Do you know the top ten scenarios of Python office automation?

In the programming world, Python is already a veritable internet celebrity. Once, a graduate student studying Chinese language asked me how to learn Python, because their course paper needed to use text analysis and use Python to run data. I told him that if you read the grammar in two days, you can start working. If you don’t know how, you can look up the information. Later, this classmate used Python to complete the paper data in half a month.

So the biggest advantage of Python is that it is easy to learn, and the threshold is much lower than Java and C. It provides non-programmers with the possibility of working with code. Of course, Python can become a popular programming tool, not only because it is easy to learn, but also because Python has thousands of toolkits spread across all walks of life.

To name a dozen common examples of public offices, Python can handle them efficiently.

1. Python processing Excel data

You can use pandas, xlwings, openpyxl and other packages to add, delete, modify, check, format, adjust, etc. Excel. You can even use Python function to analyze excel data.

Read excel table:

import xlwings as xw
wb = xw.Book()# this will create a new workbook
wb = xw.Book('FileName.xlsx')# connect to a file that is open or in the current working directory
wb = xw.Book(r'C:pathtofile.xlsx')# on Windows: use raw strings to escape backslashes

Write matplotlib drawing to excel table:

import matplotlib.pyplot as plt
import xlwings as xw

fig = plt.figure()
plt.plot([1, 2, 3])

sheet = xw.Book().sheets[0]
sheet.pictures.add(fig, name='MyPlot', update=True)

2. Python processing PDF text

PDF is almost the most common text format. Many people have various needs for processing PDF. For example, make PDF, get text, get pictures, get tables, etc. There are packages in Python such as PyPDF, pdfplumber, ReportLab, and PyMuPDF that can easily meet these needs.

Extract PDF text:

import PyPDF2
pdfFile = open('example.pdf','rb')
pdfReader = PyPDF2.PdfFileReader(pdfFile)
print(pdfReader.numPages)
page = pdfReader.getPage(0)
print(page.extractText())
pdfFile.close()

Extract PDF table:

# 提取pdf表格
import pdfplumber
with pdfplumber.open("example.pdf") as pdf:
page01 = pdf.pages[0] #指定页码
table1 = page01.extract_table()#提取单个表格
# table2 = page01.extract_tables()#提取多个表格
print(table1)

3. Python processing Email

In Python, you can use smtplib with the email library to realize automated transmission of emails, which is very convenient.

import smtplib
import email
# 负责将多个对象集合起来
from email.mime.multipart import MIMEMultipart
from email.header import Header
# SMTP服务器,这里使用163邮箱
mail_host = "smtp.163.com"
# 发件人邮箱
mail_sender = "******@163.com"
# 邮箱授权码,注意这里不是邮箱密码,如何获取邮箱授权码,请看本文最后教程
mail_license = "********"
# 收件人邮箱，可以为多个收件人
mail_receivers = ["******@qq.com","******@outlook.com"]
mm = MIMEMultipart('related')
# 邮件正文内容
body_content = """你好，这是一个测试邮件！"""
# 构造文本,参数1：正文内容，参数2：文本格式，参数3：编码方式
message_text = MIMEText(body_content,"plain","utf-8")
# 向MIMEMultipart对象中添加文本对象
mm.attach(message_text)
# 创建SMTP对象
stp = smtplib.SMTP()
# 设置发件人邮箱的域名和端口，端口地址为25
stp.connect(mail_host, 25)
# set_debuglevel(1)可以打印出和SMTP服务器交互的所有信息
stp.set_debuglevel(1)
# 登录邮箱，传递参数1：邮箱地址，参数2：邮箱授权码
stp.login(mail_sender,mail_license)
# 发送邮件，传递参数1：发件人邮箱地址，参数2：收件人邮箱地址，参数3：把邮件内容格式改为str
stp.sendmail(mail_sender, mail_receivers, mm.as_string())
print("邮件发送成功")
# 关闭SMTP对象
stp.quit()

4. Python processing database

The database is our common office application. There are various database driver interface packages in Python, which support the addition, deletion, modification and query of the database. Operation and maintenance management work. For example, the pymysql package corresponds to MySQL, the psycopg2 package corresponds to PostgreSQL, the pymssql package corresponds to sqlserver, the cxoracle package corresponds to Oracle, the PyMongo package corresponds to MongoDB, and so on.

Connection query to MySQL

import pymysql
# 打开数据库连接
db = pymysql.connect(host='localhost',
 user='testuser',
 password='test123',
 database='TESTDB') 
# 使用 cursor() 方法创建一个游标对象 cursor
cursor = db.cursor()
# 使用 execute()方法执行 SQL 查询 
cursor.execute("SELECT VERSION()")
# 使用 fetchone() 方法获取单条数据.
data = cursor.fetchone()
print ("Database version : %s " % data)
# 关闭数据库连接
db.close()

5. Python processing of batch files

For many office scenarios, batch Processing files has always been a dirty job, and Python can help you get out of it. There are many packages in Python that handle system files, such as sys, os, shutil, glob, path.py, etc.

