FAQ: Feature Engineering - Numerical Transformations - Natural Log Transformation

This community-built FAQ covers the “Natural Log Transformation” exercise from the lesson “Feature Engineering - Numerical Transformations”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Feature Engineering

FAQs on the exercise Natural Log Transformation

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!
You can also find further discussion and get answers to your questions over in #get-help.

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head to #get-help and #community:tips-and-resources. If you are wanting feedback or inspiration for a project, check out #project.

Looking for motivation to keep learning? Join our wider discussions in #community

Learn more about how to use this guide.

Found a bug? Report it online, or post in #community:Codecademy-Bug-Reporting

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

hey, there is no “cars.csv” file under local directory


I am having the same issue. How did you solve this?

import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import codecademylib3_seaborn 

## add code below
## read in csv file
cars = pd.read_csv('cars.csv')

## set you price variable
prices = cars['sellingprice']

## plot a histogram of prices
plt.hist(prices, bins = 150)

## log transform prices
log_prices = np.log(prices)

## plot a histogram of log_prices
plt.hist(log_prices, bins = 150)

My second histogram is just like the first one. It doesn’t transoform for some reasons, it’s still right-skewed.

1 Like

Resetting the workspace worked for me.