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- polars: <1.32 → <1.40 (resolves to 1.39.3), removed Windows version ceiling - pl-fuzzy-frame-match: >=0.4.0 → >=0.6.0 - polars-simed: ^0.3.4 → >=0.4.0 - polars-ds: >=0.6.0 → >=0.11.0 - polars-distance: ~0.4.3 → >=0.4.3 - polars-expr-transformer: >=0.5.0 → >=0.5.1 Breaking changes addressed: - group_by().sum/mean/median() now uses cs.numeric() selector since polars no longer allows these aggregations on string columns - Added polars.selectors (cs) to polars_code_parser safe globals - UDF serialization: detect when LazyFrame JSON serialization loses UDFs (shown as ".()") and inject precomputed results directly into flow nodes - map_elements in group_by agg context changed behavior; tests updated to use map_batches with returns_scalar=True - map_batches now requires return_dtype in lazy contexts; updated test New dtype support (Float16, UInt128, Int128): - Added to DataType enum, TypeRegistry, type mappings, code parser globals - Added to generic_datatype/get_readable_datatype_group classifications - Excluded Float16 from round precision test (insufficient precision) https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
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Binary format preserves UDFs/lambdas (JSON format dropped them in polars 1.39.3). Uses base64 encoding to embed binary data in generated code strings. Removes the UDF-loss detection and live-result injection workaround that was needed with JSON serialization. https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
…ull handling - Add Int128, UInt128, Float16 to NUMERIC_TYPES_SORTED in test_ui_components - Update flow engine pivot to fill_null(0) for sum/count/len aggregations to match native polars 1.39.3 pivot behavior https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
…ate pivot expected values - test_custom_component_integration: add Int128, UInt128, Float16 to expected numeric types - test_node_designer: add new types to get_expected_numeric_types() and both Numeric assertion sets - test_flow_data_engine: update test_pivot_numeric expected values from None to 0 for sum agg https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
…type sets - Rewrite do_pivot() to use polars LazyFrame.pivot instead of manual group_by + filter + struct approach - Unique pivot values still fetched via worker (fetch_unique_values) - Handles concat aggregation via pl.element().str.join with empty→null fixup - Supports multiple aggregations via separate pivots joined together - Update all test numeric type sets to include Int128, UInt128, Float16 https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
…um/count/len The original group_by + filter + struct approach is more readable and doesn't require DataFrame/LazyFrame guards or concat edge-case handling. https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
…value threading Instead of stashing precomputed results on self as mutable state, _add_polars_code() now returns the precomputed result (or None), and callers pass it to _create_child_frame(precomputed_result=...). This eliminates: - Mutable state leak risk if _create_child_frame is not called - Double-call ambiguity where only the first call gets the result - Side-channel attribute that could affect unrelated operations Updated call sites: with_columns, sort, filter, select, group_by agg. https://claude.ai/code/session_01PB1o78uUwTexByBiNTY5rk
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Bump the polars version to 1.39.3