np.concatenate() for joining arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.concatenate() with 1D arrays

What is the output of this code?

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5])
result = np.concatenate((arr1, arr2))
print(result)

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5])
result = np.concatenate((arr1, arr2))
print(result)

A [[1 2 3 4 5]]

B

[[1 2 3]
 [4 5]]

C

[1 2 3]
[4 5]

D [1 2 3 4 5]

💡 Hint

np.concatenate joins arrays along an existing axis. For 1D arrays, it joins elements end to end.

np.concatenate((arr1, arr2)) joins the two 1D arrays into one longer 1D array: [1 2 3 4 5].

❓ data_output

intermediate

Shape after concatenating 2D arrays along axis 0

Given two 2D arrays:

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6]])
result = np.concatenate((arr1, arr2), axis=0)
print(result.shape)

What is the shape of the result?

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6]])
result = np.concatenate((arr1, arr2), axis=0)
print(result.shape)

A (3, 2)

B (2, 3)

C (3, 3)

D (2, 2)

💡 Hint

Concatenating along axis 0 stacks rows, so rows add up, columns stay same.

arr1 has shape (2,2), arr2 has shape (1,2). Concatenating along axis 0 adds rows: 2+1=3 rows, columns remain 2.

🔧 Debug

advanced

Error when concatenating arrays with mismatched shapes

What error does this code raise?

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6, 7]])
result = np.concatenate((arr1, arr2), axis=0)

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6, 7]])
result = np.concatenate((arr1, arr2), axis=0)

A ValueError: all the input array dimensions except for the concatenation axis must match exactly

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: all the input arrays must have same number of dimensions

D IndexError: index out of bounds

💡 Hint

Arrays must have matching shapes except along the concatenation axis.

arr1 shape is (2,2), arr2 shape is (1,3). They differ in columns (axis 1), so concatenation along axis 0 fails with ValueError about dimension mismatch.

🚀 Application

advanced

Concatenate arrays along axis 1

What is the output of this code?

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5], [6]])
result = np.concatenate((arr1, arr2), axis=1)
print(result)

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5], [6]])
result = np.concatenate((arr1, arr2), axis=1)
print(result)

A

[[1 2]
 [3 4]
 [5 6]]

B

[[1 2]
 [3 4]
 [5]
 [6]]

C

[[1 2 5]
 [3 4 6]]

D [[1 2 3 4 5 6]]

💡 Hint

Concatenating along axis 1 adds columns side by side.

arr1 shape (2,2), arr2 shape (2,1). Concatenating along axis 1 adds columns, resulting in shape (2,3).

🧠 Conceptual

expert

Understanding axis parameter in np.concatenate()

Which statement best describes the role of the axis parameter in np.concatenate()?

A It specifies the axis along which the arrays will be split before joining.

B It specifies the axis along which the arrays will be joined, and all other dimensions must match exactly.

C It determines the number of arrays that can be concatenated at once.

D It controls whether the arrays are joined vertically or horizontally regardless of their shape.

💡 Hint

Think about how arrays are stacked and what must be the same for concatenation to work.

The axis parameter tells np.concatenate which dimension to join along. All other dimensions must be the same size.