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独家 | 17个可以用于工作自动化的最佳Python脚本(下集)
作者:Abdul Hannan Hassan
翻译:付雯欣
校对:赵茹萱
本文约4800字,建议阅读10分钟
让我们进入自动化的世界,探索17个可以简化工作并节省时间精力的Python脚本。
引言
目录(下篇)
10.网络自动化
10.1检查网站状态
```
# Python script to check the status of a website
import requests
def check_website_status(url):
response = requests.get(url)
if response.status_code == 200:
# Your code here to handle a successful response
else:
# Your code here to handle an unsuccessful response
```
10.2自动 FTP 传输
```
# Python script to automate FTP file transfers
from ftplib import FTP
def ftp_file_transfer(host, username, password, local_file_path, remote_file_path):
with FTP(host) as ftp:
ftp.login(user=username, passwd=password)
with open(local_file_path, 'rb') as f:
ftp.storbinary(f'STOR {remote_file_path}', f)
```
10.3网络配置设置
```
# Python script to automate network device configuration
from netmiko import ConnectHandler
def configure_network_device(host, username, password, configuration_commands):
device = {
'device_type': 'cisco_ios',
'host': host,
'username': username,
'password': password,
}
with ConnectHandler(device) as net_connect:
net_connect.send_config_set(configuration_commands)
```
11. 数据清理和转换
11.1从数据中删除重复项
```
# Python script to remove duplicates from data
import pandas as pd
def remove_duplicates(data_frame):
cleaned_data = data_frame.drop_duplicates()
return cleaned_data
```
11.2数据标准化
```
# Python script for data normalization
import pandas as pd
def normalize_data(data_frame):
normalized_data = (data_frame - data_frame.min()) / (data_frame.max() - data_frame.min())
return normalized_data
```
```
# Python script to handle missing values in data
import pandas as pd
def handle_missing_values(data_frame):
filled_data = data_frame.fillna(method='ffill')
return filled_data
```
12. 自动化 PDF 操作
12.1从PDF中提取文本
```
# Python script to extract text from PDFs
importPyPDF2
def extract_text_from_pdf(file_path):
with open(file_path, 'rb') as f:
pdf_reader = PyPDF2.PdfFileReader(f)
text = ''
for page_num in range(pdf_reader.numPages):
page = pdf_reader.getPage(page_num)
text += page.extractText()
return text
```
12.2合并多个PDF
```
# Python script to merge multiple PDFs into a single PDF
import PyPDF2
def merge_pdfs(input_paths, output_path):
pdf_merger = PyPDF2.PdfMerger()
for path in input_paths:
with open(path, 'rb') as f:
pdf_merger.append(f)
with open(output_path, 'wb') as f:
pdf_merger.write(f)
```
```
# Python script to add password protection to a PDF
import PyPDF2
def add_password_protection(input_path, output_path, password):
with open(input_path, 'rb') as f:
pdf_reader = PyPDF2.PdfFileReader(f)
pdf_writer = PyPDF2.PdfFileWriter()
for page_num in range(pdf_reader.numPages):
page = pdf_reader.getPage(page_num)
pdf_writer.addPage(page)
pdf_writer.encrypt(password)
with open(output_path, 'wb') as output_file:
pdf_writer.write(output_file)
```
13. 自动化GUI
13.1自动化鼠标和键盘
```
# Python script for GUI automation using pyautogui
import pyautogui
def automate_gui():
# Your code here for GUI automation using pyautogui
pass
```
13.2创建简单的 GUI 应用程序
```
# Python script to create simple GUI applications using tkinter
import tkinter as tk
def create_simple_gui():
# Your code here to define the GUI elements and behavior
pass
```
```
# Python script to handle GUI events using tkinter
import tkinter as tk
def handle_gui_events():
pass
def on_button_click():
# Your code here to handle button click event
root = tk.Tk()
button = tk.Button(root, text="Click Me", command=on_button_click)
button.pack()
