Generating random samples in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy random choice with replace=False

What is the output of this code snippet?

import numpy as np
np.random.seed(0)
sample = np.random.choice([10, 20, 30, 40, 50], size=3, replace=False)
print(sample)

NumPy

import numpy as np
np.random.seed(0)
sample = np.random.choice([10, 20, 30, 40, 50], size=3, replace=False)
print(sample)

A [20 40 50]

B [50 20 10]

C [10 20 30]

D [40 10 30]

💡 Hint

Remember that setting the seed fixes the random output.

With seed 0 and replace=False, numpy picks 3 unique elements in a fixed order. The output is [20 40 50].

❓ data_output

intermediate

Shape of random normal samples array

What is the shape of the array produced by this code?

import numpy as np
samples = np.random.normal(loc=5, scale=2, size=(4,3))
print(samples.shape)

NumPy

import numpy as np
samples = np.random.normal(loc=5, scale=2, size=(4,3))
print(samples.shape)

A (4, 3)

B (3, 4)

C (12,)

D (4,)

💡 Hint

The size parameter defines the shape of the output array.

The size=(4,3) means 4 rows and 3 columns, so shape is (4, 3).

❓ visualization

advanced

Histogram of uniform random samples

Which option shows the correct histogram plot for 1000 samples from a uniform distribution between 0 and 1?

NumPy

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
samples = np.random.uniform(0, 1, 1000)
plt.hist(samples, bins=10, edgecolor='black')
plt.show()

A Histogram with bars increasing from left to right

B Histogram with one tall bar at 0 and others near zero

C Histogram with a bell shape centered at 0.5

D Histogram with bars roughly equal height across bins

💡 Hint

Uniform distribution means equal chance for all values in the range.

Uniform samples spread evenly, so histogram bars are roughly equal height.

🧠 Conceptual

advanced

Effect of random seed on reproducibility

Why do we set a random seed before generating random samples in numpy?

A To limit the range of random numbers generated

B To ensure the random samples are the same every time the code runs

C To speed up the random number generation

D To make the samples more random

💡 Hint

Think about reproducibility in experiments.

Setting a seed fixes the random number generator state, so results repeat exactly.

🔧 Debug

expert

Identify the error in random sample generation

What error does this code raise?

import numpy as np
np.random.seed(2)
sample = np.random.choice([1, 2, 3], size=5, replace=False)
print(sample)

NumPy

import numpy as np
np.random.seed(2)
sample = np.random.choice([1, 2, 3], size=5, replace=False)
print(sample)

A TypeError: 'replace' argument must be boolean

B SyntaxError: invalid syntax

C ValueError: Cannot take a larger sample than population when 'replace=False'

D No error, prints 5 unique samples

💡 Hint

Check if sample size is larger than population when replace=False.

Sampling 5 unique items from a list of 3 without replacement is impossible, causing ValueError.