You can use the function to count the number of unique values in a pandas DataFrame.

This function uses the following basic syntax:

#count unique values in each column
df.nunique()

#count unique values in each row
df.nunique(axis=1)

The following examples show how to use this function in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'points': [8, 8, 13, 13, 22, 22, 25, 29],
                   'assists': [5, 8, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 11, 6, 6, 5, 9, 12]})

#view DataFrame
df

	team	points	assists	rebounds
0	A	8	5	11
1	A	8	8	8
2	A	13	7	11
3	A	13	9	6
4	B	22	12	6
5	B	22	9	5
6	B	25	9	9
7	B	29	4	12

Example 1: Count Unique Values in Each Column

The following code shows how to count the number of unique values in each column of a DataFrame:

#count unique values in each column
df.nunique()

team        2
points      5
assists     5
rebounds    6
dtype: int64

From the output we can see:

• The ‘team’ column has 2 unique values
• The ‘points’ column has 5 unique values
• The ‘assists’ column has 5 unique values
• The ‘rebounds’ column has 6 unique values

Example 2: Count Unique Values in Each Row

The following code shows how to count the number of unique values in each row of a DataFrame:

#count unique values in each row
df.nunique(axis=1)

0    4
1    2
2    4
3    4
4    4
5    4
6    3
7    4
dtype: int64

From the output we can see:

• The first row has 4 unique values
• The second row has 2 unique values
• The third row has 4 unique values

And so on.

Example 3: Count Unique Values by Group

#count unique 'points' values, grouped by team
df.groupby('team')['points'].nunique()

team
A    2
B    3
Name: points, dtype: int64

From the output we can see:

• Team ‘A’ has 2 unique ‘points’ values
• Team ‘B’ has 3 unique ‘points’ values

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