Source code for kedro.extras.datasets.pandas.parquet_dataset

"""``ParquetDataSet`` loads/saves data from/to a Parquet file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas to handle the Parquet file.
import logging
from copy import deepcopy
from io import BytesIO
from pathlib import Path, PurePosixPath
from typing import Any, Dict

import fsspec
import pandas as pd
import pyarrow.parquet as pq

from import (

logger = logging.getLogger(__name__)

# NOTE: kedro.extras.datasets will be removed in Kedro 0.19.0.
# Any contribution to datasets should be made in kedro-datasets
# in kedro-plugins (

[docs]class ParquetDataSet(AbstractVersionedDataSet[pd.DataFrame, pd.DataFrame]): """``ParquetDataSet`` loads/saves data from/to a Parquet file using an underlying filesystem (e.g.: local, S3, GCS). It uses pandas to handle the Parquet file. Example usage for the `YAML API <\ data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml boats: type: pandas.ParquetDataSet filepath: data/01_raw/boats.parquet load_args: engine: pyarrow use_nullable_dtypes: True save_args: file_scheme: hive has_nulls: False engine: pyarrow trucks: type: pandas.ParquetDataSet filepath: abfs://container/02_intermediate/trucks.parquet credentials: dev_abs load_args: columns: [name, gear, disp, wt] index: name save_args: compression: GZIP partition_on: [name] Example usage for the `Python API <\ data_catalog.html#use-the-data-catalog-with-the-code-api>`_: :: >>> from kedro.extras.datasets.pandas import ParquetDataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> data_set = ParquetDataSet(filepath="test.parquet") >>> >>> reloaded = data_set.load() >>> assert data.equals(reloaded) """ DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any] # pylint: disable=too-many-arguments
[docs] def __init__( self, filepath: str, load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, version: Version = None, credentials: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ) -> None: """Creates a new instance of ``ParquetDataSet`` pointing to a concrete Parquet file on a specific filesystem. Args: filepath: Filepath in POSIX format to a Parquet file prefixed with a protocol like `s3://`. If prefix is not provided, `file` protocol (local filesystem) will be used. The prefix should be any protocol supported by ``fsspec``. It can also be a path to a directory. If the directory is provided then it can be used for reading partitioned parquet files. Note: `http(s)` doesn't support versioning. load_args: Additional options for loading Parquet file(s). Here you can find all available arguments when reading single file: Here you can find all available arguments when reading partitioned datasets: All defaults are preserved. save_args: Additional saving options for saving Parquet file(s). Here you can find all available arguments: All defaults are preserved. ``partition_cols`` is not supported. version: If specified, should be an instance of ````. If its ``load`` attribute is None, the latest version will be loaded. If its ``save`` attribute is None, save version will be autogenerated. credentials: Credentials required to get access to the underlying filesystem. E.g. for ``GCSFileSystem`` it should look like `{"token": None}`. fs_args: Extra arguments to pass into underlying filesystem class constructor (e.g. `{"project": "my-project"}` for ``GCSFileSystem``). """ _fs_args = deepcopy(fs_args) or {} _credentials = deepcopy(credentials) or {} protocol, path = get_protocol_and_path(filepath, version) if protocol == "file": _fs_args.setdefault("auto_mkdir", True) self._protocol = protocol self._storage_options = {**_credentials, **_fs_args} self._fs = fsspec.filesystem(self._protocol, **self._storage_options) super().__init__( filepath=PurePosixPath(path), version=version, exists_function=self._fs.exists, glob_function=self._fs.glob, ) # Handle default load and save arguments self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS) if load_args is not None: self._load_args.update(load_args) self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS) if save_args is not None: self._save_args.update(save_args) if "storage_options" in self._save_args or "storage_options" in self._load_args: logger.warning( "Dropping 'storage_options' for %s, " "please specify them under 'fs_args' or 'credentials'.", self._filepath, ) self._save_args.pop("storage_options", None) self._load_args.pop("storage_options", None)
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, protocol=self._protocol, load_args=self._load_args, save_args=self._save_args, version=self._version, ) def _load(self) -> pd.DataFrame: load_path = get_filepath_str(self._get_load_path(), self._protocol) if self._fs.isdir(load_path): # It doesn't work at least on S3 if root folder was created manually # data = ( pq.ParquetDataset(load_path, filesystem=self._fs) .read(**self._load_args) .to_pandas() ) else: data = self._load_from_pandas() return data def _load_from_pandas(self): load_path = str(self._get_load_path()) if self._protocol == "file": # file:// protocol seems to misbehave on Windows # (<urlopen error file not on local host>), # so we don't join that back to the filepath; # storage_options also don't work with local paths return pd.read_parquet(load_path, **self._load_args) load_path = f"{self._protocol}{PROTOCOL_DELIMITER}{load_path}" return pd.read_parquet( load_path, storage_options=self._storage_options, **self._load_args ) def _save(self, data: pd.DataFrame) -> None: save_path = get_filepath_str(self._get_save_path(), self._protocol) if Path(save_path).is_dir(): raise DataSetError( f"Saving {self.__class__.__name__} to a directory is not supported." ) if "partition_cols" in self._save_args: raise DataSetError( f"{self.__class__.__name__} does not support save argument " f"'partition_cols'. Please use '' instead." ) bytes_buffer = BytesIO() data.to_parquet(bytes_buffer, **self._save_args) with, mode="wb") as fs_file: fs_file.write(bytes_buffer.getvalue()) self._invalidate_cache() def _exists(self) -> bool: try: load_path = get_filepath_str(self._get_load_path(), self._protocol) except DataSetError: return False return self._fs.exists(load_path) def _release(self) -> None: super()._release() self._invalidate_cache() def _invalidate_cache(self) -> None: """Invalidate underlying filesystem caches.""" filepath = get_filepath_str(self._filepath, self._protocol) self._fs.invalidate_cache(filepath)