Numerical transformations modify the distribution of a variable: logarithm or square root to reduce skewness, PowerTransformer, scaling (min-max, standardization, RobustScaler, which is robust to extreme values). They help models that are sensitive to scale or to widely spread distributions.
Example
np.log1p(df['revenu'])