demo + utils venv
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d3m1g0d
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""" parquet compat """
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from distutils.version import LooseVersion
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from warnings import catch_warnings
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from pandas.compat import string_types
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from pandas.errors import AbstractMethodError
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from pandas import DataFrame, get_option
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from pandas.io.common import get_filepath_or_buffer, is_s3_url
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def get_engine(engine):
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""" return our implementation """
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if engine == 'auto':
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engine = get_option('io.parquet.engine')
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if engine == 'auto':
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# try engines in this order
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try:
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return PyArrowImpl()
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except ImportError:
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pass
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try:
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return FastParquetImpl()
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except ImportError:
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pass
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raise ImportError("Unable to find a usable engine; "
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"tried using: 'pyarrow', 'fastparquet'.\n"
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"pyarrow or fastparquet is required for parquet "
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"support")
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if engine not in ['pyarrow', 'fastparquet']:
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raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
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if engine == 'pyarrow':
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return PyArrowImpl()
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elif engine == 'fastparquet':
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return FastParquetImpl()
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class BaseImpl(object):
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api = None # module
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@staticmethod
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def validate_dataframe(df):
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if not isinstance(df, DataFrame):
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raise ValueError("to_parquet only supports IO with DataFrames")
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# must have value column names (strings only)
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if df.columns.inferred_type not in {'string', 'unicode'}:
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raise ValueError("parquet must have string column names")
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# index level names must be strings
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valid_names = all(
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isinstance(name, string_types)
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for name in df.index.names
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if name is not None
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)
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if not valid_names:
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raise ValueError("Index level names must be strings")
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def write(self, df, path, compression, **kwargs):
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raise AbstractMethodError(self)
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def read(self, path, columns=None, **kwargs):
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raise AbstractMethodError(self)
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class PyArrowImpl(BaseImpl):
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def __init__(self):
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# since pandas is a dependency of pyarrow
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# we need to import on first use
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try:
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import pyarrow
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import pyarrow.parquet
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except ImportError:
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raise ImportError(
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"pyarrow is required for parquet support\n\n"
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"you can install via conda\n"
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"conda install pyarrow -c conda-forge\n"
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"\nor via pip\n"
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"pip install -U pyarrow\n"
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)
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if LooseVersion(pyarrow.__version__) < '0.9.0':
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raise ImportError(
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"pyarrow >= 0.9.0 is required for parquet support\n\n"
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"you can install via conda\n"
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"conda install pyarrow -c conda-forge\n"
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"\nor via pip\n"
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"pip install -U pyarrow\n"
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)
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self.api = pyarrow
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def write(self, df, path, compression='snappy',
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coerce_timestamps='ms', index=None, partition_cols=None,
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**kwargs):
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self.validate_dataframe(df)
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path, _, _, _ = get_filepath_or_buffer(path, mode='wb')
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if index is None:
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from_pandas_kwargs = {}
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else:
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from_pandas_kwargs = {'preserve_index': index}
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table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
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if partition_cols is not None:
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self.api.parquet.write_to_dataset(
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table, path, compression=compression,
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coerce_timestamps=coerce_timestamps,
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partition_cols=partition_cols, **kwargs)
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else:
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self.api.parquet.write_table(
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table, path, compression=compression,
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coerce_timestamps=coerce_timestamps, **kwargs)
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def read(self, path, columns=None, **kwargs):
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path, _, _, should_close = get_filepath_or_buffer(path)
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kwargs['use_pandas_metadata'] = True
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result = self.api.parquet.read_table(path, columns=columns,
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**kwargs).to_pandas()
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if should_close:
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try:
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path.close()
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except: # noqa: flake8
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pass
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return result
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class FastParquetImpl(BaseImpl):
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def __init__(self):
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# since pandas is a dependency of fastparquet
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# we need to import on first use
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try:
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import fastparquet
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except ImportError:
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raise ImportError(
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"fastparquet is required for parquet support\n\n"
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"you can install via conda\n"
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"conda install fastparquet -c conda-forge\n"
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"\nor via pip\n"
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"pip install -U fastparquet"
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)
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if LooseVersion(fastparquet.__version__) < '0.2.1':
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raise ImportError(
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"fastparquet >= 0.2.1 is required for parquet "
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"support\n\n"
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"you can install via conda\n"
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"conda install fastparquet -c conda-forge\n"
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"\nor via pip\n"
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"pip install -U fastparquet"
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)
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self.api = fastparquet
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def write(self, df, path, compression='snappy', index=None,
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partition_cols=None, **kwargs):
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self.validate_dataframe(df)
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# thriftpy/protocol/compact.py:339:
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# DeprecationWarning: tostring() is deprecated.
