Avoiding broadcasting mistakes in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array of zeros with shape (3, 4).

NumPy

import numpy as np
arr = np.[1]((3, 4))

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

Azeros

Bfull

Cempty

Dones

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.ones instead of np.zeros creates an array of ones.

Using np.empty creates an uninitialized array with random values.

The np.zeros function creates an array filled with zeros of the specified shape.

2fill in blank

medium

Complete the code to add a 1D array to each row of a 2D array using broadcasting.

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
add = np.array([10, 20, 30])
result = arr + [1]

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

Aadd.reshape(3, 1)

Badd

Cadd.T

Dadd.reshape(1, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Reshaping to (3, 1) tries to add column-wise, causing shape mismatch.

Using add.T does not change shape correctly for broadcasting.

Reshaping add to (1, 3) allows it to broadcast correctly across each row of arr.

3fill in blank

hard

Fix the error in the code to multiply a 2D array by a 1D array along columns using broadcasting.

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
multiplier = np.array([2, 3])
result = arr * [1]

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

Amultiplier.reshape(2, 1)

Bmultiplier

Cmultiplier.reshape(1, 2)

Dmultiplier.T

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplier without reshape causes shape mismatch error.

Reshaping to (1, 2) aligns with columns, not rows.

Reshaping multiplier to (2, 1) aligns it with the rows of arr for correct broadcasting.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword != 'dog'

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.startswith('a') filters words starting with 'a' instead of length.

Using word != 'dog' excludes 'dog' but does not check length.

The dictionary maps each word to its length, but only includes words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 4.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = {{ [1]: [2] for w in words if [3] }}

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

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper() changes the case incorrectly.

Filtering with len(w) > 3 includes shorter words than required.

The dictionary comprehension converts each word to uppercase as the key, maps it to its length, and includes only words longer than 4 characters.

Practice

1. What does broadcasting in NumPy allow you to do?

easy

A. Increase the size of arrays by duplicating elements

B. Automatically convert data types during operations

C. Prevent any shape mismatch errors in arrays

D. Perform operations on arrays of different shapes if compatible

Solution

1. Step 1: Understand broadcasting concept

   Broadcasting allows NumPy to perform element-wise operations on arrays with different shapes if they follow certain compatibility rules.

2. Step 2: Identify correct description

   Perform operations on arrays of different shapes if compatible correctly describes broadcasting as working with different shapes if compatible. Other options describe unrelated behaviors.

3. Final Answer:

   Perform operations on arrays of different shapes if compatible -> Option D

4. Quick Check:

   Broadcasting = operations on compatible shapes [OK]

Hint: Broadcasting works only if shapes are compatible [OK]

Common Mistakes:

• Thinking broadcasting changes data types
• Assuming broadcasting duplicates array elements
• Believing broadcasting prevents all errors

2. Which of the following NumPy operations will cause a broadcasting error?

easy

A. np.array([1, 2, 3]) + np.array([[1], [2]])

B. np.array([[1, 2], [3, 4]]) + np.array([1, 2])

C. np.array([1, 2]) + np.array([1, 2, 3])

D. np.array([1, 2, 3]) + np.array([4, 5, 6])

Solution

1. Step 1: Check shape compatibility for each option

   np.array([1, 2, 3]) + np.array([4, 5, 6]): shapes (3,) + (3,) compatible.
   np.array([[1, 2], [3, 4]]) + np.array([1, 2]): shapes (2,2) + (2,) compatible by broadcasting.
   np.array([1, 2, 3]) + np.array([[1], [2]]): shapes (3,) + (2,1) compatible by broadcasting.
   np.array([1, 2]) + np.array([1, 2, 3]): shapes (2,) + (3,) incompatible, causes error.

2. Step 2: Identify which causes error

   Only np.array([1, 2]) + np.array([1, 2, 3]) has incompatible shapes that cannot broadcast.

