np.broadcast_to() for explicit broadcasting in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to broadcast array a to shape (3, 3).

NumPy

import numpy as np

a = np.array([1, 2, 3])
b = np.broadcast_to(a, [1])
print(b)

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

A(3, 1)

B(3,)

C(1, 3)

D(3, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original shape (3,) which does not broadcast.

Using (1, 3) which only adds a new axis but does not repeat rows.

We use np.broadcast_to to expand a to shape (3, 3) by repeating its values along the new axis.

2fill in blank

medium

Complete the code to broadcast a 1D array arr to a 2D array with 4 rows and 5 columns.

NumPy

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
broadcasted = np.broadcast_to(arr, [1])
print(broadcasted)

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

A(4, 5)

B(5, 4)

C(1, 5)

D(5, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the dimensions and using (5, 4) which is incorrect.

Using (1, 5) which does not add the required rows.

The array arr has 5 elements. Broadcasting to (4, 5) repeats arr across 4 rows.

3fill in blank

hard

Fix the error in broadcasting a 1D array data to shape (2, 3).

NumPy

import numpy as np

data = np.array([1, 2, 3])
result = np.broadcast_to(data, [1])
print(result)

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

A(3, 2)

B(2, 2)

C(2, 3)

D(3, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using (3, 2) which swaps dimensions and causes an error.

Using (2, 2) which does not match the original array length.

The original array has shape (3,), so broadcasting to (2, 3) repeats the array over 2 rows correctly.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths broadcasted only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: len(word) for word in words if len(word) [1] 3}
broadcasted = np.broadcast_to(np.array(list(lengths.values())), [2])
print(broadcasted)

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

A>

B(2, 2)

C(4, 1)

D<

Attempts:

3 left

💡 Hint

Common Mistakes

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

Broadcasting to a shape that does not match the number of filtered lengths.

We filter words with length greater than 3, then broadcast the lengths array to shape (2, 2).

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys, their lengths as values, and broadcast lengths greater than 4 to shape (3, 2).

NumPy

words = ['tree', 'house', 'car', 'elephant']
lengths = {word[1]: [2] for word in words if len(word) [3] 4}
broadcasted = np.broadcast_to(np.array(list(lengths.values())), (3, 2))
print(broadcasted)

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

A.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting words to uppercase keys.

Using the word itself instead of its length as the dictionary value.

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

We convert words to uppercase keys, use their lengths as values, and filter lengths greater than 4 before broadcasting.

Practice

1. What does np.broadcast_to() do in NumPy?

easy

A. It sorts the elements of an array in ascending order.

B. It removes duplicate elements from an array.

C. It stretches an array to a larger shape without copying data.

D. It converts an array into a list.

Solution

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

   This function is used to stretch an array to a bigger shape so it can be used in element-wise operations with other arrays.

2. Step 2: Compare with other options

   Sorting, removing duplicates, or converting to list are unrelated to broadcasting.

3. Final Answer:

   It stretches an array to a larger shape without copying data. -> Option C

4. Quick Check:

   Broadcasting = Stretch array shape [OK]

Hint: Broadcasting means stretching shape, not sorting or converting [OK]

Common Mistakes:

• Confusing broadcasting with sorting or reshaping
• Thinking it copies data instead of reusing
• Mixing it up with array conversion functions

2. Which of the following is the correct syntax to broadcast array a to shape (3, 4)?

easy

A. np.broadcast_to(a, (3, 4))

B. np.broadcast(a, shape=(3, 4))

C. np.broadcast_to(a, shape=3, 4)

D. np.broadcast(a, (3, 4))

Solution

1. Step 1: Recall the function signature

   The correct function is np.broadcast_to(array, shape) where shape is a tuple.

2. Step 2: Check each option

   np.broadcast_to(a, (3, 4)) matches the correct syntax. np.broadcast_to(a, shape=3, 4) incorrectly uses shape=3, 4 without tuple (int shape invalid). Options B and D use a non-existent np.broadcast function.

3. Final Answer:

   np.broadcast_to(a, (3, 4)) -> Option A

4. Quick Check:

   Correct syntax = np.broadcast_to(array, shape) [OK]

Hint: Use np.broadcast_to(array, shape) with shape as tuple [OK]

Common Mistakes:

• Using np.broadcast instead of np.broadcast_to
• Forgetting tuple around shape like shape=3, 4
• Confusing function names

3. What is the output of this code?

import numpy as np
x = np.array([1, 2, 3])
y = np.broadcast_to(x, (3, 3))
print(y)

medium

A. Error: shapes not compatible

B. [[1 1 1]
[2 2 2]
[3 3 3]]

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

D. [[1 2 3]
[1 2 3]
[1 2 3]]

Solution

1. Step 1: Understand the input array and target shape

   The array x is 1D with shape (3,). Broadcasting it to (3, 3) repeats it along the new axis.

2. Step 2: Visualize the broadcasted array

   The array x is repeated 3 times as rows, so the output is a 3x3 array with each row [1 2 3].

3. Final Answer:

   [[1 2 3] [1 2 3] [1 2 3]] -> Option D

4. Quick Check:

   Broadcast repeats rows = [[1 2 3], [1 2 3], [1 2 3]] [OK]

Hint: Broadcast 1D array to 2D repeats rows [OK]

Common Mistakes:

• Thinking broadcasting repeats columns instead of rows
• Expecting a flattened array
• Assuming an error due to shape mismatch

4. What is wrong with this code?

import numpy as np
arr = np.array([1, 2])
result = np.broadcast_to(arr, (3, 3))
print(result)

medium

A. ValueError because shape (3, 3) is incompatible with input shape (2,).

B. SyntaxError due to missing parentheses.

C. TypeError because broadcast_to expects a list, not an array.

D. No error; output is a 3x3 array with repeated rows.

Solution

1. Step 1: Check input and target shapes

   The input array has shape (2,), but the target shape is (3, 3). Broadcasting requires the input shape to be compatible with the target shape.

2. Step 2: Understand broadcasting rules

   Since 2 does not match 3 in the last dimension, broadcasting cannot stretch (2,) to (3, 3). This causes a ValueError.

3. Final Answer:

   ValueError because shape (3, 3) is incompatible with input shape (2,). -> Option A

4. Quick Check:

   Shape mismatch causes ValueError [OK]

Hint: Broadcast shapes must be compatible; check dimensions carefully [OK]

Common Mistakes:

• Assuming broadcasting always works regardless of shape
• Confusing ValueError with TypeError
• Ignoring dimension mismatch

5. You have a 1D array a = np.array([5]) and want to create a 3x4 array where every element is 5 using np.broadcast_to(). Which code correctly achieves this?

hard

A. np.broadcast_to(a, (4, 3))

B. np.broadcast_to(a, (3, 4))

C. np.broadcast_to(a, (3,))

D. np.broadcast_to(a, (5, 5))

Solution

1. Step 1: Identify the input and desired output shape

   The input is a 1D array with shape (1,), and the desired output shape is (3, 4).

2. Step 2: Apply broadcasting rules

   Broadcasting can stretch (1,) to (3, 4) by repeating the single value 5 across all elements.

3. Step 3: Check options

   np.broadcast_to(a, (3, 4)) correctly uses np.broadcast_to(a, (3, 4)). np.broadcast_to(a, (4, 3)) swaps dimensions, which is not the requested shape. np.broadcast_to(a, (3,)) is only 1D, and np.broadcast_to(a, (5, 5)) has wrong shape.

4. Final Answer:

   np.broadcast_to(a, (3, 4)) -> Option B

5. Quick Check:

   Broadcast single value to (3,4) with np.broadcast_to [OK]

Hint: Broadcast single-element array to target shape tuple [OK]

Common Mistakes:

• Mixing up shape dimensions order
• Using wrong target shape tuple
• Trying to broadcast to incompatible shapes