np.vstack() and np.hstack() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.vstack() do in numpy?

np.vstack() stacks arrays vertically (row-wise). It joins arrays by adding rows on top of each other.

beginner

What is the difference between np.vstack() and np.hstack()?

np.vstack() stacks arrays vertically (adds rows).
np.hstack() stacks arrays horizontally (adds columns).

beginner

Given two arrays a = np.array([1, 2]) and b = np.array([3, 4]), what is the result of np.vstack((a, b))?

array([[1, 2],
       [3, 4]])

intermediate

What shape must arrays have to be stacked using np.hstack()?

Arrays must have the same number of rows (same first dimension) to stack horizontally.

intermediate

Why might np.vstack() or np.hstack() raise an error?

They raise errors if arrays have incompatible shapes for stacking. For vstack, columns must match; for hstack, rows must match.

What does np.hstack() do?

A Stacks arrays vertically (adds rows)

B Stacks arrays horizontally (adds columns)

C Flattens arrays into 1D

D Splits arrays into parts

np.hstack() stacks arrays side by side, adding columns.

If you have two arrays with shapes (2, 3) and (3, 3), can you use np.vstack() to stack them?

A Yes, because rows differ

B No, because rows differ

C Yes, because columns match

D No, because columns differ

np.vstack() requires arrays to have the same number of columns. Here both have 3 columns, so stacking vertically works.

What shape will the result have after np.hstack() of arrays with shapes (2, 3) and (2, 2)?

A (2, 5)

B (4, 3)

C (2, 1)

D (3, 2)

np.hstack() adds columns, so rows stay 2, columns add: 3 + 2 = 5.

Which numpy function would you use to add rows to an existing array?

A np.hstack()

B np.concatenate() with axis=1

C np.split()

D np.vstack()

np.vstack() stacks arrays vertically, adding rows.

What happens if you try to np.hstack() arrays with different number of rows?

A You get an error about shape mismatch

B They stack successfully

C Arrays get flattened

D Arrays get stacked vertically instead

np.hstack() requires arrays to have the same number of rows. Different rows cause a shape mismatch error.

Explain in your own words how np.vstack() and np.hstack() combine arrays. Give a simple example.

Think about stacking blocks either on top or side by side.

What shape conditions must arrays meet to be stacked with np.vstack() and np.hstack()? Why is this important?

Consider how rows and columns align when stacking.

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