kedro_datasets.pandas.ExcelDataset¶

class kedro_datasets.pandas.ExcelDataset(*, filepath, engine='openpyxl', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶

ExcelDataset loads/saves data from/to a Excel file using an underlying filesystem (e.g.: local, S3, GCS). It uses pandas to handle the Excel file.

Example usage for the YAML API:

rockets:
  type: pandas.ExcelDataset
  filepath: gcs://your_bucket/rockets.xlsx
  fs_args:
    project: my-project
  credentials: my_gcp_credentials
  save_args:
    sheet_name: Sheet1
  load_args:
    sheet_name: Sheet1

shuttles:
  type: pandas.ExcelDataset
  filepath: data/01_raw/shuttles.xlsx

Example usage for the Python API:

from kedro_datasets.pandas import ExcelDataset
import pandas as pd

data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})

dataset = ExcelDataset(filepath=tmp_path / "test.xlsx")
dataset.save(data)
reloaded = dataset.load()
assert data.equals(reloaded)

To save a multi-sheet Excel file, no special save_args are required. Instead, return a dictionary of Dict[str, pd.DataFrame] where the string keys are your sheet names.

Example usage for the YAML API for a multi-sheet Excel file:

trains:
  type: pandas.ExcelDataset
  filepath: data/02_intermediate/company/trains.xlsx
  load_args:
    sheet_name: [Sheet1, Sheet2, Sheet3]

Example usage for the Python API for a multi-sheet Excel file:

from kedro_datasets.pandas import ExcelDataset
import pandas as pd

dataframe = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
another_dataframe = pd.DataFrame({"x": [10, 20], "y": ["hello", "world"]})
multiframe = {"Sheet1": dataframe, "Sheet2": another_dataframe}
dataset = ExcelDataset(filepath="test.xlsx", load_args={"sheet_name": None})
dataset.save(multiframe)
reloaded = dataset.load()
assert multiframe["Sheet1"].equals(reloaded["Sheet1"])
assert multiframe["Sheet2"].equals(reloaded["Sheet2"])

Attributes

`DEFAULT_FS_ARGS`
`DEFAULT_LOAD_ARGS`
`DEFAULT_SAVE_ARGS`

Methods

`exists`()	Checks whether a dataset's output already exists by calling the provided _exists() method.
`from_config`(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
`load`()	Loads data by delegation to the provided load method.
`preview`([nrows])	Generate a preview of the dataset with a specified number of rows.
`release`()	Release any cached data.
`resolve_load_version`()	Compute the version the dataset should be loaded with.
`resolve_save_version`()	Compute the version the dataset should be saved with.
`save`(data)	Saves data by delegation to the provided save method.
`to_config`()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_FS_ARGS: dict[str, Any] = {'open_args_save': {'mode': 'wb'}}¶

DEFAULT_LOAD_ARGS = {'engine': 'openpyxl'}¶

DEFAULT_SAVE_ARGS = {'index': False}¶

__init__(*, filepath, engine='openpyxl', load_args=None, save_args=None, version=None, credentials=None, fs_args=None, metadata=None)[source]¶

Creates a new instance of ExcelDataset pointing to a concrete Excel file on a specific filesystem.

Parameters:

filepath (str) – Filepath in POSIX format to a Excel 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. Note: http(s) doesn’t support versioning.
engine (str) – The engine used to write to Excel files. The default engine is ‘openpyxl’.
load_args (Optional[dict[str, Any]]) – Pandas options for loading Excel files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html All defaults are preserved, but “engine”, which is set to “openpyxl”. Supports multi-sheet Excel files (include sheet_name = None in load_args).
save_args (Optional[dict[str, Any]]) – Pandas options for saving Excel files. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_excel.html All defaults are preserved, but “index”, which is set to False. If you would like to specify options for the ExcelWriter, you can include them under the “writer” key. Here you can find all available arguments: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.ExcelWriter.html
version (Optional[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.
credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem 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”} for GCSFileSystem). Defaults are preserved, apart from the open_args_save mode which is set to wb. Note that the save method requires bytes, so any save mode provided should include “b” for bytes.
metadata (Optional[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 while in append mode.

exists()[source]¶

Checks whether a dataset’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)[source]¶

Create a dataset instance using the configuration provided.

Parameters:

name (str) – Data set name.
config (dict[str, Any]) – Data set config dictionary.
load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.
save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()[source]¶

Loads data by delegation to the provided load method.

Return type:: DataFrame | dict[str, DataFrame]
Returns:: Data returned by the provided load method.
Raises:: DatasetError – When underlying load method raises error.

preview(nrows=5)[source]¶

Generate a preview of the dataset with a specified number of rows.

Parameters:: nrows (int) – The number of rows to include in the preview. Defaults to 5.
Returns:: A dictionary containing the data in a split format.
Return type:: dict

release()[source]¶

Release any cached data.

Raises:: DatasetError – when underlying release method raises error.
Return type:: None

resolve_load_version()[source]¶

Compute the version the dataset should be loaded with.

Return type:: Optional[str]

resolve_save_version()[source]¶

Compute the version the dataset should be saved with.

Return type:: Optional[str]

save(data)[source]¶

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame | dict[str, DataFrame]) – 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

to_config()[source]¶

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:: dict[str, Any]
Returns:: A dictionary containing the dataset’s type and initialization arguments.