polars.scan_ipc#

polars.scan_ipc( source: str | Path | list[str] | list[Path], *, n_rows: int | None = None, cache: bool = True, rechunk: bool = True, row_count_name: str | None = None, row_count_offset: int = 0, storage_options: dict[str, Any] | None = None, memory_map: bool = True, ) → LazyFrame[source]#

Lazily read from an Arrow IPC (Feather v2) file or multiple files via glob patterns.

This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.

Parameters:

source: Path to a IPC file.
n_rows: Stop reading from IPC file after reading n_rows.
cache: Cache the result after reading.
rechunk: Reallocate to contiguous memory when all chunks/ files are parsed.
row_count_name: If not None, this will insert a row count column with give name into the DataFrame
row_count_offset: Offset to start the row_count column (only use if the name is set)
storage_options: Extra options that make sense for fsspec.open() or a particular storage connection. e.g. host, port, username, password, etc.
memory_map: Try to memory map the file. This can greatly improve performance on repeated queries as the OS may cache pages. Only uncompressed IPC files can be memory mapped.