np.random.rand() and random arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output shape of np.random.rand() with multiple dimensions

What is the shape of the array produced by np.random.rand(3, 4, 2)?

NumPy

import numpy as np
arr = np.random.rand(3, 4, 2)
print(arr.shape)

A (2, 3, 4)

B (3, 2, 4)

C (4, 3, 2)

D (3, 4, 2)

💡 Hint

The arguments to np.random.rand() specify the size of each dimension in order.

np.random.rand(3, 4, 2) creates a 3D array with shape (3, 4, 2). The first argument is the size of the first dimension, the second is the second dimension, and so on.

❓ data_output

intermediate

Range of values from np.random.rand()

What is the range of values you can expect from np.random.rand(5)?

NumPy

import numpy as np
arr = np.random.rand(5)
print(arr.min(), arr.max())

A Values between 0 and 1 inclusive

B Values between 0 (inclusive) and 1 (exclusive)

C Values between -1 and 1

D Values between 0 and 5

💡 Hint

np.random.rand() generates floats in a specific range starting at zero.

np.random.rand() generates random floats in the half-open interval [0, 1), meaning 0 is included but 1 is excluded.

❓ visualization

advanced

Visualizing distribution of np.random.rand() values

Which option correctly plots a histogram showing the distribution of 1000 values generated by np.random.rand(1000)?

NumPy

import numpy as np
import matplotlib.pyplot as plt
values = np.random.rand(1000)
plt.hist(values, bins=20)
plt.show()

A A histogram with a peak near 0.5

B A histogram with a peak near 0 and decreasing towards 1

C A histogram with bars roughly uniform across the range 0 to 1

D A histogram with values only at 0 and 1

💡 Hint

np.random.rand() generates uniform random numbers between 0 and 1.

Since np.random.rand() produces uniform random numbers, the histogram bars should be roughly even across the range from 0 to 1.

🧠 Conceptual

advanced

Effect of shape argument order in np.random.rand()

If you want to create a 2D array with 5 rows and 3 columns using np.random.rand(), which call is correct?

A np.random.rand(5, 3)

B np.random.rand(3, 5)

C np.random.rand([5, 3])

D np.random.rand((5, 3))

💡 Hint

The arguments to np.random.rand() are separate integers for each dimension.

np.random.rand() takes each dimension as a separate argument, so np.random.rand(5, 3) creates an array with 5 rows and 3 columns.

🔧 Debug

expert

Identify the error in this np.random.rand() usage

What error will this code raise?

import numpy as np
arr = np.random.rand([2, 3])
print(arr.shape)

NumPy

import numpy as np
arr = np.random.rand([2, 3])
print(arr.shape)

A TypeError: 'list' object cannot be interpreted as an integer

B SyntaxError: invalid syntax

C ValueError: shape must be a tuple of integers

D No error, prints (2, 3)

💡 Hint

np.random.rand() expects separate integer arguments, not a list.

Passing a list as a single argument causes a TypeError because np.random.rand() tries to interpret it as an integer dimension size.