kedro.extras.datasets.pandas.ExcelDataSet

class kedro.extras.datasets.pandas.ExcelDataSet(filepath, engine='xlsxwriter', load_args=None, save_args=None, version=None, credentials=None, fs_args=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 adding a catalog entry with 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 using Python API:

from kedro.extras.datasets.pandas import ExcelDataSet
import pandas as pd

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

# data_set = ExcelDataSet(filepath="gcs://bucket/test.xlsx")
data_set = ExcelDataSet(filepath="test.xlsx")
data_set.save(data)
reloaded = data_set.load()
assert data.equals(reloaded)

Attributes

DEFAULT_LOAD_ARGS

DEFAULT_SAVE_ARGS

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.

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.
DEFAULT_LOAD_ARGS = {'engine': 'xlrd'}
DEFAULT_SAVE_ARGS = {'index': False}
__init__(filepath, engine='xlsxwriter', load_args=None, save_args=None, version=None, credentials=None, fs_args=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 ‘xlsxwriter’.

  • 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 “xlrd”. 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), as well as to pass to the filesystem’s open method through nested keys open_args_load and open_args_save. Here you can find all available arguments for open: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open All defaults are preserved, except mode, which is set to wb when saving.

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 (Optional[str]) – 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 (Optional[str]) – 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

Any

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

Optional[str]

resolve_save_version()

Compute the version the dataset should be saved with.

Return type

Optional[str]

save(data)

Saves data by delegation to the provided save method.

Parameters

data (Any) – 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