How to Check for Nan Values

Better Stack Team
Updated on June 19, 2024

In pandas, you can check for NaN (Not a Number) values using the isna() or isnull() methods. These methods return a DataFrame of the same shape as the original DataFrame, where each element is True if it's NaN and False otherwise. Here's how you can do it:

import pandas as pd
import numpy as np

# Create a DataFrame with NaN values
data = {'A': [1, np.nan, 3], 'B': [np.nan, 5, np.nan], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

# Check for NaN values using isna()
nan_values = df.isna()

print("\\nNaN values:")

This will output:

     A    B  C
0  1.0  NaN  7
1  NaN  5.0  8
2  3.0  NaN  9

NaN values:
       A      B      C
0  False   True  False
1   True  False  False
2  False   True  False

In this example, nan_values is a DataFrame where each True value indicates a NaN value in the corresponding position of the original DataFrame df.

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