Scaling parts of a dataset
In previous videos, you've used the QuantileTransformer on the full dataset. In this exercise, you will practice scaling only parts of a dataset. The reason for doing this is that the stocks datasets have numerically-encoded categorical features (day_of_week, day, month) that would have been incorrectly scaled if you used QuantileTransformer on the full dataset.
The transformer has been imported from sklearn along with the apple stocks dataset with the extra features.
Diese Übung ist Teil des Kurses
Anomaly Detection in Python
Anleitung zur Übung
- Create a list that contains the five numeric column names of
apple. - Initialize a
QuantileTransformerthat casts features to a normal distribution. - Scale and store the five columns in
to_scalesimultaneously.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create a list of columns
to_scale = [____]
# Initialize a QuantileTransformer
qt = ____
# Scale and store simultaneously
apple.loc[____] = ____