Using outlier limits for filtering
In the previous exercise, you found the limits you will use for categorizing outliers. In this exercise, you'll apply them to the prices
distribution to isolate the outliers.
The prices
, lower_limit
, and upper_limit
variables are available from the last exercise.
This exercise is part of the course
Anomaly Detection in Python
Exercise instructions
- Create a boolean mask named
is_lower
that checks if the values of prices are less thanlower_limit
. - Create a boolean mask named
is_higher
that checks if the values of prices are greater thanupper_limit
. - Combine the masks and use boolean subsetting to filter for outliers.
- Print the number of outliers found.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a mask for values lower than lower_limit
is_lower = ____
# Create a mask for values higher than upper_limit
is_higher = ____
# Combine the masks to filter for outliers
outliers = ____[____]
# Count and print the number of outliers
____