
# Import Dependencies
import pandas as pd
import os
import psycopg2
from sqlalchemy import create_engine
from getpass import getpass


# Folder Path
#r"C:\Users\benmo\OneDrive\Desktop\Capstone Project\ML_Data_Preprocessing\Resources\Resources_ML\names"
path = (r"C:\Users\benmo\OneDrive\Desktop\Data_Capstone_Ontario_Sunshine_List_Ben_Mogil\Resources\Resources_ML\names")
 
# Change the directory
os.chdir(path)

names_list = []

for file in os.listdir():
    file_path = rf"{path}\{file}"
    year = file[3:7]
    df_temp = pd.read_csv(file_path, names=["first_name", "gender", "frequency"])
    df_temp["year"] = year
    names_list.append(df_temp)


# Testing output
names_list[0]


# Pre-testing for next step
names_list[0].append(names_list[1]).reset_index()


# Import dependency to help combine all 'names_list' items--each US Social Security Names File DF--into one large DF
from functools import reduce

# Use dependency + lambda function to execute individual DF consolidation 
total_us_ss_df = reduce(lambda x, y: x.append(y), names_list).reset_index(drop=True)

# Display output (which is one large dataframe that has combined all of the DFs in 'names_list')
total_us_ss_df


# Grouping the above DF by first name and gender and summing the frequency 
# Purpose of grouping by first name is to have a DF consisting of only the unique names across all of the DFs (across all of the US Social Security Data's annual name files)
# Purpose of grouping by gender is to ensure that any multi-gender first name does not have its M/F frequencies combined into one frequency, but rather have one instance for each
# Purpose of summing frequency is to tally each unique name-gender combination across all of the years (1880-2020)
# *NOTE: The original US Social Security name files contain a name, associated gender and the frequency or how many people had that name and gender in that given year. Moreover, every year the frequency for each name is adjusted for births and deaths of people with that name. Further, each year, names are added and eliminated from the list based on succeeding / failing to meet the required minimum name-gender frequency of 5. Therefore, there is certainly overlap in the frequency if you consider individual people, but that is not relevant for our purposes. 
sum_frequencies_df = total_us_ss_df.groupby(["first_name", "gender"])[["frequency"]].sum().reset_index()

# Displaying output
sum_frequencies_df.head(10)


# Using inverted loc on above DF's 'first_name' column to find all of the names that do not have a duplicate 
# Purpose of this is to find all of the names that are not multi-gender and separate them from the names that are historically multi-gender--so if at any point in the US from 1880-2020 there were 5 or more instances of a first name with a Male associated gender and five or more instances of the same name with a Female associated gender they will not be included in this extraction--
not_duplicated = sum_frequencies_df.loc[~sum_frequencies_df.duplicated(subset=["first_name"], keep=False)]

# Pivoting the above DF so that the binary values within the 'gender' column become two distinct columns of their own--rather than one gender column-- and their associated values will be the frequency of each gender for each name since the 'first_name' column is set to the index
# Even though the names in the DF created above are not duplicated--meaning they will only have a value for one of the 'M' or 'F' columns this is done for later purposes when this DF will be combined with another one
not_duplicated_pivoted = not_duplicated.pivot_table(values="frequency", index="first_name", columns="gender").fillna(0)

# Removing original gender column
not_duplicated_pivoted.columns.name=None

# Resetting index
not_duplicated_final = not_duplicated_pivoted.reset_index()

# Displaying output
not_duplicated_final


# Same thing as above except with the opposite data--all multi-gender first names
duplicated = sum_frequencies_df.loc[sum_frequencies_df.duplicated(subset=["first_name"], keep=False)]
duplicated_pivoted = duplicated.pivot_table(values="frequency", index="first_name", columns="gender")
duplicated_pivoted.columns.name=None
duplicated_final = duplicated_pivoted.reset_index()


# Combining the two DFs created in the two cells above and sorting them by alphabetically by first name
final_best_df = not_duplicated_final.append(duplicated_final).sort_values(by="first_name").fillna(0)

# Changing the 'F' and 'M' frequency columns in the above DF to be an integer datatype rather than a float to get rid of the decimal for each value
final_best_df = final_best_df.astype({"F": int, "M": int})

