Am I right to say that also Standardization gets affected negatively by the extreme values as well?
Indeed you are; the scikit-learn docs themselves clearly warn for such a case:
However, when data contains outliers,
StandardScalercan often be mislead. In such cases, it is better to use a scaler that is robust against outliers.
More or less, the same holds true for the MinMaxScaler as well.
I really can’t see how the Robust Scaler improved the data because I still have extreme values in the resulted data set? Any simple -complete interpretation?
Robust does not mean immune, or invulnerable, and the purpose of scaling is not to “remove” outliers and extreme values – this is a separate task with its own methodologies; this is again clearly mentioned in the relevant scikit-learn docs:
RobustScaler
[…] Note that the outliers themselves are still present in the transformed data. If a separate outlier clipping is desirable, a non-linear transformation is required (see below).
where the “see below” refers to the QuantileTransformer and quantile_transform.