NumPy
NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides a powerful N‑dimensional array object, fast operations, and mathematical functions. It is the basis for many other data science libraries.
Installation
`pip install numpy`
Creating Arrays
Use `np.array()` from lists, or convenience functions.
import numpy as np
a = np.array([1, 2, 3])
b = np.zeros((3, 3))
c = np.ones((2, 3))
d = np.arange(10)
e = np.linspace(0, 1, 5)
Array Operations
Vectorized operations are fast.
arr = np.array([1, 2, 3])
arr2 = arr * 2 # [2,4,6]
arr3 = arr + 10 # [11,12,13]
mean = np.mean(arr)
total = np.sum(arr)
Indexing and Slicing
Similar to lists but supports fancy indexing.
matrix = np.array([[1, 2], [3, 4]])
print(matrix[0, 1]) # 2
print(matrix[:, 0]) # [1,3]
Broadcasting
Perform operations on arrays of different shapes.
Two Minute Drill
- NumPy provides fast, vectorized operations on arrays.
- Create arrays with `np.array()`, `np.zeros()`, `np.arange()`.
- Operations are element‑wise and efficient.
- Slicing works like Python lists; fancy indexing is powerful.
- Broadcasting allows operations on different shapes.
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