我很困惑，因为如果您首先执行OneHotEncoder然后StandardScaler会出现问题，因为缩放器还会缩放先前由OneHotEncoder转换的列。有没有办法同时执行编码和缩放，然后将结果连接在一起？
答案 0 :(得分：13)
当然可以。只需根据需要单独缩放和单独编码单独的列：
# Import libraries and download example data
from sklearn.preprocessing import StandardScaler, OneHotEncoder
dataset = pd.read_csv("http://www.ats.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)
答案 1 :(得分：2)
0.20版的Scikit学习提供了sklearn.compose.ColumnTransformer来做混合类型的列转换器。您可以缩放数字特征并一键式编码分类特征。下面是一个官方示例（您可以找到代码here）：
# Author: Pedro Morales <part.morales@gmail.com>
#
# License: BSD 3 clause
from __future__ import print_function
import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
np.random.seed(0)
# Read data from Titanic dataset.
titanic_url = ('https://raw.githubusercontent.com/amueller/'
'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')
data = pd.read_csv(titanic_url)
# We will train our classifier with the following features:
# Numeric Features:
# - age: float.
# - fare: float.
# Categorical Features:
# - embarked: categories encoded as strings {'C', 'S', 'Q'}.
# - sex: categories encoded as strings {'female', 'male'}.
# - pclass: ordinal integers {1, 2, 3}.
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', LogisticRegression(solver='lbfgs'))])
X = data.drop('survived', axis=1)
y = data['survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))
警告：此方法是实验性的，发行之间的某些行为可能会发生变化而不会被弃用。
答案 2 :(得分：2)
目前有许多方法可以达到OP要求的结果。做到这一点的三种方法是
np.concatenate()-请参见this answer to the OP's question, already posted
使用@Max Power here发布的示例，下面是一个最小的工作片段，该片段可以执行OP所需的工作，并将转换后的列汇总到单个Pandas数据帧中。显示了这三种方法的输出
这3种方法的通用代码是
import numpy as np
import pandas as pd
# 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")
# 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)
方法1 。请参见代码here。要显示输出，可以使用
print pd.DataFrame(processed_data).head()
方法1的输出。
0 1 2 3 4 5
0 -1.800263 0.579072 0.0 0.0 1.0 0.0
1 0.626668 0.736929 0.0 0.0 1.0 0.0
2 1.840134 1.605143 1.0 0.0 0.0 0.0
3 0.453316 -0.525927 0.0 0.0 0.0 1.0
4 -0.586797 -1.209974 0.0 0.0 0.0 1.0
方法2。
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
p = Pipeline([
('coltransformer', ColumnTransformer(transformers=[
('assessments', Pipeline([
('scale', scaler),
]), columns_to_scale),
('ranks', Pipeline([
('encode', ohe),
]), columns_to_encode),
]),
),
])
print(pd.DataFrame(p.fit_transform(dataset)).head())
方法2的输出。
0 1 2 3 4 5
0 -1.800263 0.579072 0.0 0.0 1.0 0.0
1 0.626668 0.736929 0.0 0.0 1.0 0.0
2 1.840134 1.605143 1.0 0.0 0.0 0.0
3 0.453316 -0.525927 0.0 0.0 0.0 1.0
4 -0.586797 -1.209974 0.0 0.0 0.0 1.0
方法3。
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import FeatureUnion
class ItemSelector(BaseEstimator, TransformerMixin):
def __init__(self, key):
self.key = key
def fit(self, x, y=None):
return self
def transform(self, df):
return df[self.key]
p = Pipeline([
("union", FeatureUnion(
transformer_list=[
('assessments', Pipeline([
('selector', ItemSelector(key=columns_to_scale)),
('scale', scaler),
])
),
('ranks', Pipeline([
('selector', ItemSelector(key=columns_to_encode)),
('encode', ohe),
])
),
]
)
),
])
print(pd.DataFrame(p.fit_transform(dataset)).head())
方法3的输出。
0 1 2 3 4 5
0 -1.800263 0.579072 0.0 0.0 1.0 0.0
1 0.626668 0.736929 0.0 0.0 1.0 0.0
2 1.840134 1.605143 1.0 0.0 0.0 0.0
3 0.453316 -0.525927 0.0 0.0 0.0 1.0
4 -0.586797 -1.209974 0.0 0.0 0.0 1.0
说明
方法1.已经说明。
方法2和3.接受完整的数据集，但仅对数据子集执行特定操作。修改/处理后的子集一起（合并）到最终输出中。
详细信息
pandas==0.23.4
numpy==1.15.2
scikit-learn==0.20.0
附加说明
这里显示的3种方法可能不是唯一的可能性。...我相信还有其他方法可以做到这一点。
已使用的来源
答案 3 :(得分：0)
无法获得您的观点，OneHotEncoder用于标称数据，StandardScaler用于数字数据。因此，您不应该将它们一起用于您的数据。