Add data profiling and schema inference#285
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validate_rows consumes a hand-written schema and stats.describe summarises one numeric list — nothing surveyed a whole row-set. Add profile_rows (per-column null fraction, cardinality, inferred type, top values, numeric ranges) and infer_schema, which proposes a validate_rows-compatible schema from observed data. Wired through facade, executor (AC_profile_rows / AC_infer_schema), MCP, and the Script Builder with a headless test batch and EN/Zh docs.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 60 |
| Duplication | 0 |
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What
Adds the profiler step missing from the data-quality lane: survey a whole row-set and propose a validation schema.
profile_rows(rows, columns=None)→{row_count, columns}with per-column count, null count/fraction, distinct count, uniqueness flag, inferred type (int/number/bool/str), top values, and numericmin/max/mean.infer_schema(rows, columns=None)→ avalidate_rows-compatible schema (required where non-null, unique where distinct, numeric bounds). Reusesstats.describe.validate_rowsonly consumed a hand-written schema andstats.describeonly summarized one numeric list — nothing surveyed the data or proposed a schema.Layers
utils/data_profile/(pure stdlibcollections+stats, zero PySide6).profile_rows,infer_schemaexported +__all__.AC_profile_rows,AC_infer_schema.ac_profile_rows,ac_infer_schema(read-only).test/unit_test/headless/test_data_profile_batch.py(9 tests; includes a round-trip proving the inferred schema validates its own rows).v77_features_doc.rst(EN + Zh) + toctrees + 3 README What's-new sections.Verification
pytest test/unit_test/headless/test_data_profile_batch.py→ 9 passed.ruff check je_auto_control/clean; pylint 10.00/10; bandit clean; radon CC clean.