How to check if any value is NaN in a Pandas DataFrame?

Pandas provides a few ways to detect missing values (NaNs). Below are the common approaches to determine whether any cell in a DataFrame contains a NaN.

1. Using isna() and any()

Step-by-step:

1. df.isna() creates a Boolean DataFrame of the same shape as df, with True where values are NaN and False otherwise.
2. .any() can be chained to check whether there is at least one True value across a specified axis.

Example

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "A": [1, 2, np.nan],
    "B": [4, np.nan, 6]
})

# Check if there is *any* NaN in the whole DataFrame
any_nan = df.isna().any().any()

print("Does DataFrame contain NaN?", any_nan)

• Explanation:
  • df.isna() returns a DataFrame of booleans.
  • df.isna().any() returns a Series indicating, for each column, if any value is NaN.
  • (df.isna().any()).any() checks if any of those columns contain NaN.

Result: any_nan is a boolean (True/False), telling you if the entire DataFrame has at least one NaN.

2. Summarizing NaNs with isna().sum()

If you want a quick glance at how many NaNs exist per column or if each column has any NaNs:

nan_count_per_column = df.isna().sum()
print(nan_count_per_column)

# If you only want to know if each column contains *any* NaN:
columns_with_nan = df.isna().any()
print(columns_with_nan)

• df.isna().sum(): Returns a Series with the count of NaNs in each column.
• df.isna().any(): Returns a Series of booleans indicating if each column has any NaN.

3. Combining with Conditional Logic

Sometimes you might only need to know if a specific subset of columns or rows has NaN:

• Subset of Columns:

  subset_has_nan = df[["A", "B"]].isna().any().any()  # Checks only columns 'A' and 'B'

• Specific Rows:

  row_has_nan = df.loc[2].isna().any()  # Check if row with index 2 has any NaN

4. Using df.isnull() (Synonym)

• df.isnull() is an alias for df.isna(). Both do the same thing.

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By applying any of these methods, you can quickly assess the presence and scope of NaN values in your DataFrame, allowing for more informed cleaning and preprocessing steps in your data pipeline.