You can use the following syntax to create a bar plot from a GroupBy function in pandas:

#calculate sum of values by group df_groups = df.groupby(['group_var'])['values_var'].sum() #create bar plot by group df_groups.plot(kind='bar')

The following example shows how to use this syntax in practice.

**Example: Create Bar Plot from GroupBy in Pandas**

Suppose we have the following pandas DataFrame that shows the points scored by basketball players on various teams:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C'], 'points': [12, 29, 34, 14, 10, 11, 7, 36, 34, 22, 41, 40, 45, 36, 38]}) #view first five rows of DataFrame df.head() team points 0 A 12 1 A 29 2 A 34 3 A 14 4 A 10

We can use the following syntax to calculate the sum of points scored by each team and create a bar plot to visualize the sum for each team:

import matplotlib.pyplot as plt #calculate sum of points for each team df.groupby('team')['points'].sum() #create bar plot by group df_groups.plot(kind='bar')

The x-axis shows the name of each team and the y-axis shows the sum of the points scored by each team.

We can also use the following code to make the plot look a bit better:

**import matplotlib.pyplot as plt
#calculate sum of points for each team
df_groups = df.groupby(['team'])['points'].sum()
#create bar plot with custom aesthetics
df_groups.plot(kind='bar', title='Total Points by Team',
ylabel='Total Points', xlabel='Team', figsize=(10, 6))
#rotate x-axis ticks vertically
plt.xticks(rotation=0)****
**

**Note**: You can find the complete documentation for the **GroupBy** function here.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

Pandas: How to Count Unique Values by Group

Pandas: How to Calculate Mode by Group

Pandas: How to Calculate Correlation By Group