Generating random samples in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 5 random numbers from a uniform distribution between 0 and 1.

NumPy

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

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Achoice

Brand

Cnormal

Drandint

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which generates integers, not floats.

Using normal which generates samples from a normal distribution.

The np.random.rand function generates random samples from a uniform distribution over [0, 1).

2fill in blank

medium

Complete the code to generate 10 random integers between 1 and 100 (inclusive).

NumPy

import numpy as np
ints = np.random.[1](1, 101, 10)

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Arandint

Brand

Cchoice

Drandom_sample

Attempts:

3 left

💡 Hint

Common Mistakes

Using rand which generates floats, not integers.

Using choice without specifying the range.

The np.random.randint function generates random integers from low (inclusive) to high (exclusive). Here, 101 is used to include 100.

3fill in blank

hard

Fix the error in the code to generate 7 random samples from a normal distribution with mean 0 and standard deviation 1.

NumPy

import numpy as np
samples = np.random.normal([1], 1, 7)

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A1

B7

C0

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing mean and standard deviation values.

Passing size as the mean.

The mean of the normal distribution should be 0, not 1 or 7.

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 4 characters.

NumPy

words = ['apple', 'dog', 'banana', 'cat', 'grape']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 4

Cword

Dword > 4

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using the word string in the condition instead of its length.

The dictionary comprehension uses len(word) for values and filters words with length greater than 4.

5fill in blank

hard

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

NumPy

words = ['tree', 'sun', 'flower', 'sky']
result = { [1]: [2] for w in words if [3] }

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Aw.upper()

Blen(w)

Clen(w) > 3

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Not filtering words by length correctly.

The dictionary comprehension uses uppercase words as keys, their lengths as values, and filters words longer than 3 characters.