Indexing, Slicing, and Iterating in NumPy

Indexing, Slicing, and Iterating

NumPy provides powerful ways to access sub‑arrays.

1‑D indexing

arr = np.arange(10)
print(arr[3])      # 3
print(arr[-1])     # 9

2‑D slicing

mat = np.arange(1, 13).reshape(3,4)
print(mat)
# Slice rows 0‑1 and columns 1‑3
sub = mat[0:2, 1:4]
print(sub)

Boolean indexing

mask = arr % 2 == 0   # even numbers mask
print(arr[mask])      # [0 2 4 6 8]

Fancy indexing

rows = np.array([0,2])
cols = np.array([1,3])
print(mat[rows, cols])   # [2 12]

Iterating (avoid when possible)

for row in mat:
    print(row)

Performance note: Vectorized operations are much faster than Python loops.