ak.from_parquet --------------- .. py:module: ak.from_parquet Defined in `awkward.operations.ak_from_parquet `__ on `line 16 `__. .. py:function:: ak.from_parquet(path, *, columns=None, row_groups=None, storage_options=None, max_gap=64000, max_block=256000000, footer_sample_size=1000000, generate_bitmasks=False, highlevel=True, behavior=None, attrs=None) :param path: Local filename or remote URL, passed to fsspec for resolution. May contain glob patterns. :type path: str :param columns: Glob pattern(s) with bash-like curly brackets for matching column names. Nested records are separated by dots. If a list of patterns, the logical-or is matched. If None, all columns are read. :type columns: None, str, or list of str :param row_groups: Row groups to read; must be non-negative. Order is ignored: the output array is presented in the order specified by Parquet metadata. If None, all row groups/all rows are read. :type row_groups: None or set of int :param storage_options: Passed to ``fsspec.parquet.open_parquet_file``. :param max_gap: Passed to ``fsspec.parquet.open_parquet_file``. :type max_gap: int :param max_block: Passed to ``fsspec.parquet.open_parquet_file``. :type max_block: int :param footer_sample_size: Passed to ``fsspec.parquet.open_parquet_file``. :type footer_sample_size: int :param generate_bitmasks: If enabled and Arrow/Parquet does not have Awkward metadata, ``generate_bitmasks=True`` creates empty bitmasks for nullable types that don't have bitmasks in the Arrow/Parquet data, so that the Form (BitMaskedForm vs UnmaskedForm) is predictable. :type generate_bitmasks: bool :param highlevel: If True, return an :py:obj:`ak.Array`; otherwise, return a low-level :py:obj:`ak.contents.Content` subclass. :type highlevel: bool :param behavior: Custom :py:obj:`ak.behavior` for the output array, if high-level. :type behavior: None or dict :param attrs: Custom attributes for the output array, if high-level. :type attrs: None or dict Reads data from a local or remote Parquet file or collection of files. The data are eagerly (not lazily) read and must fit into memory. Use ``columns`` and/or ``row_groups`` to select and filter manageable subsets of the data, and use :py:obj:`ak.metadata_from_parquet` to find column names and the range of row groups that a dataset has. See also :py:obj:`ak.to_parquet`, :py:obj:`ak.metadata_from_parquet`.