Methods for Handling Outliers

Trimming

• Description: Removing data points identified as outliers based on criteria such as being beyond a certain number of standard deviations from the mean or outside a specified percentile range.
• Implementation Example:

  lower_quantile = df["var1"].quantile(0.01)
  upper_quantile = df["var1"].quantile(0.99)
  df_no_outliers = df[(df["var1"] >= lower_quantile) & (df["var1"] <= upper_quantile)]

Capping or Flooring

• Description: Setting a maximum or minimum threshold beyond which data points are considered outliers and replacing them with the threshold value.

Winsorizing

• Description: Similar to capping and flooring, winsorizing replaces extreme values with less extreme values within a specified range, typically using percentiles.