明天给大家分享一篇用openpyxl操作Excel的文章。
各种数据须要导入Excel？多个Excel要合并？目前，Python解决Excel文件有很多库，openpyxl算是其中性能和性能做的比拟好的一个。接下来我将为大家介绍各种Excel操作。
关上Excel文件
新建一个Excel文件
>>> from openpyxl import Workbook >>> wb = Workbook()关上现有Excel文件
>>> from openpyxl import load_workbook >>> wb2 = load_workbook('test.xlsx')关上大文件时，依据需要应用只读或只写模式缩小内存耗费。
wb = load_workbook(filename='large_file.xlsx', read_only=True)wb = Workbook(write_only=True)获取、创立工作表
获取以后流动工作表：
>>> ws = wb.active创立新的工作表：
>>> ws1 = wb.create_sheet("Mysheet") # insert at the end (default) # or >>> ws2 = wb.create_sheet("Mysheet", 0) # insert at first position # or >>> ws3 = wb.create_sheet("Mysheet", -1) # insert at the penultimate position应用工作表名字获取工作表：
>>> ws3 = wb["New Title"]获取所有的工作表名称：
>>> print(wb.sheetnames) ['Sheet2', 'New Title', 'Sheet1']应用for循环遍历所有的工作表： >>> for sheet in wb: ... print(sheet.title)保留
保留到流中在网络中应用：
>>> from tempfile import NamedTemporaryFile >>> from openpyxl import Workbook >>> wb = Workbook() >>> with NamedTemporaryFile() as tmp: wb.save(tmp.name) tmp.seek(0) stream = tmp.read()保留到文件： >>> wb = Workbook() >>> wb.save('balances.xlsx')保留为模板： >>> wb = load_workbook('document.xlsx') >>> wb.template = True >>> wb.save('document_template.xltx')单元格
单元格地位作为工作表的键间接读取：
>>> c = ws['A4']为单元格赋值：
>>> ws['A4'] = 4 >>> c.value = 'hello, world'多个单元格 能够应用切片拜访单元格区域：
>>> cell_range = ws['A1':'C2']应用数值格局：
>>> # set date using a Python datetime >>> ws['A1'] = datetime.datetime(2010, 7, 21) >>> >>> ws['A1'].number_format 'yyyy-mm-dd h:mm:ss'应用公式：
>>> # add a simple formula >>> ws["A1"] = "=SUM(1, 1)"合并单元格时，除左上角单元分外，所有单元格都将从工作表中删除：
>>> ws.merge_cells('A2:D2') >>> ws.unmerge_cells('A2:D2') >>> >>> # or equivalently >>> ws.merge_cells(start_row=2, start_column=1, end_row=4, end_column=4) >>> ws.unmerge_cells(start_row=2, start_column=1, end_row=4, end_column=4) 行、列
能够独自指定行、列、或者行列的范畴：
>>> colC = ws['C'] >>> col_range = ws['C:D'] >>> row10 = ws[10] >>> row_range = ws[5:10]能够应用Worksheet.iter_rows()办法遍历行：
>>> for row in ws.iter_rows(min_row=1, max_col=3, max_row=2): ... for cell in row: ... print(cell) <Cell Sheet1.A1> <Cell Sheet1.B1> <Cell Sheet1.C1> <Cell Sheet1.A2> <Cell Sheet1.B2> <Cell Sheet1.C2>同样的Worksheet.iter_cols()办法将遍历列：
>>> for col in ws.iter_cols(min_row=1, max_col=3, max_row=2): ... for cell in col: ... print(cell) <Cell Sheet1.A1> <Cell Sheet1.A2> <Cell Sheet1.B1> <Cell Sheet1.B2> <Cell Sheet1.C1> <Cell Sheet1.C2>遍历文件的所有行或列，能够应用Worksheet.rows属性：
>>> ws = wb.active >>> ws['C9'] = 'hello world' >>> tuple(ws.rows) ((<Cell Sheet.A1>, <Cell Sheet.B1>, <Cell Sheet.C1>), (<Cell Sheet.A2>, <Cell Sheet.B2>, <Cell Sheet.C2>), (<Cell Sheet.A3>, <Cell Sheet.B3>, <Cell Sheet.C3>), (<Cell Sheet.A4>, <Cell Sheet.B4>, <Cell Sheet.C4>), (<Cell Sheet.A5>, <Cell Sheet.B5>, <Cell Sheet.C5>), (<Cell Sheet.A6>, <Cell Sheet.B6>, <Cell Sheet.C6>), (<Cell Sheet.A7>, <Cell Sheet.B7>, <Cell Sheet.C7>), (<Cell Sheet.A8>, <Cell Sheet.B8>, <Cell Sheet.C8>), (<Cell Sheet.A9>, <Cell Sheet.B9>, <Cell Sheet.C9>))或Worksheet.columns属性：
>>> tuple(ws.columns) ((<Cell Sheet.A1>, <Cell Sheet.A2>, <Cell Sheet.A3>, <Cell Sheet.A4>, <Cell Sheet.A5>, <Cell Sheet.A6>, ... <Cell Sheet.B7>, <Cell Sheet.B8>, <Cell Sheet.B9>), (<Cell Sheet.C1>, <Cell Sheet.C2>, <Cell Sheet.C3>, <Cell Sheet.C4>, <Cell Sheet.C5>, <Cell Sheet.C6>, <Cell Sheet.C7>, <Cell Sheet.C8>, <Cell Sheet.C9>))应用Worksheet.append()或者迭代应用Worksheet.cell()新增一行数据：
>>> for row in range(1, 40): ... ws1.append(range(600)) >>> for row in range(10, 20): ... for col in range(27, 54): ... _ = ws3.cell(column=col, row=row, value="{0}".format(get_column_letter(col)))插入操作比拟麻烦。能够应用Worksheet.insert_rows()插入一行或几行：
>>> from openpyxl.utils import get_column_letter >>> ws.insert_rows(7) >>> row7 = ws[7] >>> for col in range(27, 54): ... _ = ws3.cell(column=col, row=7, value="{0}".format(get_column_letter(col)))Worksheet.insert_cols()操作相似。Worksheet.delete_rows()和Worksheet.delete_cols()用来批量删除行和列。
只读取值
应用Worksheet.values属性遍历工作表中的所有行，但只返回单元格值：
for row in ws.values: for value in row: print(value)Worksheet.iter_rows()和Worksheet.iter_cols()能够设置values_only参数来仅返回单元格的值：
>>> for row in ws.iter_rows(min_row=1, max_col=3, max_row=2, values_only=True): ... print(row) (None, None, None) (None, None, None)以上就是本次分享的所有内容，如果你感觉文章还不错，欢送关注公众号：Python编程学习圈，每日干货分享，发送“J”还可支付大量学习材料，内容笼罩Python电子书、教程、数据库编程、Django，爬虫，云计算等等。或是返回编程学习网，理解更多编程技术常识。