How to differentiate between datatypes of columns of dataframe and validate them?

Here in for loop I have tried to get col 1 by 1 and

• if its data type is integer - passed to validate the data by calling is_Datanumeric function

• else any else data type(string) - passed to valdate the data by calling isDataaplha function

  def is_Data_alpha(ch):
      if ch.rlike(obj['char_exp']):
          return 0
      else:
          return obj['in_valid']
  
  
  def is_Data_numeric(ch):
      if ch.isNotNull():
          return 0
      else:
          return obj['in_valid']
  
  
  for x in df1.columns:
      if dict(df1.dtypes)[x] == 'int':
          re = is_Data_numeric(x)
          print(re)
      else:
          re = is_Data_alpha(x)
          print(re)
  

But got an error :

Traceback (most recent call last):
  File "/home/zmo-ubt-preetim-01/PycharmProjects/pythonProject/script1.py", line 41, in <module>
    re = is_Data_numeric(x)
  File "/home/zmo-ubt-preetim-01/PycharmProjects/pythonProject/script1.py", line 32, in is_Data_numeric
    if ch.isNotNull():
AttributeError: 'str' object has no attribute 'isNotNull'
sys:1: ResourceWarning: unclosed file <_io.TextIOWrapper name='inputVariables.json' mode='r' encoding='UTF-8'>

I want to validate data by their data types.