Python Libraries and Frameworks 🧰: Extending Python’s Capabilities

Intermediate

Python's strength lies in its vast ecosystem of libraries and frameworks that accelerate development.

Popular Libraries:

  • NumPy for numerical computations
  • pandas for data manipulation
  • Matplotlib and Seaborn for data visualization
  • scikit-learn for machine learning
  • Requests for HTTP requests

Frameworks:

  • Django and Flask for web development
  • TensorFlow and PyTorch for AI and deep learning

Installing Libraries:

pip install numpy pandas matplotlib

Using Libraries:

import numpy as np
array = np.array([1, 2, 3])
print(array.mean())

Leverage these tools to tackle complex problems efficiently, harnessing Python's versatility across domains.