ufunc performance considerations in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array and apply a ufunc to square each element.

NumPy

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

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Asquare

Bsqrt

Clog

Dexp

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.sqrt which calculates square root instead of square.

Using np.log or np.exp which perform logarithm or exponentiation.

The np.square ufunc squares each element of the array efficiently.

2fill in blank

medium

Complete the code to apply a ufunc that adds 5 to each element of the array.

NumPy

import numpy as np
arr = np.array([10, 20, 30])
result = np.[1](arr, 5)

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Asubtract

Bmultiply

Cadd

Ddivide

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.multiply which multiplies elements instead of adding.

Using np.subtract which subtracts instead of adding.

The np.add ufunc adds the second argument to each element of the array.

3fill in blank

hard

Fix the error in the code to correctly apply a ufunc that computes the natural logarithm of each element.

NumPy

import numpy as np
arr = np.array([1, 10, 100])
log_values = np.[1](arr)

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Alog10

Blog

Clog1p

Dlog2

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.log10 which calculates base-10 logarithm.

Using np.log2 which calculates base-2 logarithm.

The np.log ufunc computes the natural logarithm (base e) of each element.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using 'word > 3' which compares string to number and causes error.

We use len(word) to get the length and filter words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase word to its length only if the length is greater than 2.

NumPy

words = ['cat', 'dog', 'elephant']
result = { [1]: [2] for word in words if [3] }

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Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Not filtering words by length correctly.

The keys are uppercase words using word.upper(), values are lengths with len(word), and the condition filters words longer than 2 characters.