NumPy Tutorial 🔢
Numerical Python
NumPy is the fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
✨ Why NumPy?
- Speed: NumPy arrays are up to 50x faster than Python lists.
- Memory: NumPy arrays take up less memory.
- Convenience: Makes complex mathematical operations easy.
📦 Creating Arrays
The main object in NumPy is the ndarray.
python
import numpy as np
# 1-D Array
arr1 = np.array([1, 2, 3, 4, 5])
# 2-D Array (Matrix)
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2.shape) # (2, 3) - 2 rows, 3 columns🧮 Array Operations
You can perform element-wise operations easily.
python
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
# Addition
print(a + b) # [5 7 9]
# Multiplication
print(a * 2) # [2 4 6]
# Dot Product
print(np.dot(a, b)) # 32✨ Pro Tip: Slicing
NumPy slicing is powerful and similar to Python lists, but works on multiple dimensions.
python
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[0, 1:4]) # [2 3 4] (Row 0, indices 1 to 3)