The preferred practice is not to use StandardScaler on one-hot-encoded columns. The first example below demonstrates the application of OHE on the categorical variables and StandardScaler on the numeric columns. The second example, shows the sequential application of OHE on selected columns and StandardScaler on all columns, but this is not recommended.

Example_1:

import numpy as np
import pandas as pd

from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer
from sklearn.pipeline import Pipeline

df = pd.DataFrame({'Cat_Var': np.random.choice(['a', 'b'], size=5),
                   'Num_Var': np.arange(5)})

cat_cols = ['Cat_Var']
num_cols = ['Num_Var']

col_transformer = make_column_transformer(
        (OneHotEncoder(), cat_cols),
        remainder=StandardScaler())

X = col_transformer.fit_transform(df)

Output:

df
Out[57]: 
  Cat_Var  Num_Var
0       b        0
1       a        1
2       b        2
3       a        3
4       a        4

X
Out[58]: 
array([[ 0.        ,  1.        , -1.41421356],
       [ 1.        ,  0.        , -0.70710678],
       [ 0.        ,  1.        ,  0.        ],
       [ 1.        ,  0.        ,  0.70710678],
       [ 1.        ,  0.        ,  1.41421356]])

Example 2:

col_transformer_2 = ColumnTransformer(
        [('cat_transform', OneHotEncoder(), cat_cols)],
        remainder='passthrough'
        )

pipe = Pipeline(
        [
         ('col_tranform', col_transformer_2),
         ('standard_scaler', StandardScaler())
         ])

X_2 = pipe.fit_transform(df)

Output:

X_2
Out[62]: 
array([[-1.22474487,  1.22474487, -1.41421356],
       [ 0.81649658, -0.81649658, -0.70710678],
       [-1.22474487,  1.22474487,  0.        ],
       [ 0.81649658, -0.81649658,  0.70710678],
       [ 0.81649658, -0.81649658,  1.41421356]])

It is a bit strange to encode continuous data as Salary. It makes no sense unless you have binned your salary to certain ranges/categories. If I were you I would do:

import pandas as pd
import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder



numeric_features = ['Salary']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['Age','Country']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

from here you can pipe it with a classifier e.g.

clf = Pipeline(steps=[('preprocessor', preprocessor),
                  ('classifier', LogisticRegression(solver='lbfgs'))])  
                  

Use it as so:

clf.fit(X_train,y_train)

this will apply the preprocessor and then pass transformed data to the predictor.

Updates:

If we want to select data types on the fly, we can modify our preprocessor to use column selector by data dtypes:

from sklearn.compose import make_column_selector as selector

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, selector(dtype_include="numeric")),
        ('cat', categorical_transformer, selector(dtype_include="category"))])

Using GridSearch

param_grid = {
    'preprocessor__num__imputer__strategy': ['mean', 'median'],
    'classifier__C': [0.1, 1.0, 10, 100],
    'classifier__solver': ['lbfgs', 'sag'],
}

grid_search = GridSearchCV(clf, param_grid, cv=10)
grid_search.fit(X_train,y_train)

Getting names of features

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, selector(dtype_include="numeric")),
        ('cat', categorical_transformer, selector(dtype_include="category"))],
    verbose_feature_names_out=False, # added this line
)

# now we can access feature names with

clf[:-1]. get_feature_names_out() # step before estimator

You need to convert it to a dense array before putting it into a data.frame, see help page too:

pd.DataFrame(df_transformed.toarray())

Or you set the transformer to always return a dense array, see the sparse threshold option

transformer = ColumnTransformer([("enc", 
                                  enc,
                                  cat_features)],
                                  remainder="passthrough",sparse_threshold=0)