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# Use tobytes() instead.
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if 'partition_on' in kwargs and partition_cols is not None:
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raise ValueError("Cannot use both partition_on and "
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"partition_cols. Use partition_cols for "
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"partitioning data")
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elif 'partition_on' in kwargs:
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partition_cols = kwargs.pop('partition_on')
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if partition_cols is not None:
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kwargs['file_scheme'] = 'hive'
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if is_s3_url(path):
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# path is s3:// so we need to open the s3file in 'wb' mode.
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# TODO: Support 'ab'
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path, _, _, _ = get_filepath_or_buffer(path, mode='wb')
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# And pass the opened s3file to the fastparquet internal impl.
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kwargs['open_with'] = lambda path, _: path
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else:
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path, _, _, _ = get_filepath_or_buffer(path)
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with catch_warnings(record=True):
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self.api.write(path, df, compression=compression,
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write_index=index, partition_on=partition_cols,
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**kwargs)
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def read(self, path, columns=None, **kwargs):
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if is_s3_url(path):
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# When path is s3:// an S3File is returned.
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# We need to retain the original path(str) while also
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# pass the S3File().open function to fsatparquet impl.
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s3, _, _, should_close = get_filepath_or_buffer(path)
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try:
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parquet_file = self.api.ParquetFile(path, open_with=s3.s3.open)
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finally:
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s3.close()
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else:
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path, _, _, _ = get_filepath_or_buffer(path)
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parquet_file = self.api.ParquetFile(path)
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return parquet_file.to_pandas(columns=columns, **kwargs)
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def to_parquet(df, path, engine='auto', compression='snappy', index=None,
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partition_cols=None, **kwargs):
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"""
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Write a DataFrame to the parquet format.
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Parameters
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----------
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path : str
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File path or Root Directory path. Will be used as Root Directory path
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while writing a partitioned dataset.
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.. versionchanged:: 0.24.0
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engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
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Parquet library to use. If 'auto', then the option
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``io.parquet.engine`` is used. The default ``io.parquet.engine``
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behavior is to try 'pyarrow', falling back to 'fastparquet' if
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'pyarrow' is unavailable.
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compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
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Name of the compression to use. Use ``None`` for no compression.
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index : bool, default None
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If ``True``, include the dataframe's index(es) in the file output. If
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``False``, they will not be written to the file. If ``None``, the
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engine's default behavior will be used.
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.. versionadded 0.24.0
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partition_cols : list, optional, default None
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Column names by which to partition the dataset
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Columns are partitioned in the order they are given
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.. versionadded:: 0.24.0
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kwargs
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Additional keyword arguments passed to the engine
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"""
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impl = get_engine(engine)
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return impl.write(df, path, compression=compression, index=index,
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partition_cols=partition_cols, **kwargs)
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def read_parquet(path, engine='auto', columns=None, **kwargs):
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"""
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Load a parquet object from the file path, returning a DataFrame.
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.. versionadded 0.21.0
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Parameters
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----------
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path : string
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File path
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columns : list, default=None
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If not None, only these columns will be read from the file.
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.. versionadded 0.21.1
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engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
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Parquet library to use. If 'auto', then the option
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``io.parquet.engine`` is used. The default ``io.parquet.engine``
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behavior is to try 'pyarrow', falling back to 'fastparquet' if
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'pyarrow' is unavailable.
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kwargs are passed to the engine
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Returns
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-------
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DataFrame
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"""
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impl = get_engine(engine)
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return impl.read(path, columns=columns, **kwargs)
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