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.
How to install fabric-pandas-perf-remediate?
patrickgallucci/fabric-skills/fabric-pandas-perf-remediate
$npx -y skills add patrickgallucci/fabric-skills --skill fabric-pandas-perf-remediateInstalls into the current project.
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Run `npx skills use "https://github.com/patrickgallucci/fabric-skills" --skill "patrickgallucci/fabric-skills/fabric-pandas-perf-remediate"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/patrickgallucci/fabric-skills" that are relevant to the current task. Run `npx skills add "https://github.com/patrickgallucci/fabric-skills"` and select the relevant skills, then follow their instructions.
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