How to Solve Python ValueError: Cannot mask with non-boolean array containing NA/NaN values

Introduction

If you’ve ever worked with a Pandas DataFrame and tried filtering rows based on conditions, you may have encountered this common error:

ValueError: Cannot mask with non-boolean array containing NA/NaN values

This error occurs when a mask used for filtering contains invalid values, such as NaN. In this post, we’ll show you how to reproduce this error and provide simple solutions to fix it.

Reproducing the Error

Let’s start by creating a DataFrame where one of the columns contains NaN values, and we attempt to filter rows based on a string condition.

Example Code to Reproduce the Error

import pandas as pd
import numpy as np

# Create a DataFrame with NaN values
df = pd.DataFrame({'department': ['HR', 'HR', 'Finance', 'Marketing', 'HR'],
                   'role': ['Manager', np.nan, 'Analyst', 'Manager', 'Intern'],
                   'salary': [50000, 45000, 60000, 70000, 30000]})

# View the DataFrame
print(df)

The DataFrame looks like this:

  department      role  salary
0         HR   Manager   50000
1         HR       NaN   45000
2    Finance   Analyst   60000
3  Marketing   Manager   70000
4         HR    Intern   30000

Now, let’s attempt to filter rows where the role column contains the word ‘Manager’:

# Access all rows where the 'role' column contains 'Manager'
df[df['role'].str.contains('Manager')]

When you run this code, you will get the following error:

ValueError: Cannot mask with non-boolean array containing NA / NaN values

Why Does this Error Occur?

The problem arises because the str.contains() function returns NaN for any NaN value in the role column. When Pandas attempts to use this mask (which contains NaN) to filter the DataFrame, it throws the error because it requires boolean (True/False) values.

Here’s what the mask would look like if we printed it:

0     True
1      NaN
2    False
3     True
4    False
Name: role, dtype: object

The NaN value in the mask is causing the error, as Pandas cannot use it to filter the DataFrame.

How to Fix the Error

There are a couple of ways to fix this issue, depending on your use case.

1. Use na=False in str.contains()

Pandas provides an option to handle NaN values directly using str.contains(). You can specify na=False, which will treat any NaN in the column as False in the mask:

Output:

  department      role  salary
0         HR   Manager   50000
3  Marketing   Manager   70000

In this case, the NaN value in the role column is treated as False, so it is excluded from the filtered DataFrame.

2. Drop Rows Containing NaN in the Column

If you prefer to remove rows with NaN values in the role column before applying the filter, you can use dropna():

# Drop rows with NaN in the 'role' column
df_clean = df.dropna(subset=['role'])

# Now apply the filtering condition
df_filtered = df_clean[df_clean['role'].str.contains('Manager')]

print(df_filtered)

Output:

  department      role  salary
0         HR   Manager   50000
3  Marketing   Manager   70000

This approach removes rows with NaN values before applying the filtering condition, so the error is avoided.

Summary

• The error occurs because Pandas expects a boolean mask when filtering, but NaN values in the mask prevent it from working correctly.
• You can fix this by:
  • Using str.contains() with na=False to handle NaN as False.
  • Dropping rows with NaN values in the relevant column before applying the filter.

By adequately handling NaN values, you can avoid this error and filter your DataFrames without issues.

For further reading on errors involving Pandas, go to the articles:

• How to Solve Pandas TypeError: empty ‘dataframe’ no numeric data to plot.
• How to Solve Python ValueError: Can only compare identically-labeled DataFrame objects
• How to Solve Python ValueError: no axis named for object type dataframe

To learn more about Python for data science and machine learning, go to the online courses page on Python for the most comprehensive courses available.

Have fun and happy researching!