Source code for pyarrow.feather

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import os

from pyarrow.pandas_compat import _pandas_api  # noqa
from pyarrow.lib import (Codec, Table,  # noqa
                         concat_tables, schema)
import pyarrow.lib as ext
from pyarrow import _feather
from pyarrow._feather import FeatherError  # noqa: F401


class FeatherDataset:
    """
    Encapsulates details of reading a list of Feather files.

    Parameters
    ----------
    path_or_paths : List[str]
        A list of file names
    validate_schema : bool, default True
        Check that individual file schemas are all the same / compatible
    """

    def __init__(self, path_or_paths, validate_schema=True):
        self.paths = path_or_paths
        self.validate_schema = validate_schema

    def read_table(self, columns=None):
        """
        Read multiple feather files as a single pyarrow.Table

        Parameters
        ----------
        columns : List[str]
            Names of columns to read from the file

        Returns
        -------
        pyarrow.Table
            Content of the file as a table (of columns)
        """
        _fil = read_table(self.paths[0], columns=columns)
        self._tables = [_fil]
        self.schema = _fil.schema

        for path in self.paths[1:]:
            table = read_table(path, columns=columns)
            if self.validate_schema:
                self.validate_schemas(path, table)
            self._tables.append(table)
        return concat_tables(self._tables)

    def validate_schemas(self, piece, table):
        if not self.schema.equals(table.schema):
            raise ValueError('Schema in {!s} was different. \n'
                             '{!s}\n\nvs\n\n{!s}'
                             .format(piece, self.schema,
                                     table.schema))

    def read_pandas(self, columns=None, use_threads=True):
        """
        Read multiple Parquet files as a single pandas DataFrame

        Parameters
        ----------
        columns : List[str]
            Names of columns to read from the file
        use_threads : bool, default True
            Use multiple threads when converting to pandas

        Returns
        -------
        pandas.DataFrame
            Content of the file as a pandas DataFrame (of columns)
        """
        return self.read_table(columns=columns).to_pandas(
            use_threads=use_threads)


def check_chunked_overflow(name, col):
    if col.num_chunks == 1:
        return

    if col.type in (ext.binary(), ext.string()):
        raise ValueError("Column '{}' exceeds 2GB maximum capacity of "
                         "a Feather binary column. This restriction may be "
                         "lifted in the future".format(name))
    else:
        # TODO(wesm): Not sure when else this might be reached
        raise ValueError("Column '{}' of type {} was chunked on conversion "
                         "to Arrow and cannot be currently written to "
                         "Feather format".format(name, str(col.type)))


_FEATHER_SUPPORTED_CODECS = {'lz4', 'zstd', 'uncompressed'}


[docs]def write_feather(df, dest, compression=None, compression_level=None, chunksize=None, version=2): """ Write a pandas.DataFrame to Feather format. Parameters ---------- df : pandas.DataFrame or pyarrow.Table Data to write out as Feather format. dest : str Local destination path. compression : string, default None Can be one of {"zstd", "lz4", "uncompressed"}. The default of None uses LZ4 for V2 files if it is available, otherwise uncompressed. compression_level : int, default None Use a compression level particular to the chosen compressor. If None use the default compression level chunksize : int, default None For V2 files, the internal maximum size of Arrow RecordBatch chunks when writing the Arrow IPC file format. None means use the default, which is currently 64K version : int, default 2 Feather file version. Version 2 is the current. Version 1 is the more limited legacy format """ if _pandas_api.have_pandas: if (_pandas_api.has_sparse and isinstance(df, _pandas_api.pd.SparseDataFrame)): df = df.to_dense() if _pandas_api.is_data_frame(df): # Feather v1 creates a new column in the resultant Table to # store index information if index type is not RangeIndex if version == 1: preserve_index = False elif version == 2: preserve_index = None else: raise ValueError("Version value should either be 1 or 2") table = Table.from_pandas(df, preserve_index=preserve_index) if version == 1: # Version 1 does not chunking for i, name in enumerate(table.schema.names): col = table[i] check_chunked_overflow(name, col) else: table = df if version == 1: if len(table.column_names) > len(set(table.column_names)): raise ValueError("cannot serialize duplicate column names") if compression is not None: raise ValueError("Feather V1 files do not support compression " "option") if chunksize is not None: raise ValueError("Feather V1 files do not support chunksize " "option") else: if compression is None and Codec.is_available('lz4_frame'): compression = 'lz4' elif (compression is not None and compression not in _FEATHER_SUPPORTED_CODECS): raise ValueError('compression="{}" not supported, must be ' 'one of {}'.format(compression, _FEATHER_SUPPORTED_CODECS)) try: _feather.write_feather(table, dest, compression=compression, compression_level=compression_level, chunksize=chunksize, version=version) except Exception: if isinstance(dest, str): try: os.remove(dest) except os.error: pass raise
[docs]def read_feather(source, columns=None, use_threads=True, memory_map=False, **kwargs): """ Read a pandas.DataFrame from Feather format. To read as pyarrow.Table use feather.read_table. Parameters ---------- source : str file path, or file-like object You can use MemoryMappedFile as source, for explicitly use memory map. columns : sequence, optional Only read a specific set of columns. If not provided, all columns are read. use_threads : bool, default True Whether to parallelize reading using multiple threads. If false the restriction is used in the conversion to Pandas as well as in the reading from Feather format. memory_map : boolean, default False Use memory mapping when opening file on disk, when source is a str. **kwargs Additional keyword arguments passed on to `pyarrow.Table.to_pandas`. Returns ------- df : pandas.DataFrame The contents of the Feather file as a pandas.DataFrame """ return (read_table( source, columns=columns, memory_map=memory_map, use_threads=use_threads).to_pandas(use_threads=use_threads, **kwargs))
[docs]def read_table(source, columns=None, memory_map=False, use_threads=True): """ Read a pyarrow.Table from Feather format Parameters ---------- source : str file path, or file-like object You can use MemoryMappedFile as source, for explicitly use memory map. columns : sequence, optional Only read a specific set of columns. If not provided, all columns are read. memory_map : boolean, default False Use memory mapping when opening file on disk, when source is a str use_threads : bool, default True Whether to parallelize reading using multiple threads. Returns ------- table : pyarrow.Table The contents of the Feather file as a pyarrow.Table """ reader = _feather.FeatherReader( source, use_memory_map=memory_map, use_threads=use_threads) if columns is None: return reader.read() column_types = [type(column) for column in columns] if all(map(lambda t: t == int, column_types)): table = reader.read_indices(columns) elif all(map(lambda t: t == str, column_types)): table = reader.read_names(columns) else: column_type_names = [t.__name__ for t in column_types] raise TypeError("Columns must be indices or names. " "Got columns {} of types {}" .format(columns, column_type_names)) # Feather v1 already respects the column selection if reader.version < 3: return table # Feather v2 reads with sorted / deduplicated selection elif sorted(set(columns)) == columns: return table else: # follow exact order / selection of names return table.select(columns)