Fabric pandas perf remediate
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Select the narrowest matching method below, then read its full entrypoint and the resources it requires. If multiple methods cover the same outcome, choose the one matching the user's environment and inputs; do not run them all. Retain each method's input requirements, decision steps, validation, output structure, and failure handling. If prerequisites are absent, use its honest proposal-only fallback or state that its documented path is blocked.
- **PatrickGallucci/fabric-pandas-perf-remediate**: Troubleshoot and optimize pandas performance in Microsoft Fabric Spark notebooks. Use when diagnosing slow pandas operations, toPandas() out-of-memory errors, pandas API on Spark (pyspark.pandas) bottlenecks, DataFrame conversion failures, collect() memory issues, driver memory exhaustion, notebook cell timeouts, or when optimizing pandas workloads for Fabric capacity. Covers pandas vs Spark DataFrame conversion, memory profiling, broadcast joins, shuffle tuning, resource profiles, and Native Execution Engine integration. Read `references/PatrickGallucci/skills/fabric-pandas-perf-remediate/SKILL.md`.
Return the requested deliverable with evidence, assumptions, validation performed, unresolved dependencies, and actual execution status. Source-specific results must be verified independently; importing these methods does not establish a working service connection.