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Python Libraries

Python libraries are reusable collections of modules and packages that extend Python’s capabilities for a wide range of tasks, from data analysis and web development to machine learning and automation. Libraries help developers avoid reinventing the wheel and accelerate development.

🌟 Popular Python Libraries​

  • NumPy: Fundamental package for numerical computing and arrays.
  • Pandas: Data analysis and manipulation, especially with tabular data.
  • Matplotlib / Seaborn / Plotly: Data visualization and plotting.
  • Requests: Simple HTTP library for making web requests.
  • Flask / Django / FastAPI: Web development frameworks.
  • BeautifulSoup / Scrapy: Web scraping and parsing HTML/XML.
  • SQLAlchemy: Database toolkit and ORM.
  • pytest / unittest: Testing frameworks.
  • scikit-learn: Machine learning algorithms and tools.
  • TensorFlow / PyTorch: Deep learning frameworks.
  • OpenCV: Computer vision and image processing.
  • Pillow: Image manipulation.
  • asyncio / aiohttp: Asynchronous programming and HTTP requests.

🛠️ Example: Installing and Using a Library​

pip install requests
import requests

response = requests.get('https://api.github.com')
print(response.json())

📚 Useful Links​

📝 Notes​

  • Use pip or conda to install libraries.
  • Virtual environments (venv, virtualenv, conda) help manage dependencies.
  • Many libraries are open source and have active communities.