输出结果

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 768 entries, 0 to 767

Data columns (total 9 columns):

#   Column                    Non-Null Count  Dtype  

---  ------                    --------------  -----  

0   Pregnancies               768 non-null    int64  

1   Glucose                   768 non-null    int64  

2   BloodPressure             768 non-null    int64  

3   SkinThickness             768 non-null    int64  

4   Insulin                   768 non-null    int64  

5   BMI                       768 non-null    float64

6   DiabetesPedigreeFunction  768 non-null    float64

7   Age                       768 non-null    int64  

8   Outcome                   768 non-null    int64  

dtypes: float64(2), int64(7)

memory usage: 54.1 KB

None

dict_keys(['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__annotations__', '__builtins__', '__file__', '__cached__', 'plt', 'pd', 'data_frame', 'col_label', 'cols_other', 'data_X', 'data_y_label_μ'])

data_X

data_y_label_μ

data_dall02

data_dall02 not in locals().keys()!

实现代码

# Python编程语言学习：利用locals函数判断某个变量参数之前是否已经被定义/存在/出现

import pandas as pd

data_frame=pd.read_csv('data_csv_xls\diabetes\diabetes.csv')

col_label='Outcome'

cols_other=['Pregnancies','Glucose','BloodPressure','SkinThickness','BMI']

data_X=data_frame[cols_other]

data_y_label_μ=data_frame[col_label]

# 判断某个参数之前是否已经被定义/存在/出现

print(locals().keys())

param_lists=['data_X','data_y_label_μ','data_dall02']

for param in param_lists:

   print(param)

   if param not in locals().keys():

       print('%s not in locals().keys()!'%param)