Python pyarrow.parquet方法代碼示例

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
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'}:
            raise ValueError("parquet must have string column names")

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

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def __init__(self):
        # since pandas is a dependency of pyarrow
        # we need to import on first use
        try:
            import pyarrow
            import pyarrow.parquet
        except ImportError:
            raise ImportError(
                "pyarrow is required for parquet support\n\n"
                "you can install via conda\n"
                "conda install pyarrow -c conda-forge\n"
                "\nor via pip\n"
                "pip install -U pyarrow\n"
            )
        if LooseVersion(pyarrow.__version__) < '0.9.0':
            raise ImportError(
                "pyarrow >= 0.9.0 is required for parquet support\n\n"
                "you can install via conda\n"
                "conda install pyarrow -c conda-forge\n"
                "\nor via pip\n"
                "pip install -U pyarrow\n"
            )

        self.api = pyarrow 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
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) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def write(self, df, path, compression='snappy',
              coerce_timestamps='ms', **kwargs):
        self.validate_dataframe(df)
        if self._pyarrow_lt_070:
            self._validate_write_lt_070(df)
        path, _, _, _ = get_filepath_or_buffer(path, mode='wb')

        if self._pyarrow_lt_060:
            table = self.api.Table.from_pandas(df, timestamps_to_ms=True)
            self.api.parquet.write_table(
                table, path, compression=compression, **kwargs)

        else:
            table = self.api.Table.from_pandas(df)
            self.api.parquet.write_table(
                table, path, compression=compression,
                coerce_timestamps=coerce_timestamps, **kwargs) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def read(self, path, columns=None, **kwargs):
        path, _, _, should_close = get_filepath_or_buffer(path)
        if self._pyarrow_lt_070:
            result = self.api.parquet.read_pandas(path, columns=columns,
                                                  **kwargs).to_pandas()
        else:
            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 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def to_parquet(df, path, engine='auto', compression='snappy', **kwargs):
    """
    Write a DataFrame to the parquet format.

    Parameters
    ----------
    df : DataFrame
    path : string
        File path
    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.
    kwargs
        Additional keyword arguments passed to the engine
    """
    impl = get_engine(engine)
    return impl.write(df, path, compression=compression, **kwargs) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def write(self, df, path, compression='snappy',
              coerce_timestamps='ms', **kwargs):
        self.validate_dataframe(df)
        if self._pyarrow_lt_070:
            self._validate_write_lt_070(df)
        path, _, _ = get_filepath_or_buffer(path)

        if self._pyarrow_lt_060:
            table = self.api.Table.from_pandas(df, timestamps_to_ms=True)
            self.api.parquet.write_table(
                table, path, compression=compression, **kwargs)

        else:
            table = self.api.Table.from_pandas(df)
            self.api.parquet.write_table(
                table, path, compression=compression,
                coerce_timestamps=coerce_timestamps, **kwargs) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def to_parquet(df, path, engine='auto', compression='snappy', **kwargs):
    """
    Write a DataFrame to the parquet format.

    Parameters
    ----------
    df : DataFrame
    path : string
        File path
    engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
        Parquet reader library to use. If 'auto', then the option
        'io.parquet.engine' is used. If 'auto', then the first
        library to be installed is used.
    compression : str, optional, default 'snappy'
        compression method, includes {'gzip', 'snappy', 'brotli'}
    kwargs
        Additional keyword arguments passed to the engine
    """
    impl = get_engine(engine)
    return impl.write(df, path, compression=compression, **kwargs) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def parquet_file(
    table: Union[Dict[str, List[Any]], pyarrow.Table],
    dir: Optional[pathlib.Path] = None,
) -> ContextManager[pathlib.Path]:
    """
    Yield a filename with `table` written to a Parquet file.
    """
    if isinstance(table, dict):
        table = pyarrow.table(table)

    with tempfile_context(dir=dir) as parquet_path:
        pyarrow.parquet.write_table(
            table,
            parquet_path,
            version="2.0",
            compression="SNAPPY",
            use_dictionary=[
                name.encode("utf-8")
                for name, column in zip(table.column_names, table.columns)
                if pyarrow.types.is_dictionary(column.type)
            ],
        )
        yield parquet_path 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
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

    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() 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def __init__(self):
        # since pandas is a dependency of pyarrow
        # we need to import on first use

        try:
            import pyarrow
            import pyarrow.parquet
        except ImportError:
            raise ImportError("pyarrow is required for parquet support\n\n"
                              "you can install via conda\n"
                              "conda install pyarrow -c conda-forge\n"
                              "\nor via pip\n"
                              "pip install -U pyarrow\n")

        if LooseVersion(pyarrow.__version__) < '0.4.1':
            raise ImportError("pyarrow >= 0.4.1 is required for parquet"
                              "support\n\n"
                              "you can install via conda\n"
                              "conda install pyarrow -c conda-forge\n"
                              "\nor via pip\n"
                              "pip install -U pyarrow\n")

        self._pyarrow_lt_050 = LooseVersion(pyarrow.__version__) < '0.5.0'
        self._pyarrow_lt_060 = LooseVersion(pyarrow.__version__) < '0.6.0'
        self.api = pyarrow 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def read_parquet(path, engine='auto', **kwargs):
    """
    Load a parquet object from the file path, returning a DataFrame.

    .. versionadded 0.21.0

    Parameters
    ----------
    path : string
        File path
    engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
        Parquet reader library to use. If 'auto', then the option
        'io.parquet.engine' is used. If 'auto', then the first
        library to be installed is used.
    kwargs are passed to the engine

    Returns
    -------
    DataFrame

    """

    impl = get_engine(engine)
    return impl.read(path) 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
def df_from_bytes_parquet_(bytes_: bytes) -> pd.DataFrame:
    """
    Since pyabc 0.9.14, pandas DataFrames are converted using
    pyarrow parquet. If the conversion to DataFrame fails,
    then `df_from_bytes_msgpack_` is tried, which was the formerly
    used method. This is in particular useful for databases that
    still employ the old format. In case errors occur here, it may
    be necessary to use a pandas version prior to 0.25.0.
    """
    try:
        b = BytesIO(bytes_)
        table = parquet.read_table(b)
        df = table.to_pandas()
    except pyarrow.lib.ArrowIOError:
        df = df_from_bytes_msgpack_(bytes_)
    return df 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
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() 

# 需要導入模塊: import pyarrow [as 別名]
# 或者: from pyarrow import parquet [as 別名]
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