Monte Carlo simulation basics in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to generate 1000 random numbers between 0 and 1 using NumPy.

NumPy

import numpy as np
random_numbers = np.random.[1](1000)

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Arandint

Brand

Crandom_sample

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using randint which generates integers, not floats.

Using random_sample which is an alias but not the most common.

The np.random.rand function generates random numbers between 0 and 1.

2fill in blank

medium

Complete the code to estimate the probability of getting a value less than 0.5 from the random numbers.

NumPy

prob = np.mean(random_numbers [1] 0.5)

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A==

B>

C<

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using greater than operator which counts wrong values.

Using equality which is too strict.

We check which numbers are less than 0.5 and take the mean to estimate the probability.

3fill in blank

hard

Fix the error in the code to simulate rolling a six-sided die 1000 times.

NumPy

rolls = np.random.randint(1, [1], size=1000)

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A5

B6

C0

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using 6 as upper bound excludes 6 from results.

Using 0 as upper bound causes an error.

The upper bound in randint is exclusive, so to include 6, we use 7.

4fill in blank

hard

Fill both blanks to create a dictionary with counts of rolls greater than 4 and less than or equal to 4.

NumPy

counts = { 'gt_4': np.sum(rolls [1] 4), 'le_4': np.sum(rolls [2] 4) }

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A>

B<=

C<

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of <= for 'le_4'.

Mixing up operators causing wrong counts.

We count rolls greater than 4 and rolls less than or equal to 4 using > and <= operators.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that counts how many rolls are equal to each face from 1 to 6.

NumPy

face_counts = { [1]: np.sum(rolls [2] [3]) for [1] in range(1, 7) }

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Aface

B==

Drolls

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rolls' as key instead of 'face'.

Using assignment '=' instead of comparison '=='.

The dictionary comprehension uses face as key, checks where rolls equal face with ==, and sums those occurrences.