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.
Diese Übung ist Teil des Kurses
Anomaly Detection in Python
Anleitung zur Übung
- Create a boolean mask named
is_lowerthat checks if the values of prices are less thanlower_limit. - Create a boolean mask named
is_higherthat 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.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# 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
____