Pandas: How to Use GroupBy and Value Counts

---

You can use the following basic syntax to count the frequency of unique values by group in a pandas DataFrame:

df.groupby(['column1', 'column2']).size().unstack(fill_value=0)

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

Example: Use GroupBy and Value Counts in Pandas

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],
                   'position':['G', 'G', 'F', 'F', 'C', 'G', 'F', 'F', 'F', 'F'],
                   'points': [8, 8, 10, 10, 11, 8, 9, 10, 10, 10]})

#view DataFrame
print(df)

  team position  points
0    A        G       8
1    A        G       8
2    A        F      10
3    A        F      10
4    A        C      11
5    B        G       8
6    B        F       9
7    B        F      10
8    B        F      10
9    B        F      10

We can use the following syntax to count the frequency of the points values, grouped by the team and position columns:

#count frequency of points values, grouped by team and position
df.groupby(['team', 'position', 'points']).size().unstack(fill_value=0)

	points	8	9	10	11
team	position				
A	C	0	0	0	1
        F	0	0	2	0
        G	2	0	0	0
B	F	0	1	3	0
        G	1	0	0	0

Here’s how to interpret the output:

• The value 8 occurred in the points column 0 times for players on team A and position C.
• The value 9 occurred in the points column 0 times for players on team A and position C.
• The value 10 occurred in the points column 0 times for players on team A and position C.
• The value 11 occurred in the points column 1 time for players on team A and position C.

And so on.

We could also use the following syntax to count the frequency of the positions, grouped by team:

#count frequency of positions, grouped by team
df.groupby(['team', 'position']).size().unstack(fill_value=0)

position	C	F	G
team			
A	        1	2	2
B	        0	4	1

Here’s how to interpret the output:

• The value ‘C’ occurred 1 time on team A.
• The value ‘F’ occurred 2 times on team A.
• The value ‘G’ occurred 2 times on team A.
• The value ‘C’ occurred 0 times on team B.
• The value ‘F’ occurred 4 times on team B.
• The value ‘G’ occurred 1 time on team B.

And so on.

Additional Resources

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

How to Perform a GroupBy Sum in Pandas
How to Count Unique Values Using GroupBy in Pandas
How to Use Groupby and Plot in Pandas