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

目录

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

输出结果

实现代码

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

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)
  
 

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/115333677