Pandas Index.isnull()-Python

Index.isnull() function in pandas detects missing values (NaN or None) in a pandas Index. It returns a boolean array where True indicates a missing value and False indicates a valid (non-null) value. Example:

import pandas as pd
import numpy as np
idx = pd.Index(['a', None, 'c', 'd'])
print(idx.isnull())

[False  True False False]

Explanation: Only the second value is None (null), so it's marked True. All other values are valid.

Syntax

Index.isnull()

Parameters: This method does not take any parameters.

Returns: A NumPy boolean array: True for missing values like NaN or None and False for valid ones.

Examples

Example 1: In this, we filter only the null values from the Index using boolean indexing.

import pandas as pd
import numpy as np

idx = pd.Index([10, 20, np.nan, 40])
null_vals = idx[idx.isnull()]
print(null_vals)

Index([nan], dtype='float64')

Explanation: This filters out the null (np.nan) from the Index. The dtype becomes float because of the presence of nan.

Example 2: In this, we verify that isnull() is the logical opposite of notnull().

import pandas as pd
import numpy as np

idx = pd.Index([None, 5, np.nan])
print(idx.isnull() == ~idx.notnull())

[ True  True  True]

Explanation: Each element in the result is True, confirming both methods are logical opposites.

Example 3: In this, we check if all values in the Index are null using all().

import pandas as pd
import numpy as np

idx = pd.Index([None, np.nan])
print(idx.isnull().all())

True

Explanation: Every value in the Index is null, so isnull().all() returns True.

Example 4: In this, we check if any value in the Index is null using any().

import pandas as pd
import numpy as np

idx = pd.Index([1, 2, np.nan])
print(idx.isnull().any())

True

Explanation: Since there is at least one missing value (np.nan), the result is True.

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