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.