is there a way to delete or replace any row on my data that it’s type is ‘datetime.datetime’

I have 1200000 rows x 96 columns dataframe, they are numbers, except for a few of them whose types are date and time.

The Question is:

I’d like to remove any row whose type is datetime.datetime and convert the rest to float if they are number but their type is string

Answer

This should get you the results you requested

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'Column1' : [123213123, '2022-01-01', '0111'],
    'Column2' : ['2022-01-01', 111, '21398021']
})

for x in range(0, len(df.columns)):
    df[df.columns[x]] = df[df.columns[x]].astype(str)
    df[df.columns[x]] = np.where(df[df.columns[x]].str.contains('-'), np.nan, df[df.columns[x]])
    df[df.columns[x]] = df[df.columns[x]].astype(float)
    
df

If you are wanting to delete any row who had a date string in that row you can simply replace the last line of the previous code with df = df.dropna() and that will remove all rows that contain an np.nan from before