エラーの対処方法がわからない

前提・実現したいこと

Wineデータセットを読み込み、MinMaaxスケーリングをしたい。

発生している問題・エラーメッセージ

ValueError                                Traceback (most recent call last)
<ipython-input-163-4541c75c7333> in <module>
      1 from sklearn.preprocessing import MinMaxScaler
      2 mms = MinMaxScaler()
----> 3 X_train_norm = mms.fit_transform(X_train)
      4 X_test_norm = mms.transform(X_test)

~\anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    569         if y is None:
    570             # fit method of arity 1 (unsupervised transformation)
--> 571             return self.fit(X, **fit_params).transform(X)
    572         else:
    573             # fit method of arity 2 (supervised transformation)

~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py in fit(self, X, y)
    337         # Reset internal state before fitting
    338         self._reset()
--> 339         return self.partial_fit(X, y)
    340 
    341     def partial_fit(self, X, y=None):

~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py in partial_fit(self, X, y)
    371         X = check_array(X,
    372                         estimator=self, dtype=FLOAT_DTYPES,
--> 373                         force_all_finite="allow-nan")
    374 
    375         data_min = np.nanmin(X, axis=0)

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    554                     "Reshape your data either using array.reshape(-1, 1) if "
    555                     "your data has a single feature or array.reshape(1, -1) "
--> 556                     "if it contains a single sample.".format(array))
    557 
    558         # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[13.71 12.22 13.27 13.16 13.86 12.85 13.84 13.3  13.05 12.51 12.29 12.77
 12.96 13.67 13.16 12.37 12.47 11.81 13.24 14.1  11.61 12.99 12.77 13.48
 11.46 12.07 11.82 13.45 12.2  13.05 13.17 12.08 12.17 12.42 11.87 12.21
 13.94 14.1  12.67 14.75 12.25 12.85 13.73 14.06 13.63 12.33 13.82 12.08
 13.03 14.37 13.17 12.72 14.39 13.34 11.66 11.84 12.86 13.75 12.53 13.11
 14.38 14.23 12.   13.23 14.38 14.12 12.   12.34 13.48 13.29 13.41 13.71
 12.37 13.2  11.45 13.62 13.88 12.42 12.81 12.58 13.83 13.07 12.7  13.77
 12.84 12.37 13.51 13.87 12.08 13.58 13.08 11.79 12.45 13.68 13.52 13.5
 12.87 14.02 12.29 12.08 12.7  11.03 13.32 14.13 13.49 11.84 13.05 12.72
 12.82 13.4  14.22 13.72 12.93 11.64 12.29 11.65 13.28 12.93 13.86 11.82
 12.37 12.42 13.9  14.16].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

該当のソースコード

Python
1df_wine = pd.read_csv(
2    'https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Datasets/wine.data.csv', header = 0)
3
4df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash', 
5                   'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 
6                   'Nonflavanoid phenols', 'Proanthocyanis', 'Color intenesity', 'Hue', 
7                   'OD280/OD315 of diluted wines', 'Proline']
8
9
10print('Class labels', np.unique(df_wine['Class label']))
11df_wine.head()
12
13from sklearn.model_selection import train_test_split
14X, y = df_wine.iloc[:, 1].values, df_wine.iloc[:, 0].values
15
16X_train, X_test, y_train, y_test = \
17    train_test_split(X, y, test_size = 0.3, random_state = 0)
18
19from sklearn.preprocessing import MinMaxScaler
20mms = MinMaxScaler()
21X_train_norm = mms.fit_transform(X_train)
22X_test_norm = mms.transform(X_test)

試したこと

教科書(Python machine learning 2nd edition)では、random_state = 0の直後にstratify = y、'~ csv'の直後にheader = Noneが入っており、それぞれエラーが発生していましたが、それは削除することで解決しました。しかし、このエラーだけはどのように解決すればよいのかがわかりません。
わかる方、お願いします。