This walkthrough is planned but not published yet. The transcript below covers the same material if you would rather read it now.
Split a dataframe workload across workers without rewriting your analysis.
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.