IndexError：用作索引的数组必须是整数（或布尔）类型错误在 OneHotEncoding

Question

我有一个包含分类变量的数据框，我想应用 OneHotEncoder。我的问题是在 OneHotEncoder 之前使用 LabelEncoder 解决的，但这对我来说没有意义，因为最新的更新 OneHotEncoder 接受分类变量的字符串。

您可以测试代码的示例数据框：

data = pd.DataFrame({'col1': {0: 'ab321', 1: 'ab568', 2: 'mkld78'},
 'col2': {0: 'Red', 1: 'Blue', 2: 'Green'},
 'col3': {0: 'First', 1: 'Second', 2: 'Third'},
 'col4': {0: 'Wisconsin', 1: 'California', 2: 'Portland'},
 'col5': {0: 'a', 1: 'f', 2: 'g'},
 'col6': {0: 1, 1: 2, 2: 3},
 'target': {0: 0, 1: 0, 2: 1}})

这是我尝试过的：

我已经尝试同时使用索引值和列名来解决错误：

#Index
# OneHotEncoding

from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd

#Load data
train = pd.read_csv("data_train.csv")
test = pd.read_csv("data_test.csv")

X= train.drop(["target"], axis = 1)
y= train["target"]
# Filter categorical columns
categorical_columns = ["col1","col2","col3","col4","col5"]
categorical_indexes = np.where(X.dtypes == 'object')[0]

# OHE
ohe = OneHotEncoder(categorical_features = categorical_columns)
# reshape data
for index in categorical_indexes:
    X.iloc[:,index] = ohe.fit_transform(X.iloc[:,index].values.reshape(-1,1))

---

#Column Names

# OneHotEncoding

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

train = pd.read_csv("data_train.csv")
test = pd.read_csv("data_test.csv")

X= train.drop(["target"], axis = 1)
y= train["target"]

# Filter categorical columns
categorical_columns = ["col1","col2","col3","col4","col5"]
categorical_indexes = np.where(X.dtypes == 'object')[0]

# OHE
ohe = OneHotEncoder(categorical_features = categorical_columns)
# reshape data
for column in categorical_columns:
    X[column] = ohe.fit_transform(X[column].values.reshape(-1,1))

错误回溯：

IndexError                                Traceback (most recent call last)
<ipython-input-86-17c86bf649e2> in <module>
     11 # reshape data
     12 for index in categorical_indexes:
---> 13     X.iloc[:,index] = ohe.fit_transform(X.iloc[:,index].values.reshape(-1,1))
     14 

c:\users\m\appdata\local\programs\python\python37\lib\site-packages\sklearn\preprocessing\_encoders.py in fit_transform(self, X, y)
    622         self._validate_keywords()
    623 
--> 624         self._handle_deprecations(X)
    625 
    626         if self._legacy_mode:

c:\users\m\appdata\local\programs\python\python37\lib\site-packages\sklearn\preprocessing\_encoders.py in _handle_deprecations(self, X)
    453                 n_features = X.shape[1]
    454                 sel = np.zeros(n_features, dtype=bool)
--> 455                 sel[np.asarray(self.categorical_features)] = True
    456                 if sum(sel) == 0:
    457                     self.categories_ = []

IndexError: arrays used as indices must be of integer (or boolean) type

Answer 1

您缺少OnehotEncoder 的概念。使用它的方法是在整个训练集上拟合它。

使用这个：

data = pd.DataFrame({'col1': {0: 'ab321', 1: 'ab568', 2: 'mkld78'},
 'col2': {0: 'Red', 1: 'Blue', 2: 'Green'},
 'col3': {0: 'First', 1: 'Second', 2: 'Third'},
 'col4': {0: 'Wisconsin', 1: 'California', 2: 'Portland'},
 'col5': {0: 'a', 1: 'f', 2: 'g'},
 'col6': {0: 1, 1: 2, 2: 3},
 'target': {0: 0, 1: 0, 2: 1}})


# OneHotEncoding

import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

train = data.iloc[0:2,:]
test = data.iloc[2:,:]

X= train.drop(["target"], axis = 1)
y= train["target"]

# Filter categorical columns
categorical_columns = ["col1","col2","col3","col4","col5"]
categorical_indexes = np.where(X.dtypes == 'object')[0]

# OHE
ohe = OneHotEncoder()
X_ = ohe.fit_transform(X)

X_
# <2x12 sparse matrix of type '<type 'numpy.float64'>'
#  with 12 stored elements in Compressed Sparse Row format>