NumPy Indexing & Slicing

Why indexing & slicing matter

Indexing and slicing let you access and transform subsets of data without loops. Most slices return views (no copy), making them fast and memory-efficient.

1D indexing & slicing

import numpy as np

a = np.array([10, 20, 30, 40, 50])
print(a[0])        # 10 (first element)
print(a[-1])       # 50 (last element)

# slice: start:stop (stop is exclusive)
print(a[1:4])      # [20 30 40]
print(a[:3])       # [10 20 30]
print(a[3:])       # [40 50]

# step / stride
print(a[::2])      # [10 30 50]
print(a[::-1])     # [50 40 30 20 10]  (reverse)

2D indexing (rows, columns)

b = np.array([[ 1,  2,  3],
              [10, 20, 30],
              [40, 50, 60]])

print(b[0, 1])     # 2  (row 0, col 1)
print(b[1])        # [10 20 30] (row 1)
print(b[:, 0])     # [ 1 10 40] (first column)

# Sub-matrix: rows 0..1, cols 1..2
print(b[0:2, 1:3]) # [[ 2  3],
                   #  [20 30]]

# Every row, every other col
print(b[:, ::2])   # [[ 1  3],
                   #  [10 30],
                   #  [40 60]]

3D indexing (example)

c = np.arange(2*3*4).reshape(2,3,4)  # shape (depth=2, rows=3, cols=4)
# Pick depth=1, rows 0..1, cols 2..3
print(c[1, 0:2, 2:4])
# Ellipsis to keep all middle dims:
print(c[..., 0])   # first column from each 2D slice

Views vs Copies (important!)

a = np.array([0, 1, 2, 3, 4, 5])
s = a[1:4]     # slice -> view (shares memory)
s[0] = 99
print(a)       # [ 0 99  2  3  4  5]  (a changed!)

# Force a copy if you need independence:
s_copy = a[1:4].copy()
s_copy[0] = -1
print(a[1:4])  # original remains unchanged now

Boolean indexing & where()

Use boolean masks to filter arrays. np.where builds masks or chooses values conditionally.

x = np.array([5, 12, 7, 22, 3, 18])
mask = x >= 10
print(mask)         # [False  True False  True False  True]
print(x[mask])      # [12 22 18]

# Conditional replace: values < 10 become 0
y = np.where(x < 10, 0, x)
print(y)            # [ 0 12  0 22  0 18]

Common patterns & pitfalls

• Stop is exclusive: a[m:n] includes m but not n.
• Negative indices: -1 is last element, -2 second last, etc.
• Strides: a[start:stop:step]; reverse with step=-1.
• Row vs column: In 2D, a[i] is a row; use a[:, j] for a column.
• Views change the original: copy with .copy() when you need isolation.

Practice: quick exercises

# 1) Extract every 3rd element from 0..29 starting at 2
a = np.arange(30)
print(a[2::3])

# 2) From a 6x6 array, get the 3x3 center block
b = np.arange(36).reshape(6,6)
print(b[1:4, 1:4])

# 3) Replace all odd numbers in 0..19 with -1 using boolean mask
c = np.arange(20)
c[c % 2 == 1] = -1
print(c)

Subhendu Mohapatra

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