np.vstack() and np.hstack() in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to stack arrays vertically using numpy.

NumPy

import numpy as np

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

Drag options to blanks, or click blank then click option'

Ahstack

Bvstack

Cconcatenate

Dstack

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.hstack() which stacks arrays horizontally.

Using np.concatenate() without specifying axis.

Using np.stack() which requires axis argument.

np.vstack() stacks arrays vertically (row-wise), creating a 2D array with arr1 and arr2 as rows.

2fill in blank

medium

Complete the code to stack arrays horizontally using numpy.

NumPy

import numpy as np

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

Drag options to blanks, or click blank then click option'

Aconcatenate

Bvstack

Chstack

Dstack

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.vstack() which stacks arrays vertically.

Using np.concatenate() without specifying axis.

Using np.stack() which requires axis argument.

np.hstack() stacks arrays horizontally (column-wise), joining arr1 and arr2 side by side.

3fill in blank

hard

Fix the error in the code to stack two 2D arrays vertically.

NumPy

import numpy as np

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

Drag options to blanks, or click blank then click option'

Ahstack

Bconcatenate

Cstack

Dvstack

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.hstack() which requires matching row counts.

Using np.concatenate() without axis=0.

Using np.stack() without axis argument.

np.vstack() stacks arrays vertically. Here arr2 has one row, so vertical stacking works.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

NumPy

words = ['cat', 'house', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for value.

Using '<' instead of '>' in condition.

Not filtering words by length.

We use len(word) to get length and filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

data = {'a': 1, 'b': -2, 'c': 3}
result = { [1]: [2] for k, v in data.items() if v [3] 0 }
print(result)

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase.

Filtering values less than or equal to zero.

Swapping keys and values in dictionary comprehension.

Keys are converted to uppercase with k.upper(), values are v, and filter keeps values greater than 0.

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