问raise ValueError ValueError:找到具有0个特征(shape=(124，0))的数组，但至少需要1个特征

EN

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
    'machine-learning-databases/wine/wine.data', header=None)

from sklearn.model_selection import train_test_split
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
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
X_test_std = sc.transform(X_test)

from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
# initializing the PCA transformer and
# logistic regression estimator:
pca = PCA() #prof recommends getting rid of m_components = 3 
lr = LogisticRegression()
# dimensionality reduction:
X_train_pca = pca.fit_transform(X_train_std)
X_test_pca = pca.transform(X_test_std)

"""
rows = len(X_train_pca)
columns = len(X_train_pca[0])
print(rows)
print(columns)
"""

# fitting the logistic regression model on the reduced dataset:
for i in range(12):
    lr.fit(X_train_pca[:, :i], y_train)
    y_train_pca = lr.predict(X_train_pca[:, :i])
    print('Training accuracy:', lr.score(X_train_pca[:, :i], y_train))