NumPy is a core library for numerical computing in Python, providing support for arrays, matrices, and many mathematical functions. It's essential for data analysis, machine learning, and scientific computing. Let's dive into its key features!
🔍 Why Use NumPy?
- Efficient Array Operations: Handles multi-dimensional arrays with speed and memory efficiency
- Broadcasting: Simplifies operations on arrays of different shapes
- Integration: Works seamlessly with pandas, matplotlib, and scikit-learn
🧠 Key Concepts
- N-dimensional Arrays
import numpy as np arr = np.array([[1, 2], [3, 4]]) # 2D array - Vectorized Operations
Avoid loops with built-in functions:np.sqrt(arr) # Square root of each element - Random Number Generation
np.random.seed(0) np.random.rand(3, 3) # Random matrix
📚 Expand Your Knowledge
Want to explore how NumPy integrates with pandas? Check out our Python for Data Analysis Series for hands-on examples.
For advanced topics like broadcasting or linear algebra, visit NumPy Documentation for detailed guides.