1. <class 'pandas.core.frame.DataFrame'>
2. RangeIndex: 768 entries, 0 to 767
3. Data columns (total 9 columns):
4. #   Column                    Non-Null Count  Dtype  
5. ---  ------                    --------------  -----  
6. 0   Pregnancies               768 non-null    int64  
7. 1   Glucose                   768 non-null    int64  
8. 2   BloodPressure             768 non-null    int64  
9. 3   SkinThickness             768 non-null    int64  
10. 4   Insulin                   768 non-null    int64  
11. 5   BMI                       768 non-null    float64
12. 6   DiabetesPedigreeFunction  768 non-null    float64
13. 7   Age                       768 non-null    int64  
14. 8   Outcome                   768 non-null    int64  
15. dtypes: float64(2), int64(7)
16. memory usage: 54.1 KB
17. None
18.    Pregnancies  Glucose  BloodPressure  SkinThickness   BMI  Outcome
19. 0            6      148             72             35  33.6        1
20. 1            1       85             66             29  26.6        0
21. 2            8      183             64              0  23.3        1
22. 3            1       89             66             23  28.1        0
23. 4            0      137             40             35  43.1        1

1. # ML之DS：特征工程中的特征拼接处理(常用于横向拼接自变量特征和因变量特征)
2. import pandas as pd
3. 
4. data_frame=pd.read_csv('data_csv_xls\diabetes\diabetes.csv')
5. print(data_frame.info())
6. 
7. col_label='Outcome'
8. cols_other=['Pregnancies','Glucose','BloodPressure','SkinThickness','BMI']
9. data_X=data_frame[cols_other]
10. data_y_label_μ=data_frame[col_label]
11. data_dall = pd.concat([data_X, data_y_label_μ], axis=1)
12. print(data_dall.head())