Excel Loader

Pandas Excel reader.

Pandas parser for .xlsx files.

PandasExcelReader

Bases: BaseReader

Pandas-based CSV parser.

Parses CSVs using the separator detection from Pandas read_csv function. If special parameters are required, use the pandas_config dict.

Args:

pandas_config (dict): Options for the `pandas.read_excel` function call.
    Refer to https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html
    for more information. Set to empty dict by default,
    this means defaults will be used.

Source code in libs\kotaemon\kotaemon\loaders\excel_loader.py

class PandasExcelReader(BaseReader):
    r"""Pandas-based CSV parser.

    Parses CSVs using the separator detection from Pandas `read_csv` function.
    If special parameters are required, use the `pandas_config` dict.

    Args:

        pandas_config (dict): Options for the `pandas.read_excel` function call.
            Refer to https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html
            for more information. Set to empty dict by default,
            this means defaults will be used.

    """

    def __init__(
        self,
        *args: Any,
        pandas_config: Optional[dict] = None,
        row_joiner: str = "\n",
        col_joiner: str = " ",
        **kwargs: Any,
    ) -> None:
        """Init params."""
        super().__init__(*args, **kwargs)
        self._pandas_config = pandas_config or {}
        self._row_joiner = row_joiner if row_joiner else "\n"
        self._col_joiner = col_joiner if col_joiner else " "

    def load_data(
        self,
        file: Path,
        include_sheetname: bool = False,
        sheet_name: Optional[Union[str, int, list]] = None,
        extra_info: Optional[dict] = None,
        **kwargs,
    ) -> List[Document]:
        """Parse file and extract values from a specific column.

        Args:
            file (Path): The path to the Excel file to read.
            include_sheetname (bool): Whether to include the sheet name in the output.
            sheet_name (Union[str, int, None]): The specific sheet to read from,
                default is None which reads all sheets.

        Returns:
            List[Document]: A list of`Document objects containing the
                values from the specified column in the Excel file.
        """
        import itertools

        try:
            import pandas as pd
        except ImportError:
            raise ImportError(
                "install pandas using `pip3 install pandas` to use this loader"
            )

        if sheet_name is not None:
            sheet_name = (
                [sheet_name] if not isinstance(sheet_name, list) else sheet_name
            )

        dfs = pd.read_excel(file, sheet_name=sheet_name, **self._pandas_config)
        sheet_names = dfs.keys()
        df_sheets = []

        for key in sheet_names:
            sheet = []
            if include_sheetname:
                sheet.append([key])
            sheet.extend(dfs[key].values.astype(str).tolist())
            df_sheets.append(sheet)

        text_list = list(
            itertools.chain.from_iterable(df_sheets)
        )  # flatten list of lists

        output = [
            Document(
                text=self._row_joiner.join(
                    self._col_joiner.join(sublist) for sublist in text_list
                ),
                metadata=extra_info or {},
            )
        ]

        return output

load_data

load_data(file, include_sheetname=False, sheet_name=None, extra_info=None, **kwargs)

Parse file and extract values from a specific column.

Parameters:

Name	Type	Description	Default
file	Path	The path to the Excel file to read.	required
include_sheetname	bool	Whether to include the sheet name in the output.	False
sheet_name	Union[str, int, None]	The specific sheet to read from, default is None which reads all sheets.	None

Returns:

Type	Description
List[Document]	List[Document]: A list of`Document objects containing the values from the specified column in the Excel file.

Source code in libs\kotaemon\kotaemon\loaders\excel_loader.py

def load_data(
    self,
    file: Path,
    include_sheetname: bool = False,
    sheet_name: Optional[Union[str, int, list]] = None,
    extra_info: Optional[dict] = None,
    **kwargs,
) -> List[Document]:
    """Parse file and extract values from a specific column.

    Args:
        file (Path): The path to the Excel file to read.
        include_sheetname (bool): Whether to include the sheet name in the output.
        sheet_name (Union[str, int, None]): The specific sheet to read from,
            default is None which reads all sheets.

    Returns:
        List[Document]: A list of`Document objects containing the
            values from the specified column in the Excel file.
    """
    import itertools

    try:
        import pandas as pd
    except ImportError:
        raise ImportError(
            "install pandas using `pip3 install pandas` to use this loader"
        )

    if sheet_name is not None:
        sheet_name = (
            [sheet_name] if not isinstance(sheet_name, list) else sheet_name
        )

    dfs = pd.read_excel(file, sheet_name=sheet_name, **self._pandas_config)
    sheet_names = dfs.keys()
    df_sheets = []

    for key in sheet_names:
        sheet = []
        if include_sheetname:
            sheet.append([key])
        sheet.extend(dfs[key].values.astype(str).tolist())
        df_sheets.append(sheet)

    text_list = list(
        itertools.chain.from_iterable(df_sheets)
    )  # flatten list of lists

    output = [
        Document(
            text=self._row_joiner.join(
                self._col_joiner.join(sublist) for sublist in text_list
            ),
            metadata=extra_info or {},
        )
    ]

    return output