np.vstack() and np.hstack() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.vstack() 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, 6])
result = np.vstack((arr1, arr2))
print(result)

NumPy

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

A

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

B

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

C [1 2 3 4 5 6]

D

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

💡 Hint

np.vstack stacks arrays vertically, adding rows.

np.vstack stacks arrays vertically, so two 1D arrays become two rows in a 2D array.

❓ Predict Output

intermediate

Output of np.hstack() with 2D arrays

What is the output of this code?

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
result = np.hstack((arr1, arr2))
print(result)

NumPy

import numpy as np
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
result = np.hstack((arr1, arr2))
print(result)

A [[1 2 3 4 5 6 7 8]]

B

[[1 2]
 [3 4]
 [5 6]
 [7 8]]

C

[[1 5]
 [2 6]
 [3 7]
 [4 8]]

D

[[1 2 5 6]
 [3 4 7 8]]

💡 Hint

np.hstack stacks arrays horizontally, adding columns.

np.hstack joins arrays side by side, so columns are added horizontally.

❓ data_output

advanced

Shape after stacking arrays with np.vstack and np.hstack

Given these arrays:

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

What is the shape of the result after applying np.vstack((arr1, arr2)) and np.hstack((arr1, arr2)) respectively?

NumPy

import numpy as np
arr1 = np.array([[1, 2, 3]])
arr2 = np.array([[4, 5, 6]])
vstack_result = np.vstack((arr1, arr2))
hstack_result = np.hstack((arr1, arr2))
print(vstack_result.shape, hstack_result.shape)

A (2, 3) and (1, 6)

B (2, 6) and (1, 3)

C (3, 2) and (6, 1)

D (1, 6) and (2, 3)

💡 Hint

vstack adds rows, hstack adds columns.

vstack stacks arrays vertically increasing rows; hstack stacks horizontally increasing columns.

🔧 Debug

advanced

Error when stacking arrays with incompatible shapes

What error does this code raise?

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

NumPy

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

A TypeError: unsupported operand type(s) for +: 'int' and 'list'

B No error, runs successfully

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

D IndexError: index out of range

💡 Hint

np.vstack requires arrays to have the same number of dimensions.

vstack requires all arrays to have the same number of dimensions; mixing 1D and 2D arrays causes ValueError.

🚀 Application

expert

Combining arrays for data analysis

You have two numpy arrays representing data from two sensors:

sensor1 = np.array([[10, 20], [30, 40]])
sensor2 = np.array([[50, 60], [70, 80]])

You want to create a single array where sensor2 data is stacked below sensor1 data, and then another array where sensor2 data is stacked to the right of sensor1 data.
Which code correctly creates these two arrays?

NumPy

import numpy as np
sensor1 = np.array([[10, 20], [30, 40]])
sensor2 = np.array([[50, 60], [70, 80]])

A

vertical_stack = np.concatenate((sensor1, sensor2), axis=0)
horizontal_stack = np.concatenate((sensor1, sensor2), axis=1)

B

vertical_stack = np.hstack((sensor1, sensor2))
horizontal_stack = np.vstack((sensor1, sensor2))

C

vertical_stack = np.vstack((sensor1, sensor2))
horizontal_stack = np.hstack((sensor1, sensor2))

D

vertical_stack = np.concatenate((sensor1, sensor2), axis=1)
horizontal_stack = np.concatenate((sensor1, sensor2), axis=0)

💡 Hint

axis=0 stacks rows (vertical), axis=1 stacks columns (horizontal).

np.vstack is equivalent to np.concatenate with axis=0; np.hstack is equivalent to np.concatenate with axis=1.

Practice

1. What does np.vstack() do with arrays?

easy

A. Stacks arrays vertically, adding rows

B. Stacks arrays horizontally, adding columns

C. Flattens arrays into one dimension

D. Splits arrays into smaller parts

Solution

1. Step 1: Understand vertical stacking

   np.vstack() stacks arrays by adding rows one below another.

2. Step 2: Compare with horizontal stacking

   np.hstack() stacks arrays side by side by adding columns, so it is different.

