Source code for

"""``MemoryDataset`` is a data set implementation which handles in-memory data.
from __future__ import annotations

import copy
from typing import Any

from import AbstractDataset, DatasetError

_EMPTY = object()

[docs] class MemoryDataset(AbstractDataset): """``MemoryDataset`` loads and saves data from/to an in-memory Python object. The `_EPHEMERAL` attribute is set to True to indicate MemoryDataset's non-persistence. Example: :: >>> from import MemoryDataset >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> dataset = MemoryDataset(data=data) >>> >>> loaded_data = dataset.load() >>> assert loaded_data.equals(data) >>> >>> new_data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5]}) >>> >>> reloaded_data = dataset.load() >>> assert reloaded_data.equals(new_data) """
[docs] def __init__( self, data: Any = _EMPTY, copy_mode: str | None = None, metadata: dict[str, Any] | None = None, ): """Creates a new instance of ``MemoryDataset`` pointing to the provided Python object. Args: data: Python object containing the data. copy_mode: The copy mode used to copy the data. Possible values are: "deepcopy", "copy" and "assign". If not provided, it is inferred based on the data type. metadata: Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins. """ self._data = _EMPTY self._copy_mode = copy_mode self.metadata = metadata self._EPHEMERAL = True if data is not _EMPTY: self._save(data)
def _load(self) -> Any: if self._data is _EMPTY: raise DatasetError("Data for MemoryDataset has not been saved yet.") copy_mode = self._copy_mode or _infer_copy_mode(self._data) data = _copy_with_mode(self._data, copy_mode=copy_mode) return data def _save(self, data: Any) -> None: copy_mode = self._copy_mode or _infer_copy_mode(data) self._data = _copy_with_mode(data, copy_mode=copy_mode) def _exists(self) -> bool: return self._data is not _EMPTY def _release(self) -> None: self._data = _EMPTY def _describe(self) -> dict[str, Any]: if self._data is not _EMPTY: return {"data": f"<{type(self._data).__name__}>"} # the string representation of datasets leaves out __init__ # arguments that are empty/None, equivalent here is _EMPTY return {"data": None} # pragma: no cover
def _infer_copy_mode(data: Any) -> str: """Infers the copy mode to use given the data type. Args: data: The data whose type will be used to infer the copy mode. Returns: One of "copy", "assign" or "deepcopy" as the copy mode to use. """ try: import pandas as pd except ImportError: # pragma: no cover pd = None # type: ignore[assignment] # pragma: no cover try: import numpy as np except ImportError: # pragma: no cover np = None # type: ignore[assignment] # pragma: no cover if pd and isinstance(data, pd.DataFrame) or np and isinstance(data, np.ndarray): copy_mode = "copy" elif type(data).__name__ == "DataFrame": copy_mode = "assign" else: copy_mode = "deepcopy" return copy_mode def _copy_with_mode(data: Any, copy_mode: str) -> Any: """Returns the copied data using the copy mode specified. If no copy mode is provided, then it is inferred based on the type of the data. Args: data: The data to copy. copy_mode: The copy mode to use, one of "deepcopy", "copy" and "assign". Raises: DatasetError: If copy_mode is specified, but isn't valid (i.e: not one of deepcopy, copy, assign) Returns: The data copied according to the specified copy mode. """ if copy_mode == "deepcopy": copied_data = copy.deepcopy(data) elif copy_mode == "copy": copied_data = data.copy() elif copy_mode == "assign": copied_data = data else: raise DatasetError( f"Invalid copy mode: {copy_mode}. " f"Possible values are: deepcopy, copy, assign." ) return copied_data