parquet.py

""" parquet compat """

from warnings import catch_warnings

from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError

from pandas import DataFrame, get_option

from pandas.io.common import get_filepath_or_buffer, is_s3_url


def get_engine(engine):
    """ return our implementation """

    if engine == "auto":
        engine = get_option("io.parquet.engine")

    if engine == "auto":
        # try engines in this order
        try:
            return PyArrowImpl()
        except ImportError:
            pass

        try:
            return FastParquetImpl()
        except ImportError:
            pass

        raise ImportError(
            "Unable to find a usable engine; "
            "tried using: 'pyarrow', 'fastparquet'.\n"
            "pyarrow or fastparquet is required for parquet "
            "support"
        )

    if engine not in ["pyarrow", "fastparquet"]:
        raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")

    if engine == "pyarrow":
        return PyArrowImpl()
    elif engine == "fastparquet":
        return FastParquetImpl()


class BaseImpl:

    api = None  # module

    @staticmethod
    def validate_dataframe(df):

        if not isinstance(df, DataFrame):
            raise ValueError("to_parquet only supports IO with DataFrames")

        # must have value column names (strings only)
        if df.columns.inferred_type not in {"string", "unicode", "empty"}:
            raise ValueError("parquet must have string column names")

        # index level names must be strings
        valid_names = all(
            isinstance(name, str) for name in df.index.names if name is not None
        )
        if not valid_names:
            raise ValueError("Index level names must be strings")

    def write(self, df, path, compression, **kwargs):
        raise AbstractMethodError(self)

    def read(self, path, columns=None, **kwargs):
        raise AbstractMethodError(self)


class PyArrowImpl(BaseImpl):
    def __init__(self):
        pyarrow = import_optional_dependency(
            "pyarrow", extra="pyarrow is required for parquet support."
        )
        import pyarrow.parquet

        self.api = pyarrow

    def write(
        self,
        df,
        path,
        compression="snappy",
        coerce_timestamps="ms",
        index=None,
        partition_cols=None,
        **kwargs
    ):
        self.validate_dataframe(df)
        path, _, _, _ = get_filepath_or_buffer(path, mode="wb")

        if index is None:
            from_pandas_kwargs = {}
        else:
            from_pandas_kwargs = {"preserve_index": index}
        table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
        if partition_cols is not None:
            self.api.parquet.write_to_dataset(
                table,
                path,
                compression=compression,
                coerce_timestamps=coerce_timestamps,
                partition_cols=partition_cols,
                **kwargs
            )
        else:
            self.api.parquet.write_table(
                table,
                path,
                compression=compression,
                coerce_timestamps=coerce_timestamps,
                **kwargs
            )

    def read(self, path, columns=None, **kwargs):
        path, _, _, should_close = get_filepath_or_buffer(path)

        kwargs["use_pandas_metadata"] = True
        result = self.api.parquet.read_table(
            path, columns=columns, **kwargs
        ).to_pandas()
        if should_close:
            try:
                path.close()
            except:  # noqa: flake8
                pass

        return result


class FastParquetImpl(BaseImpl):
    def __init__(self):
        # since pandas is a dependency of fastparquet
        # we need to import on first use
        fastparquet = import_optional_dependency(
            "fastparquet", extra="fastparquet is required for parquet support."
        )
        self.api = fastparquet

    def write(
        self, df, path, compression="snappy", index=None, partition_cols=None, **kwargs
    ):
        self.validate_dataframe(df)
        # thriftpy/protocol/compact.py:339:
        # DeprecationWarning: tostring() is deprecated.
        # Use tobytes() instead.

        if "partition_on" in kwargs and partition_cols is not None:
            raise ValueError(
                "Cannot use both partition_on and "
                "partition_cols. Use partition_cols for "
                "partitioning data"
            )
        elif "partition_on" in kwargs:
            partition_cols = kwargs.pop("partition_on")

        if partition_cols is not None:
            kwargs["file_scheme"] = "hive"

        if is_s3_url(path):
            # path is s3:// so we need to open the s3file in 'wb' mode.
            # TODO: Support 'ab'

            path, _, _, _ = get_filepath_or_buffer(path, mode="wb")
            # And pass the opened s3file to the fastparquet internal impl.
            kwargs["open_with"] = lambda path, _: path
        else:
            path, _, _, _ = get_filepath_or_buffer(path)

        with catch_warnings(record=True):
            self.api.write(
                path,
                df,
                compression=compression,
                write_index=index,
                partition_on=partition_cols,
                **kwargs
            )

    def read(self, path, columns=None, **kwargs):
        if is_s3_url(path):
            # When path is s3:// an S3File is returned.
            # We need to retain the original path(str) while also
            # pass the S3File().open function to fsatparquet impl.
            s3, _, _, should_close = get_filepath_or_buffer(path)
            try:
                parquet_file = self.api.ParquetFile(path, open_with=s3.s3.open)
            finally:
                s3.close()
        else:
            path, _, _, _ = get_filepath_or_buffer(path)
            parquet_file = self.api.ParquetFile(path)

        return parquet_file.to_pandas(columns=columns, **kwargs)


def to_parquet(
    df,
    path,
    engine="auto",
    compression="snappy",
    index=None,
    partition_cols=None,
    **kwargs
):
    """
    Write a DataFrame to the parquet format.

    Parameters
    ----------
    path : str
        File path or Root Directory path. Will be used as Root Directory path
        while writing a partitioned dataset.

        .. versionchanged:: 0.24.0

    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.
    compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
        Name of the compression to use. Use ``None`` for no compression.
    index : bool, default None
        If ``True``, include the dataframe's index(es) in the file output. If
        ``False``, they will not be written to the file. If ``None``, the
        engine's default behavior will be used.

        .. versionadded 0.24.0

    partition_cols : list, optional, default None
        Column names by which to partition the dataset
        Columns are partitioned in the order they are given

        .. versionadded:: 0.24.0

    kwargs
        Additional keyword arguments passed to the engine
    """
    impl = get_engine(engine)
    return impl.write(
        df,
        path,
        compression=compression,
        index=index,
        partition_cols=partition_cols,
        **kwargs
    )


def read_parquet(path, engine="auto", columns=None, **kwargs):
    """
    Load a parquet object from the file path, returning a DataFrame.

    .. versionadded 0.21.0

    Parameters
    ----------
    path : str, path object or file-like object
        Any valid string path is acceptable. The string could be a URL. Valid
        URL schemes include http, ftp, s3, and file. For file URLs, a host is
        expected. A local file could be:
        ``file://localhost/path/to/table.parquet``.

        If you want to pass in a path object, pandas accepts any
        ``os.PathLike``.

        By file-like object, we refer to objects with a ``read()`` method,
        such as a file handler (e.g. via builtin ``open`` function)
        or ``StringIO``.
    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.
    columns : list, default=None
        If not None, only these columns will be read from the file.

        .. versionadded 0.21.1
    **kwargs
        Any additional kwargs are passed to the engine.

    Returns
    -------
    DataFrame
    """

    impl = get_engine(engine)
    return impl.read(path, columns=columns, **kwargs)