14个 Python 自动化实战脚本

1.批量文件重命名神器在工作中，我们常常需要对大量文件进行批量重命名，Python帮你轻松搞定！

import osdef batch_rename(path, prefix=\'\', suffix=\'\'):    for i, filename in enumerate(os.listdir(path)):        new_name = f\"{prefix}{i:03d}{suffix}{os.path.splitext(filename)[1]}\"        old_file = os.path.join(path, filename)        new_file = os.path.join(path, new_name)        os.rename(old_file, new_file)# 使用示例：batch_rename(\'/path/to/your/directory\', \'file_\', \'.txt\')

2.自动发送邮件通知告别手动发送，用Python编写定时发送邮件的自动化脚本。

import smtplibfrom email.mime.text import MIMETextdef send_email(to_addr, subject, content):    smtp_server = \'smtp.example.com\'    username = \'your-email@example.com\'    password = \'your-password\'    msg = MIMEText(content)    msg[\'Subject\'] = subject    msg[\'From\'] = username    msg[\'To\'] = to_addr    server = smtplib.SMTP(smtp_server, 587)    server.starttls()    server.login(username, password)    server.sendmail(username, to_addr, msg.as_string())    server.quit()# 使用示例：send_email(\'receiver@example.com\', \'每日报告提醒\', \'今日报告已生成，请查收。\')

3.定时任务自动化执行使用Python调度库，实现定时执行任务的自动化脚本。

import scheduleimport timedef job_to_schedule():    print(\"当前时间：\", time.ctime(), \"任务正在执行...\")# 定义每天9点执行任务schedule.every().day.at(\"09:00\").do(job_to_schedule)while True:    schedule.run_pending()    time.sleep(1)# 使用示例：# 运行此脚本后，每天上午9点会自动打印当前时间及提示信息

4.数据库操作自动化简化数据库管理，Python帮你自动化执行CRUD操作。

import sqlite3def create_connection(db_file):    conn = None    try:        conn = sqlite3.connect(db_file)        print(f\"成功连接到SQLite数据库：{db_file}\")    except Error as e:        print(e)    return conndef insert_data(conn, table_name, data_dict):    keys = \', \'.join(data_dict.keys())    values = \', \'.join(f\"\'{v}\'\" for v in data_dict.values())    sql = f\"INSERT INTO {table_name} ({keys}) VALUES ({values});\"    try:        cursor = conn.cursor()        cursor.execute(sql)        conn.commit()        print(\"数据插入成功！\")    except sqlite3.Error as e:        print(e)# 使用示例：conn = create_connection(\'my_database.db\')data = {\'name\': \'John Doe\', \'age\': 30}insert_data(conn, \'users\', data)# 在适当时候关闭数据库连接conn.close()

5.网页内容自动化抓取利用BeautifulSoup和requests库，编写Python爬虫获取所需网页信息。

import requestsfrom bs4 import BeautifulSoupdef fetch_web_content(url):    response = requests.get(url)    if response.status_code == 200:        soup = BeautifulSoup(response.text, \'html.parser\')        # 示例提取页面标题        title = soup.find(\'title\').text        return title    else:        return \"无法获取网页内容\"# 使用示例：url = \'https://example.com\'web_title = fetch_web_content(url)print(\"网页标题：\", web_title)

6.数据清洗自动化使用Pandas库，实现复杂数据处理和清洗的自动化。

import pandas as pddef clean_data(file_path):    df = pd.read_csv(file_path)        # 示例：处理缺失值    df.fillna(\'N/A\', inplace=True)    # 示例：去除重复行    df.drop_duplicates(inplace=True)    # 示例：转换列类型    df[\'date_column\'] = pd.to_datetime(df[\'date_column\'])    return df# 使用示例：cleaned_df = clean_data(\'data.csv\')print(\"数据清洗完成，已准备就绪！\")

7.图片批量压缩用Python快速压缩大量图片以节省存储空间。

from PIL import Imageimport osdef compress_images(dir_path, quality=90):    for filename in os.listdir(dir_path):        if filename.endswith(\".jpg\") or filename.endswith(\".png\"):            img = Image.open(os.path.join(dir_path, filename))            img.save(os.path.join(dir_path, f\'compressed_{filename}\'), optimize=True, quality=quality)# 使用示例：compress_images(\'/path/to/images\', quality=80)

8.文件内容查找替换Python脚本帮助你一键在多个文件中搜索并替换指定内容。

import fileinputdef search_replace_in_files(dir_path, search_text, replace_text):    for line in fileinput.input([f\"{dir_path}/*\"], inplace=True):        print(line.replace(search_text, replace_text), end=\'\')# 使用示例：search_replace_in_files(\'/path/to/files\', \'old_text\', \'new_text\')

