perf: batch scoped-property resolution via props_for_scopes - #12
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Replace per-(property × timespan × category) calls to prop_for_scope with a single props_for_scopes call per property. The new API validates once and explodes the sc_ column once, then filters the pre-built exploded DataFrame for each requested scope—eliminating O(T×C) redundant deepcopy + schema-validation + explode passes per property. Benchmark on stpaul (66k links, ~6,600 scoped): ~188ms mean vs the previous O(T×C×N) loop (~10s estimated for full time-period×category matrix). Adds fixtures and TestBenchmarkSplitProperties to make the improvement reproducible across branches. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Summary
(property × timespan × category)calls toprop_for_scopewith a singleprops_for_scopescall per property insplit_properties_by_time_period_and_categorysc_column once, then filters the pre-built exploded DataFrame for each requested scope — eliminating O(T×C) redundant deepcopy + schema-validation + explode passes per propertystpaul_netandstpaul_net_with_scopedfixtures plusTestBenchmarkSplitPropertiesto make the improvement reproducible across branchesPerformance
Benchmark on stpaul (66k links, ~6,600 scoped links, 5 time periods × 4 vehicle categories):
prop_for_scopein nested loop (O(T×C) calls)props_for_scopesbatch (1 call per property)~50× speedup for the full time-period × category matrix.
Dependencies
Requires network_wrangler PR #452 (
props_for_scopesbatch API).Test plan
pytest tests/ -m "not benchmark")pytest tests/test_benchmark.py::TestBenchmarkSplitProperties -m benchmark)feature/perf-link-changesusing--benchmark-save/--benchmark-compare🤖 Generated with Claude Code