Uniform random with random() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy.random.random() shape

What is the shape of the array produced by numpy.random.random((3, 4))?

NumPy

import numpy as np
arr = np.random.random((3, 4))
print(arr.shape)

A (12,)

B (4, 3)

C (3, 4)

D (3,)

💡 Hint

The argument to random() defines the shape of the output array.

The function numpy.random.random() takes a shape tuple and returns an array of that shape filled with random floats between 0 and 1.

❓ data_output

intermediate

Range of values from numpy.random.random()

What is the range of values produced by numpy.random.random(5)?

NumPy

import numpy as np
arr = np.random.random(5)
print(arr)

A Values between 0 and 1, including 0 but excluding 1

B Values between 0 and 1, including both 0 and 1

C Values between -1 and 1

D Values between 1 and 10

💡 Hint

random() generates floats in the half-open interval [0.0, 1.0).

numpy.random.random() generates floats from 0 (inclusive) up to but not including 1.

❓ visualization

advanced

Histogram of uniform random numbers

Which option shows the correct histogram plot code for 1000 samples from numpy.random.random()?

NumPy

import numpy as np
import matplotlib.pyplot as plt
samples = np.random.random(1000)

A plt.plot(samples); plt.show()

B plt.hist(samples, bins=10, range=(0,1)); plt.show()

C plt.scatter(range(1000), samples); plt.show()

D plt.bar(range(10), samples[:10]); plt.show()

💡 Hint

Histograms show frequency distribution of values.

Using plt.hist with bins and range shows the distribution of uniform random samples between 0 and 1.

🧠 Conceptual

advanced

Effect of seed on numpy.random.random()

What happens if you set the seed using np.random.seed(42) before calling np.random.random(3) twice?

A The second call produces an error

B Both calls produce different arrays of 3 random numbers

C The seed only affects integers, not floats

D Both calls produce the same array of 3 random numbers

💡 Hint

Seed fixes the random number generator state.

Setting the seed makes the random numbers reproducible, so repeated calls produce the same output.

🔧 Debug

expert

Identify error in numpy.random.random() usage

What error does this code raise?

import numpy as np
arr = np.random.random(3,4)
print(arr)

A TypeError: random() takes from 0 to 1 positional arguments but 2 were given

B ValueError: shape must be a tuple of integers

C SyntaxError: invalid syntax

D No error, prints a 3x4 array

💡 Hint

random() expects a single argument for shape, which should be a tuple.

random() expects one argument for shape, passing two separate arguments causes TypeError.