标签编码中的for循环和一个热编码器答案

【问题标题】：For loop in Label encoding and one hot encoder标签编码中的for循环和一个热编码器
【发布时间】：2020-06-14 12:36:49
【问题描述】：

我的数据集包含分类变量，所以我使用标签编码和一个热编码器，我的代码如下

我可以使用循环来确保我的代码包含较少的代码？

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_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:]

如何使用 for 循环 来优化代码行数？？请帮忙！

【问题讨论】：

创建列表labelencoder_y = []并使用labelencoder_y.append(LabelEncoder())在for-loop中添加LabelEncoder。你可以使用labelencoder_y[0]、labelencoder_y[1]来访问它 - labelencoder_y[5].fit_transform(X[:, 5])

标签： python machine-learning one-hot-encoding label-encoding

【解决方案1】：

当然可以！我建议使用字典来存储编码器

label_encoders = {}
categorical_columns = [0, 1, 2, 3]  # I would recommend using columns names here if you're using pandas. If you're using numpy then stick with range(n) instead

for column in categorical_columns:
    label_encoders[column] = LabelEncoder()
    X[column] = label_encoders[column].fit_transform(X[column])  # if numpy instead of pandas use X[:, column] instead

【讨论】：