Question

我有pandas DataFrame df。我想使用不同的编码器对df的连续和分类特征进行编码。我发现使用make_column_transformer很舒服，但是下面显示的代码对于LabelEncoder()失败，但是对于OneHotEncoder(handle_unknown='ignore'))可以正常工作。错误消息是：

TypeError：fit_transform（）需要2个位置参数，但3个是给

我不清楚如何解决此问题。

代码：

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (categorical_features,LabelEncoder()),
    (continuous_features, RobustScaler()))

X_enc = column_trans.fit_transform(df)

Answer 1

根据https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html。

make_column_transformer(
...     (StandardScaler(), ['numerical_column']),
...     (OneHotEncoder(), ['categorical_column']))

所以对于您的情况：

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (OneHotEncoder(), categorical_features),
    (RobustScaler(), continuous_features))

X_enc = column_trans.fit_transform(df)

如果要使用LabelEncoder()，则只能传递一列，不能传递两列！

希望这会有所帮助。

TypeError：fit_transform（）接受2个位置参数，但给出了3个

1 个答案: