我的数据集包含分类变量，因此我使用了标签编码和独热编码，我的代码如下

我可以使用循环来确保我的代码包含更少的行数吗？

from sklearn.preprocessing import LabelEncoder, OneHotEncoderlabelencoder_X_0 = LabelEncoder()X[:, 0] = labelencoder_X_0.fit_transform(X[:, 0])labelencoder_X_1 = LabelEncoder()X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])labelencoder_X_2 = LabelEncoder()X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])labelencoder_X_3 = LabelEncoder()X[:, 3] = labelencoder_X_3.fit_transform(X[:, 3])labelencoder_X_4 = LabelEncoder()X[:, 4] = labelencoder_X_4.fit_transform(X[:, 4])labelencoder_X_5 = LabelEncoder()X[:, 5] = labelencoder_X_5.fit_transform(X[:, 5])labelencoder_X_6 = LabelEncoder()X[:, 6] = labelencoder_X_6.fit_transform(X[:, 6])labelencoder_X_7 = LabelEncoder()X[:, 7] = labelencoder_X_7.fit_transform(X[:, 7])labelencoder_X_8 = LabelEncoder()X[:, 8] = labelencoder_X_8.fit_transform(X[:, 8])labelencoder_X_13 = LabelEncoder()X[:, 13] = labelencoder_X_13.fit_transform(X[:, 13])labelencoder_X_14 = LabelEncoder()X[:, 14] = labelencoder_X_14.fit_transform(X[:, 14])labelencoder_X_15 = LabelEncoder()X[:, 15] = labelencoder_X_15.fit_transform(X[:, 15])labelencoder_y_16 = LabelEncoder()y[:, ] = labelencoder_y_16.fit_transform(y[:, ])onehotencoder = OneHotEncoder(categorical_features = [1])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]onehotencoder = OneHotEncoder(categorical_features = [14])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]onehotencoder = OneHotEncoder(categorical_features = [27])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]onehotencoder = OneHotEncoder(categorical_features = [29])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]onehotencoder = OneHotEncoder(categorical_features = [38])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]onehotencoder = OneHotEncoder(categorical_features = [40])X = onehotencoder.fit_transform(X).toarray()X = X[:, 1:]

我如何使用for循环来优化代码行数？请帮助我！

回答：

当然可以！我建议使用字典来存储你的编码器

label_encoders = {}categorical_columns = [0, 1, 2, 3]  # 如果你使用的是pandas，我建议使用列名。如果使用的是numpy，则使用range(n)for column in categorical_columns:    label_encoders[column] = LabelEncoder()    X[column] = label_encoders[column].fit_transform(X[column])  # 如果使用的是numpy而不是pandas，请使用X[:, column]