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taipy.core.data.ExcelDataNode

Bases: DataNode, _AbstractFileDataNode, _AbstractTabularDataNode

Data Node stored as an Excel file.

The Excel file format is xlsx.

Attributes:

Name Type Description
config_id str

Identifier of this data node configuration. It must be a valid Python identifier.

scope Scope

The scope of this data node.

id str

The unique identifier of this data node.

owner_id str

The identifier of the owner (sequence_id, scenario_id, cycle_id) or None.

parent_ids Optional[Set[str]]

The identifiers of the parent tasks or None.

last_edit_date datetime

The date and time of the last modification.

edits List[Edit]

The ordered list of edits for that job.

version str

The string indicates the application version of the data node to instantiate. If not provided, the current version is used.

validity_period Optional[timedelta]

The duration implemented as a timedelta since the last edit date for which the data node can be considered up-to-date. Once the validity period has passed, the data node is considered stale and relevant tasks will run even if they are skippable (see the Task management page for more details). If validity_period is set to None, the data node is always up-to-date.

edit_in_progress bool

True if a task computing the data node has been submitted and not completed yet. False otherwise.

editor_id Optional[str]

The identifier of the user who is currently editing the data node.

editor_expiration_date Optional[datetime]

The expiration date of the editor lock.

path str

The path to the Excel file.

properties dict[str, Any]

A dictionary of additional properties. The properties must have a "default_path" or "path" entry with the path of the Excel file:

  • "default_path" (str): The path of the Excel file.

  • "has_header" (bool): If True, indicates that the Excel file has a header.

  • "sheet_name" (Union[List[str], str]): The list of sheet names to be used. This can be a unique name.

  • "exposed_type": The exposed type of the data read from Excel file. The default value is pandas.

write_with_column_names(data, columns=None, job_id=None)

Write a set of columns.

Parameters:

Name Type Description Default
data Any

The data to write.

required
columns List[str]

The list of column names to write.

None
job_id JobId

An optional identifier of the writer.

None