Pandas nunique()

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 2],
        'B': [4, 5, 6]}

df = pd.DataFrame(data)

# calculate the number of unique values in each column
unique_values = df.nunique()

print(unique_values)

'''
Output

A    2
B    3
dtype: int64
'''

nunique() Syntax

The syntax of the nunique() method in Pandas is:

df.nunique(axis=0, dropna=True)

nunique() Arguments

The nunique() method has the following arguments:

• axis (optional): the axis to compute the number of unique values along
• dropna (optional): if False, NaN values are also counted

nunique() Return Value

The nunique() method returns a scalar if applied to a Series or a Series if applied to a DataFrame.

Example 1: Counting Unique Values in a Series

import pandas as pd

# sample Series
data = pd.Series([1, 2, 2, 3, 3, 3])

# calculate the number of unique values
unique_count = data.nunique()

print(unique_count)

Output

3

Here, we calculated the number of unique values in a Series.

Example 2: Including NaN values in the Count

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, None],
        'B': [4, None, None]}
df = pd.DataFrame(data)

# calculate the number of unique values including nan
unique_count = df.nunique(dropna=False)

print(unique_count)

Output

A    3
B    2
dtype: int64

In this example, we set dropna=False to include NaN values in the count of unique values.

Example 3: Unique Values in Rows

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 2, 1]}
df = pd.DataFrame(data)

# calculate the number of unique values in rows
unique_count = df.nunique(axis=1)

print(unique_count)

Output

0    2
1    1
2    2
dtype: int64

In this example, we changed the axis to 1 to count unique values across rows.