python自动化测试学习笔记-6excel操作xlwt、xlrd、xlutils模块

时间:2021-11-04 06:54:52

python中通过xlwt、xlrd和xlutils操作xls

  • xlwt模块用于在内存中生成一个xls/xlsx对象,增加表格数据,并把内存中的xls对象保存为本地磁盘xls文件;
  • xlrd模块用于把本地xls文件加载到内存中,可以读取xls文件的表格数据,查询xls文件的相关信息;
  • xlwt可以生成xls文件,xlrd可以读取已经存在的xls文件,但是如果要修改本地已经存在的xls文件,就需要用到xlutils模块。
  • xlutils模块是xlrd和xlwt之间的桥梁,可以使用xlutils模块中的copy模块,拷贝一份通过xlrd读取到内存中的xls对象,就可以在拷贝对象上像xlwt中那样修改xls表格的内容,并保存到本地

要使用这些模块首先要安装导入:

pip install xlrd

pip install xlwt

pip install xluntils

安装好后进行导入

import xlrd
import xlwt
from xlutils.copy import copy
#创建一个excel
book=xlwt.Workbook()
#添加一个sheet
sheet=book.add_sheet('sheet1')
#向sheet中添加数据,行、列、value值
sheet.write(0,0,'id')
sheet.write(0,1,'name')
sheet.write(0,2,'age')
sheet.write(0,3,'sex')
#保存xls,微软的office不能以xlsx为结尾,wps随意
book.save('peitest.xls')

执行后生成了一个excel文件,查看内容

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pmPfdwLbN+6tpnmucoisI+fzQW4RX3cuDmzsFerlwO3HQ11rFr1EJerSgoIko1vetaZv3WlD+3fOraZ4qitMvX1TUaP9mHa5n13NOqK0tba6yWS1trLuiK8t6UCnSdS8Y1x4WrPa1q4P4PB+rTcn1vu3ydfTIredUFuqK8z8XUvqvG3tFY5NaUX7U3J6fau84l41yGxJrB8D+Gvi5+DnRFOa6lMl01vaWrsW7/h4PRWIQd7qp/nUvG9384KD5Ray4ZNxP3g6aT7pHb8Y0X8Y0X7pHbulNN0BXluJaKdVUXTl37TFdX9k+5khsniyi50S+7QF6tKCgiyv8BhxfZJSc6BPsAAAAASUVORK5CYII=" 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但是像这样一个单元格一个单元格的插入又很浪费时间,我们可以用循环来实现,如下:

##############################
#利用循环写数据
#创建一个excel
book=xlwt.Workbook()
#添加一个sheet
sheet=book.add_sheet('sheet2')
row=
col=
list=[
['id','name','age','sex'],
['01','wang','13','女'],
['02','li','23','女'],
['03','hang','34','男'],
['04','wu','16','女'],
['05','ma','22','女']
]
#循环行
for r in range(6):
#循环列
for c in range(4):
#根据行和列找到要赋值的value
sheet.write(r,c,list[r][c])
c+=
r+=
#保存excel
book.save('peitest.xls')

执行查看结果:

aaarticlea/png;base64,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" alt="" />

上述方法是已知数据的行和列来循环的,如果行和列太多的时候就不方便了,我们可以通过循环list来添加数据,如下:

#创建一个excel
book=xlwt.Workbook()
#添加一个sheet
sheet=book.add_sheet('sheet3')
list=[
['id','name','age','sex'],
['01','wang','13','女'],
['02','li','23','女'],
['03','hang','34','男'],
['04','wu','16','女'],
['05','ma','22','女']
] r=0
for stu in list:
c=0
for s in stu:
sheet.write(r,c,s)
c+=1
r+=1
book.save('peitest.xls')

执行查看结果:

aaarticlea/png;base64,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" alt="" />

读取excel的数据是通过xlrd模块来实现的,如下:

###############################################################################
#读取excel数据
book=xlrd.open_workbook('peitest.xls')
#获取sheet,通过index
sheet=book.sheet_by_index(0)
#获取sheet通过sheet名称
#sheet=book.sheet_by_name('sheet3')
value=sheet.cell(0,0).value
value1=sheet.cell(0,1).value
value2=sheet.cell(0,2).value
value3=sheet.cell(0,3).value print(value)
print(value1)
print(value2)
print(value3)

执行查看结果:

id
name
age
sex

同样的,如果我们要读取出excel所有的数据,也可以用循环来实现:

#####################################33
#循环来实现读取excel数据
#打开excel
book=xlrd.open_workbook('peitest.xls')
#获取sheet
sheet=book.sheet_by_name('sheet3')
#获取sheet中的行数
row=sheet.nrows
#获取sheet中的列数
col=sheet.ncols
#获取每一行的数据
for i in sheet.get_rows():
print(i)
#获取某一行的数据,通过循环获取出所有数据
for r in range(row):
print(sheet.row_values(r))
#获取某一列的数据,通过循环获取出所有数据
for c in range(col):
print(sheet.col_values(c))

执行查看结果:

[text:'id', text:'name', text:'age', text:'sex']
[text:'01', text:'wang', text:'13', text:'女']
[text:'02', text:'li', text:'23', text:'女']
[text:'03', text:'hang', text:'34', text:'男']
[text:'04', text:'wu', text:'16', text:'女']
[text:'05', text:'ma', text:'22', text:'女']
['id', 'name', 'age', 'sex']
['01', 'wang', '13', '女']
['02', 'li', '23', '女']
['03', 'hang', '34', '男']
['04', 'wu', '16', '女']
['05', 'ma', '22', '女']
['id', '01', '02', '03', '04', '05']
['name', 'wang', 'li', 'hang', 'wu', 'ma']
['age', '13', '23', '34', '16', '22']
['sex', '女', '女', '男', '女', '女']

我们看到上面第一种方法,每一个数据都带一个text,不方便进行后续操作,所以一般用第二种方式来实现。

修改文件

#######################################
import xlrd
import xlwt
from xlutils.copy import copy
#修改excel文件
#打开文件
book=xlrd.open_workbook('peitest.xls')
#复制一份文件用于修改
book2=copy(book)
#获取要修改的sheet
sheet=book2.get_sheet(0)
#修改指定的行和列
sheet.write(0,0,'序号')
#保存为新的excel
book2.save('peitest1.xls')

执行查看结果:

aaarticlea/png;base64,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" 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