Delete folders with the same name in different folders in batches:

import os,shutil
import sys
import numpy as np
def arrange_file(dir_path0):
for dirpath,dirnames,filenames in os.walk(dir_path0):
if 'my_result' in dirpath:
# print(dirpath)
shutil.rmtree(dirpath)

Modify file suffixes in batches:

import os
def file_rename():
path = input("请输入你需要修改的目录(格式如'F:\test')：")
old_suffix = input('请输入你需要修改的后缀（需要加点.）：')
new_suffix = input('请输入你要改成的后缀（需要加点.）：')
file_list = os.listdir(path)
for file in file_list:
old_dir = os.path.join(path, file)
print('当前文件：', file)
if os.path.isdir(old_dir):
continue
if old_suffix != os.path.splitext(file)[1]:
continue
filename = os.path.splitext(file)[0]
new_dir = os.path.join(path, filename + new_suffix)
os.rename(old_dir, new_dir)
if __name__ == '__main__':
file_rename()

6. Python control mouse

This is the need of many people to realize automatic control of the mouse and do some assembly line work, such as software testing.

Python has a pyautogui library that can control your mouse arbitrarily.

Control mouse left-click/right-click/double-click function and test source code:

# 获取鼠标位置
import pyautogui as pg
try:
while True:
x, y = pg.position()
print(str(x) + " " + str(y))#输出鼠标位置
 
if 1746 < x < 1800 and 2 < y < 33:
pg.click()#左键单击
if 1200 < x < 1270 and 600 < y < 620:
pg.click(button='right')#右键单击
if 1646 < x < 1700 and 2 < y < 33:
pg.doubleClick()#左键双击
except KeyboardInterrupt:
print("n")

7、Python控制键盘

同样的，Python也可以通过pyautogui控制键盘。

键盘写入：

import pyautogui
#typewrite()无法输入中文内容，中英文混合的只能输入英文
#interval设置文本输入速度，默认值为0
pyautogui.typewrite('你好，world!',interval=0.5)

8、Python压缩文件

压缩文件是办公中常见的操作，一般压缩会使用压缩软件，需要手动操作。

Python中有很多包支持文件压缩，可以让你自动化压缩或者解压缩本地文件，或者将内存中的分析结果进行打包。比如zipfile、zlib、tarfile等可以实现对.zip、.rar、.7z等压缩文件格式的操作。

压缩文件：

import zipfile
try:
with zipfile.ZipFile("c://test.zip",mode="w") as f:
f.write("c://test.txt")#写入压缩文件，会把压缩文件中的原有覆盖
except Exception as e:
print("异常对象的类型是:%s"%type(e))
print("异常对象的内容是:%s"%e)
finally:
f.close()

解压文件：

import zipfile
try:
with zipfile.ZipFile("c://test.zip",mode="a") as f:
 f.extractall("c://",pwd=b"root") ##将文件解压到指定目录，解压密码为root
except Exception as e:
 print("异常对象的类型是:%s"%type(e))
 print("异常对象的内容是:%s"%e)
finally:
 f.close()

9、Python爬取网络数据

python爬虫应该是最受欢迎的功能，也是广大Python爱好者们入坑的主要的原因。

Python中有非常多的包支持爬虫，而爬虫包又分为抓取、解析两种。

比如说requests、urllib这种是网络数据请求工具，也就是抓取包；xpath、re、bs4这种会对抓取下来的网页内容进行解析，称为解析包。

爬取百度首页图片，并保存到本地：

# 导入urlopen
from urllib.request import urlopen
# 导入BeautifulSoup
from bs4 import BeautifulSoup as bf
# 导入urlretrieve函数，用于下载图片
from urllib.request import urlretrieve
# 请求获取HTML
html = urlopen("http://www.baidu.com/")
# 用BeautifulSoup解析html
obj = bf(html.read(),'html.parser')
# 从标签head、title里提取标题
title = obj.head.title
# 只提取logo图片的信息
logo_pic_info = obj.find_all('img',class_="index-logo-src")
# 提取logo图片的链接
logo_url = "https:"+logo_pic_info[0]['src']
# 使用urlretrieve下载图片
urlretrieve(logo_url, 'logo.png')

10、Python处理图片图表

图片处理、图表可视化涉及到图像处理，这也是Python的强项，现在诸如图像识别、计算机视觉等前沿领域也都会用到Python。

在Python中处理图像的包有scikit Image、PIL、OpenCV等，处理图表的包有matplotlib、plotly、seaborn等。

对图片进行黑白化处理：

from PIL import Image
from PIL import ImageEnhance
img_main = Image.open(u'E:/login1.png')
img_main = img_main.convert('L')
threshold1 = 138
table1 = []
for i in range(256):
if i < threshold1:
table1.append(0)
else:
table1.append(1)
img_main = img_main.point(table1, "1")
img_main.save(u'E:/login3.png')

生成统计图表：

import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N)# the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width,
 bottom=menMeans, yerr=womenStd)
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
plt.show()

小结

总之Python会成为大众化的编程语言，帮助到更多需要的人。

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