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Home Interviews How to access NumPy Array Indexing – MindStick
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ICSM Computer
ICSM Computer 05 Nov 2025
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NumPy Array Indexing refers to the way you access or modify elements in a NumPy array. It’s similar to Python list indexing but much more powerful — supporting slicing, multi-dimensional access, boolean indexing, and fancy indexing.

1. Basic Indexing

Just like Python lists:

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
print(arr[0])   # 10
print(arr[-1])  # 50

2. 2D Array Indexing

Use [row, column]:

arr2d = np.array([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])

print(arr2d[0, 0])  # 1  (first row, first column)
print(arr2d[1, 2])  # 6  (second row, third column)

You can also access entire rows or columns:

print(arr2d[1])      # [4 5 6]  → 2nd row
print(arr2d[:, 2])   # [3 6 9]  → 3rd column

3. Slicing

You can extract a range of elements:

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])   # [20 30 40]
print(arr[:3])    # [10 20 30]
print(arr[::2])   # [10 30 50] (step = 2)

For 2D arrays:

arr2d = np.array([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])

print(arr2d[0:2, 1:3])
# [[2 3]
#  [5 6]]

4. Integer (Fancy) Indexing

Use lists or arrays of indices:

arr = np.array([10, 20, 30, 40, 50])
indices = [0, 2, 4]
print(arr[indices])   # [10 30 50]

For 2D arrays:

arr2d = np.array([[10, 20], [30, 40], [50, 60]])
print(arr2d[[0, 2], [1, 0]])  # [20 50]

Explanation:

(0,1) and (2,0) are the chosen coordinates.

5. Boolean Indexing

Select elements that satisfy a condition:

arr = np.array([10, 20, 30, 40, 50])
print(arr[arr > 25])  # [30 40 50]

You can also combine conditions:

print(arr[(arr > 15) & (arr < 45)])  # [20 30 40]

For 2D arrays:

arr2d = np.array([[1, 2, 3],
                  [4, 5, 6]])
print(arr2d[arr2d > 3])
# [4 5 6]

6. Assigning Values

You can modify elements directly:

arr = np.array([10, 20, 30, 40, 50])
arr[1:4] = 0
print(arr)  # [10  0  0  0 50]

Boolean assignment:

arr[arr > 25] = 99
print(arr)  # [10 20 99 99 99]

7. Using np.i() for Multi-dimensional Selection

If you want to select specific rows and columns:

arr = np.arange(1, 13).reshape(3, 4)
# arr =
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]

rows = [0, 2]
cols = [1, 3]
print(arr[np.i(rows, cols)])
# [[ 2  4]
#  [10 12]]

Summary Table

Type Example Description
Basic arr[2] Access single element
2D arr[1, 2] Row 1, Column 2
Slice arr[1:4] Range of elements
Fancy arr[[0,2,4]] Use list of indices
Boolean arr[arr > 5] Filter by condition
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