groupby and map values

import pandas as pd
import numpy as np

# some data
data3 = [["Alex","Tampa","A23","1","Ax","Red"],
         ["Alex","Tampa","A23","1","Ay","Blue"],
         ["Alex","Tampa","B43","1","Bx","Green"],
         ["Alex","Tampa","B43","1","By","White"],
         ["Alex","Tampa","C55","1","Cx","Red"],
         ["Alex","Tampa","C55","1","Cy","White"],
         ["Alex","Tampa","C55","2","Cx","Purple"],
         ["Alex","Tampa","C55","2","Cy","Black"],
         ["Tim","San Diego","A23","1","Ax","Green"],
         ["Tim","San Diego","A23","1","Ay","Black"],
         ["Tim","San Diego","B43","1","Bx","Yellow"],
         ["Tim","San Diego","B43","1","By","Black"],
         ["Tim","San Diego","C55","1","Cx","Pink"],
         ["Tim","San Diego","C55","1","Cy","Orange"],
         ["Tim","San Diego","A23","2","Ax","Green"],
         ["Tim","San Diego","A23","2","Ay","Red"],
         ["Tim","San Diego","B43","2","Bx",""],
         ["Tim","San Diego","B43","2","By",""],
         ["Mark","Houston","A23","1","Ax","Purple"],
         ["Mark","Houston","A23","1","Ay","Yellow"],
         ["Mark","Houston","B43","1","Bx","Gray"],
         ["Mark","Houston","B43","1","By","White"],
         ["Mark","Houston","C55","1","Cx",""],
         ["Mark","Houston","C55","1","Cy",""],
         ["Anthony","Seattle","A23","","Ax","Orange"],
         ["Anthony","Seattle","A23","","Ay","Black"],
         ["Anthony","Seattle","B43","","Bx","Red"],
         ["Anthony","Seattle","B43","","By","Black"],
         ["Anthony","Seattle","C55","","Cx","Blue"],
         ["Anthony","Seattle","C55","","Cy","Pink"]]

# create dataframe
df3 = pd.DataFrame(data3,columns=[
        "Name","City","Domain","Sequence","Group","Value"])

# add Compared and Type columns
df3["Compared Group"] = ""
df3["Compared Value"] = ""
df3["Type"] = ""

# replace nulls with np.NaN
df3.replace(r"^s*$", np.nan, regex=True, inplace = True)

# fillna for missing Sequence
df3.fillna({"Sequence":"N/A","Value":"NULL"},inplace=True)

# map groups with dictionary
group_dict = {"Ax":"Ay","Bx":"By","Cx":"Cy"}

# groupby
grouped = df3.groupby(["Name","Sequence","Domain","Group"], group_keys=False)

# print groups
for group_name, df_group in grouped:
    print(group_name)


# expected result
result = [["Alex","Tampa","A23","1","Ax","Red","Ay","Blue"],
          ["Alex","Tampa","B43","1","Bx","Green","By","White"],
          ["Alex","Tampa","C55","1","Cx","Red","Cy","White"],
          ["Alex","Tampa","C55","2","Cx","Purple","Cy","Black"],
         ["Tim","San Diego","A23","1","Ax","Green","Ay","Black"],
         ["Tim","San Diego","A23","2","Ax","Green","Ay","Red"],
         ["Tim","San Diego","B43","1","Bx","Yellow","By","Black"],
         ["Tim","San Diego","B43","2","Bx","NULL","By","NULL"],
         ["Tim","San Diego","C55","1","Cx","Pink","Cy","Orange"],
         ["Mark","Houston","A23","1","Ax","Purple","Ay","Yellow"],
         ["Mark","Houston","B43","1","Bx","Gray","By","White"],
         ["Mark","Houston","C55","1","Cx","NULL","Cy","NULL"],
         ["Anthony","Seattle","A23","","Ax","Orange","Ay","Black"],
         ["Anthony","Seattle","B43","","Bx","Red","By","Black"],
         ["Anthony","Seattle","C55","","Cx","Blue","Cy","Pink"]]

result_df = pd.DataFrame(result,columns=[
        "Name","City","Domain","Sequence","Group",
        "Value","Compared Group","Compared Value"])


df3.to_csv("df3.csv", index = False)
result_df.to_csv("result_df.csv", index = False)