我有一个训练数据集，其中包含连续值和类别值。我已经使用scikit-learn创建了一个包含类别特征的训练集（x_train_1hot），我也有一个包含数值特征的训练集（x_train_num）。
x_train_num = []x_test_num = []x_train_1hot = []x_test_1hot = []x_train_full = []x_test_full = []cat_feats = []cat_feats_test = []for instance in x_train: num_instance = [] num_instance.append(instance[0]) num_instance.append(instance[2]) num_instance.append(instance[4]) num_instance.append(instance[10]) num_instance.append(instance[11]) num_instance.append(instance[12]) x_train_num.append(num_instance) cat_instance = [] cat_instance.append(instance[1]) cat_instance.append(instance[3]) cat_instance.append(instance[5]) cat_instance.append(instance[6]) cat_instance.append(instance[7]) cat_instance.append(instance[8]) cat_instance.append(instance[9]) cat_instance.append(instance[13]) cat_feats.append(cat_instance) for instance in x_test: num_instance = [] num_instance.append(int(instance[0])) num_instance.append(int(instance[2])) num_instance.append(int(instance[4])) num_instance.append(int(instance[10])) num_instance.append(int(instance[11])) num_instance.append(int(instance[12])) x_test_num.append(num_instance) cat_instance = [] cat_instance.append(instance[1]) cat_instance.append(instance[3]) cat_instance.append(instance[5]) cat_instance.append(instance[6]) cat_instance.append(instance[7]) cat_instance.append(instance[8]) cat_instance.append(instance[9]) cat_instance.append(instance[13]) cat_feats_test.append(cat_instance)enc = OneHotEncoder(handle_unknown='ignore')X = numpy.array(cat_feats)x_train_1hot = enc.fit_transform(X).toarray()如何将这些合并成一个完整的训练集（x_train_full）？我尝试过添加或连接数组，但这会遇到很多错误。我觉得我可能在根本上误解了某些东西？
我希望仅使用scikit-learn或纯Python来完成这项任务，避免使用pandas。
编辑：这是训练数据集（x_train）的一个样本：
[['39', ' State-gov', ' 77516', ' Bachelors', ' 13', ' Never-married', ' Adm-clerical', ' Not-in-family', ' White', ' Male', ' 2174', ' 0', ' 40', ' United-States'], ['50', ' Self-emp-not-inc', ' 83311', ' Bachelors', ' 13', ' Married-civ-spouse', ' Exec-managerial', ' Husband', ' White', ' Male', ' 0', ' 0', ' 13', ' United-States'], ['38', ' Private', ' 215646', ' HS-grad', ' 9', ' Divorced', ' Handlers-cleaners', ' Not-in-family', ' White', ' Male', ' 0', ' 0', ' 40', ' United-States'], ['53', ' Private', ' 234721', ' 11th', ' 7', ' Married-civ-spouse', ' Handlers-cleaners', ' Husband', ' Black', ' Male', ' 0', ' 0', ' 40', ' United-States'], ['28', ' Private', ' 338409', ' Bachelors', ' 13', ' Married-civ-spouse', ' Prof-specialty', ' Wife', ' Black', ' Female', ' 0', ' 0', ' 40', ' Cuba'], ['37', ' Private', ' 284582', ' Masters', ' 14', ' Married-civ-spouse', ' Exec-managerial', ' Wife', ' White', ' Female', ' 0', ' 0', ' 40', ' United-States'], ['49', ' Private', ' 160187', ' 9th', ' 5', ' Married-spouse-absent', ' Other-service', ' Not-in-family', ' Black', ' Female', ' 0', ' 0', ' 16', ' Jamaica'], ['52', ' Self-emp-not-inc', ' 209642', ' HS-grad', ' 9', ' Married-civ-spouse', ' Exec-managerial', ' Husband', ' White', ' Male', ' 0', ' 0', ' 45', ' United-States'], ['31', ' Private', ' 45781', ' Masters', ' 14', ' Never-married', ' Prof-specialty', ' Not-in-family', ' White', ' Female', ' 14084', ' 0', ' 50', ' United-States'], ['42', ' Private', ' 159449', ' Bachelors', ' 13', ' Married-civ-spouse', ' Exec-managerial', ' Husband', ' White', ' Male', ' 5178', ' 0', ' 40', ' United-States']]完整的原始数据集可在此处找到： http://archive.ics.uci.edu/ml/datasets/Adult
回答：
我注意到您没有将x_train_num转换为int。但您应该可以像这样进行连接：
x_train_num = np.array(x_train_num, dtype=int)x_train = np.concatenate([x_train_num, x_train_1hot], axis=1)print(x_train.shape)# (10, 33)