hLike[str]``), or file-like
        object implementing a binary ``read()`` function.
        The string could be a URL. Valid URL schemes include http, ftp, s3,
        gs, and file. For file URLs, a host is expected. A local file could be:
        ``file://localhost/path/to/table.parquet``.
        A file URL can also be a path to a directory that contains multiple
        partitioned parquet files. Both pyarrow and fastparquet support
        paths to directories as well as file URLs. A directory path could be:
        ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
    engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
        Parquet library to use. If 'auto', then the option
        ``io.parquet.engine`` is used. The default ``io.parquet.engine``
        behavior is to try 'pyarrow', falling back to 'fastparquet' if
        'pyarrow' is unavailable.

        When using the ``'pyarrow'`` engine and no storage options are provided
        and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
        (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
        Use the filesystem keyword with an instantiated fsspec filesystem
        if you wish to use its implementation.
    columns : list, default=None
        If not None, only these columns will be read from the file.
    {storage_options}

        .. versionadded:: 1.3.0

    use_nullable_dtypes : bool, default False
        If True, use dtypes that use ``pd.NA`` as missing value indicator
        for the resulting DataFrame. (only applicable for the ``pyarrow``
        engine)
        As new dtypes are added that support ``pd.NA`` in the future, the
        output with this option will change to use those dtypes.
        Note: this is an experimental option, and behaviour (e.g. additional
        support dtypes) may change without notice.

        .. deprecated:: 2.0

    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
        Back-end data type applied to the resultant :class:`DataFrame`
        (still experimental). Behaviour is as follows:

        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
          (default).
        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
          DataFrame.

        .. versionadded:: 2.0

    filesystem : fsspec or pyarrow filesystem, default None
        Filesystem object to use when reading the parquet file. Only implemented
        for ``engine="pyarrow"``.

        .. versionadded:: 2.1.0

    filters : List[Tuple] or List[List[Tuple]], default None
        To filter out data.
        Filter syntax: [[(column, op, val), ...],...]
        where op is [==, =, >, >=, <, <=, !=, in, not in]
        The innermost tuples are transposed into a set of filters applied
        through an `AND` operation.
        The outer list combines these sets of filters through an `OR`
        operation.
        A single list of tuples can also be used, meaning that no `OR`
        operation between set of filters is to be conducted.

        Using this argument will NOT result in row-wise filtering of the final
        partitions unless ``engine="pyarrow"`` is also specified.  For
        other engines, filtering is only performed at the partition level, that is,
        to prevent the loading of some row-groups and/or files.

        .. versionadded:: 2.1.0

    **kwargs
        Any additional kwargs are passed to the engine.

    Returns
    -------
    DataFrame

    See Also
    --------
    DataFrame.to_parquet : Create a parquet object that serializes a DataFrame.

    Examples
    --------
    >>> original_df = pd.DataFrame(
    ...     {{"foo": range(5), "bar": range(5, 10)}}
    ...    )
    >>> original_df
       foo  bar
    0    0    5
    1    1    6
    2    2    7
    3    3    8
    4    4    9
    >>> df_parquet_bytes = original_df.to_parquet()
    >>> from io import BytesIO
    >>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
    >>> restored_df
       foo  bar
    0    0    5
    1    1    6
    2    2    7
    3    3    8
    4    4    9
    >>> restored_df.equals(original_df)
    True
    >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
    >>> restored_bar
        bar
    0    5
    1    6
    2    7
    3    8
    4    9
    >>> restored_bar.equals(original_df[['bar']])
    True

    The function uses `kwargs` that are passed directly to the engine.
    In the following example, we use the `filters` argument of the pyarrow
    engine to filter the rows of the DataFrame.

    Since `pyarrow` is the default engine, we can omit the `engine` argument.
    Note that the `filters` argument is implemented by the `pyarrow` engine,
    which can benefit from multithreading and also potentially be more
    economical in terms of memory.

    >>> sel = [("foo", ">", 2)]
    >>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
    >>> restored_part
        foo  bar
    0    3    8
    1    4    9
    zYThe argument 'use_nullable_dtypes' is deprecated and will be removed in a future version.TzFUse dtype_backend='numpy_nullable' instead of use_nullable_dtype=True.)