Pandas DataFrame notnull() Method

DataFrame.notnull() function in pandas detect non-missing (non-NaN/None) values in a DataFrame. It returns a DataFrame of the same shape as the original, with boolean values indicating whether each element is not null. Example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': ['x', 'y', None]
})
print(df.notnull())

       A      B
0   True   True
1  False   True
2   True  False

Explanation: Returns a DataFrame of the same shape where True means the value is not null i.e., valid and False means the value is NaN or None.

Syntax

DataFrame.notnull()

Parameters: This method does not take any parameters.

Returns: A DataFrame of boolean values, with True where values are not null and False where values are NaN or None.

Examples

Example 1: In this example, we check which values in the DataFrame are not missing.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': ['a', 'b', None]})
print(df.notnull())

       A      B
0   True   True
1  False   True
2   True  False

Explanation: This returns a DataFrame with True where values are not null and False where values are NaN or None.

Example 2: Filter rows where column 'A' is not null using boolean indexing.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': ['a', 'b', None]})
print(df[df['A'].notnull()])

     A     B
0  1.0     a
2  3.0  None

Explanation: Only rows where 'A' is not null are returned. The row with NaN in column 'A' is excluded.

Example 3: Compare notnull() with the inverse of isnull() to verify they are logical opposites.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': ['a', 'b', None]})
print(df.notnull() == ~df.isnull())

      A     B
0  True  True
1  True  True
2  True  True

Explanation: notnull() returns the opposite of isnull() and the comparison confirms this by returning True for all elements.

Example 4: In this example, we check if all values in each column are not null using all().

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': ['a', 'b', None]})
print(df.notnull().all())

A    False
B    False
dtype: bool

Explanation: False indicates that not all values in the columns are non-null both columns contain at least one missing value.

Example 5: In this example, we check if any value in each row is not null using any(axis=1).

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3], 'B': ['a', 'b', None]})
print(df.notnull().any(axis=1))

0    True
1    True
2    True
dtype: bool

Explanation: True means that each row contains at least one non-null value, even if some values in the row are missing.

Related articles:

• Pandas
• NumPy