Why random generation matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 5 random numbers between 0 and 1 using NumPy.

NumPy

import numpy as np
random_numbers = np.random.[1](5)
print(random_numbers)

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Arand

Bchoice

Crandint

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.randint which generates integers, not floats.

Using np.random.choice without specifying a list.

Using np.random.random which is a valid function but not the best here.

The np.random.rand function generates random floats between 0 and 1. Here, it creates 5 such numbers.

2fill in blank

medium

Complete the code to generate 5 random integers between 1 and 10 (inclusive) using NumPy.

NumPy

import numpy as np
random_ints = np.random.[1](1, 11, 5)
print(random_ints)

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Arandom

Brand

Crandint

Dchoice

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.random.rand which generates floats, not integers.

Using np.random.random which generates floats.

Using np.random.choice without a list.

The np.random.randint function generates random integers. The arguments specify the start (inclusive), end (exclusive), and number of values.

3fill in blank

hard

Fix the error in the code to generate 3 random numbers from a normal distribution with mean 0 and standard deviation 1.

NumPy

import numpy as np
samples = np.random.normal([1], 1, 3)
print(samples)

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A3

B1

C-1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting mean to 1 instead of 0.

Confusing mean with standard deviation.

Using sample size as mean.

The mean of the normal distribution should be 0 to get standard normal samples.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only include words longer than 3 letters.

NumPy

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

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Comparing the word string directly to 3.

Not filtering words by length.

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

5fill in blank

hard

Fill both blanks to create a dictionary with uppercase words as keys and their lengths as values, including only words longer than 3 letters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word.upper(): {BLANK_2}} for word in words if {{BLANK_2}}
print(lengths)

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Aword.upper()

Blen(word)

Clen(word) > 3

D{

Attempts:

3 left

💡 Hint

Common Mistakes

Not starting the dictionary with a curly brace.

Using the word itself as key instead of uppercase.

Not filtering words by length.

The dictionary starts with {. Keys are uppercase words using word.upper(), values are lengths, and filter checks length > 3.