polars.scan_parquet#

polars.scan_parquet(file: 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, object] | None = None, low_memory: bool = False) 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:
file

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: bool

Reduce memory pressure at the expense of performance.