How to handle outliers in Python data.

There are many different methods for dealing with abnormal data values, here are some commonly used methods:

1. Remove outliers: You can directly delete rows or columns containing outliers, but this may result in losing some valuable information.
2. Replace outliers: you can use some reasonable values to replace outliers, such as using the mean, median, mode, etc. to replace outliers.
3. Fill in missing values through interpolation: Interpolation methods such as linear interpolation, Lagrange interpolation, etc., can be used to predict missing values based on known data points.
4. Outlier processing: methods such as box plots or the 3 sigma rule can be used to detect and deal with outliers.
5. Statistical models can be used to detect outliers, such as using clustering algorithms or outlier detection algorithms.

The specific method chosen will depend on the characteristics of the data and actual requirements, and should be selected and processed according to the specific situation.