Why reshaping arrays matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape a 1D array into a 2D array with 3 rows.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.[1]((3, 2))
print(reshaped)

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

Areshape

Bresize

Cflatten

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

Using resize instead of reshape.

Using flatten which converts to 1D instead of reshaping.

The reshape method changes the shape of the array without changing its data.

2fill in blank

medium

Complete the code to reshape a 2D array into a 1D array.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
flat = arr.[1]()
print(flat)

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

Aexpand_dims

Breshape

Cresize

Dflatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape without specifying shape.

Using resize which modifies the original array.

The flatten method returns a copy of the array collapsed into one dimension.

3fill in blank

hard

Fix the error in reshaping an array with incompatible size.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
reshaped = arr.[1]((2, 3))
print(reshaped)

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

Aresize

Bflatten

Creshape

Dravel

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to reshape to a shape that doesn't match total elements.

Using resize which changes data and size.

The error is due to shape (2, 3) not matching total elements (5). Use reshape with compatible shape like (1, 5) or (5, 1).

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 letters.

NumPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using wrong comparison operator in the condition.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

data = {'a': 1, 'b': -2, 'c': 3}
result = { [1]: [2] for k, v in data.items() if v [3] 0 }
print(result)

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase conversion.

Using wrong comparison operator or filtering condition.

The dictionary comprehension uses uppercase keys with k.upper(), values v, and filters values greater than 0.

Practice

1. Why is reshaping an array important in numpy?

easy

A. It sorts the array elements.

B. It deletes elements to reduce size.

C. It converts arrays into lists.

D. It changes the shape without altering the data content.

Solution

1. Step 1: Understand reshaping purpose

   Reshaping changes how data is organized in rows and columns but keeps all elements intact.

2. Step 2: Compare options

   Only It changes the shape without altering the data content. correctly states reshaping changes shape without changing data content.

3. Final Answer:

   It changes the shape without altering the data content. -> Option D

4. Quick Check:

   Reshape = Change shape, keep data [OK]

Hint: Reshape changes shape, not data values [OK]

Common Mistakes:

• Thinking reshaping deletes or adds elements
• Confusing reshaping with sorting
• Believing reshaping converts data types

2. Which of the following is the correct syntax to reshape a numpy array arr into 2 rows and 3 columns?

easy

A. arr.reshape(2;3)

B. arr.reshape(3, 2)

C. arr.reshape(2, 3)

D. arr.reshape([2, 3])

Solution

1. Step 1: Recall numpy reshape syntax

   The reshape method takes dimensions as separate arguments, e.g., reshape(2, 3).

2. Step 2: Check options

   arr.reshape(2, 3) uses correct syntax with comma-separated integers. arr.reshape(3, 2) would create 3 rows and 2 columns instead. The option using a semicolon (;) is invalid syntax. The option passing a list [2, 3] is incorrect as reshape expects separate arguments.

3. Final Answer:

   arr.reshape(2, 3) -> Option C

4. Quick Check:

   reshape(rows, cols) = reshape(2, 3) [OK]

Hint: Use commas, not semicolons or lists in reshape() [OK]

Common Mistakes:

• Using semicolons instead of commas
• Passing a list instead of separate arguments
• Swapping row and column order

3. What is the shape of the array after executing this code?

import numpy as np
arr = np.arange(12)
reshaped = arr.reshape(3, 4)
print(reshaped.shape)

medium

A. (3, 4)

B. (2, 6)

C. (12,)

D. (4, 3)

Solution

1. Step 1: Understand the code

   arr is created with 12 elements from 0 to 11. Then reshaped into 3 rows and 4 columns.

2. Step 2: Determine the shape

   The reshape(3, 4) changes shape to (3, 4). Printing shape outputs this tuple.

3. Final Answer:

   (3, 4) -> Option A

4. Quick Check:

   reshape(3,4) shape = (3, 4) [OK]

Hint: reshape(3,4) means 3 rows, 4 columns [OK]

Common Mistakes:

• Confusing rows and columns order
• Expecting original shape (12,)
• Mixing up shape tuple format

4. What error will occur when running this code?

import numpy as np
arr = np.arange(10)
reshaped = arr.reshape(3, 4)

medium

A. ValueError: cannot reshape array of size 10 into shape (3,4)

B. TypeError: reshape arguments must be integers

C. SyntaxError: invalid syntax

D. No error, reshaping works fine

Solution

1. Step 1: Check array size and reshape target

   arr has 10 elements. Reshape tries to make shape (3,4) which needs 12 elements.

2. Step 2: Identify error type

   Since total elements mismatch, numpy raises ValueError about impossible reshape.

3. Final Answer:

   ValueError: cannot reshape array of size 10 into shape (3,4) -> Option A

4. Quick Check:

   Mismatch size causes ValueError [OK]

Hint: Total elements must match for reshape [OK]

Common Mistakes:

• Ignoring total element count mismatch
• Expecting reshape to truncate or pad data
• Confusing ValueError with SyntaxError

5. Given a numpy array arr = np.arange(24), which reshape command correctly reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation?

hard

A. arr.reshape(2, 3, 4)

B. arr.reshape(-1, 3, 4)

C. arr.reshape(3, -1, 4)

D. arr.reshape(2, 4, -1)

Solution

1. Step 1: Understand array size and target shape

   arr has 24 elements. Target shape is (2, 3, 4) which multiplies to 24.

2. Step 2: Use -1 to let numpy calculate dimension

   Using -1 lets numpy infer that dimension. arr.reshape(-1, 3, 4) uses -1 in first dimension, so shape becomes (2,3,4) automatically.

3. Step 3: Check other options

   arr.reshape(2, 3, 4) works but does not use automatic calculation. arr.reshape(3, -1, 4) results in (3, 2, 4). arr.reshape(2, 4, -1) results in (2, 4, 3).

4. Final Answer:

   arr.reshape(-1, 3, 4) -> Option B

5. Quick Check:

   Use -1 for auto dimension: reshape(-1, 3, 4) [OK]

Hint: Use -1 where you want numpy to infer dimension [OK]

Common Mistakes:

• Placing -1 in wrong dimension causing errors
• Not matching total elements after reshape
• Assuming multiple -1s allowed in reshape