The main goals are as follows:

Apply

`StandardScaler`

to continuous variablesApply

`LabelEncoder`

and`OnehotEncoder`

to categorical variables

The continuous variables need to be scaled, but at the same time, a couple of categorical variables are also of integer type. Applying `StandardScaler`

would result in undesired effects.

On the flip side, the `StandardScaler`

would scale the integer based categorical variables, which is also not what we want.

Since continuous variables and categorical ones are mixed in a single `Pandas`

DataFrame, what’s the recommended workflow to approach this kind of problem?

The best example to illustrate my point is the Kaggle Bike Sharing Demand dataset, where `season`

and `weather`

are integer categorical variables