# Parallel pandas in practice

Split a dataframe workload across workers without rewriting your analysis.

Source: https://university.eugo.io/videos/parallel-pandas-in-practice

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- Level: Intermediate
- Duration: 7m
- Topics: data-io, distributed-compute, performance
- Status: not yet recorded — the transcript below covers the same material

## What this video covers

The pattern is partition, distribute, combine. Split the input into chunks that can be processed independently, wrap the per-chunk work in a distributed function, collect the futures, then concatenate the results. Your analysis code inside the function is unchanged pandas. The important judgment is chunk size: too small and scheduling overhead dominates, too large and you lose parallelism at the tail.