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