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103 lines
3.7 KiB
103 lines
3.7 KiB
# pyright: reportMissingImports=false
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from __future__ import annotations
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from pandas._typing import (
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FilePath,
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ReadBuffer,
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Scalar,
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StorageOptions,
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)
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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: FilePath | ReadBuffer[bytes],
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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: FilePath | ReadBuffer[bytes]):
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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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data: list[list[Scalar]] = []
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prevous_row_number = -1
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# When sparse=True the rows can have different lengths and empty rows are
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# not returned. The cells are namedtuples of row, col, value (r, c, v).
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for row in sheet.rows(sparse=True):
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row_number = row[0].r
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converted_row = [self._convert_cell(cell, convert_float) for cell in row]
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while converted_row and converted_row[-1] == "":
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# trim trailing empty elements
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converted_row.pop()
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if converted_row:
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data.extend([[]] * (row_number - prevous_row_number - 1))
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data.append(converted_row)
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prevous_row_number = row_number
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if data:
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# extend rows to max_width
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max_width = max(len(data_row) for data_row in data)
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if min(len(data_row) for data_row in data) < max_width:
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empty_cell: list[Scalar] = [""]
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data = [
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data_row + (max_width - len(data_row)) * empty_cell
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for data_row in data
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]
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return data
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