# Displaying output
final_best_df.head(10)


# db_password = 'db_password'
db_password = getpass ('Enter in Password')

Enter in Password········


#Define the database credentials
credentials = {
'host':'localhost',
'database':'Final_Project_SunshineList',
'user':'postgres',
'password' : db_password
}


def connect(credentials):
    """ Connect to the PostgreSQL database server """
    conn = None
    try:
        # connect to the PostgreSQL server
        print('Connecting to the PostgreSQL database...')
        conn = psycopg2.connect(**credentials)
    except (Exception, psycopg2.DatabaseError) as error:
        print(error)
        sys.exit(1)
    print("Connection successful")
    return conn


def postgresql_to_dataframe(conn, select_query, column_names):
    """
    Tranform a SELECT query into a pandas dataframe
    """
    cursor = conn.cursor()
    try:
        cursor.execute(select_query)
    except (Exception, psycopg2.DatabaseError) as error:
        print("Error: %s" % error)
        cursor.close()
        return 1
    # Naturally we get a list of tupples
    tupples = cursor.fetchall()
    cursor.close()
    # We just need to turn it into a pandas dataframe
    df = pd.DataFrame(tupples, columns=column_names)
    return df


# Connect to Database
conn=connect(credentials)
column_names=['first_name',
              'gender']

# Execute the Select * query
test_names_df=postgresql_to_dataframe(conn,"select * from ml_first_names", column_names)
test_names_df.head()

Connecting to the PostgreSQL database...
Connection successful


# test_names_df = pd.read_csv("../SunShine_FirstNames.csv")
# test_names_df


# Defining 'generate_preds' function and 'df_train' and 'df_test' as parameters to function
# *NOTE: 'df_test' is the input DF given by function user--the function will then use preprocessed data to output a gender prediction for each name wihtin 'test_df'--
# *NOTE: 'df_train' represents the data preprocessed in step 1 that will be used in below algorithm to calculate probability of input_df name being either male/female
def generate_preds(df_train, df_test):
    
    # Strips all values of input DF 'first_name' column to eliminate any formating errors that could cause function to output an error
    df_test.first_name = df_test.first_name.str.strip()
    # Sorts test values alphabetically by first name (not really needed I guess)
    df_test = df_test.sort_values(by='first_name')
    
    # Calculate probability of each name in preprocessed data being male by using unique name instances with respective male/female gender frequency from preprocessed data and insert a new column in final preprocessed DF (final_best_df) to store values
    df_train['pom'] = ((df_train.M)/(df_train.M + df_train.F))*100
     # Calculate probability of each name in preprocessed data being female by using unique name instances with respective male/female gender frequency from preprocessed data and insert a new column in final preprocessed DF (final_best_df) to store values
    df_train['pof'] = ((df_train.F)/(df_train.M + df_train.F))*100
    
    # Executing left join on 'df_test' with 'df_train' on 'df_train' 'first_name' column so that all of the (first name) data from 'df_test' is kept and only the gender probabilities (new columns being merged to 'df_test') that occur in both DFs are added to 'df_test' rather than also adding all of the names from 'df_train' that are not in 'df_test' and their respective probabilities because the function user only wants the function to output a prediction for the names they gave the function ('df_test') 
    df_merged = df_test.merge(df_train, on='first_name', how='left')
    
    # Instantiating empty list to store values output by below loop
    preds = []
    # Iterating through each row in the merged DF 
    # *NOTE: 'itertuples()' method is used for DF looping efficiency and since it provides ability to access each DF column using dot notation
    for t in df_merged.itertuples():
        # States that if merged DF 'pom'--probability of male-- column is above 50% and 'pof'--probability of female-- column is below 50% to add an 'M' to the 'preds' list because the model has dictated that the gender prediction for the respective name is male 
        if (t.pom > 50 and t.pof < 50):
            preds.append('M')
        # States that if merged DF 'pof'--probability of female-- column is above 50% and 'pom'--probability of male-- column is below 50% to add an 'F' to the 'preds' list because the model has dictated that the gender prediction for the respective name is female
        elif (t.pof > 50 and t.pom < 50):
            preds.append('F')
        # States that if the probability of a name being male/female is 50%-50% to output 'EV'--standing for even-- to the 'preds' list since the respective gender neutral name's gender cannot be predicted using this model due to insufficient data 
        elif (t.pom == 50 and t.pof == 50):
            preds.append('EV')
        # States that if none of the above can be executed to output 'U'--standing for unknown-- to the 'preds' list since the name cannot be predicted by the model due to there being no data on this name (name is not included in 'df_train')
        else:
            preds.append('U')
    # Creating a new column in the 'df_test' DF and assigning it to the 'preds' list which contains all of the gender predictions output by the model
    df_test['gender'] = preds 
    
    # Returns the same DF that the function-user input with another column that contains a gender prediction for each name from the input DF
    return df_test