3. Final Answer:

   np.array([1, 2]) + np.array([1, 2, 3]) -> Option C

4. Quick Check:

   Shape mismatch causes error = np.array([1, 2]) + np.array([1, 2, 3]) [OK]

Hint: Check array shapes before adding to avoid errors [OK]

Common Mistakes:

• Assuming all arrays add without error
• Ignoring shape differences in 1D arrays
• Confusing broadcasting rules for different dimensions

3. What is the output of this code?

import numpy as np
x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([10, 20, 30])
result = x + y
print(result)

medium

A. [[11 12 13]
[14 15 16]]

B. [[11 22 33]
[14 25 36]]

C. [[10 20 30]
[10 20 30]]

D. ValueError due to shape mismatch

Solution

1. Step 1: Analyze shapes of x and y

   x shape is (2,3), y shape is (3,). y broadcasts to (2,3) by repeating along first axis.

2. Step 2: Perform element-wise addition

   Add each element of y to corresponding column in x:
   Row 1: [1+10, 2+20, 3+30] = [11, 22, 33]
   Row 2: [4+10, 5+20, 6+30] = [14, 25, 36]

3. Final Answer:

   [[11 22 33] [14 25 36]] -> Option B

4. Quick Check:

   Broadcast y across rows = [[11 22 33] [14 25 36]] [OK]

Hint: Broadcast 1D array across matching dimension in 2D [OK]

Common Mistakes:

• Expecting error due to different shapes
• Adding y only to first row
• Confusing element-wise addition with matrix multiplication

4. Identify the mistake in this code snippet that causes a broadcasting error:

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

medium

A. arr1 and arr2 have incompatible shapes for broadcasting

B. arr2 should be reshaped to (2,1) before addition

C. arr1 should be flattened before addition

D. No mistake; code runs without error

Solution

1. Step 1: Check shapes of arr1 and arr2

   arr1 shape is (2,2), arr2 shape is (3,). These shapes are not compatible for broadcasting.

2. Step 2: Understand broadcasting rules

   For broadcasting, trailing dimensions must be equal or one must be 1. Here, 2 vs 3 mismatch causes error.

3. Final Answer:

   arr1 and arr2 have incompatible shapes for broadcasting -> Option A

4. Quick Check:

   Shape mismatch causes error = arr1 and arr2 have incompatible shapes for broadcasting [OK]

Hint: Check trailing dimensions match or are 1 [OK]

Common Mistakes:

• Assuming arrays with different lengths always broadcast
• Trying to flatten arrays unnecessarily
• Ignoring shape of second array

5. You have a 3D array data with shape (4, 3, 2) representing 4 samples, 3 features, and 2 measurements each. You want to add a 1D array offset of shape (2,) to each measurement across all samples and features without errors. Which code correctly avoids broadcasting mistakes?

hard

A. result = data + offset.reshape(1, 1, 2)

B. result = data + offset.reshape(4, 3, 2)

C. result = data + offset.reshape(2, 1, 1)

D. result = data + offset.reshape(2, 1)

Solution

1. Step 1: Understand shapes and broadcasting

   data shape is (4,3,2), offset shape is (2,). To broadcast across samples (4) and features (3) matching measurements (2), need compatible shape.

2. Step 2: Reshape offset to (1,1,2) to broadcast properly

   Reshaping offset to (1,1,2) aligns dimensions: 1->4, 1->3, 2=2.

3. Step 3: Check other options

   A: reshape(2,1) to (2,1), aligns as (2,1) vs (4,3,2): dimension mismatch.
   B: reshape(4,3,2) ValueError (size 2≠24).
   C: reshape(2,1,1) (2,1,1) vs (4,3,2): dim0 2≠4 incompatible.

4. Final Answer:

   result = data + offset.reshape(1, 1, 2) -> Option A

5. Quick Check:

   Reshape offset to match trailing dimension [OK]

Hint: Reshape 1D array to (1,1,n) for 3D addition [OK]

Common Mistakes:

• Reshaping to wrong dimensions like (2,1)
• Reshaping to full data shape unnecessarily
• Trying to reshape to (2,1,1) mismatching leading dimensions