Synthetic data quality evaluation with Polars-native performance and interactive HTML reports.
Project description
dataxid-syntheval
Synthetic data quality evaluation — compare original and synthetic datasets with interactive HTML reports.
Quickstart
import polars as pl
from dataxid_syntheval import SynthEval
original = pl.read_csv("original.csv")
synthetic = pl.read_csv("synthetic.csv")
se = SynthEval(original=original, synthetic=synthetic)
se.to_html("report.html")
Programmatic access:
diffs = se.diff
diffs["column_diffs"] # per-column stat deltas
diffs["alert_diff"] # new / resolved alerts
diffs["distribution_overlays"] # histogram & frequency overlays
diffs["correlation_diffs"] # correlation matrix differences
Features
- Column-level stat comparison — mean, std, median, min/max, missing %, distinct count and more
- Alert change detection — new and resolved data quality alerts between profiles
- Distribution overlays — proportion-based histograms and categorical frequency charts for fair comparison across different dataset sizes
- Correlation matrix diffs — Pearson, Spearman, Kendall, Cramér's V, Phik
- Interactive HTML report — tabbed column comparison, ECharts visualizations, lazy chart rendering
- Built on dataxid-profiling and Polars
Installation
pip install dataxid-syntheval
Contributing
Contributions are welcome. See CONTRIBUTING.md for details.
Links
License
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