# Run 'generate_preds' function with final preprocessed US Social Security names DF (final_best_df) and unique sunshine first name DF (test_names_df)
sunshine_first_pass = generate_preds(final_best_df, test_names_df)
sunshine_first_pass


# Display gender column value counts
sunshine_first_pass.value_counts("gender")

gender
U     14549
F      8058
M      6158
EV        9
dtype: int64


# Import dependencies 
import random
from nltk.corpus import names
import nltk
import sklearn
import pandas as pd
import numpy as np


# Creating new variable for convenience and assigning to a groupby of the 'total_us_ss_df' by first name and gender and summing on frequency to get a unique name-gender combination DF from 'total_us_ss_df' which contains all the US Social Security names data 
nltk_training_data = total_us_ss_df.groupby(["first_name", "gender"])[["frequency"]].sum().reset_index()
nltk_training_data


# Creating a new variable for the above DF but exlcuding frequency and converting values to list
labeled_names = nltk_training_data[["first_name", "gender"]].to_numpy().tolist()

# Creating a function that takes a string input and and returns a dictionary with a key of 'last three letters' and a value of the the last three letters of the input string
# *NOTE: This function and the list comp below were taken from the NLTK library's documentation explaining how to use the library
def gender_features(word):
    return {'last three letters' :word[-3:]}

# Using list comprehension and the function created above as the expression output to return a new list that includes the last three letters of each name and the gender from each item in 'labeled_names'
featuresets = [(gender_features(n), gender)
               for (n, gender)in labeled_names]

