
- pd.crosstab(df['gender'], df['class']) can also be used to reshape with an aggregation.
Alternatively, use seaborn.countplot and hue='class', or the figure level version seaborn.catplot with kind='count', both of which can create the desired plot without reshaping the dataframe.
If one of the desired columns is in the index, either specify df.index or reset the index with df = df.reset_index()

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

data = {'gender': ['male', 'female', 'male', 'female', 'male', 'female'], 'class': ['A', 'A', 'B', 'B', 'B', 'A']}
df = pd.DataFrame(data)

# pivot the data and aggregate
dfp = df.pivot_table(index='gender', columns='class', values='class', aggfunc='size')

# plot
dfp.plot(kind='bar', figsize=(5, 3), rot=0)
plt.show()

plt.figure(figsize=(5, 3))
sns.countplot(data=df, x='gender', hue='class')
plt.show()

sns.catplot(kind='count', data=df, x='gender', hue='class', height=3, aspect=1.4)
plt.show()

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