hydromt.data_catalog.DataCatalog.get_dataframe#

DataCatalog.get_dataframe(data_like: str | SourceSpecDict | Path | DataFrame | DataFrameSource, variables: List | None = None, time_range: Annotated[Tuple[datetime, datetime], BeforeValidator(func=_time_range_from_str, json_schema_input_type=PydanticUndefined), AfterValidator(func=_time_range_validate)] | None = None, handle_nodata: NoDataStrategy = NoDataStrategy.RAISE, provider: str | None = None, version: str | None = None, **kwargs) → DataFrame[source]#

Return a clipped, sliced and unified DataFrame.

Parameters:

  • data_like (Union[str, SourceSpecDict, Path, pd.DataFrame, DataFrameSource]) – Data catalog key, path to tabular data file, DataFrameSource object or tabular pandas dataframe. The catalog key can be a string or a dictionary with the following keys: {‘name’, ‘provider’, ‘version’}. If a path to a tabular data file is provided it will be added to the catalog with its based on the file basename.

  • variables (Optional[List], optional) – Names of DataFrame variables to return, or all if None, by default None

  • time_range (Optional[TimeRange], optional) – Start and end date of period of interest, or entire period if None, by default None

  • handle_nodata (NoDataStrategy, optional) – How to react when no data is found, by default NoDataStrategy.RAISE

  • provider (Optional[str], optional) – Specifies a data provider, by default None

  • version (Optional[str], optional) – Specifies a data version, by default None

  • **kwargs – Extra keyword arguments passed to the DataFrameSource construction

Returns:

A unified and sliced DataFrame

Return type:

pd.DataFrame

Raises:

  • ValueError – On unknown type of data_like

  • NoDataException – If no data is found and handle_nodata is NoDataStrategy.RAISE