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144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
from __future__ import annotations
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from datetime import time
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import math
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from typing import TYPE_CHECKING
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import numpy as np
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from pandas.compat._optional import import_optional_dependency
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from pandas.util._decorators import doc
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from pandas.core.shared_docs import _shared_docs
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from pandas.io.excel._base import BaseExcelReader
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if TYPE_CHECKING:
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from xlrd import Book
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from pandas._typing import (
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Scalar,
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StorageOptions,
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)
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class XlrdReader(BaseExcelReader["Book"]):
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@doc(storage_options=_shared_docs["storage_options"])
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def __init__(
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self,
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filepath_or_buffer,
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storage_options: StorageOptions | None = None,
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engine_kwargs: dict | None = None,
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) -> None:
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"""
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Reader using xlrd 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}
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engine_kwargs : dict, optional
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Arbitrary keyword arguments passed to excel engine.
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"""
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err_msg = "Install xlrd >= 2.0.1 for xls Excel support"
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import_optional_dependency("xlrd", extra=err_msg)
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super().__init__(
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filepath_or_buffer,
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storage_options=storage_options,
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engine_kwargs=engine_kwargs,
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)
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@property
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def _workbook_class(self) -> type[Book]:
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from xlrd import Book
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return Book
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def load_workbook(self, filepath_or_buffer, engine_kwargs) -> Book:
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from xlrd import open_workbook
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if hasattr(filepath_or_buffer, "read"):
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data = filepath_or_buffer.read()
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return open_workbook(file_contents=data, **engine_kwargs)
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else:
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return open_workbook(filepath_or_buffer, **engine_kwargs)
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@property
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def sheet_names(self):
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return self.book.sheet_names()
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def get_sheet_by_name(self, name):
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self.raise_if_bad_sheet_by_name(name)
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return self.book.sheet_by_name(name)
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def get_sheet_by_index(self, index):
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self.raise_if_bad_sheet_by_index(index)
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return self.book.sheet_by_index(index)
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def get_sheet_data(
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self, sheet, file_rows_needed: int | None = None
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) -> list[list[Scalar]]:
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from xlrd import (
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XL_CELL_BOOLEAN,
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XL_CELL_DATE,
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XL_CELL_ERROR,
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XL_CELL_NUMBER,
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xldate,
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)
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epoch1904 = self.book.datemode
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def _parse_cell(cell_contents, cell_typ):
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"""
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converts the contents of the cell into a pandas appropriate object
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"""
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if cell_typ == XL_CELL_DATE:
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# Use the newer xlrd datetime handling.
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try:
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cell_contents = xldate.xldate_as_datetime(cell_contents, epoch1904)
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except OverflowError:
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return cell_contents
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# Excel doesn't distinguish between dates and time,
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# so we treat dates on the epoch as times only.
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# Also, Excel supports 1900 and 1904 epochs.
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year = (cell_contents.timetuple())[0:3]
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if (not epoch1904 and year == (1899, 12, 31)) or (
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epoch1904 and year == (1904, 1, 1)
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):
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cell_contents = time(
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cell_contents.hour,
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cell_contents.minute,
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cell_contents.second,
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cell_contents.microsecond,
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)
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elif cell_typ == XL_CELL_ERROR:
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cell_contents = np.nan
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elif cell_typ == XL_CELL_BOOLEAN:
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cell_contents = bool(cell_contents)
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elif cell_typ == XL_CELL_NUMBER:
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# GH5394 - Excel 'numbers' are always floats
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# it's a minimal perf hit and less surprising
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if math.isfinite(cell_contents):
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# GH54564 - don't attempt to convert NaN/Inf
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val = int(cell_contents)
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if val == cell_contents:
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cell_contents = val
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return cell_contents
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data = []
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nrows = sheet.nrows
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if file_rows_needed is not None:
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nrows = min(nrows, file_rows_needed)
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for i in range(nrows):
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row = [
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_parse_cell(value, typ)
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for value, typ in zip(sheet.row_values(i), sheet.row_types(i))
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]
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data.append(row)
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return data
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