PCA: standardize the wine dataframe

standardize_data.py

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# split into training and testing sets
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3,
    stratify=y, random_state=0
)
# standardize the features
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)