You can use the nunique() 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**

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

#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

**Additional Resources**

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

How to Count Observations by Group in Pandas

How to Count Missing Values in Pandas

How to Use Pandas value_counts() Function