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, use_statistics: bool = True, rechunk: bool = True, ) → 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, files in that directory will all be read. If fsspec is 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.
use_statistics: Use statistics in the parquet to determine if pages can be skipped from reading.
rechunk: Make sure that all columns are contiguous in memory by aggregating the chunks into a single array.

Returns:

DataFrame