This pull request introduces safeguards against division by zero errors in the _is_int_column and _is_positive_or_negative_column methods within the sdgx/data_models/inspectors/numeric.py file. The changes ensure that before any operations that could potentially lead to division by zero, we first check if the numeric values extracted from the column series are empty. If there are no numeric values, the methods will return False, thereby preventing any division operations that could result in errors.
Motivation and Context
Division by zero is a common issue that can lead to runtime errors and unexpected behavior in data processing applications. This change is required to enhance the robustness of the data inspection methods by ensuring that they handle cases where numeric data may be absent. By implementing these checks, we can avoid potential crashes and improve the reliability of the Synthetic Data Generator framework.
This PR addresses the need for better error handling in the numeric column inspection methods, which is crucial for maintaining data integrity and application stability.
How has this been tested?
The changes have been tested by running the modified methods with various input scenarios, including:
Columns with all non-numeric values, which should return False.
Columns with numeric values, ensuring that the methods return the expected results without triggering any division by zero errors.
Edge cases where columns contain NaN values or are completely empty.
The testing environment included the latest version of pandas, and unit tests were updated to cover these new scenarios, confirming that the changes do not introduce any regressions.
Types of changes
[ ] Maintenance (no change in code, maintain the project's CI, docs, etc.)
[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
Checklist:
[x] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
Description
This pull request introduces safeguards against division by zero errors in the
_is_int_columnand_is_positive_or_negative_columnmethods within thesdgx/data_models/inspectors/numeric.pyfile. The changes ensure that before any operations that could potentially lead to division by zero, we first check if the numeric values extracted from the column series are empty. If there are no numeric values, the methods will returnFalse, thereby preventing any division operations that could result in errors.Motivation and Context
Division by zero is a common issue that can lead to runtime errors and unexpected behavior in data processing applications. This change is required to enhance the robustness of the data inspection methods by ensuring that they handle cases where numeric data may be absent. By implementing these checks, we can avoid potential crashes and improve the reliability of the Synthetic Data Generator framework.
This PR addresses the need for better error handling in the numeric column inspection methods, which is crucial for maintaining data integrity and application stability.
How has this been tested?
The changes have been tested by running the modified methods with various input scenarios, including:
False.The testing environment included the latest version of pandas, and unit tests were updated to cover these new scenarios, confirming that the changes do not introduce any regressions.
Types of changes
Checklist: