np.broadcast_to() for explicit broadcasting in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.broadcast_to() with 1D to 2D array

What is the output of this code snippet?

NumPy

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

A

[[1 1 1]
 [2 2 2]
 [3 3 3]]

B

[[1 2 3]
 [2 3 1]
 [3 1 2]]

C

[[1 2 3]
 [1 2 3]
 [1 2 3]]

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

💡 Hint

Broadcasting repeats the original array along new dimensions without copying data.

np.broadcast_to repeats the original 1D array along the new axis to match the target shape (3,3). So each row is the original array.

❓ data_output

intermediate

Shape after broadcasting a scalar

What is the shape of the array after broadcasting this scalar to shape (4, 5)?

NumPy

import numpy as np
scalar = np.array(7)
broadcasted = np.broadcast_to(scalar, (4, 5))
print(broadcasted.shape)

A (1, 1)

B (4,)

C (5, 4)

D (4, 5)

💡 Hint

Broadcasting a scalar repeats it to fill the target shape.

Broadcasting a scalar to (4,5) creates a 2D array with shape (4,5) filled with the scalar value.

🔧 Debug

advanced

Error when broadcasting incompatible shapes

What error does this code raise?

NumPy

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

A ValueError: shape mismatch: objects cannot be broadcast to a single shape

B TypeError: unsupported operand type(s)

C IndexError: index out of bounds

D No error, output is [[1 2] [3 1]]

💡 Hint

Broadcasting requires the original shape to be compatible with the target shape.

The original array shape (3,) cannot be broadcast to (2,2) because the last dimension sizes differ and are incompatible.

🚀 Application

advanced

Using np.broadcast_to to add bias to a matrix

Given a 2D array of shape (3, 4) and a 1D bias array of shape (4,), which option correctly broadcasts the bias to add it to each row of the matrix?

NumPy

import numpy as np
matrix = np.array([[1, 2, 3, 4],
                   [5, 6, 7, 8],
                   [9, 10, 11, 12]])
bias = np.array([10, 20, 30, 40])

A matrix + np.broadcast_to(bias, (3, 4))

B matrix + np.broadcast_to(bias, (4, 3))

C matrix + np.broadcast_to(bias, (3,))

D matrix + np.broadcast_to(bias, (1, 4))

💡 Hint

The bias must be broadcast to match the matrix shape exactly.

Broadcasting bias to (3,4) repeats it for each row, allowing element-wise addition with the matrix.

🧠 Conceptual

expert

Memory behavior of np.broadcast_to()

Which statement about np.broadcast_to() is TRUE regarding memory usage?

A np.broadcast_to() creates a new array with copied data to match the target shape.

B np.broadcast_to() returns a view that shares the original data without copying.

C np.broadcast_to() always returns a deep copy of the original array.

D np.broadcast_to() modifies the original array in-place to match the new shape.

💡 Hint

Broadcasting tries to avoid copying data for efficiency.

np.broadcast_to() returns a read-only view that shares the original data, avoiding memory copies.

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