engine-pandas-fdd

Automate fault detection in pandas DataFrames by loading YAML-defined validation rules and applying them to your dataset, which generates boolean flag columns to indicate pass or fail status for each row.

YAML-based data quality checks define validation logic in configuration files, allowing you to separate fault detection rules from code and apply them automatically to pandas DataFrames for anomaly identification.

Yes, you can use this for batch processing sensor data streams to identify anomalies, applying YAML fault detection rules to pandas DataFrames to flag data quality issues without manual review.

Yes, you need pandas for in-memory DataFrame processing and the open-fdd rule engine to evaluate the YAML fault detection rules when running automated validation on CSV data.

The best way to generate boolean flag columns for diagnostics is to evaluate YAML-defined fault detection rules directly on a pandas DataFrame, automatically creating columns that indicate rule pass or fail status.

You should avoid using pandas DataFrames for fault detection when processing datasets that exceed available memory, as this approach requires in-memory processing and is designed for batch analytics rather than real-time streaming.