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Excel2table

Excel2Table

Bases: ExcelParseBase

Source code in Docs2KG/parser/excel/excel2table.py 
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class Excel2Table(ExcelParseBase):
    def __init__(self, *args, **kwargs):
        """
        Initialize the Excel2Table class.
        """
        super().__init__(*args, **kwargs)
        self.table_output_dir = self.output_dir / "tables"
        self.table_output_dir.mkdir(parents=True, exist_ok=True)

    @staticmethod
    def find_table_start(df, min_non_nan=3):
        """
        Identify the most likely starting row of a table in a DataFrame.

        Parameters:
        df (pd.DataFrame): The DataFrame to inspect.
        min_non_nan (int): Minimum number of non-NaN values in a row to consider it as the start.

        Returns:
        int: Index of the row most likely to be the start of the table.
        """
        for i, row in df.iterrows():
            non_nan_count = row.count() - row.isna().sum()
            if non_nan_count >= min_non_nan:
                # Check if subsequent rows have similar non-NaN counts
                if (
                    df.iloc[i + 1 : i + 5]
                    .apply(lambda x: x.count() - x.isna().sum(), axis=1)
                    .mean()
                    >= min_non_nan
                ):
                    return i
        return 0

    def extract_tables_from_excel(self):
        # A simple example to extract tables from Excel
        tables = []

        df = pd.read_excel(self.excel_file, sheet_name=None)
        index = 0
        for sheet_name, sheet_data in df.items():
            start_row = self.find_table_start(sheet_data)
            sheet_data = sheet_data.iloc[start_row:].reset_index(drop=True)
            sheet_data.to_csv(self.table_output_dir / f"{sheet_name}.csv", index=False)
            tables.append(
                {
                    "page_index": index,
                    "table_index": 1,
                    "filename": f"{sheet_name}.csv",
                    "file_path": f"{self.table_output_dir}/{sheet_name}.csv",
                    "sheet_name": sheet_name,
                }
            )
            index += 1
        logger.info(f"Tables extracted from {self.excel_file}")
        table_df = pd.DataFrame(tables)
        table_df.to_csv(self.table_output_dir / "tables.csv", index=False)
        return self.table_output_dir

__init__(*args, **kwargs)

Initialize the Excel2Table class.

Source code in Docs2KG/parser/excel/excel2table.py 
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def __init__(self, *args, **kwargs):
    """
    Initialize the Excel2Table class.
    """
    super().__init__(*args, **kwargs)
    self.table_output_dir = self.output_dir / "tables"
    self.table_output_dir.mkdir(parents=True, exist_ok=True)

find_table_start(df, min_non_nan=3) staticmethod

Identify the most likely starting row of a table in a DataFrame.

Parameters: df (pd.DataFrame): The DataFrame to inspect. min_non_nan (int): Minimum number of non-NaN values in a row to consider it as the start.

Returns: int: Index of the row most likely to be the start of the table.

Source code in Docs2KG/parser/excel/excel2table.py 
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@staticmethod
def find_table_start(df, min_non_nan=3):
    """
    Identify the most likely starting row of a table in a DataFrame.

    Parameters:
    df (pd.DataFrame): The DataFrame to inspect.
    min_non_nan (int): Minimum number of non-NaN values in a row to consider it as the start.

    Returns:
    int: Index of the row most likely to be the start of the table.
    """
    for i, row in df.iterrows():
        non_nan_count = row.count() - row.isna().sum()
        if non_nan_count >= min_non_nan:
            # Check if subsequent rows have similar non-NaN counts
            if (
                df.iloc[i + 1 : i + 5]
                .apply(lambda x: x.count() - x.isna().sum(), axis=1)
                .mean()
                >= min_non_nan
            ):
                return i
    return 0