How to Fix the use_inf_as_na FutureWarning in Seaborn and Pandas

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Introduction

If you’re a Python developer working with data visualization and analysis, you may have encountered the following warning:

/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):

This FutureWarning appears when using Seaborn and Pandas, indicating that the use_inf_as_na option will be removed in future versions. In this blog post, we’ll break down what this warning means, why it occurs, and how you can fix it in your code to ensure compatibility with future versions of Pandas and Seaborn.

What Causes This FutureWarning?

This warning is triggered when Seaborn internally sets Pandas options using:

with pd.option_context('mode.use_inf_as_na', True):

In previous versions of Pandas, this option allowed users to automatically treat infinite (inf) values as missing (NaN). However, Pandas is deprecating this option, meaning it will no longer be supported in upcoming releases.

futurewarning: use_inf_as_na option is deprecated and will be removed in a future version. convert inf values to nan before operating instead.

Why is Pandas Removing use_inf_as_na?

1. Explicit Data Handling: Users are encouraged to manually handle infinite values before performing operations.
2. Performance Optimization: Reducing implicit conversions can improve Pandas’ efficiency.
3. Code Clarity: Explicit conversions make the intent of data handling clearer.

How to Fix This Warning in Your Code

1. Manually Convert inf to NaN Before Processing

Since use_inf_as_na=True automatically converted infinite values to NaN, you should manually apply this conversion in your dataset. Use the following Pandas method:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, np.inf, 4],
    'B': [-np.inf, 3, 4, 5]
})

# Replace inf values with NaN
df.replace([np.inf, -np.inf], np.nan, inplace=True)

print(df)

This ensures that all infinite values are properly replaced before further analysis or visualization.

use_inf_as_na option is deprecated and will be removed in a future version. convert inf values to nan before operating instead.

2. Use Try-Except to Catch Warnings (Temporary Solution)

If you’re working with an older version of Seaborn or Pandas and cannot update your code immediately, you can suppress the warning temporarily using Python’s warnings module:

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

However, this is not a long-term fix—you should aim to update your code accordingly.

/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: futurewarning: use_inf_as_na option is deprecated and will be removed in a future version. convert inf values to nan before operating instead. with pd.option_context('mode.use_inf_as_na', true):

3. Upgrade Pandas and Seaborn to the Latest Versions

Since this warning is related to future deprecation, keeping your libraries up to date ensures that you are using the latest best practices:

pip install --upgrade pandas seaborn

Check your installed versions using:

import pandas as pd
import seaborn as sns
print(pd.__version__)
print(sns.__version__)

Best Practices for Handling Infinite Values in Data Analysis

Here are some best practices to avoid issues with infinite values in your Pandas and Seaborn workflows:

• Check for Infinite Values Before Processing np.isinf(df).sum() This helps identify columns with inf values.
• Replace Infinites at Data Ingestion df[df == np.inf] = np.nan This ensures you don’t run into processing errors later.
• Validate Data Before Visualization When working with Seaborn plots, ensure your dataset doesn’t contain unexpected infinite values: df.dropna(inplace=True) sns.histplot(df)

futurewarning: use_inf_as_na option is deprecated and will be removed in a future version. convert inf values to nan before operating instead. with pd.option_context('mode.use_inf_as_na', true):

Conclusion

This FutureWarning serves as a heads-up that Pandas is removing the use_inf_as_na option, requiring developers to explicitly handle infinite values in their datasets. To avoid issues:

✅ Manually replace inf values with NaN before processing. ✅ Keep your Pandas and Seaborn libraries updated. ✅ Validate and clean your data before visualization.

By following these best practices, you can ensure your code remains compatible with future Pandas versions while maintaining clean and reliable data processing workflows.

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