I want to check Excel data by numbers or non-numeric ones.

If the specified column is recognized as a numeric item, isdigit() should get an error like this=>AttributeError: 'int' object has no attribute'

その If such an error occurs, it would be better to specify it in the question

You may want to use .astype(str) to treat such data as strings

>>import pandas as pd
>>>df=pd.DataFrame({'A': [100,200], 'B': [110,120], 'C': [210,300]})
>>df
     ABC Corporation
0  100  110  210
1  200  120  300
>>df.dtypes
Aint64
Bint64
Cint64
dtype:object
>>>df['C'].astype(str)
0    210
1    300
Name:C, dtype:object
>> [v.isdigit() for vindf ['C'].astype(str)]
[True, True]

If you do not need to use pandas, you can use openpyxl to load Excel to get the cell type.

Below is a sample code that retrieves the cell type with the data_type property of the cell.(pip install openpyxl)

sample code

from openpyxl import load_workbook
wb=load_workbook("test.xlsx")
ws = wb ["Sheet1" ]
column=ws ["A"]
for row in range (1,len(column)):
    cell=column [row]
    ts = "Number" if cell.data_type == "n" else "Non-numeric" 
    print(f"{cell.value} is {ts}")

"Because the conditions for judging as ""numeric"" are ambiguous, we decided whether it was an integer value or not."
Also, I don't know if I want to judge input values such as '0003 as strings or numbers, so I decided to judge them as numbers this time.

 [Enter the following data in column 'A' of 'Sheet1']
1
'0002.2
'0003
'a'
'0004
'0005

import pandas as pd
import pathlib

file_path=pathlib.Path(__file__).parent/'test.xlsx'

df=pd.read_excel(file_path, sheet_name=0, header=None)
for df [0]:
    isDigit=False
    if isinstance(d,str):
        if d.isdigit():
            isDigit = True
    elif isinstance(d, int):
        isDigit = True

    ifisDigit:
        print(repr(d), 'Integer value.')
    else:
        print(repr(d), 'Non-integer value.')

1 integer value.
Other than the '0002.2' integer value.
Integer value '0003'.
Other than the 'a' integer value.
Integer value '0005'.

You will need to prepare and process various things in advance.

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For example, it's going to be one of these things.
Read all data as a string:

import pandas as pd
df=pd.read_excel("test.xlsx", sheet_name="sheet1", header=None, names=['column', dtype=str)
for df_chindf ['column']:
   if df_ch.isdigit():
      print ("Number")
   else:
      print ("Non-numeric")

Convert only that column to a string when checking:

import pandas as pd
df=pd.read_excel("test.xlsx", sheet_name="sheet1", header=None, names=['column'])
for df_chimpd.Series(df['column', dtype='string'):
   if df_ch.isdigit():
      print ("Number")
   else:
      print ("Non-numeric")

add

I tried my first answer and got an error because there was a blank somewhere.
With this article in mind, you can add keep_default_na=False to the read_excel() parameter, or you can use df=df.filna(') to process the read df.
Python Pandas read_excel dtype str replace nan by blank(') when reading or when writing via to_csv
pandas.read_excel
pandas.DataFrame.filna

It is recommended to add keep_default_na=False to the parameters.
If df=df.filna(') is processed, the entire column is treated as a floating-point number when read_excel() has at least one data that can be considered a floating-point number instead of NaN.