Identifying outliers
Let's keep at it with our laptops
dataset and tackle some outliers hiding away. In this exercise, we'll stick to the first technique we discussed in the slides using standard deviations to identify extreme values, since this method is more common in practice.
You'll compute the descriptive statistics and outlier boundaries, and then identify the rows with them before dropping them from the dataset. You'll be working primarily with the Price
column here.
This exercise is part of the course
Practicing Statistics Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the mean and std
mean, std = ____, ____