# Displaying output
featuresets

[({'last three letters': 'ban'}, 'M'),
 ({'last three letters': 'bha'}, 'F'),
 ({'last three letters': 'bid'}, 'M'),
 ({'last three letters': 'dah'}, 'F'),
 ({'last three letters': 'bir'}, 'M'),
 ({'last three letters': 'lla'}, 'F'),
 ({'last three letters': 'ada'}, 'F'),
 ({'last three letters': 'dam'}, 'M'),
 ({'last three letters': 'dan'}, 'M'),
 ({'last three letters': 'rsh'}, 'M'),
 ({'last three letters': 'dav'}, 'M'),
 ({'last three letters': 'aya'}, 'F'),
 ({'last three letters': 'den'}, 'F'),
 ({'last three letters': 'den'}, 'M'),
 ({'last three letters': 'esh'}, 'M'),
 ({'last three letters': 'han'}, 'M'),
 ({'last three letters': 'hav'}, 'M'),
 ({'last three letters': 'van'}, 'M'),
 ({'last three letters': 'dhi'}, 'M'),
 ({'last three letters': 'ira'}, 'F'),
 ({'last three letters': 'ran'}, 'M'),
 ({'last three letters': 'vik'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'hya'}, 'F'),
 ({'last three letters': 'yan'}, 'M'),
 ({'last three letters': 'ddy'}, 'F'),
 ({'last three letters': 'sri'}, 'F'),
 ({'last three letters': 'adi'}, 'F'),
 ({'last three letters': 'adi'}, 'M'),
 ({'last three letters': 'ian'}, 'M'),
 ({'last three letters': 'dil'}, 'M'),
 ({'last three letters': 'ynn'}, 'F'),
 ({'last three letters': 'din'}, 'M'),
 ({'last three letters': 'ish'}, 'M'),
 ({'last three letters': 'son'}, 'F'),
 ({'last three letters': 'dit'}, 'M'),
 ({'last three letters': 'ith'}, 'M'),
 ({'last three letters': 'hya'}, 'M'),
 ({'last three letters': 'tri'}, 'F'),
 ({'last three letters': 'tya'}, 'M'),
 ({'last three letters': 'div'}, 'M'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'don'}, 'M'),
 ({'last three letters': 'ian'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'rit'}, 'M'),
 ({'last three letters': 'iti'}, 'F'),
 ({'last three letters': 'dvi'}, 'F'),
 ({'last three letters': 'vik'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'dya'}, 'F'),
 ({'last three letters': 'ant'}, 'M'),
 ({'last three letters': 'dyn'}, 'F'),
 ({'last three letters': 'dyn'}, 'M'),
 ({'last three letters': 'fia'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'een'}, 'F'),
 ({'last three letters': 'gam'}, 'M'),
 ({'last three letters': 'age'}, 'M'),
 ({'last three letters': 'gna'}, 'F'),
 ({'last three letters': 'got'}, 'F'),
 ({'last three letters': 'aan'}, 'M'),
 ({'last three letters': 'han'}, 'M'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'eli'}, 'F'),
 ({'last three letters': 'hid'}, 'M'),
 ({'last three letters': 'hil'}, 'M'),
 ({'last three letters': 'hir'}, 'M'),
 ({'last three letters': 'ani'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'hna'}, 'F'),
 ({'last three letters': 'ron'}, 'M'),
 ({'last three letters': 'dan'}, 'M'),
 ({'last three letters': 'den'}, 'M'),
 ({'last three letters': 'dyn'}, 'M'),
 ({'last three letters': 'ila'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ima'}, 'F'),
 ({'last three letters': 'ira'}, 'F'),
 ({'last three letters': 'rah'}, 'F'),
 ({'last three letters': 'sha'}, 'F'),
 ({'last three letters': 'hah'}, 'F'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'iza'}, 'F'),
 ({'last three letters': 'aja'}, 'F'),
 ({'last three letters': 'jah'}, 'F'),
 ({'last three letters': 'lah'}, 'F'),
 ({'last three letters': 'jon'}, 'M'),
 ({'last three letters': 'sha'}, 'F'),
 ({'last three letters': 'rsh'}, 'M'),
 ({'last three letters': 'ash'}, 'M'),
 ({'last three letters': 'eem'}, 'M'),
 ({'last three letters': 'kif'}, 'M'),
 ({'last three letters': 'kil'}, 'M'),
 ({'last three letters': 'lah'}, 'F'),
 ({'last three letters': 'ira'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'iti'}, 'F'),
 ({'last three letters': 'ala'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'lam'}, 'M'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'nah'}, 'F'),
 ({'last three letters': 'ani'}, 'F'),
 ({'last three letters': 'lap'}, 'M'),
 ({'last three letters': 'sia'}, 'F'),
 ({'last three letters': 'aya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'jah'}, 'F'),
 ({'last three letters': 'yna'}, 'F'),
 ({'last three letters': 'sha'}, 'F'),
 ({'last three letters': 'sia'}, 'F'),
 ({'last three letters': 'lea'}, 'F'),
 ({'last three letters': 'eah'}, 'F'),
 ({'last three letters': 'hya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ena'}, 'F'),
 ({'last three letters': 'eya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'eia'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'gha'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ena'}, 'F'),
 ({'last three letters': 'xis'}, 'F'),
 ({'last three letters': 'xus'}, 'F'),
 ({'last three letters': 'eya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ali'}, 'M'),
 ({'last three letters': 'lia'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'ias'}, 'M'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ayh'}, 'F'),
 ({'last three letters': 'cia'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'iha'}, 'F'),
 ({'last three letters': 'jah'}, 'F'),
 ({'last three letters': 'jah'}, 'M'),
 ({'last three letters': 'lah'}, 'F'),
 ({'last three letters': 'lim'}, 'M'),
 ({'last three letters': 'mah'}, 'F'),
