pandas groupby and compare dates

import pandas as pd
import numpy as np
import os

data = [[100,'A','1','','','8/1/2015','CA2.2','','','','','','','',''],
        [100,'A','2','','','7/1/2019','CA2.0','8/21/2019','','','','','','',''],
        [100,'B','1','','','','','','CA2.2','6/15/2018','6/15/2016','8/1/2019','','',''],
        [100,'B','2','','','','','','CA2.0','12/15/2018','12/15/2016','','','',''],
        [100,'B','3','','','','','','CA2.0','12/15/2018','12/15/2016','8/21/2019','','',''],
        [100,'C','1','','','','','','','','','','6/15/2016','CA2.2',''],
        [100,'C','2','','','','','','','','','','12/15/2017','CA2.0','8/21/2019'],
        [100,'Rec','','6/12/2019','8/1/2019','','','','','','','','','',''],
        [200,'A','1','','','8/1/2015','CA2.2','','','','','','','',''],
        [200,'A','2','','','7/1/2015','CA2.0','8/21/2019','','','','','','',''],
        [200,'B','1','','','','','','CA2.2','6/15/2018','6/15/2016','8/1/2019','','',''],
        [200,'B','2','','','','','','CA2.0','12/15/2018','12/15/2016','','','',''],
        [200,'B','3','','','','','','CA2.0','12/15/2018','12/15/2016','8/21/2019','','',''],
        [200,'C','1','','','','','','','','','','6/15/2016','CA2.2',''],
        [200,'C','2','','','','','','','','','','12/15/2017','CA2.0','8/21/2019'],
        [200,'Rec','','6/12/2019','8/1/2019','','','','','','','','','',''],]

df = pd.DataFrame(data,columns=['Cust_ID','Dep','Order_Num','Rec_Date1',
                                'Rec_DateX','A_Date1','A_Loc1','A_DateX',
                                'B_Loc1','B_Date1','B_Date2','B_DateX',
                                'C_Date1','C_Loc1','C_DateX'])

# replace blanks with np.NaN
df.replace(r"^s*$", np.nan, regex=True, inplace = True)

# Add loc column and populate
df.insert(loc=3, column="Site", value=np.nan)
df.insert(loc=0, column="Result", value=np.nan)

df.loc[df["A_Loc1"].notna(),
       "Site"] = df["A_Loc1"]

df.loc[df["B_Loc1"].notna(),
       "Site"] = df["B_Loc1"]

df.loc[df["C_Loc1"].notna(),
       "Site"] = df["C_Loc1"

df.to_csv("test_df.csv", index = False)