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)
