satisfaction_level last_evaluation number_project average_montly_hours time_spend_company Work_accident left promotion_last_5years dept salary
0.38 0.53 2 157 3 0 1 0 TECHNICAL low
0.8 0.86 5 262 6 0 1 0 HR medium
0.11 0.88 7 272 4 0 1 0 HR medium
0.72 0.87 5 223 5 0 1 0 FINANCE low
0.37 0.52 2 159 3 0 1 0 MARKETING low
0.41 0.5 2 153 3 0 1 0 TECHNICAL low
0.1 0.77 6 247 4 0 1 0 HR low
0.92 0.85 5 259 5 0 1 0 FINANCE low
0.89 1 5 224 5 0 1 0 HR low
我使用了上面的数据，并尝试使用 DictVectorizer 转换 C。代码如下
import pandas as pd
from sklearn.feature_extraction import DictVectorizer
dv=DictVectorizer()
hr_data=pd.read_csv(r"C:\Users\IBM_ADMIN\Desktop\data\HR_comma_sep.csv")
dv.fit_transform(X=hr_data.dept)
但是它抛出了错误:
'str' object has no attribute 'items'
请您参考如下方法:
使用LabelEncoder :
In [304]: df.dept
Out[304]:
0 TECHNICAL
1 HR
2 HR
3 FINANCE
4 MARKETING
5 TECHNICAL
6 HR
7 FINANCE
8 HR
Name: dept, dtype: object
In [305]: from sklearn.preprocessing import LabelEncoder
In [306]: le = LabelEncoder()
In [307]: df['dept'] = le.fit_transform(df['dept'])
In [308]: df.dept
Out[308]:
0 3
1 1
2 1
3 0
4 2
5 3
6 1
7 0
8 1
Name: dept, dtype: int64
In [309]: le.classes_
Out[309]: array(['FINANCE', 'HR', 'MARKETING', 'TECHNICAL'], dtype=object)
