np.exp() and np.log() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.exp() on a numpy array

What is the output of the following code?

NumPy

import numpy as np
arr = np.array([0, 1, 2])
result = np.exp(arr)
print(result)

A [0 1 2]

B [1. 2.71828183 7.3890561 ]

C [1 1 1]

D [0. 1. 2.]

💡 Hint

Recall that np.exp(x) calculates e to the power of x for each element.

np.exp() calculates the exponential of each element. e^0=1, e^1≈2.718, e^2≈7.389.

❓ Predict Output

intermediate

Output of np.log() on a numpy array

What is the output of this code snippet?

NumPy

import numpy as np
arr = np.array([1, np.e, np.e**2])
result = np.log(arr)
print(result)

A [1. 1. 1.]

B [1. 2. 3.]

C [0. 1. 2.]

D [0. 0. 0.]

💡 Hint

np.log() returns the natural logarithm (base e) of each element.

Since log(e^x) = x, the output is [0, 1, 2].

❓ data_output

advanced

Result of combining np.exp() and np.log()

What is the output of this code?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.exp(np.log(arr))
print(result)

A [1. 2. 3.]

B [0. 0. 0.]

C [1 1 1]

D [2.71828183 7.3890561 20.08553692]

💡 Hint

Think about what happens when you apply exp to the log of a number.

np.exp(np.log(x)) returns x for positive x, so the output matches the original array.

🧠 Conceptual

advanced

Behavior of np.log() with zero and negative inputs

What happens when you run np.log() on an array containing zero or negative numbers?

NumPy

import numpy as np
arr = np.array([0, -1, 1])
result = np.log(arr)
print(result)

A It returns [-inf, nan, 0.] with a runtime warning.

B It returns [0, 0, 0] without any warnings.

C It raises a ValueError and stops execution.

D It returns [inf, -inf, 1] without warnings.

💡 Hint

Logarithm of zero or negative numbers is not defined in real numbers.

np.log(0) returns -inf, np.log(negative) returns nan, and a runtime warning is issued.

🚀 Application

expert

Calculate the geometric mean using np.log() and np.exp()

Given a numpy array of positive numbers, which code correctly calculates the geometric mean?

NumPy

import numpy as np
arr = np.array([1, 10, 100])

A np.mean(np.exp(arr))

B np.exp(np.sum(arr))

C np.log(np.mean(arr))

D np.exp(np.mean(np.log(arr)))

💡 Hint

Geometric mean is the nth root of the product of n numbers, which can be calculated using logs and exponentials.

Taking the log of numbers, averaging, then exponentiating gives the geometric mean.