polars.read_parquet#
- polars.read_parquet(source: str | Path | BinaryIO | BytesIO | bytes, columns: list[int] | list[str] | None = None, n_rows: int | None = None, use_pyarrow: bool = False, memory_map: bool = True, storage_options: dict[str, Any] | None = None, parallel: ParallelStrategy = 'auto', row_count_name: str | None = None, row_count_offset: int = 0, low_memory: bool = False, pyarrow_options: dict[str, Any] | None = None) DataFrame[source]#
Read into a DataFrame from a parquet file.
- Parameters:
- source
Path to a file, or a file-like object. If the path is a directory, that directory will be used as partition aware scan. If
fsspecis installed, it will be used to open remote files.- columns
Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.
- n_rows
Stop reading from parquet file after reading
n_rows. Only valid when use_pyarrow=False.- use_pyarrow
Use pyarrow instead of the Rust native parquet reader. The pyarrow reader is more stable.
- memory_map
Memory map underlying file. This will likely increase performance. Only used when
use_pyarrow=True.- storage_options
Extra options that make sense for
fsspec.open()or a particular storage connection, e.g. host, port, username, password, etc.- parallel{‘auto’, ‘columns’, ‘row_groups’, ‘none’}
This determines the direction of parallelism. ‘auto’ will try to determine the optimal direction.
- 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).
- low_memory
Reduce memory pressure at the expense of performance.
- pyarrow_options
Keyword arguments for pyarrow.parquet.read_table.
- Returns:
- DataFrame
Notes
This operation defaults to a rechunk operation at the end, meaning that all data will be stored continuously in memory. Set rechunk=False if you are benchmarking the parquet-reader. A rechunk is an expensive operation.