feat: add first-class DateTime support#66
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maskedsyntax merged 2 commits intomainfrom Mar 2, 2026
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… summaries
- type_inference.py: detect datetime64 dtypes and parse object columns
(>80% parseable via pd.to_datetime → classified as "DateTime"); datetime
detection runs before cardinality-based categorical check so date columns
with few unique values are not misclassified
- config.py: add DateTimeThresholds (parse threshold, future-date ratio,
gap multipliers, min rows) wired into HashPrepConfig
- checks/datetime_checks.py: three new checks —
- datetime_future_dates: flags values timestamped in the future
- datetime_gaps: detects anomalously large gaps vs. median gap
- datetime_monotonicity: warns when a high-uniqueness date column is
out of temporal order
- summaries/variables.py: enhance _summarize_datetime with weekday
distribution, hour distribution (sub-day precision), gap statistics,
monotonicity, future_count, and has_time_component flag; removed
deprecated infer_datetime_format argument across all callers
- checks/__init__.py + core/analyzer.py: register three new checks in
CHECKS registry and ALL_CHECKS list
- tests/test_datetime.py: 31 tests covering type inference, each check
unit, summary fields, and end-to-end DatasetAnalyzer integration
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Summary
type_inference.pynow detectsdatetime64native dtypes and auto-parses object columns (>80% parseable →"DateTime"). Detection runs before cardinality-based categorical classification, so date columns with few unique values are no longer misclassified.DateTimeThresholdsdataclass inconfig.pywith tunable parse threshold, future-date ratio, gap multipliers, and minimum row count.checks/datetime_checks.py:datetime_future_dates— flags values timestamped in the future (data entry errors, clock skew)datetime_gaps— detects anomalously large gaps vs. the median gap in a time seriesdatetime_monotonicity— warns when a high-uniqueness date column is out of temporal order_summarize_datetimenow includes weekday distribution, hour distribution (sub-day precision), gap statistics (median/mean/min/max gap), monotonicity, future value count, and ahas_time_componentflag.infer_datetime_formatargument from allpd.to_datetimecalls.tests/test_datetime.pycovering type inference, each check unit, summary fields, and end-to-endDatasetAnalyzerintegration. All 150 tests pass (119 existing + 31 new).Test plan
uv run pytest tests/test_datetime.py -v→ 31/31 passuv run pytest tests/ --ignore=tests/test_datetime.py→ 119/119 pass (no regressions)