check values across dataframes

import pandas as pd
import numpy as np

# create data mapping fields to code names
map_data = [['Gender','gender_codes'],['Race','race_codes'],
         ['Ethnicity','ethnicity_codes']]
# create dataframe
map_df = pd.DataFrame(map_data,columns=['Field Name','Code Name'])

# create data of codes
codes_data = {'gender_codes':['1','2',np.nan,np.nan,np.nan,np.nan,np.nan],
              'race_codes':['1','2','3','4',np.nan,np.nan,np.nan],
              'ethnicity_codes':['1','2','3','4','5','6','7']}
# create dataframe
codes_df = pd.DataFrame(codes_data)


# create data of data to check
data = [['Alex','99','1',np.nan],
        ['Cindy',np.nan,'4','5'],
        ['Tom','1','99','1']]
# create dataframe
data_df = pd.DataFrame(data,columns=['Name','Gender','Race','Ethnicity'])

# code column to list
gender_codes = codes_df["gender_codes"].tolist()

# remove nan string
gender_codes = [gender_codes
               for gender_codes in gender_codes
               if  str(gender_codes) != "nan"]

# check each value against code list
result_df = data_df.loc[(~data_df.Gender.isin(gender_codes))]
# filter dataframe
result_df = result_df.filter(items = ["Name","Gender"])
# rename column
result_df.rename(columns = {"Gender":"Value"}, inplace = True)
# add new column
result_df['Column'] = 'Gender'