def add_feed_dict(self,
feed_dict: Dict[str, Any],
replace: bool = False) -> None:
"""Adds instances of ``MemoryDataSet``, containing the data provided
through feed_dict.
Args:
feed_dict: A feed dict with data to be added in memory.
replace: Specifies whether to replace an existing ``DataSet``
with the same name is allowed.
Example:
::
>>> import pandas as pd
>>>
>>> df = pd.DataFrame({'col1': [1, 2],
>>> 'col2': [4, 5],
>>> 'col3': [5, 6]})
>>>
>>> io = DataCatalog()
>>> io.add_feed_dict({
>>> 'data': df
>>> }, replace=True)
>>>
>>> assert io.load("data").equals(df)
"""
for data_set_name in feed_dict:
if isinstance(feed_dict[data_set_name], AbstractDataSet):
data_set = feed_dict[data_set_name]
else:
data_set = MemoryDataSet(data=feed_dict[data_set_name])
self.add(data_set_name, data_set, replace)
def __init__(
self,
dataset: Union[AbstractDataSet, Dict],
version: Version = None,
copy_mode: str = None,
):
"""Creates a new instance of ``CachedDataSet`` pointing to the
provided Python object.
Args:
dataset: A Kedro DataSet object or a dictionary to cache.
version: If specified, should be an instance of
``kedro.io.core.Version``. If its ``load`` attribute is
None, the latest version will be loaded. If its ``save``
attribute is None, save version will be autogenerated.
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.
Raises:
ValueError: If the provided dataset is not a valid dict/YAML
representation of a dataset or an actual dataset.
"""
if isinstance(dataset, dict):
self._dataset = self._from_config(dataset, version)
elif isinstance(dataset, AbstractDataSet):
self._dataset = dataset
else:
raise ValueError(
"The argument type of `dataset` should be either a dict/YAML "
"representation of the dataset, or the actual dataset object.")
self._cache = MemoryDataSet(copy_mode=copy_mode)