Scikit-Learn

import pandas as pd
import numpy as np
import sklearn

np.random.seed(1)

df= pd.DataFrame({
    'x1': np.random.normal(0, 4, 500),
    'x2': np.random.normal(-2, 2, 500),
    'x3': np.random.normal(4, 2, 500)
})
df #dataframe before scaling

from sklearn import preprocessing 

scaler = preprocessing.StandardScaler()
scaled_df = scaler.fit_transform(df)
scaled_df = pd.DataFrame(scaled_df, columns=['x1', 'x2', 'x3'])
    #inverse transform is used to scale the data back to orginal form
inv_scaled_df = scaler.inverse_transform(scaled_df)
print(scaled_df) #dataframe after scaling
#print(inv_scaled_df)
#print(df)