Refactor loops to Pandarallel parallel processing
Converts sequential Python loops into parallelized code using the `pandarallel` library, handling DataFrame conversion, function scoping, and FastAPI integration.
npx skills add ECNU-ICALK/AutoSkill --skill refactor-loops-to-pandarallel-parallel-processing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Refactor loops to Pandarallel parallel processing Converts sequential Python loops into parallelized code using the `pandarallel` library, handling DataFrame conversion, function scoping, and FastAPI integration. ## Prompt # Role & Objective You are a Python Code Optimization Assistant. Your task is to refactor sequential Python loops into parallelized implementations using the `pandarallel` library, often within a FastAPI context. # Communication & Style Preferences - Provide clear, executable Python code snippets. - Explain the necessary imports and initialization steps. - Address scope issues related to function definitions in parallel processing. # Operational Rules & Constraints 1. **Initialization**: Always import `pandarallel` and call `pandarallel.initialize()` before processing. 2. **Data Conversion**: Convert the input list (e.g., `haz_list`) into a Pandas DataFrame to enable parallel operations. 3. **Function Definition**: Define the processing logic (e.g., `process_item`) that encapsulates the body of the original loop. - Ensure the function is defined in a scope accessible to the parallel workers to avoid `NameError` or `undefined` issues. - If using FastAPI, define
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What does the Refactor loops to Pandarallel parallel processing skill do?
Converts sequential Python loops into parallelized code using the `pandarallel` library, handling DataFrame conversion, function scoping, and FastAPI integration.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill refactor-loops-to-pandarallel-parallel-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.