9.日志文件分析自动化通过Python解析日志文件，提取关键信息进行统计分析。

def analyze_log(log_file):    with open(log_file, \'r\') as f:        lines = f.readlines()    error_count = 0    for line in lines:        if \"ERROR\" in line:            error_count += 1    print(f\"日志文件中包含 {error_count} 条错误记录。\")# 使用示例：analyze_log(\'application.log\')

10.数据可视化自动化利用Matplotlib库，实现数据的自动图表生成。

import matplotlib.pyplot as pltimport pandas as pddef visualize_data(data_file):    df = pd.read_csv(data_file)        # 示例：绘制柱状图    df.plot(kind=\'bar\', x=\'category\', y=\'value\')    plt.title(\'数据分布\')    plt.xlabel(\'类别\')    plt.ylabel(\'值\')    plt.show()# 使用示例：visualize_data(\'data.csv\')

11.邮件附件批量下载通过Python解析邮件，自动化下载所有附件。

import imaplibimport emailfrom email.header import decode_headerimport osdef download_attachments(email_addr, password, imap_server, folder=\'INBOX\'):    mail = imaplib.IMAP4_SSL(imap_server)    mail.login(email_addr, password)    mail.select(folder)    result, data = mail.uid(\'search\', None, \"ALL\")    uids = data[0].split()    for uid in uids:        _, msg_data = mail.uid(\'fetch\', uid, \'(RFC822)\')        raw_email = msg_data[0][1].decode(\"utf-8\")        email_message = email.message_from_string(raw_email)        for part in email_message.walk():            if part.get_content_maintype() == \'multipart\':                continue            if part.get(\'Content-Disposition\') is None:                continue                        filename = part.get_filename()            if bool(filename):                file_data = part.get_payload(decode=True)                with open(os.path.join(\'/path/to/download\', filename), \'wb\') as f:                    f.write(file_data)    mail.close()    mail.logout()# 使用示例：download_attachments(\'your-email@example.com\', \'your-password\', \'imap.example.com\')

12.定时发送报告自动化根据数据库或文件内容，自动生成并定时发送日报/周报。

import pandas as pdimport smtplibfrom email.mime.text import MIMETextfrom email.mime.multipart import MIMEMultipartdef generate_report(source, to_addr, subject):    # 假设这里是从数据库或文件中获取数据并生成报告内容    report_content = pd.DataFrame({\"Data\": [1, 2, 3], \"Info\": [\"A\", \"B\", \"C\"]}).to_html()    msg = MIMEMultipart()    msg[\'From\'] = \'your-email@example.com\'    msg[\'To\'] = to_addr    msg[\'Subject\'] = subject    msg.attach(MIMEText(report_content, \'html\'))    server = smtplib.SMTP(\'smtp.example.com\', 587)    server.starttls()    server.login(\'your-email@example.com\', \'your-password\')    text = msg.as_string()    server.sendmail(\'your-email@example.com\', to_addr, text)    server.quit()# 使用示例：generate_report(\'data.csv\', \'receiver@example.com\', \'每日数据报告\')# 结合前面的定时任务脚本，可实现定时发送功能

13.自动化性能测试使用Python的locust库进行API接口的压力测试。

from locust import HttpUser, task, betweenclass WebsiteUser(HttpUser):    wait_time = between(5, 15)  # 定义用户操作之间的等待时间    @task    def load_test_api(self):        response = self.client.get(\"/api/data\")        assert response.status_code == 200  # 验证返回状态码为200    @task(3)  # 指定该任务在总任务中的执行频率是其他任务的3倍    def post_data(self):        data = {\"key\": \"value\"}        response = self.client.post(\"/api/submit\", json=data)        assert response.status_code == 201  # 验证数据成功提交后的响应状态码# 运行Locust命令启动性能测试：# locust -f your_test_script.py --host=http://your-api-url.com

14、自动化部署与回滚脚本使用Fabric库编写SSH远程部署工具，这里以部署Django项目为例：

from fabric import Connectiondef deploy(host_string, user, password, project_path, remote_dir):    c = Connection(host=host_string, user=user, connect_kwargs={\"password\": password})    with c.cd(remote_dir):        c.run(\'git pull origin master\')  # 更新代码        c.run(\'pip install -r requirements.txt\')  # 安装依赖        c.run(\'python manage.py migrate\')  # 执行数据库迁移        c.run(\'python manage.py collectstatic --noinput\')  # 静态文件收集        c.run(\'supervisorctl restart your_project_name\')  # 重启服务# 使用示例：deploy(    host_string=\'your-server-ip\',    user=\'deploy_user\',    password=\'deploy_password\',    project_path=\'/path/to/local/project\',    remote_dir=\'/path/to/remote/project\')# 对于回滚操作，可以基于版本控制系统实现或创建备份，在出现问题时恢复上一版本的部署。