root.mainloop()
```
14. 自动化测试
14.1使用 Python 进行单元测试
```
# Python script for unit testing with the unittest module
import unittest
def add(a, b):
return a + b
class TestAddFunction(unittest.TestCase):
def test_add_positive_numbers(self):
self.assertEqual(add(2, 3), 5)
def test_add_negative_numbers(self):
self.assertEqual(add(-2, -3), -5)
def test_add_zero(self):
self.assertEqual(add(5, 0), 5)
if __name__ == '__main__':
unittest.main()
```
14.2用于 Web 测试的 Selenium
```
# Python script for web testing using Selenium
from selenium import webdriver
def perform_web_test():
driver = webdriver.Chrome()
driver.get("https://www.example.com")
# Your code here to interact with web elements and perform tests
driver.quit()
```
```
# Python script for building test automation frameworks
# Your code here to define the framework architecture and tools
```
15. 自动化云服务
15.1向云空间上传文件
```
# Python script to automate uploading files to cloud storage
# Your code here to connect to a cloud storage service (e.g., AWS S3, Google Cloud Storage)
# Your code here to upload files to the cloud storage
```
15.2管理AWS资源
```
# Python script to manage AWS resources using Boto3
import boto3
def create_ec2_instance(instance_type, image_id, key_name, security_group_ids):
ec2 = boto3.resource('ec2')
instance = ec2.create_instances(
ImageId=image_id,
InstanceType=instance_type,
KeyName=key_name,
SecurityGroupIds=security_group_ids,
MinCount=1,
MaxCount=1
)
return instance[0].id
```
```
# Python script to automate interactions with Google Drive
# Your code here to connect to Google Drive using the respective API
# Your code here to perform tasks such as uploading files, creating folders, etc.
```
16. 财务自动化
16.1分析股票价格
```
# Python script for stock price analysis
# Your code here to fetch stock data using a financial API (e.g., Yahoo Finance)
# Your code here to analyze the data and derive insights
```
16.2货币汇率
```
# Python script to fetch currency exchange rates
# Your code here to connect to a currency exchange API (e.g., Fixer.io, Open Exchange Rates)
# Your code here to perform currency conversions and display exchange rates
```
```
# Python script for budget tracking and analysis
# Your code here to read financial transactions from a CSV or Excel file
# Your code here to calculate income, expenses, and savings
# Your code here to generate reports and visualize budget data
```
17. 自然语言处理
17.1情感分析
```
# Python script for sentiment analysis using NLTK or other NLP libraries
importnltk
fromnltk.sentiment import SentimentIntensityAnalyzer
defanalyze_sentiment(text):
nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()
sentiment_score = sia.polarity_scores(text)
return sentiment_score
```
17.2文本摘要
```
# Python script for text summarization using NLP techniques
# Your code here to read the text data and preprocess it (e.g., removing stop words)
# Your code here to generate the summary using techniques like TF-IDF, TextRank, or BERT```
```
# Python script for language translation using NLP libraries
# Your code here to connect to a translation API (e.g., Google Translate, Microsoft Translator)
# Your code here to translate text between different languages```
结论
一些经常被问到的问题
1.Python适合自动化吗?
2.使用 Python 自动化任务有哪些好处?
3. 我可以在我的项目中使用这些脚本吗?
4. 我需要安装任何库来运行这些脚本吗?
5. 我可以将这些脚本用于商业用途吗?
6. 如何针对我的特定项目进一步优化这些脚本?
7. 我可以使用Python自动执行复杂的任务吗?
8. 自动化任务时是否有任何安全考虑?
译者简介
付雯欣,中国人民大学统计学专业硕士研究生在读,数据科学道路上的探索者一枚。小时候梦想做数学家,现在依旧着迷于数据背后的世界。热爱阅读,热爱遛弯儿,不停感受打开生命大门的瞬间。欢迎大家和我一起用概率的视角看世界~
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