Numpy Array Indexing

Numpy Array indexing covers below:

1D Indexing — positive and negative index

1D Slicing — start/stop/step, reverse

2D Indexing — [row, col] access

2D Slicing — rows, cols, blocks

Fancy Indexing — list of indices

Boolean Indexing — filter by condition

Modifying — assign via index or condition

np.where — conditional value selection

import numpy as np

# ── 1. 1D Array Indexing ──────────────────────────────────────────────────────

# Access elements by position (0-based index)

a = np.array([10, 20, 30, 40, 50])

print(a[0]) # 10 → first element

print(a[2]) # 30 → third element

print(a[-1]) # 50 → last element

print(a[-2]) # 40 → second from last

# ── 2. 1D Array Slicing ───────────────────────────────────────────────────────

# Syntax: array[start:stop:step] (stop is exclusive)

print(a[1:4]) # [20 30 40] → index 1 to 3

print(a[:3]) # [10 20 30] → from start to index 2

print(a[2:]) # [30 40 50] → from index 2 to end

print(a[::2]) # [10 30 50] → every 2nd element

print(a[::-1]) # [50 40 30 20 10] → reversed

# ── 3. 2D Array Indexing ──────────────────────────────────────────────────────

# Syntax: array[row, col]

b = np.array([[10, 20, 30],

[40, 50, 60],

[70, 80, 90]])

print(b[0, 0]) # 10 → row 0, col 0

print(b[1, 2]) # 60 → row 1, col 2

print(b[2, 1]) # 80 → row 2, col 1

print(b[-1, -1]) # 90 → last row, last col

# ── 4. 2D Array Slicing ───────────────────────────────────────────────────────

# Syntax: array[row_start:row_stop, col_start:col_stop]

print(b[0, :]) # [10 20 30] → entire row 0

print(b[:, 1]) # [20 50 80] → entire col 1

print(b[0:2, 0:2]) # [[10 20] → top-left 2×2 block

# [40 50]]

print(b[1:, 1:]) # [[50 60] → bottom-right 2×2 block

# [80 90]]

print(b[:, 0]) # [10 40 70] → entire first column

# ── 5. Fancy Indexing ─────────────────────────────────────────────────────────

# Pass a list of indices to select multiple specific elements

a = np.array([10, 20, 30, 40, 50])

print(a[[0, 2, 4]]) # [10 30 50] → elements at index 0,2,4

print(a[[1, 3]]) # [20 40] → elements at index 1,3

b = np.array([[10, 20, 30],

[40, 50, 60],

[70, 80, 90]])

print(b[[0, 2]]) # [[10 20 30] → rows 0 and 2

# [70 80 90]]

# ── 6. Boolean Indexing ───────────────────────────────────────────────────────

# Filter elements using a condition (returns elements where condition is True)

a = np.array([10, 20, 30, 40, 50])

print(a > 25) # [False False True True True]

print(a[a > 25]) # [30 40 50] → elements greater than 25

print(a < 25) # [ True True False False False]

print(a[a < 25]) # [10 20] → elements less than 25

print(a[a % 20 == 0]) # [20 40] → elements divisible by 20

b = np.array([[1, 2, 3],

[4, 5, 6],

[7, 8, 9]])

print(b[b > 5]) # [6 7 8 9] → flattened result

# ── 7. Modifying with Indexing ────────────────────────────────────────────────

# Assign new values using index or condition

a = np.array([10, 20, 30, 40, 50])

a[0] = 99 # change single element

print(a) # [99 20 30 40 50]

a[1:3] = 0 # change a slice

print(a) # [99 0 0 40 50]

a[a > 40] = -1 # change elements matching condition

print(a) # [-1 0 0 40 -1]

a[:] = 0 # change ALL elements at once

print(a) # [0 0 0 0 0]

# ── 8. np.where ───────────────────────────────────────────────────────────────

# Conditional selection: np.where(condition, value_if_true, value_if_false)

a = np.array([10, 20, 30, 40, 50])

result = np.where(a > 25, a, 0) # keep value if > 25 else 0

print(result) # [ 0 0 30 40 50]

result = np.where(a % 20 == 0, "even20", "other")

print(result) # ['other' 'even20' 'other' 'even20' 'other']

# ── 9. copy() vs View ─────────────────────────────────────────────────────────

# Slicing returns a VIEW — modifying it changes the original

# copy() creates an independent array — changes do NOT affect original

a = np.array([10, 20, 30, 40, 50])

# View (no copy) — shares memory with original

view = a[1:4]

view[0] = 99

print(a) # [10 99 30 40 50] ← original changed!

# copy() — independent array

a = np.array([10, 20, 30, 40, 50])

copy = a[1:4].copy()

copy[0] = 99

print(copy) # [99 30 40] ← copy changed

print(a) # [10 20 30 40 50] ← original unchanged

# Full array copy

b = a.copy()

b[0] = 0

print(b) # [ 0 20 30 40 50]

print(a) # [10 20 30 40 50] ← original safe