NumPy Array Indexing Practice Questions with Solutions

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Introduction

NumPy array indexing allows you to access individual elements from one-dimensional, two-dimensional, and multi-dimensional arrays. Understanding indexing is essential for data analysis, machine learning, and scientific computing. In this chapter, you’ll practice the most commonly used NumPy array indexing questions with complete solutions. NumPy Array Indexing practice questions with solutions help to build concepts.

1. Python Program to Access the First Element of a NumPy Array

Problem Statement

Write a Python program to print the first element of a NumPy array.

Python Solution

import numpy as np

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

print(numbers[0])

Sample Output

10

Explanation

Array indexing starts from 0. Therefore, index 0 returns the first element.

Concepts Covered

  • Array Indexing
  • First Element
  • Index Position

2. Python Program to Access the Last Element of a NumPy Array

Problem Statement

Write a Python program to print the last element of a NumPy array using negative indexing.

Python Solution

import numpy as np

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

print(numbers[-1])

Sample Output

50

Explanation

Negative indexing starts from the end of the array. The index -1 always represents the last element.

Concepts Covered

  • Negative Indexing
  • Last Element

3. Python Program to Access the Third Element of a NumPy Array

Problem Statement

Write a Python program to print the third element of a NumPy array.

Python Solution

import numpy as np

numbers = np.array([5, 10, 15, 20, 25])

print(numbers[2])

Sample Output

15

Explanation

Since indexing begins with 0, the third element is located at index 2.

Concepts Covered

  • Index Position
  • One-Dimensional Array

4. Python Program to Access an Element from a Two-Dimensional Array

Problem Statement

Write a Python program to print the value 6 from the following array.

Python Solution

import numpy as np

numbers = np.array([
    [1, 2, 3],
    [4, 5, 6]
])

print(numbers[1, 2])

Sample Output

6

Explanation

The first index represents the row, and the second index represents the column.

Concepts Covered

  • Two-Dimensional Array
  • Row and Column Indexing

5. Python Program to Access an Element from a Three-Dimensional Array

Problem Statement

Write a Python program to print the value 8 from a three-dimensional NumPy array.

Python Solution

import numpy as np

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

print(numbers[1, 1, 1])

Sample Output

8

Explanation

For a three-dimensional array, indexing follows the order: array, row, column.

Concepts Covered

  • Three-Dimensional Array
  • Multi-Dimensional Indexing

6. Python Program to Change an Array Element

Problem Statement

Write a Python program to change the second element of a NumPy array to 100.

Python Solution

import numpy as np

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

numbers[1] = 100

print(numbers)

Sample Output

[ 10 100  30  40]

Explanation

You can modify an array element by assigning a new value using its index.

Concepts Covered

  • Updating Array Elements
  • Array Assignment

7. Python Program to Change an Element in a Two-Dimensional Array

Problem Statement

Write a Python program to replace the value 5 with 50.

Python Solution

import numpy as np

numbers = np.array([
    [1, 2, 3],
    [4, 5, 6]
])

numbers[1, 1] = 50

print(numbers)

Sample Output

[[ 1  2  3]
 [ 4 50  6]]

Explanation

Use row and column indexes to update elements in a two-dimensional array.

Concepts Covered

  • Updating 2D Arrays
  • Row and Column Indexing

8. Python Program to Access Multiple Elements Using Indexes

Problem Statement

Write a Python program to print the first and last elements of a NumPy array.

Python Solution

import numpy as np

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

print(numbers[0], numbers[-1])

Sample Output

10 50

Explanation

You can access multiple elements by using multiple index positions in the same statement.

Concepts Covered

  • Positive Indexing
  • Negative Indexing

9. Python Program to Access an Entire Row

Problem Statement

Write a Python program to print the second row of a two-dimensional array.

Python Solution

import numpy as np

numbers = np.array([
    [10, 20, 30],
    [40, 50, 60]
])

print(numbers[1])

Sample Output

[40 50 60]

Explanation

Providing only the row index returns the complete row.

Concepts Covered

  • Row Selection
  • Two-Dimensional Arrays

10. Python Program to Access an Entire Column

Problem Statement

Write a Python program to print the first column of a two-dimensional array.

Python Solution

import numpy as np

numbers = np.array([
    [10, 20, 30],
    [40, 50, 60]
])

print(numbers[:, 0])

Sample Output

[10 40]

Explanation

The colon (:) selects all rows, while the column index selects the required column.

Concepts Covered

  • Column Selection
  • Array Indexing

11. Python Program to Access Specific Elements from a 2D NumPy Array

Problem Statement

Write a Python program to create a 4×4 NumPy array and display the following elements:

  • First element
  • Last element
  • Element at second row and third column
  • Element at fourth row and second column

Python Solution

import numpy as np

array = np.array([
    [10, 20, 30, 40],
    [50, 60, 70, 80],
    [90, 100, 110, 120],
    [130, 140, 150, 160]
])

print("Original Array:")
print(array)

print("\nFirst Element:", array[0, 0])
print("Last Element:", array[-1, -1])
print("Second Row, Third Column:", array[1, 2])
print("Fourth Row, Second Column:", array[3, 1])

Sample Output

Original Array:
[[ 10  20  30  40]
 [ 50  60  70  80]
 [ 90 100 110 120]
 [130 140 150 160]]

First Element: 10
Last Element: 160
Second Row, Third Column: 70
Fourth Row, Second Column: 140

Explanation

The program demonstrates how to access elements using row and column indices in a 2D NumPy array.

