pandas2df: fast-path nullable Int64/UInt64 columns - #4
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reticulate::py_to_r() walks pandas nullable integer extension columns (dtype Int64/UInt64) cell by cell, which is pathologically slow on the large id columns that dominate synapse / partner queries (~24x the whole conversion; ~58s for a 327k-row synapse frame in fafbseg). Convert those columns up front via the existing string -> bit64 path (classify_integer_strings, honouring bigint) and filter them out of the frame handed to py_to_r, then splice them back into their original column positions. Columns the fast path declines (all-NA) stay in the py_to_r pass and keep their native R type. Output is unchanged. Ported from natverse/fafbseg's fix/pandas2df-nullable-int64-speed and adapted to nat.python's bigint / classify_integer_strings refactor. The empty-frame check now reads nrow() from the original frame, since an all-fast frame yields a 0-column res whose nrow() would misreport as 0. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DUcpVWK2jMbGn5GkXhWTmz
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reticulate::py_to_r() walks pandas nullable integer extension columns
(Int64/UInt64) cell-by-cell, which is pathologically slow: ~58s for a
327k-row × 12-col synapse frame. This extracts those columns directly to
bit64::integer64 and drops them from the py_to_r() pass, falling back to
py_to_r for any column the fast path declines (fits int32, all-NA).
~24× faster on that frame (58s → 2.5s); output byte-for-byte identical.
Object/datetime column handling and nullable-int NA precision preserved.
Branches off before the "minimal" bundle merge but only touches
R/convert.R + its test, so main's env.R minimal bundle merges cleanly.
Needed on main for the v0.2.0 release (NEWS credits this speedup).
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.8 noreply@anthropic.com