By Team Hypomnema · Data
Compare numeric and categorical distributions across two datasets.
Use the guide alongside the working tool.
Open Dataset Drift Detector →Explicit working sample
group,score
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C,12Compare numeric and categorical distributions across two datasets. Reference dataset is the baseline. Numeric bins use baseline quantiles, duplicate boundaries collapse; population stability index uses 0.5 smoothing. Categorical total variation compares proportions with missing as its own category. Scores are descriptive, not significance tests. Numeric means exclude missing cells; nonnumeric values in selected numeric fields are rejected.
Prepare the input
Compare numeric and categorical distributions across two datasets.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Reference dataset is the baseline. Numeric bins use baseline quantiles, duplicate boundaries collapse; population stability index uses 0.5 smoothing. Categorical total variation compares proportions with missing as its own category. Scores are descriptive, not significance tests. Numeric means exclude missing cells; nonnumeric values in selected numeric fields are rejected.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Dataset Drift Detector →Explore a related workflow
- JSON Schema Validator: methods and data limits
Validate JSON against a supplied schema with precise error paths.
- Dataset Schema Change Checker: methods and data limits
Review observed field and type changes between JSON datasets.
- Data Dictionary Builder: methods and data limits
Build an editable field dictionary with definitions, units and ownership.
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.