One-Hot-Encode 分类变量并同时缩放连续变量 [英] One-Hot-Encode categorical variables and scale continuous ones simultaneouely

问题描述

我很困惑，因为如果您先执行 OneHotEncoder 然后执行 StandardScaler 会出现问题，因为缩放器还会缩放先前由 转换的列OneHotEncoder.有没有办法同时执行编码和缩放，然后将结果连接在一起?

I'm confused because it's going to be a problem if you first do OneHotEncoder and then StandardScaler because the scaler will also scale the columns previously transformed by OneHotEncoder. Is there a way to perform encoding and scaling at the same time and then concatenate the results together?

推荐答案

当然可以.只需根据需要单独缩放和单热编码单独的列:

Sure thing. Just separately scale and one-hot-encode the separate columns as needed:

# Import libraries and download example data
from sklearn.preprocessing import StandardScaler, OneHotEncoder

dataset = pd.read_csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
print(dataset.head(5))

# Define which columns should be encoded vs scaled
columns_to_encode = ['rank']
columns_to_scale  = ['gre', 'gpa']

# Instantiate encoder/scaler
scaler = StandardScaler()
ohe    = OneHotEncoder(sparse=False)

# Scale and Encode Separate Columns
scaled_columns  = scaler.fit_transform(dataset[columns_to_scale]) 
encoded_columns =    ohe.fit_transform(dataset[columns_to_encode])

# Concatenate (Column-Bind) Processed Columns Back Together
processed_data = np.concatenate([scaled_columns, encoded_columns], axis=1)

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