kedro_datasets.pandas.ParquetDataset¶
- class kedro_datasets.pandas.ParquetDataset(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶
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:
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:
from kedro_datasets.pandas import ParquetDataset import pandas as pd data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]}) dataset = ParquetDataset(filepath=tmp_path / "test.parquet") dataset.save(data) reloaded = dataset.load() assert data.equals(reloaded)
Attributes
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.
Compute the version the dataset should be loaded with.
Compute the version the dataset should be saved with.
save
(data)Saves data by delegation to the provided save method.
- DEFAULT_LOAD_ARGS: dict[str, Any] = {}¶
- DEFAULT_SAVE_ARGS: dict[str, Any] = {}¶
- __init__(*, filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶
Creates a new instance of
ParquetDataset
pointing to a concrete Parquet file on a specific filesystem.- Parameters:
filepath (
str
) – 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 byfsspec
. 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 (
Optional
[dict
[str
,Any
]]) – Additional options for loading Parquet file(s). Here you can find all available arguments when reading single file: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html Here you can find all available arguments when reading partitioned datasets: https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html#pyarrow.parquet.ParquetDataset.read All defaults are preserved.save_args (
Optional
[dict
[str
,Any
]]) – Additional saving options for saving Parquet file(s). Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html All defaults are preserved.partition_cols
is not supported.version (
Optional
[Version
]) – If specified, should be an instance ofkedro.io.core.Version
. If itsload
attribute is None, the latest version will be loaded. If itssave
attribute is None, save version will be autogenerated.credentials (
Optional
[dict
[str
,Any
]]) – Credentials required to get access to the underlying filesystem. E.g. forGCSFileSystem
it should look like {“token”: None}.fs_args (
Optional
[dict
[str
,Any
]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} forGCSFileSystem
).metadata (
Optional
[dict
[str
,Any
]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.
- 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
- resolve_load_version()¶
Compute the version the dataset should be loaded with.
- Return type:
str | None
- resolve_save_version()¶
Compute the version the dataset should be saved with.
- Return type:
str | 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