You can use the following methods to plot histograms by group in a pandas DataFrame:

Method 1: Plot Histograms by Group Using Multiple Plots

df['values_var'].hist(by=df['group_var'])

Method 2: Plot Histograms by Group Using One Plot

plt.hist(group1, alpha=0.5, label='group1')plt.hist(group2, alpha=0.5, label='group2')plt.hist(group3, alpha=0.5, label='group3')

The following examples show how to use each method in practice with the following pandas DataFrame that shows the points scored by basketball players on three different teams:

import pandas as pd
import numpy as np

#make this example reproducible
np.random.seed(1)

#create DataFrame
df = pd.DataFrame({'team': np.repeat(['A', 'B', 'C'], 100),
                   'points': np.random.normal(loc=20, scale=2, size=300)})

#view head of DataFrame
print(df.head())

  team     points
0    A  23.248691
1    A  18.776487
2    A  18.943656
3    A  17.854063
4    A  21.730815    

Example 1: Plot Histograms by Group Using Multiple Plots

The following code shows how to create three histograms that display the distribution of points scored by players on each of the three teams:

#create histograms of points by team
df['points'].hist(by=df['team'])

We can also use the edgecolor argument to add edge lines to each histogram and the figsize argument to increase the size of each histogram to make them easier to view:

#create histograms of points by team
df['points'].hist(by=df['team'], edgecolor='black', figsize = (8,6)) 

Example 2: Plot Histograms by Group Using One Plot

The following code shows how to create three histograms and place them all on the same plot:

import matplotlib.pyplot as plt

#define points values by group
A = df.loc[df['team'] == 'A', 'points']
B = df.loc[df['team'] == 'B', 'points']
C = df.loc[df['team'] == 'C', 'points']

#add three histograms to one plot
plt.hist(A, alpha=0.5, label='A')
plt.hist(B, alpha=0.5, label='B')
plt.hist(C, alpha=0.5, label='C')

#add plot title and axis labels
plt.title('Points Distribution by Team')
plt.xlabel('Points')
plt.ylabel('Frequency')

#add legend
plt.legend(title='Team')

#display plot
plt.show()

The end result is one plot that displays three overlaid histograms.

Note: The alpha argument specifies the transparency of each histogram. This value can range from 0 to 1. By setting this value equal to 0.5, we’re able to better view each overlaid histogram.

The following tutorials explain how to create other common plots in Python: