polars.scan_parquet#

polars.scan_parquet( source: str | Path, *, n_rows: int | None = None, cache: bool = True, parallel: ParallelStrategy = 'auto', rechunk: bool = True, row_count_name: str | None = None, row_count_offset: int = 0, storage_options: dict[str, Any] | None = None, low_memory: bool = False, use_statistics: bool = True, ) → LazyFrame[source]#

Lazily read from a parquet 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 file.
n_rows: Stop reading from parquet file after reading n_rows.
cache: Cache the result after reading.
parallel{‘auto’, ‘columns’, ‘row_groups’, ‘none’}: This determines the direction of parallelism. ‘auto’ will try to determine the optimal direction.
rechunk: In case of reading multiple files via a glob pattern rechunk the final DataFrame into contiguous memory chunks.
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.
low_memory: Reduce memory pressure at the expense of performance.
use_statistics: Use statistics in the parquet to determine if pages can be skipped from reading.