Python Libraries and Frameworks 🧰: Extending Python’s Capabilities
Python's strength lies in its vast ecosystem of libraries and frameworks that accelerate development.
Popular Libraries:
NumPy
for numerical computationspandas
for data manipulationMatplotlib
andSeaborn
for data visualizationscikit-learn
for machine learningRequests
for HTTP requests
Frameworks:
Django
andFlask
for web developmentTensorFlow
andPyTorch
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.