openpyxl Python中的Excel公式

如果不满足指定条件,则需要使用openpyxl库删除excel中的行。行中的单元格包含一些带有相对单元格引用的公式。删除行时,以下单元格中的公式不会更新。但是,当我们在excel中执行相同操作时,以下单元格中的公式将自动更新。我不知道我哪里错了。任何建议都会非常有帮助。下面是示例代码

import base64
import os
import io
import openpyxl

data =b'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decrypted=base64.b64decode(data)
xls_filelike = io.BytesIO(decrypted)
wb = openpyxl.load_workbook(xls_filelike)
sheet=wb['Sheet1']

if int(sheet['D4'].value)<75:
    sheet.delete_rows(4,1)

wb.save('Sample.xlsx')

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