Introduction
NumPy provides powerful functions to join and split arrays. These operations are commonly used in data analysis, machine learning, and scientific computing when combining datasets or dividing large arrays into smaller parts. In this chapter, you’ll practice the most important NumPy join and split array questions with complete solutions. NumPy Join and Split Arrays practice Questions with solutions help to understand the concepts.
1. Python Program to Join Two NumPy Arrays Using concatenate()
Problem Statement
Write a Python program to join two one-dimensional NumPy arrays using the concatenate() function.
Python Solution
import numpy as np
array1 = np.array([10, 20, 30])
array2 = np.array([40, 50, 60])
result = np.concatenate((array1, array2))
print(result)
Sample Output
[10 20 30 40 50 60]
Explanation
The concatenate() function joins two or more arrays into a single array.
Concepts Covered
concatenate()- Joining Arrays
2. Python Program to Join Two 2D Arrays Row-wise
Problem Statement
Write a Python program to join two two-dimensional arrays vertically.
Python Solution
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
result = np.vstack((array1, array2))
print(result)
Sample Output
[[1 2]
[3 4]
[5 6]
[7 8]]
Explanation
The vstack() function joins arrays vertically by adding rows.
Concepts Covered
vstack()- Vertical Join
3. Python Program to Join Two 2D Arrays Column-wise
Problem Statement
Write a Python program to join two two-dimensional arrays horizontally.
Python Solution
import numpy as np
array1 = np.array([[1, 2], [3, 4]])
array2 = np.array([[5, 6], [7, 8]])
result = np.hstack((array1, array2))
print(result)
Sample Output
[[1 2 5 6]
[3 4 7 8]]
Explanation
The hstack() function joins arrays horizontally by adding columns.
Concepts Covered
hstack()- Horizontal Join
4. Python Program to Join Arrays Using stack()
Problem Statement
Write a Python program to join two arrays using the stack() function.
Python Solution
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
result = np.stack((array1, array2))
print(result)
Sample Output
[[1 2 3]
[4 5 6]]
Explanation
The stack() function joins arrays by creating a new axis.
Concepts Covered
stack()- New Axis
5. Python Program to Split an Array into Two Equal Parts
Problem Statement
Write a Python program to split a NumPy array into two equal parts.
Python Solution
import numpy as np
numbers = np.array([10, 20, 30, 40])
result = np.split(numbers, 2)
print(result)
Sample Output
[array([10, 20]), array([30, 40])]
Explanation
The split() function divides an array into equal-sized parts.
Concepts Covered
split()- Equal Splitting
6. Python Program to Split an Array into Three Parts
Problem Statement
Write a Python program to split an array into three equal parts.
Python Solution
import numpy as np
numbers = np.array([3, 6, 9, 12, 15, 18])
result = np.split(numbers, 3)
print(result)
Sample Output
[array([3, 6]), array([ 9, 12]), array([15, 18])]
Explanation
The number of elements must be divisible by the number of splits.
Concepts Covered
- Multiple Splits
split()
7. Python Program to Split an Array Using array_split()
Problem Statement
Write a Python program to split an array into unequal parts.
Python Solution
import numpy as np
numbers = np.array([10, 20, 30, 40, 50])
result = np.array_split(numbers, 3)
print(result)
Sample Output
[array([10, 20]), array([30, 40]), array([50])]
Explanation
The array_split() function allows unequal splitting when equal division is not possible.
Concepts Covered
array_split()- Unequal Splitting
8. Python Program to Join Three NumPy Arrays
Problem Statement
Write a Python program to join three one-dimensional arrays into a single array.
Python Solution
import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
c = np.array([5, 6])
result = np.concatenate((a, b, c))
print(result)
Sample Output
[1 2 3 4 5 6]
Explanation
The concatenate() function can join more than two arrays.
Concepts Covered
- Multiple Arrays
concatenate()
9. Python Program to Split a 2D Array
Problem Statement
Write a Python program to split a two-dimensional NumPy array row-wise.
