from typing import List from pandas._typing import FilePathOrBuffer, Scalar, StorageOptions from pandas.compat._optional import import_optional_dependency from pandas.io.excel._base import BaseExcelReader class PyxlsbReader(BaseExcelReader): def __init__( self, filepath_or_buffer: FilePathOrBuffer, storage_options: StorageOptions = None, ): """ Reader using pyxlsb engine. Parameters ---------- filepath_or_buffer : str, path object, or Workbook Object to be parsed. storage_options : dict, optional passed to fsspec for appropriate URLs (see ``_get_filepath_or_buffer``) """ import_optional_dependency("pyxlsb") # This will call load_workbook on the filepath or buffer # And set the result to the book-attribute super().__init__(filepath_or_buffer, storage_options=storage_options) @property def _workbook_class(self): from pyxlsb import Workbook return Workbook def load_workbook(self, filepath_or_buffer: FilePathOrBuffer): from pyxlsb import open_workbook # TODO: hack in buffer capability # This might need some modifications to the Pyxlsb library # Actual work for opening it is in xlsbpackage.py, line 20-ish return open_workbook(filepath_or_buffer) @property def sheet_names(self) -> List[str]: return self.book.sheets def get_sheet_by_name(self, name: str): self.raise_if_bad_sheet_by_name(name) return self.book.get_sheet(name) def get_sheet_by_index(self, index: int): self.raise_if_bad_sheet_by_index(index) # pyxlsb sheets are indexed from 1 onwards # There's a fix for this in the source, but the pypi package doesn't have it return self.book.get_sheet(index + 1) def _convert_cell(self, cell, convert_float: bool) -> Scalar: # TODO: there is no way to distinguish between floats and datetimes in pyxlsb # This means that there is no way to read datetime types from an xlsb file yet if cell.v is None: return "" # Prevents non-named columns from not showing up as Unnamed: i if isinstance(cell.v, float) and convert_float: val = int(cell.v) if val == cell.v: return val else: return float(cell.v) return cell.v def get_sheet_data(self, sheet, convert_float: bool) -> List[List[Scalar]]: return [ [self._convert_cell(c, convert_float) for c in r] for r in sheet.rows(sparse=False) ]