Capping or censoring outliers

Capping or censoring is the process of transforming the data by limiting the extreme values, as in the outliers, to a certain maximum or minimum arbitrary value. With this procedure, the outliers are not removed but are instead replaced by other values. A typical strategy involves setting outliers to a specified percentile. For example, we can set all data below the 5th percentile to the value at the 5th percentile and all data greater than the 95th percentile to the value at the 95th percentile. Alternatively, we can cap the variable at the limits determined by the IQR proximity rule or at the mean plus and minus three times the standard deviation. In this recipe, we will cap variables at arbitrary values determined by the mean plus and minus three times the standard deviation using pandas and Feature-engine.

How to do it...

Let’s first import the Python libraries and load the data:

1. Import the required Python libraries:

   import numpy...