Broadcasting errors and debugging in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array of shape (3, 1).

NumPy

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

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

A.shape

B.reshape(3, 1)

C.size

D.ndim

Attempts:

3 left

💡 Hint

Common Mistakes

Using .size returns total elements, not shape.

Using .ndim returns number of dimensions, not shape.

The .shape attribute shows the dimensions of the array. Here, it will print (3, 1).

2fill in blank

medium

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

NumPy

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

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

A*

B/

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using * multiplies instead of adds.

Using / causes division, not addition.

The + operator adds arrays element-wise. Broadcasting allows adding a 1D array to each row of a 2D array.

3fill in blank

hard

Fix the error in the code that tries to add arrays with incompatible shapes.

NumPy

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

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

Areshape

Bresize

Cflatten

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

flatten or ravel make B 1D, which is still incompatible.

resize modifies in place and repeats elements if necessary, not suitable here.

To fix the shape mismatch error, use B.reshape(2, 1) to make B shape (2, 1) which broadcasts correctly when added to A (2, 3).

4fill in blank

hard

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

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Using less than < instead of greater than >.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 3 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
[1] = {word.[2](): [3] for word in words if len(word) > 3}
print([1])

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

Aresult

Bupper

Clen(word)

Dlower

Attempts:

3 left

💡 Hint

Common Mistakes

Using lower() instead of upper().

Not assigning the dictionary to a variable.

Using the word itself instead of its length for values.

The code creates a dictionary named result. It uses word.upper() for keys and len(word) for values, filtering words longer than 3 characters.

Practice

1. What causes a broadcasting error in NumPy when working with arrays?

easy

A. When arrays contain only zeros

B. When arrays have the same shape

C. When array shapes are not compatible for element-wise operations

D. When arrays are one-dimensional

Solution

1. Step 1: Understand broadcasting rules

   Broadcasting requires arrays to have compatible shapes, meaning dimensions must be equal or one of them is 1.

2. Step 2: Identify cause of error

   If shapes cannot be matched or stretched to match, a broadcasting error occurs.

3. Final Answer:

   When array shapes are not compatible for element-wise operations -> Option C

4. Quick Check:

   Broadcasting error = incompatible shapes [OK]

Hint: Check if array shapes can align or stretch [OK]

Common Mistakes:

• Assuming arrays with same number of elements always broadcast
• Ignoring dimension sizes when broadcasting
• Thinking zeros cause broadcasting errors

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

easy

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

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

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

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

Solution

1. Step 1: Check shapes of arrays in each option

   np.array([1, 2, 3]) + np.array([[1, 2], [3, 4]]) adds shape (3,) and (2,2), which are incompatible for broadcasting.

2. Step 2: Confirm broadcasting compatibility

   Other options have compatible shapes: (3,) and (3,1), (3,) and (3,), (3,1) and (1,).

3. Final Answer:

   np.array([1, 2, 3]) + np.array([[1, 2], [3, 4]]) -> Option A

4. Quick Check:

   Shape mismatch causes error [OK]

Hint: Compare array shapes dimension-wise from right [OK]

Common Mistakes:

• Ignoring that (3,) and (2,2) cannot broadcast
• Confusing 1D and 2D shapes
• Assuming all arrays add without error

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])
result = x + y
print(result)

medium

A. Broadcasting error

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

C. [[11 12 13]
[24 25 26]]

D. [[11 22 13]
[14 25 16]]

Solution

1. Step 1: Check shapes of x and y

   x shape is (2,3), y shape is (2,). These shapes are not compatible for broadcasting because trailing dimensions differ and y cannot be stretched to match x.

2. Step 2: Predict result of x + y

   Since shapes are incompatible, NumPy raises a broadcasting error.

3. Final Answer:

   Broadcasting error -> Option A

4. Quick Check:

   Shape (2,3) + (2,) = error [OK]

Hint: Match shapes from right; trailing dims must be equal or 1 [OK]

Common Mistakes:

• Assuming (2,) broadcasts to (2,3)
• Expecting element-wise addition without shape check
• Ignoring broadcasting rules for 2D arrays

4. You get a broadcasting error when adding arrays a with shape (3, 1) and b with shape (3,). How can you fix it?

medium

A. Reshape a to (3,) before adding

B. No fix needed; shapes are compatible

C. Transpose a to (1, 3) before adding

D. Reshape b to (3, 1) before adding

Solution

1. Step 1: Analyze shapes

   a (3,1), b (3,): pad b to (1,3). Trailing: 1 vs 3 (1 stretches), leading: 3 vs 1 (1 stretches). Compatible, result shape (3,3).

2. Step 2: No error occurs

   Direct addition works fine.

3. Final Answer:

   No fix needed; shapes are compatible -> Option B

4. Quick Check:

   (3,1) + (3,) works [OK]

Hint: Verify compatibility: pad lower-rank with leading 1s [OK]

Common Mistakes:

• Thinking (3,1) + (3,) errors
• Reshaping when automatic broadcasting works
• Confusing padding position

5. You have a 3D array arr with shape (4, 1, 5) and a 2D array mat with shape (1, 3). You want to add them without error. What is the correct way?

hard

A. Add directly; broadcasting works automatically

B. Reshape arr to (4, 3, 5) before adding

C. Reshape mat to (1, 1, 3) before adding

D. Reshape mat to (1, 3, 1) before adding

Solution

1. Step 1: Check shapes and broadcasting rules

   arr shape: (4,1,5), mat shape: (1,3). To add, shapes must be compatible from right: 5 vs missing, 1 vs 3, 4 vs 1.

2. Step 2: Reshape mat to match arr's dimensions

   Reshape mat to (1,3,1) so shapes become (4,1,5) and (1,3,1), compatible for broadcasting.

3. Final Answer:

   Reshape mat to (1, 3, 1) before adding -> Option D

4. Quick Check:

   Match dimensions by adding missing axis [OK]

Hint: Add missing dimensions with reshape for broadcasting [OK]

Common Mistakes:

• Assuming direct add works without reshape
• Reshaping arr incorrectly
• Confusing dimension order in reshape