kedro_datasets.partitions.PartitionedDataset¶
- class kedro_datasets.partitions.PartitionedDataset(*, path, dataset, filepath_arg='filepath', filename_suffix='', credentials=None, load_args=None, fs_args=None, overwrite=False, metadata=None)[source]¶
PartitionedDataset
loads and saves partitioned file-like data using the underlying dataset definition. For filesystem level operations it uses fsspec: https://github.com/intake/filesystem_spec.It also supports advanced features like lazy saving.
Example usage for the YAML API:
station_data: type: PartitionedDataset path: data/03_primary/station_data dataset: type: pandas.CSVDataset load_args: sep: '\t' save_args: sep: '\t' index: true filename_suffix: '.dat'
Example usage for the Python API:
import pandas as pd from kedro_datasets.partitions import PartitionedDataset # Create a fake pandas dataframe with 10 rows of data df = pd.DataFrame([{"DAY_OF_MONTH": str(i), "VALUE": i} for i in range(1, 11)]) # Convert it to a dict of pd.DataFrame with DAY_OF_MONTH as the dict key dict_df = { ... day_of_month: df[df["DAY_OF_MONTH"] == day_of_month] ... for day_of_month in df["DAY_OF_MONTH"] ... } # Save it as small paritions with DAY_OF_MONTH as the partition key dataset = PartitionedDataset( ... path=tmp_path / "df_with_partition", ... dataset="pandas.CSVDataset", ... filename_suffix=".csv", ... ) # This will create a folder `df_with_partition` and save multiple files # with the dict key + filename_suffix as filename, i.e. 1.csv, 2.csv etc. dataset.save(dict_df) # This will create lazy load functions instead of loading data into memory immediately. loaded = dataset.load() # Load all the partitions for partition_id, partition_load_func in loaded.items(): ... # The actual function that loads the data ... partition_data = partition_load_func() ... # Add the processing logic for individual partition HERE print(partition_data)
You can also load multiple partitions from a remote storage and combine them like this:
import pandas as pd from kedro_datasets.partitions import PartitionedDataset # these credentials will be passed to both 'fsspec.filesystem()' call # and the dataset initializer credentials = {"key1": "secret1", "key2": "secret2"} dataset = PartitionedDataset( ... path="s3://bucket-name/path/to/folder", ... dataset="pandas.CSVDataset", ... credentials=credentials, ... ) loaded = dataset.load() # assert isinstance(loaded, dict) combine_all = pd.DataFrame() for partition_id, partition_load_func in loaded.items(): ... partition_data = partition_load_func() ... combine_all = pd.concat([combine_all, partition_data], ignore_index=True, sort=True) ... new_data = pd.DataFrame({"new": [1, 2]}) # creates "s3://bucket-name/path/to/folder/new/partition.csv" dataset.save({"new/partition.csv": new_data})
Methods
exists
()Checks whether a data set's output already exists by calling the provided _exists() method.
from_config
(name, config[, load_version, ...])Create a data set instance using the configuration provided.
load
()Loads data by delegation to the provided load method.
release
()Release any cached data.
save
(data)Saves data by delegation to the provided save method.
- __init__(*, path, dataset, filepath_arg='filepath', filename_suffix='', credentials=None, load_args=None, fs_args=None, overwrite=False, metadata=None)[source]¶
Creates a new instance of
PartitionedDataset
.- Parameters:
path (str) – Path to the folder containing partitioned data. If path starts with the protocol (e.g.,
s3://
) then the correspondingfsspec
concrete filesystem implementation will be used. If protocol is not specified,fsspec.implementations.local.LocalFileSystem
will be used. Note: Some concrete implementations are bundled withfsspec
, while others (likes3
orgcs
) must be installed separately prior to usage of thePartitionedDataset
.dataset (str | type[AbstractDataset] | dict[str, Any]) – Underlying dataset definition. This is used to instantiate the dataset for each file located inside the
path
. Accepted formats are: a) object of a class that inherits fromAbstractDataset
b) a string representing a fully qualified class name to such class c) a dictionary withtype
key pointing to a string from b), other keys are passed to the Dataset initializer. Credentials for the dataset can be explicitly specified in this configuration.filepath_arg (str) – Underlying dataset initializer argument that will contain a path to each corresponding partition file. If unspecified, defaults to “filepath”.
filename_suffix (str) – If specified, only partitions that end with this string will be processed.
credentials (dict[str, Any]) – Protocol-specific options that will be passed to
fsspec.filesystem
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.filesystem and the dataset initializer. If the dataset config contains explicit credentials spec, then such spec will take precedence. All possible credentials management scenarios are documented here: https://kedro.readthedocs.io/en/stable/data/kedro_io.html#partitioned-dataset-credentialsload_args (dict[str, Any]) – Keyword arguments to be passed into
find()
method of the filesystem implementation.fs_args (dict[str, Any]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for
GCSFileSystem
).overwrite (bool) – If True, any existing partitions will be removed.
metadata (dict[str, Any]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.
- Raises:
DatasetError – If versioning is enabled for the underlying dataset.
- exists()¶
Checks whether a data set’s output already exists by calling the provided _exists() method.
- Return type:
bool
- Returns:
Flag indicating whether the output already exists.
- Raises:
DatasetError – when underlying exists method raises error.
- classmethod from_config(name, config, load_version=None, save_version=None)¶
Create a data set instance using the configuration provided.
- Parameters:
name (str) – Data set name.
config (dict[str, Any]) – Data set config dictionary.
load_version (str | None) – Version string to be used for
load
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.save_version (str | None) – Version string to be used for
save
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
- Return type:
AbstractDataset
- Returns:
An instance of an
AbstractDataset
subclass.- Raises:
DatasetError – When the function fails to create the data set from its config.
- load()¶
Loads data by delegation to the provided load method.
- Return type:
TypeVar
(_DO
)- Returns:
Data returned by the provided load method.
- Raises:
DatasetError – When underlying load method raises error.
- release()¶
Release any cached data.
- Raises:
DatasetError – when underlying release method raises error.
- Return type:
None
- save(data)¶
Saves data by delegation to the provided save method.
- Parameters:
data (
TypeVar
(_DI
)) – the value to be saved by provided save method.- Raises:
DatasetError – when underlying save method raises error.
FileNotFoundError – when save method got file instead of dir, on Windows.
NotADirectoryError – when save method got file instead of dir, on Unix.
- Return type:
None