这是包含3列和3行的数据集
名称组织部门
Manie ABC2财务
乔伊斯ABC1 HR
AMI NSV2 HR
这是我的代码:
现在一切都好了,我该如何删除每个虚拟变量列的第一个虚拟变量列呢?
# 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()
最佳答案
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
使用以下命令更新有关错误的信息:
根据documentation page,
pd.get_dummies(X, columns =[1:]
参数采用“列名”。因此,以下代码可以工作: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
如果确实要按位置定义列,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)