3. Final Answer:

   Stacks arrays vertically, adding rows -> Option A

4. Quick Check:

   np.vstack() = vertical stack [OK]

Hint: Remember 'v' in vstack means vertical stacking [OK]

Common Mistakes:

• Confusing vertical and horizontal stacking
• Thinking vstack flattens arrays
• Assuming vstack splits arrays

2. Which of the following is the correct syntax to horizontally stack two arrays a and b using NumPy?

easy

A. np.hstack(a, b)

B. np.hstack[a, b]

C. np.hstack((a, b))

D. np.hstack{a, b}

Solution

1. Step 1: Check function call syntax

   NumPy functions use parentheses and tuples for multiple arrays, so np.hstack((a, b)) is correct.

2. Step 2: Identify incorrect syntax

   Square brackets, missing parentheses, or curly braces are invalid for function calls in Python.

3. Final Answer:

   np.hstack((a, b)) -> Option C

4. Quick Check:

   Correct function call uses parentheses and tuple [OK]

Hint: Use double parentheses for multiple arrays in hstack [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Passing arrays without tuple parentheses
• Using curly braces which are invalid

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], [7, 8]])
result = np.vstack((x, y))
print(result)

medium

A. [[1 2 5 6]
[3 4 7 8]]

B. [[1 2 3 4]
[5 6 7 8]]

C. [[1 2]
[5 6]
[3 4]
[7 8]]

D. [[1 2]
[3 4]
[5 6]
[7 8]]

Solution

1. Step 1: Understand np.vstack behavior

   It stacks arrays vertically, so rows of y come below rows of x.

2. Step 2: Combine arrays row-wise

   Result has 4 rows: first two from x, next two from y, each with 2 columns.

3. Final Answer:

   [[1 2] [3 4] [5 6] [7 8]] -> Option D

4. Quick Check:

   Vertical stack adds rows below [OK]

Hint: vstack adds rows; hstack adds columns [OK]

Common Mistakes:

• Confusing vstack with hstack output
• Expecting horizontal concatenation
• Misreading array shapes

4. What error will occur when running this code?

import numpy as np
a = np.array([1, 2, 3])
b = np.array([[4, 5, 6], [7, 8, 9]])
result = np.hstack((a, b))
print(result)

medium

A. No error, outputs concatenated array

B. TypeError because inputs are not lists

C. SyntaxError due to wrong function call

D. ValueError due to incompatible shapes

Solution

1. Step 1: Check shapes of arrays

   a is shape (3,), b is shape (2,3). They differ in first dimension.

2. Step 2: Understand hstack requirements

   np.hstack() stacks arrays along their second axis if possible. Here, a is 1D and b is 2D, but NumPy broadcasts a to shape (1,3) and stacks along axis 1, resulting in shape (3,3).

3. Step 3: Result

   No error occurs; arrays are concatenated.

4. Final Answer:

   No error, outputs concatenated array -> Option A

5. Quick Check:

   NumPy broadcasts 1D array and concatenates [OK]

Hint: NumPy can broadcast 1D arrays when stacking [OK]

Common Mistakes:

• Assuming hstack requires same number of rows
• Confusing ValueError with TypeError
• Ignoring NumPy broadcasting rules

5. Given two arrays:
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6, 7], [8, 9, 10]])
Which method correctly combines them into a single array with shape (2, 5) by stacking horizontally?

hard

A. np.vstack((a, b))

B. np.hstack((a, b))

C. np.hstack((a.T, b.T)).T

D. np.vstack((a.T, b.T)).T

Solution

1. Step 1: Check shapes of arrays

   a shape is (2, 2), b shape is (2, 3). Both have 2 rows, so horizontal stacking is possible.

2. Step 2: Apply np.hstack

   np.hstack((a, b)) concatenates columns, resulting in shape (2, 5).

3. Step 3: Verify other options

   np.vstack((a, b)) would fail due to different column counts; transposes complicate shape and are unnecessary here.

4. Final Answer:

   np.hstack((a, b)) -> Option B

5. Quick Check:

   Same rows, hstack adds columns [OK]

Hint: For same rows, use hstack to add columns [OK]

Common Mistakes:

• Using vstack with different column counts
• Overcomplicating with transpose
• Mixing up row and column dimensions