Python Solution
import numpy as np
numbers = np.array([
[1, 2],
[3, 4],
[5, 6],
[7, 8]
])
result = np.vsplit(numbers, 2)
print(result)
Sample Output
[array([[1, 2],
[3, 4]]), array([[5, 6],
[7, 8]])]
Explanation
The vsplit() function splits a two-dimensional array vertically.
Concepts Covered
vsplit()- 2D Array Splitting
10. Python Program to Split a 2D Array Column-wise
Problem Statement
Write a Python program to split a two-dimensional array into two column groups.
Python Solution
import numpy as np
numbers = np.array([
[10, 20, 30, 40],
[50, 60, 70, 80]
])
result = np.hsplit(numbers, 2)
print(result)
Sample Output
[array([[10, 20],
[50, 60]]), array([[30, 40],
[70, 80]])]
Explanation
The hsplit() function divides a two-dimensional array horizontally into column groups.
Concepts Covered
hsplit()- Column Splitting
11. Python Program to Join Two NumPy Arrays Using concatenate()
Problem Statement
Write a Python program to join two one-dimensional NumPy arrays using the concatenate() function.
Python Solution
import numpy as np
array1 = np.array([10, 20, 30])
array2 = np.array([40, 50, 60])
joined_array = np.concatenate((array1, array2))
print("First Array:")
print(array1)
print("\nSecond Array:")
print(array2)
print("\nJoined Array:")
print(joined_array)
Sample Output
First Array:
[10 20 30]
Second Array:
[40 50 60]
Joined Array:
[10 20 30 40 50 60]
Explanation
The np.concatenate() function combines two or more arrays along an existing axis.
Concepts Covered
- concatenate()
- 1D Array Joining
- Array Combination
12. Python Program to Join Two 2D Arrays Row-Wise
Problem Statement
Write a Python program to join two 2D NumPy arrays vertically using axis=0.
Python Solution
import numpy as np
array1 = np.array([
[1, 2, 3],
[4, 5, 6]
])
array2 = np.array([
[7, 8, 9],
[10, 11, 12]
])
joined_array = np.concatenate((array1, array2), axis=0)
print("Joined Array Row Wise:")
print(joined_array)
Sample Output
Joined Array Row Wise:
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
Explanation
When axis=0 is used, arrays are joined vertically by adding rows.
Concepts Covered
- 2D Array Joining
- axis=0
- Vertical Stacking
13. Python Program to Join Two 2D Arrays Column-Wise
Problem Statement
Write a Python program to combine two NumPy arrays horizontally by joining columns.
Python Solution
import numpy as np
array1 = np.array([
[1, 2],
[3, 4],
[5, 6]
])
array2 = np.array([
[7, 8],
[9, 10],
[11, 12]
])
joined_array = np.concatenate((array1, array2), axis=1)
print("Column Wise Joined Array:")
print(joined_array)
Sample Output
Column Wise Joined Array:
[[ 1 2 7 8]
[ 3 4 9 10]
[ 5 6 11 12]]
Explanation
Using axis=1 joins arrays horizontally by adding columns.
Concepts Covered
- concatenate()
- axis=1
- Horizontal Joining
14. Python Program to Split a NumPy Array into Multiple Parts Using split()
Problem Statement
Write a Python program to split a NumPy array into three equal parts using the split() function.
Python Solution
import numpy as np
array = np.arange(1, 13)
print("Original Array:")
print(array)
split_arrays = np.split(array, 3)
print("\nSplit Arrays:")
for part in split_arrays:
print(part)
Sample Output
Original Array:
[ 1 2 3 4 5 6 7 8 9 10 11 12]
Split Arrays:
[1 2 3 4]
[5 6 7 8]
[ 9 10 11 12]
Explanation
The np.split() function divides an array into equal sections.
Concepts Covered
- split()
- Array Division
- 1D Arrays
15. Python Program to Split a 2D Array Row-Wise
Problem Statement
Write a Python program to split a 4×4 NumPy matrix into two parts row-wise.
Python Solution
import numpy as np
matrix = np.arange(1, 17).reshape(4, 4)
print("Original Matrix:")
print(matrix)
parts = np.split(matrix, 2, axis=0)
print("\nSplit Matrices:")
for part in parts:
print(part)
Sample Output
Original Matrix:
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]
[13 14 15 16]]
Split Matrices:
[[1 2 3 4]
[5 6 7 8]]
[[ 9 10 11 12]
[13 14 15 16]]
Explanation
axis=0 divides the matrix by rows.
