np.exp() and np.log() in NumPy - Mini Project: Build & Apply

Using np.exp() and np.log() for Data Transformation

📖 Scenario: Imagine you are analyzing daily growth rates of a small plant species. You have recorded the growth rates as percentages, and you want to understand the exponential growth and also convert back from exponential values to the original growth rates.

🎯 Goal: You will create a numpy array of growth rates, apply the exponential function to simulate growth, then use the natural logarithm to retrieve the original rates. This helps understand how exponential and logarithmic functions work in data science.

📋 What You'll Learn

Create a numpy array with exact growth rates

Create a variable to hold the base of natural logarithm (e)

Use np.exp() to calculate exponential growth

Use np.log() to convert exponential values back to original growth rates

Print the final results

💡 Why This Matters

🌍 Real World

Exponential and logarithmic functions are used in biology to model growth, in finance to calculate compound interest, and in many data science tasks to transform data for analysis.

💼 Career

Understanding how to apply np.exp() and np.log() is important for data scientists working with growth models, time series data, and feature engineering.

1

Create the growth rates array

Import numpy as np and create a numpy array called growth_rates with these exact values: 0.05, 0.10, 0.15, 0.20, 0.25.

NumPy

import numpy as np
# Create the growth_rates array with the exact values
# Your code here

Need a hint?

Use np.array() to create the array with the given values.

2

Create the constant for natural logarithm base

Create a variable called e and assign it the value of the mathematical constant e using np.e.

NumPy

import numpy as np
growth_rates = np.array([0.05, 0.10, 0.15, 0.20, 0.25])
# Create the variable e using np.e
# Your code here

Need a hint?

Use np.e to get the value of e.

3

Calculate exponential growth and apply logarithm

Create a variable called exp_growth by applying np.exp() to growth_rates. Then create a variable called log_values by applying np.log() to exp_growth.

NumPy

import numpy as np
growth_rates = np.array([0.05, 0.10, 0.15, 0.20, 0.25])
e = np.e
# Calculate exp_growth using np.exp on growth_rates
# Calculate log_values using np.log on exp_growth
# Your code here

Need a hint?

Use np.exp() to get exponential values and np.log() to get back the original values.

4

Print the exponential and logarithmic results

Print the variables exp_growth and log_values each on a separate line.

NumPy

import numpy as np
growth_rates = np.array([0.05, 0.10, 0.15, 0.20, 0.25])
e = np.e
exp_growth = np.exp(growth_rates)
log_values = np.log(exp_growth)
# Print exp_growth
# Print log_values
# Your code here

Need a hint?

Use two print() statements to display the arrays.