python - 如何将 StandardScaler() 转换回数据框？

Question

我正在使用模型，在拆分为训练和测试后，我想应用 StandardScaler()。但是，这种转换将我的数据转换为数组，我想保持以前的格式。我怎样才能做到这一点？

基本上，我有：

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

X = df[features]
y = df[["target"]]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=0.7, random_state=42
)

sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)

我怎样才能X_train_sc回到原来的格式X_train？

更新：我不想X_train_sc回到被缩放之前。我只想X_train_sc以最简单的方式成为一个数据框。

标签： pythonpandasdataframescikit-learnsklearn-pandas

Answer 1

As you mentioned, applying the scaling results in a numpy array, to get a dataframe you can initialize a new one:

import pandas as pd

cols = X_train.columns
sc = StandardScaler()
X_train_sc = pd.DataFrame(sc.fit_transform(X_train), columns=cols)
X_test_sc = pd.DataFrame(sc.transform(X_test), columns=cols)