Normal distribution with normal() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy normal() with fixed seed

What is the output of this code snippet that generates 3 random numbers from a normal distribution with mean 0 and standard deviation 1?

NumPy

import numpy as np
np.random.seed(0)
samples = np.random.normal(0, 1, 3)
print(samples)

A [0.49671415 -0.1382643 0.64768854]

B [1.76405235 0.40015721 0.97873798]

C [ 0.76103773 0.12167502 0.44386323]

D [ 1.86755799 -0.97727788 0.95008842]

💡 Hint

Remember to set the random seed before generating samples to get reproducible results.

Setting np.random.seed(0) fixes the random number generator state. The first three samples from np.random.normal(0,1,3) with this seed are [1.76405235, 0.40015721, 0.97873798].

❓ data_output

intermediate

Shape of output array from normal()

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

NumPy

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

A (4, 3)

B (4,)

C (12,)

D (3, 4)

💡 Hint

The size parameter defines the shape of the output array.

The size=(4,3) argument tells numpy to generate a 2D array with 4 rows and 3 columns, so the shape is (4, 3).

❓ visualization

advanced

Histogram of samples from normal distribution

Which option shows the correct histogram plot code for 1000 samples from a normal distribution with mean 0 and standard deviation 1?

NumPy

import numpy as np
import matplotlib.pyplot as plt
samples = np.random.normal(0, 1, 1000)
# Fill in the code to plot histogram

A

plt.bar(range(30), samples[:30])
plt.title('Bar plot of first 30 samples')
plt.show()

B

plt.plot(samples)
plt.title('Line plot of samples')
plt.show()

C

plt.scatter(range(1000), samples)
plt.title('Scatter plot of samples')
plt.show()

D

plt.hist(samples, bins=30, color='blue', edgecolor='black')
plt.title('Histogram of Normal Samples')
plt.show()

💡 Hint

Histograms show frequency distribution of data values.

Option D correctly uses plt.hist() to plot the frequency distribution of the 1000 samples with 30 bins, which is the standard way to visualize a normal distribution sample.

🧠 Conceptual

advanced

Effect of scale parameter in normal()

What happens to the spread of data when you increase the scale parameter in np.random.normal(loc=0, scale=scale_value, size=1000)?

A The data points become more concentrated around the mean, decreasing the standard deviation.

B The mean of the data shifts to the scale value.

C The data points become more spread out, increasing the standard deviation.

D The number of data points generated increases.

💡 Hint

Scale is the standard deviation of the normal distribution.

The scale parameter controls the standard deviation. Increasing it makes the data spread wider around the mean.

🔧 Debug

expert

Identify error in normal() usage

What error will this code raise and why? import numpy as np samples = np.random.normal(0, -1, 5) print(samples)

NumPy

import numpy as np
samples = np.random.normal(0, -1, 5)
print(samples)

A ValueError: scale < 0

B TypeError: loc must be a float

C SyntaxError: invalid syntax

D No error, prints 5 samples

💡 Hint

Scale (standard deviation) must be positive.

The scale parameter cannot be negative. Passing -1 causes numpy to raise a ValueError indicating scale must be non-negative.