Concepts Covered
- 2D Split
- axis=0
- Matrix Division
16. Python Program to Split a 2D Array Column-Wise
Problem Statement
Write a Python program to split a 4×4 NumPy matrix into two parts column-wise.
Python Solution
import numpy as np
matrix = np.arange(1, 17).reshape(4, 4)
print("Original Matrix:")
print(matrix)
parts = np.split(matrix, 2, axis=1)
print("\nColumn Split:")
for part in parts:
print(part)
Sample Output
Column Split:
[[ 1 2]
[ 5 6]
[ 9 10]
[13 14]]
[[ 3 4]
[ 7 8]
[11 12]
[15 16]]
Explanation
axis=1 splits the array based on columns.
Concepts Covered
- Column Splitting
- axis=1
- Matrix Operations
17. Python Program to Join Arrays Using Stack Functions
Problem Statement
Write a Python program to join two arrays using:
- vstack()
- hstack()
Python Solution
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
vertical = np.vstack((array1, array2))
horizontal = np.hstack((array1, array2))
print("Vertical Stack:")
print(vertical)
print("\nHorizontal Stack:")
print(horizontal)
Sample Output
Vertical Stack:
[[1 2 3]
[4 5 6]]
Horizontal Stack:
[1 2 3 4 5 6]
Explanation
vstack()joins arrays vertically.hstack()joins arrays horizontally.
Concepts Covered
- vstack()
- hstack()
- Array Joining
18. Python Program to Split an Array Using hsplit() and vsplit()
Problem Statement
Write a Python program to split a matrix using:
hsplit()for columnsvsplit()for rows
Python Solution
import numpy as np
matrix = np.arange(1, 13).reshape(3, 4)
print("Original Matrix:")
print(matrix)
row_split = np.vsplit(matrix, 3)
column_split = np.hsplit(matrix, 2)
print("\nVertical Split:")
for part in row_split:
print(part)
print("\nHorizontal Split:")
for part in column_split:
print(part)
Sample Output
Original Matrix:
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
Vertical Split:
[[1 2 3 4]]
[[5 6 7 8]]
[[ 9 10 11 12]]
Horizontal Split:
[[1 2]
[5 6]
[9 10]]
[[ 3 4]
[ 7 8]
[11 12]]
Explanation
vsplit()divides an array vertically by rows.hsplit()divides an array horizontally by columns.
Concepts Covered
- vsplit()
- hsplit()
- Matrix Splitting
- Array Manipulation
Chapter Summary
In this chapter, you learned how to join and split NumPy arrays using concatenate(), stack(), vstack(), hstack(), split(), array_split(), vsplit(), and hsplit(). These operations are widely used for preparing and organizing datasets in data analysis and machine learning.
Key Takeaways
concatenate()joins multiple arrays into one.stack()joins arrays by creating a new axis.vstack()combines arrays vertically.hstack()combines arrays horizontally.split()divides arrays into equal parts.array_split()supports unequal splitting.vsplit()andhsplit()are useful for splitting 2D arrays.
Frequently Asked Questions (FAQs)
1. What is the difference between concatenate() and stack()?
concatenate() joins arrays along an existing axis, while stack() creates a new axis when combining arrays.
2. When should I use vstack()?
Use vstack() when you want to join arrays vertically by adding rows.
3. What is the purpose of hstack()?
hstack() joins arrays horizontally by adding columns.
4. What is the difference between split() and array_split()?
split() requires equal-sized divisions, while array_split() can create unequal-sized divisions.
5. Can I join more than two NumPy arrays?
Yes. Functions like concatenate() and stack() can join multiple arrays.
6. What is vsplit() used for?
vsplit() divides a two-dimensional array into multiple parts row-wise.
7. Why are join and split operations important in NumPy?
Join and split operations help combine datasets, divide large datasets, and prepare data for machine learning and data analysis.
Written by Shubhranshu Shekhar, who has trained 20000+ students in coding.
