4个必学的Python自动化技巧分享

import os
 
# 设置文件夹路径
folder_path = 'path/to/your/folder'
 
# 获取文件夹中的所有文件
files = os.listdir(folder_path)
 
# 过滤出图片文件（以.jpg结尾）
image_files = [f for f in files if f.endswith('.jpg')]
 
# 重命名文件
for i, file_name in enumerate(image_files, start=1):
    new_file_name = f'{i}.jpg'
    old_file_path = os.path.join(folder_path, file_name)
    new_file_path = os.path.join(folder_path, new_file_name)
    os.rename(old_file_path, new_file_path)
 
print('文件重命名完成！')

import requests
 
# 设置目标URL
url = 'http://example.com'
 
# 发起HTTP GET请求
response = requests.get(url)
 
# 检查请求是否成功
if response.status_code == 200:
    # 获取网页内容
    web_content = response.text
    
    # 设置保存路径和文件名
    save_path = 'path/to/save/file.html'
    
    # 将网页内容写入文件
    with open(save_path, 'w', encoding='utf-8') as file:
        file.write(web_content)
    
    print('网页内容已保存为文件！')
else:
    print(f'请求失败，状态码：{response.status_code}')

import schedule
import time
import smtplib
from email.mime.text import MIMEText
 
# 设置邮件发送函数
def send_email():
    # 邮件服务器设置
    smtp_server = 'smtp.example.com'
    smtp_port = 587
    sender_email = 'your_email@example.com'
    sender_password = 'your_password'
    
    # 邮件内容设置
    receiver_email = 'receiver_email@example.com'
    subject = '工作提醒'
    body = '今天是XX月XX日，别忘了完成以下工作任务：...'
    
    # 创建邮件对象
    message = MIMEText(body, 'plain', 'utf-8')
    message['From'] = sender_email
    message['To'] = receiver_email
    message['Subject'] = subject
    
    # 发送邮件
    with smtplib.SMTP(smtp_server, smtp_port) as server:
        server.starttls()
        server.login(sender_email, sender_password)
        server.sendmail(sender_email, receiver_email, message.as_string())
    
    print('邮件已发送！')
 
# 设置定时任务：每天下午5点发送邮件
schedule.every().day.at("17:00").do(send_email)
 
# 运行定时任务
while True:
    schedule.run_pending()
    time.sleep(1)

import pandas as pd
 
# 读取CSV文件
df = pd.read_csv('path/to/your/file.csv')
 
# 查看数据
print(df.head())
 
# 删除缺失值
df_dropna = df.dropna()
 
# 删除异常值（假设异常值为小于0或大于100的值）
df_clean = df_dropna[(df_dropna >= 0) & (df_dropna <= 100)]
 
# 保存清洗后的数据
df_clean.to_csv('path/to/save/cleaned_file.csv', index=False)
 
print('数据清洗完成！')