如何将OneHotEncoder用于多列并自动删除每列的第一个虚拟变量？

Question

这是包含3个col和3行的数据集

名称组织部

Manie ABC2 FINANCE

Joyce ABC1 HR

Ami NSV2 HR

这是我的代码:

现在它很好,直到这里,我如何删除每个的第一个虚拟变量列？

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Data1.csv',encoding = "cp1252")
X = dataset.values


# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_0 = LabelEncoder()
X[:, 0] = labelencoder_X_0.fit_transform(X[:, 0])
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])

onehotencoder = OneHotEncoder(categorical_features = "all")
X = onehotencoder.fit_transform(X).toarray()

Answer 1

import pandas as pd
df = pd.DataFrame({'name': ['Manie', 'Joyce', 'Ami'],
                   'Org':  ['ABC2', 'ABC1', 'NSV2'],
                   'Dept': ['Finance', 'HR', 'HR']        
        })


df_2 = pd.get_dummies(df,drop_first=True)

测试:

print(df_2)
   Dept_HR  Org_ABC2  Org_NSV2  name_Joyce  name_Manie
0        0         1         0           0           1
1        1         0         0           1           0
2        1         0         1           0           0 

关于您的错误更新pd.get_dummies(X, columns =[1:]:

根据文档页面,该columns参数采用"列名称".所以下面的代码可以工作:

df_2 = pd.get_dummies(df, columns=['Org', 'Dept'], drop_first=True)

输出:

    name  Org_ABC2  Org_NSV2  Dept_HR
0  Manie         1         0        0
1  Joyce         0         0        1
2    Ami         0         1        1

如果你真的想要定位你的列,你可以这样做:

column_names_for_onehot = df.columns[1:]
df_2 = pd.get_dummies(df, columns=column_names_for_onehot, drop_first=True)

Answer 2

我使用我自己的模板来做到这一点：

from sklearn.base import TransformerMixin
import pandas as pd
import numpy as np
class DataFrameEncoder(TransformerMixin):

    def __init__(self):
        """Encode the data.

        Columns of data type object are appended in the list. After 
        appending Each Column of type object are taken dummies and 
        successively removed and two Dataframes are concated again.

        """
    def fit(self, X, y=None):
        self.object_col = []
        for col in X.columns:
            if(X[col].dtype == np.dtype('O')):
                self.object_col.append(col)
        return self

    def transform(self, X, y=None):
        dummy_df = pd.get_dummies(X[self.object_col],drop_first=True)
        X = X.drop(X[self.object_col],axis=1)
        X = pd.concat([dummy_df,X],axis=1)
        return X

对于使用此代码，只需将此模板放在当前目录中，文件名让我们假设 CustomeEncoder.py 并输入您的代码：

from customEncoder import DataFrameEncoder
data = DataFrameEncoder().fit_transormer(data)

并且所有对象类型数据被删除、编码、首先删除并连接在一起以提供最终所需的输出。
PS：这个模板的输入文件是 Pandas Dataframe。