Broadcasting compatibility check in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if two arrays can be broadcast together using numpy.

NumPy

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([[1], [2], [3]])
can_broadcast = np.[1](arr1, arr2)
print(can_broadcast.shape)

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

Abroadcast_arrays

Bbroadcast

Cshape

Dbroadcast_shapes

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.broadcast_arrays instead of np.broadcast.

Trying to use np.shape to check broadcasting.

The function np.broadcast(arr1, arr2) returns a broadcast object if the arrays can be broadcast together.

2fill in blank

medium

Complete the code to get the broadcasted shape of two arrays using numpy.

NumPy

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

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

Abroadcast_shapes

Bbroadcast_arrays

Cbroadcast

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.broadcast instead of np.broadcast_shapes.

Trying to broadcast actual arrays instead of shapes.

np.broadcast_shapes returns the shape tuple resulting from broadcasting the input shapes.

3fill in blank

hard

Fix the error in the code to check if arrays of shapes (2,3) and (3,) can be broadcast together.

NumPy

import numpy as np
arr1 = np.zeros((2, 3))
arr2 = np.zeros((3,))
can_broadcast = np.broadcast(arr1, [1])
print(can_broadcast.shape)

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

Aarr1

B(3,)

C(3, 2)

Darr2

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same array twice to np.broadcast.

Passing shape tuples instead of arrays.

To check broadcasting compatibility, both arrays must be passed to np.broadcast. Here arr2 is the second array.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths only for words longer than 4 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 4}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the dictionary value.

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

We want the length of each word (len(word)) and filter words longer than 4 (len(word) > 4).

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 = ['sun', 'moon', 'star', 'sky']
result = { [1]: [2] for w in words if len(w) [3] 3 }

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

Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using original words as keys instead of uppercase.

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

The keys are uppercase words (w.upper()), values are lengths (len(w)), and filter words longer than 3 (len(w) > 3).

Practice

1. Which of the following pairs of shapes are compatible for broadcasting in NumPy?

easy

A. (1, 2, 3) and (2, 3, 1)

B. (2, 3) and (3, 2)

C. (4, 5) and (4, 5, 1)

D. (3, 1) and (1, 4)

Solution

1. Step 1: Understand broadcasting rules

   Broadcasting compares shapes from right to left. Dimensions must be equal or one must be 1.

2. Step 2: Check each pair

   For (3,1) and (1,4), rightmost dims 1 and 4 are compatible (1 can broadcast to 4), left dims 3 and 1 compatible (1 can broadcast to 3). So compatible.

3. Final Answer:

   (3, 1) and (1, 4) are compatible -> Option D

4. Quick Check:

   Broadcast dims right to left, 1 can expand [OK]

Hint: Check shapes from right; 1 or equal dims broadcast [OK]

Common Mistakes:

• Assuming all different shapes are compatible
• Not comparing dimensions from right to left
• Ignoring that 1 can broadcast to any size

2. Which of the following is the correct way to check broadcasting compatibility of shapes (2, 3) and (3,) using NumPy?

easy

A. numpy.broadcast_shapes((2, 3), (3,))

B. numpy.broadcast((2, 3), (3,))

C. numpy.broadcast_shapes([2, 3], 3)

D. numpy.broadcast((2, 3), (3))

Solution

1. Step 1: Identify the correct function

   NumPy provides numpy.broadcast_shapes to check shape compatibility, which takes tuples as shape arguments.

2. Step 2: Check argument types

   numpy.broadcast_shapes((2, 3), (3,)) uses numpy.broadcast_shapes with tuples, which is correct. numpy.broadcast_shapes([2, 3], 3) uses a scalar for the second argument, which is invalid for this function.

3. Final Answer:

   numpy.broadcast_shapes((2, 3), (3,)) -> Option A

4. Quick Check:

   Use numpy.broadcast_shapes with tuples [OK]

Hint: Use numpy.broadcast_shapes with tuples for shape check [OK]

Common Mistakes:

• Using numpy.broadcast instead of numpy.broadcast_shapes
• Passing scalars instead of sequences
• Missing parentheses or wrong argument types

3. What will be the output of the following code?

import numpy as np
shape = np.broadcast_shapes((4, 1, 3), (1, 5, 3))
print(shape)

medium

A. (4, 1, 3)

B. (4, 5, 3)

C. (1, 5, 3)

D. ValueError: shapes not compatible

Solution

1. Step 1: Compare shapes dimension-wise from right

   Rightmost dims: 3 and 3 match. Next dims: 1 and 5, 1 broadcasts to 5. Leftmost dims: 4 and 1, 1 broadcasts to 4.

2. Step 2: Determine broadcasted shape

   Broadcasted shape is (4, 5, 3) combining max of each dimension.

3. Final Answer:

   (4, 5, 3) -> Option B

4. Quick Check:

   Broadcast dims max or 1 expands [OK]

Hint: Broadcast dims take max or 1 expands to max [OK]

Common Mistakes:

• Confusing which dimension broadcasts to which
• Expecting error when shapes are compatible
• Mixing up order of dimensions

4. The following code throws an error. What is the cause?

import numpy as np
np.broadcast_shapes((3, 2), (2, 3))

medium

A. Shapes are not compatible for broadcasting

B. Function broadcast_shapes does not exist

C. Tuples must be of same length

D. Missing import statement

Solution

1. Step 1: Check broadcasting rules for shapes (3, 2) and (2, 3)

   Compare from right: 2 vs 3 are different and neither is 1, so incompatible.

2. Step 2: Confirm error cause

   Since shapes cannot broadcast, numpy.broadcast_shapes raises ValueError.

3. Final Answer:

   Shapes are not compatible for broadcasting -> Option A

4. Quick Check:

   Broadcast dims must be equal or 1 [OK]

Hint: Check dims right to left; mismatch without 1 causes error [OK]

Common Mistakes:

• Thinking function is missing or typo
• Assuming tuples must be same length
• Ignoring broadcasting dimension rules

5. You have arrays with shapes (7, 1, 5) and (1, 3, 1). You want to add them using broadcasting. What will be the resulting shape?

hard

A. (1, 3, 5)

B. (7, 1, 1)

C. (7, 3, 5)

D. Cannot broadcast these shapes

Solution

1. Step 1: Compare shapes from right to left

   Right dims: 5 and 1, 1 broadcasts to 5. Middle dims: 1 and 3, 1 broadcasts to 3. Left dims: 7 and 1, 1 broadcasts to 7.

2. Step 2: Combine broadcasted dimensions

   Resulting shape is (7, 3, 5) taking max of each dimension.

3. Final Answer:

   (7, 3, 5) -> Option C

4. Quick Check:

   Broadcast dims max or 1 expands [OK]

Hint: Broadcast dims max or 1 expands to max [OK]

Common Mistakes:

• Mixing up dimension order
• Assuming shapes must be identical
• Forgetting 1 can broadcast to any size