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.

Practice

1. What does the function np.concatenate() do in NumPy?

easy

A. Splits an array into multiple smaller arrays

B. Joins two or more arrays along an existing axis

C. Sorts the elements of an array in ascending order

D. Creates a new array filled with zeros

Solution

1. Step 1: Understand the purpose of np.concatenate()

   It is used to join arrays along a specified axis, combining them into one larger array.

2. Step 2: Compare with other array functions

   Splitting, sorting, or creating zero arrays are different functions like np.split(), np.sort(), and np.zeros().

3. Final Answer:

   Joins two or more arrays along an existing axis -> Option B

4. Quick Check:

   np.concatenate() = join arrays [OK]

Hint: Remember: concatenate means join arrays along axis [OK]

Common Mistakes:

• Confusing concatenate with split or sort functions
• Thinking it modifies original arrays
• Assuming it creates arrays from scratch

2. Which of the following is the correct syntax to join two 1D arrays a and b using np.concatenate()?

easy

A. np.concatenate([a, b])

B. np.concatenate(a, axis=1)

C. np.concatenate(a + b)

D. np.concatenate(a, b)

Solution

1. Step 1: Check the function signature

   np.concatenate() expects a single argument: a sequence (like a list or tuple) of arrays to join.

2. Step 2: Analyze each option

   np.concatenate([a, b]) correctly passes a list of arrays [a, b]. np.concatenate(a, b) passes two separate arguments, which is invalid. np.concatenate(a + b) tries to add arrays before concatenation, which is not the function's purpose. np.concatenate(a, axis=1) passes axis=1 for 1D arrays, which is invalid.

3. Final Answer:

   np.concatenate([a, b]) -> Option A

4. Quick Check:

   Pass arrays as a list to concatenate() [OK]

Hint: Always pass arrays inside a list or tuple to concatenate [OK]

Common Mistakes:

• Passing arrays as separate arguments instead of a list
• Using plus (+) operator instead of concatenate
• Setting wrong axis for 1D arrays

3. What is the output of the following code?

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

medium

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

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

C. [[1 2]
[3 4]]

D. Error: shapes do not match

Solution

1. Step 1: Check shapes of arrays

   x has shape (2, 2), y has shape (1, 2). They match in columns (axis=1) but differ in rows (axis=0).

2. Step 2: Concatenate along axis=0 (rows)

   Joining along rows stacks y below x, resulting in a (3, 2) array with rows from x then y.

3. Final Answer:

   [[1 2] [3 4] [5 6]] -> Option A

4. Quick Check:

   Concatenate rows stacks arrays vertically [OK]

Hint: Concatenate axis=0 stacks rows; axis=1 stacks columns [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting horizontal stacking with axis=0
• Ignoring shape compatibility

4. What is wrong with this code snippet?

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

medium

A. Error because y must be reshaped to 3D

B. No error, output is concatenated array

C. Error because axis=0 is invalid for 2D arrays

D. Error because arrays have incompatible shapes along axis=0

Solution

1. Step 1: Check shapes of arrays

   x shape is (2, 2), y shape is (3,). They differ in both dimensions.

2. Step 2: Understand concatenate axis rules

   Arrays must match in all dimensions except the concatenation axis. Here, axis=0 means rows, but y has no matching columns.

3. Final Answer:

   Error because arrays have incompatible shapes along axis=0 -> Option D

4. Quick Check:

   Shapes must match except on concat axis [OK]

Hint: Check array shapes match except on concat axis [OK]

Common Mistakes:

• Ignoring shape mismatch errors
• Assuming 1D array can concatenate with 2D without reshape
• Misunderstanding axis parameter

5. You have three arrays:

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

You want to join them into one array with shape (4, 2). Which code correctly does this?

hard

A. np.concatenate((a, b), axis=0) + c

B. np.concatenate([a, b, c], axis=1)

C. np.concatenate((a, b, c), axis=0)

D. np.concatenate((a, b, c), axis=2)

Solution

1. Step 1: Check shapes and desired output

   a shape is (2, 2), b and c are (1, 2). Joining along rows (axis=0) stacks them vertically to get (4, 2).

2. Step 2: Analyze options

   np.concatenate((a, b, c), axis=0) correctly concatenates all three arrays along axis=0. np.concatenate([a, b, c], axis=1) tries axis=1 which would require matching rows, which is false. np.concatenate((a, b), axis=0) + c adds arrays after concatenation, which is invalid. np.concatenate((a, b, c), axis=2) uses axis=2 which is invalid for 2D arrays.

3. Final Answer:

   np.concatenate((a, b, c), axis=0) -> Option C

4. Quick Check:

   Stack arrays vertically with axis=0 [OK]

Hint: Use axis=0 to stack arrays vertically when shapes match columns [OK]

Common Mistakes:

• Using wrong axis for concatenation
• Trying to add arrays instead of concatenate
• Using invalid axis number for 2D arrays