我试图使用category_encoders.OrdinalEncoder在pandas数据框中将分类映射到整数。但我遇到了以下错误，没有其他有用的提示。
TypeError: 'NoneType' object is not iterable代码在没有尝试映射的情况下运行正常，但我想进行映射。
代码如下：
import category_encoders as ceordinal_cols = [ "ExterQual",]ordinal_cols_mapping = [{ "ExterQual": { 'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'NA': NaN }},]encoder = ce.OrdinalEncoder( mapping = ordinal_cols_mapping, return_df = True, cols = ordinal_cols,) df_train = encoder.fit_transform(train_data)print(df_train)我做错了什么？
mapping: 列表中的字典，用于编码的类别到标签的映射，可选。
http://contrib.scikit-learn.org/categorical-encoding/ordinal.html
完整的堆栈跟踪：
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-56-4944c8d41d07> in <module>() 150 # use the Ordinal Encoder to map the ordinal data to interval and then fit transform 151 encoder = ce.OrdinalEncoder( return_df = True, cols = ordinal_cols, mapping = ordinal_cols_mapping) #NaNs get -1, mapping = ordinal_cols_mapping removed due to error--> 152 X = encoder.fit_transform(X)/opt/conda/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params) 515 if y is None: 516 # fit method of arity 1 (unsupervised transformation)--> 517 return self.fit(X, **fit_params).transform(X) 518 else: 519 # fit method of arity 2 (supervised transformation)/opt/conda/lib/python3.6/site-packages/category_encoders/ordinal.py in fit(self, X, y, **kwargs) 130 cols=self.cols, 131 impute_missing=self.impute_missing,--> 132 handle_unknown=self.handle_unknown 133 ) 134 self.mapping = categories/opt/conda/lib/python3.6/site-packages/category_encoders/ordinal.py in ordinal_encoding(X_in, mapping, cols, impute_missing, handle_unknown) 249 for switch in mapping: 250 X[str(switch.get('col')) + '_tmp'] = np.nan--> 251 for category in switch.get('mapping'): 252 X.loc[X[switch.get('col')] == category[0], str(switch.get('col')) + '_tmp'] = str(category[1]) 253 del X[switch.get('col')]TypeError: 'NoneType' object is not iterable示例数据：
0 01 12 03 14 0Name: ExterQual, dtype: int64回答：
您错误地使用了'mapping'参数。
格式应该是这样的：
'mapping'参数应为list中的dicts，其中内部的dicts应包含键'col'和'mapping'，而'mapping'键应具有格式为(original_label, encoded_label)的元组列表作为值。
应该像这样：
ordinal_cols_mapping = [{ "col":"ExterQual", "mapping": [ ('Ex',5), ('Gd',4), ('TA',3), ('Fa',2), ('Po',1), ('NA',np.nan) ]},]然后无需单独设置'cols'参数。列名将从'mapping'参数中使用。
只需这样做：
encoder = OrdinalEncoder(mapping = ordinal_cols_mapping, return_df = True) df_train = encoder.fit_transform(train_data)希望这能清楚地说明问题。