Concepts Covered

  • 2D Array Indexing
  • Positive Indexing
  • Negative Indexing

12. Python Program to Extract Entire Rows and Columns from a NumPy Array

Problem Statement

Write a Python program to extract:

  • First row
  • Last row
  • Second column
  • Fourth column

from a NumPy array.

Python Solution

import numpy as np

array = np.array([
    [5, 10, 15, 20],
    [25, 30, 35, 40],
    [45, 50, 55, 60],
    [65, 70, 75, 80]
])

print("Original Array:")
print(array)

print("\nFirst Row:")
print(array[0])

print("\nLast Row:")
print(array[-1])

print("\nSecond Column:")
print(array[:, 1])

print("\nFourth Column:")
print(array[:, 3])

Sample Output

First Row:
[ 5 10 15 20]

Last Row:
[65 70 75 80]

Second Column:
[10 30 50 70]

Fourth Column:
[20 40 60 80]

Explanation

The : operator selects all rows while specifying a column index extracts an entire column.

Concepts Covered

  • Row Selection
  • Column Selection
  • Array Indexing

13. Python Program to Access Elements Using Negative Indexing

Problem Statement

Write a Python program to demonstrate negative indexing in a NumPy array by displaying:

  • Last row
  • Last column
  • Second last row
  • Second last column

Python Solution

import numpy as np

array = np.array([
    [11, 22, 33],
    [44, 55, 66],
    [77, 88, 99]
])

print("Original Array:")
print(array)

print("\nLast Row:")
print(array[-1])

print("\nLast Column:")
print(array[:, -1])

print("\nSecond Last Row:")
print(array[-2])

print("\nSecond Last Column:")
print(array[:, -2])

Sample Output

Last Row:
[77 88 99]

Last Column:
[33 66 99]

Second Last Row:
[44 55 66]

Second Last Column:
[22 55 88]

Explanation

Negative indexing starts from the end of the array.

  • -1 → Last element
  • -2 → Second last element

Concepts Covered

  • Negative Indexing
  • Rows
  • Columns
  • NumPy Arrays

14. Python Program to Replace Specific Elements Using Indexing

Problem Statement

Write a Python program to replace:

  • First element with 100
  • Last element with 500
  • Center element with 999

using NumPy indexing.

Python Solution

import numpy as np

array = np.array([
    [10, 20, 30],
    [40, 50, 60],
    [70, 80, 90]
])

print("Original Array:")
print(array)

array[0, 0] = 100
array[-1, -1] = 500
array[1, 1] = 999

print("\nUpdated Array:")
print(array)

Sample Output

Updated Array:
[[100  20  30]
 [ 40 999  60]
 [ 70  80 500]]

Explanation

NumPy arrays are mutable, so individual elements can be modified using indexing.

Concepts Covered

  • Updating Array Values
  • Index Assignment
  • Mutable Arrays

15. Python Program to Find the Maximum and Minimum Elements Using Index Positions

Problem Statement

Write a Python program to display:

  • Maximum value
  • Minimum value
  • Position (index) of the maximum value
  • Position (index) of the minimum value

Python Solution

import numpy as np

array = np.array([
    [25, 18, 92],
    [40, 75, 12],
    [88, 56, 61]
])

print("Original Array:")
print(array)

max_value = np.max(array)
min_value = np.min(array)

max_position = np.unravel_index(np.argmax(array), array.shape)
min_position = np.unravel_index(np.argmin(array), array.shape)

print("\nMaximum Value:", max_value)
print("Maximum Position:", max_position)

print("\nMinimum Value:", min_value)
print("Minimum Position:", min_position)

Sample Output

Maximum Value: 92
Maximum Position: (0, 2)

Minimum Value: 12
Minimum Position: (1, 2)

Explanation

  • np.argmax() returns the flattened index of the maximum element.
  • np.argmin() returns the flattened index of the minimum element.
  • np.unravel_index() converts the flattened index into row and column coordinates.

Concepts Covered

  • Array Indexing
  • argmax()
  • argmin()
  • unravel_index()
  • Finding Element Positions

Chapter Summary

In this chapter, you learned how to access and modify elements in one-dimensional, two-dimensional, and three-dimensional NumPy arrays. You also practiced positive indexing, negative indexing, row selection, column selection, and updating array values.

Key Takeaways

  • NumPy indexing starts from 0.
  • Negative indexing accesses elements from the end of the array.
  • Multi-dimensional arrays use row and column indexes.
  • Three-dimensional arrays require three indexes.
  • Array elements can be modified using index assignment.
  • The : operator selects all rows or columns.
  • Indexing is one of the most important NumPy concepts for data manipulation.

Frequently Asked Questions (FAQs)

1. What is array indexing in NumPy?

Array indexing is the process of accessing individual elements in a NumPy array using their index positions.

2. Does NumPy indexing start from 0?

Yes. Like Python lists, NumPy arrays use zero-based indexing.

3. What is negative indexing in NumPy?

Negative indexing accesses elements from the end of the array. For example, -1 returns the last element.

4. How do I access an element in a 2D NumPy array?

Use the syntax array[row, column]. For example, array[1, 2].

5. Can I modify array elements using indexing?

Yes. Simply assign a new value using the index, such as array[0] = 100.

6. How do I access an entire row in a NumPy array?

Use the row index only. For example, array[1] returns the second row.

7. How do I access an entire column in a NumPy array?

Use : for all rows and specify the column index. For example, array[:, 0] returns the first column.

Written by Shubhranshu Shekhar, who has trained 20000+ students in coding.

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