Pandas notnull()

In this tutorial, you will learn about the notnull() method in Pandas with the help of examples.

The notnull() method in Pandas is used to detect existing (non-missing) values in the data.

Example

import pandas as pd

# sample DataFrame with missing values
data = {'A': [1, None, 3],
        'B': [4, 5, None]}

df = pd.DataFrame(data)

# detect non-missing values
not_null_values = df.notnull()

print(not_null_values)
 
'''
Output

       A      B
0   True   True
1  False   True
2   True  False
'''

notnull() Syntax

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

df.notnull()

notnull() Arguments

The notnull() method does not take any arguments.

notnull() Return Value

The notnull() method returns a Boolean same-sized object indicating if the values are non-NA. True stands for non-missing values and False stands for missing values.

Example: Filtering Data using notnull()

import pandas as pd

# sample DataFrame with missing values
data = {'A': [1, None, 3],
        'B': [4, 5, None]}

df = pd.DataFrame(data)

# detect non-missing values
filtered_df = df[df['A'].notnull()]

print(filtered_df)

Output

     A    B
0  1.0  4.0
2  3.0  NaN

In this example, we used notnull() in combination with indexing to filter rows based on non-missing values in column A.