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 ofDict[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
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.
preview
([nrows])Generate a preview of the dataset with a specified number of rows.
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 = {'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 byfsspec
. 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.htmlversion (
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.
- Raises:
DatasetError – If versioning is enabled while in append mode.
- exists()¶
Checks whether a data set’s output already exists by calling the provided _exists() method.
- Return type:
- 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.load_version (
Optional
[str
]) – Version string to be used forload
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 forsave
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
- Return type:
- 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:
- 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.
- 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: