Working with results

Stub

Final content will be a tour of the Result wrapper — how to get your data out as pandas, Polars, Arrow, or parquet, and when to pick which.

Topics planned:

  • A cheat-sheet table of every Result method: arrow, df, to_pandas, to_polars, batches, head, column, write_parquet.
  • When the data lives in memory vs on disk (auto-spill, inmem_row_limit).
  • .batches() for streaming through results that don't fit in RAM.
  • Result.write_parquet(path, layout="hats" | "single") — the two output shapes.
  • A "Want it faster?" callout on pandas vs Polars: lead with pandas because that's what everyone knows, then nudge readers to r.to_polars() for anything beyond inspection (filtering, group-by, joins on multi-million-row results are routinely 5–50× faster). Include a side-by-side pandas / astropy.Table / Polars example for the same filter-group-mean operation.

For now, the Python API surface in the README lists every method.