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)

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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)

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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)

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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)

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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.