如何在训练测试拆分后仅标准化 int64 列？

Question

I have a dataframe ready for modelling, it contains continuous variables and one-hot-encoded variables

ID   Limit   Bill_Sep  Bill_Aug  Payment_Sep   Payment_Aug   Gender_M   Gender_F  Edu_Uni DEFAULT_PAYMT
1    10000   2000      350       1000          350           1          0         1          1
2    30000   3000      5000      500           500           0          1         0          0
3    20000   8000      10000     8000          5000          1          0         1          1
4    45000   450       250       450           250           0          1         0          1
5    60000   700       1000      700           1000          1          0         1          1
6    8000    300       5000      300           2000          1          0         1          0
7    30000   3000      10000     1000          5000          0          1         1          1
8    15000   1000      1250      500           1750          0          1         1          1

All the numerical variables are 'int64' while the one-hot-encoded variables are 'uint8'. The binary outcome variable is DEFAULT_PAYMT.

I have gone down the usual manner of train test split here, but i wanted to see if i could apply the standardscaler only for the int64 variables (ie, the variables that were not one-hot-encoded)?

featurelist = df.drop(['ID','DEFAULT_PAYMT'],axis = 1)
X = featurelist
y = df['DEFAULT_PAYMT']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform (X_test)

Am attempting the following code and seems to work, however, am not sure how to merge the categorical variables (that were not scaled) back into the X_scaled_tr and X_scaled_t arrays. Appreciate any form of help, thank you!

featurelist = df.drop(['ID','DEFAULT_PAYMT'],axis = 1)
X = featurelist
y = df['DEFAULT_PAYMT']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

sc = StandardScaler()
X_scaled_tr = X_train.select_dtypes(include=['int64'])
X_scaled_t = X_test.select_dtypes(include=['int64'])

X_scaled_tr = sc.fit_transform(X_scaled_tr)
X_scaled_t = sc.transform(X_scaled_t)

Answer 1

Managed to address the question with the following code where standardscaler is only applied to the continuous variables and NOT the one-hot-encoded variables

from sklearn.compose import ColumnTransformer

ct = ColumnTransformer([('X_train', StandardScaler(), ['LIMIT','BILL_SEP','BILL_AUG','PAYMENT_SEP','PAYMENT_AUG'])], remainder ='passthrough')

X_train_scaled = ct.fit_transform(X_train)
X_test_scaled = ct.transform(X_test)