 ({'last three letters': 'lin'}, 'F'),
 ({'last three letters': 'ina'}, 'F'),
 ({'last three letters': 'nah'}, 'F'),
 ({'last three letters': 'isa'}, 'F'),
 ({'last three letters': 'sha'}, 'F'),
 ({'last three letters': 'via'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yaa'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'yah'}, 'M'),
 ({'last three letters': 'aha'}, 'F'),
 ({'last three letters': 'rie'}, 'F'),
 ({'last three letters': 'ose'}, 'F'),
 ({'last three letters': 'yan'}, 'F'),
 ({'last three letters': 'yan'}, 'M'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'nna'}, 'F'),
 ({'last three letters': 'yha'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'lok'}, 'M'),
 ({'last three letters': 'lon'}, 'M'),
 ({'last three letters': 'oni'}, 'F'),
 ({'last three letters': 'lya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'cia'}, 'F'),
 ({'last three letters': 'yha'}, 'F'),
 ({'last three letters': 'yia'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'yna'}, 'F'),
 ({'last three letters': 'sia'}, 'F'),
 ({'last three letters': 'ssa'}, 'F'),
 ({'last three letters': 'via'}, 'F'),
 ({'last three letters': 'nda'}, 'F'),
 ({'last three letters': 'nee'}, 'F'),
 ({'last three letters': 'ani'}, 'F'),
 ({'last three letters': 'mar'}, 'M'),
 ({'last three letters': 'ara'}, 'F'),
 ({'last three letters': 'ari'}, 'F'),
 ({'last three letters': 'ari'}, 'M'),
 ({'last three letters': 'ion'}, 'M'),
 ({'last three letters': 'aya'}, 'F'),
 ({'last three letters': 'ber'}, 'F'),
 ({'last three letters': 'ena'}, 'F'),
 ({'last three letters': 'mer'}, 'M'),
 ({'last three letters': 'mia'}, 'F'),
 ({'last three letters': 'mil'}, 'M'),
 ({'last three letters': 'lah'}, 'F'),
 ({'last three letters': 'ina'}, 'F'),
 ({'last three letters': 'nah'}, 'F'),
 ({'last three letters': 'mir'}, 'F'),
 ({'last three letters': 'mir'}, 'M'),
 ({'last three letters': 'ira'}, 'F'),
 ({'last three letters': 'rah'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'mna'}, 'F'),
 ({'last three letters': 'oni'}, 'F'),
 ({'last three letters': 'ora'}, 'F'),
 ({'last three letters': 'rah'}, 'F'),
 ({'last three letters': 'ori'}, 'F'),
 ({'last three letters': 'mya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'and'}, 'M'),
 ({'last three letters': 'nav'}, 'M'),
 ({'last three letters': 'nay'}, 'M'),
 ({'last three letters': 'aya'}, 'F'),
 ({'last three letters': 'hal'}, 'F'),
 ({'last three letters': 'nia'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'jah'}, 'F'),
 ({'last three letters': 'nik'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'sah'}, 'F'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'lah'}, 'F'),
 ({'last three letters': 'nsh'}, 'M'),
 ({'last three letters': 'shi'}, 'F'),
 ({'last three letters': 'nvi'}, 'F'),
 ({'last three letters': 'nya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'yla'}, 'F'),
 ({'last three letters': 'pri'}, 'F'),
 ({'last three letters': 'qib'}, 'M'),
 ({'last three letters': 'qil'}, 'M'),
 ({'last three letters': 'ara'}, 'F'),
 ({'last three letters': 'lla'}, 'F'),
 ({'last three letters': 'bhi'}, 'F'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'dhy'}, 'M'),
 ({'last three letters': 'hya'}, 'F'),
 ({'last three letters': 'raf'}, 'M'),
 ({'last three letters': 'aiz'}, 'M'),
 ({'last three letters': 'ral'}, 'F'),
 ({'last three letters': 'lyn'}, 'F'),
 ({'last three letters': 'ynn'}, 'F'),
 ({'last three letters': 'ran'}, 'M'),
 ({'last three letters': 'nya'}, 'F'),
 ({'last three letters': 'aon'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'rav'}, 'M'),
 ({'last three letters': 'avi'}, 'F'),
 ({'last three letters': 'ddy'}, 'M'),
 ({'last three letters': 'aya'}, 'F'),
 ({'last three letters': 'ren'}, 'F'),
 ({'last three letters': 'ren'}, 'M'),
 ({'last three letters': 'eon'}, 'F'),
 ({'last three letters': 'eon'}, 'M'),
 ({'last three letters': 'ona'}, 'F'),
 ({'last three letters': 'ari'}, 'F'),
 ({'last three letters': 'ari'}, 'M'),
 ({'last three letters': 'ria'}, 'F'),
 ({'last three letters': 'iah'}, 'F'),
 ({'last three letters': 'ian'}, 'F'),
 ({'last three letters': 'ian'}, 'M'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'nna'}, 'F'),
 ({'last three letters': 'rib'}, 'M'),
 ({'last three letters': 'ric'}, 'M'),
 ({'last three letters': 'ica'}, 'F'),
 ({'last three letters': 'ick'}, 'M'),
 ({'last three letters': 'rie'}, 'F'),
 ({'last three letters': 'iel'}, 'F'),
 ({'last three letters': 'lla'}, 'F'),
 ({'last three letters': 'lle'}, 'F'),
 ({'last three letters': 'ien'}, 'M'),
 ({'last three letters': 'rik'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'eth'}, 'M'),
 ({'last three letters': 'lyn'}, 'F'),
 ({'last three letters': 'ynn'}, 'F'),
 ({'last three letters': 'rin'}, 'F'),
 ({'last three letters': 'rin'}, 'M'),
 ({'last three letters': 'ini'}, 'F'),
 ({'last three letters': 'ion'}, 'F'),
 ({'last three letters': 'ion'}, 'M'),
 ({'last three letters': 'ona'}, 'F'),
 ({'last three letters': 'nna'}, 'F'),
 ({'last three letters': 'ris'}, 'F'),
 ({'last three letters': 'ris'}, 'M'),
 ({'last three letters': 'ish'}, 'M'),
 ({'last three letters': 'rit'}, 'M'),
 ({'last three letters': 'riv'}, 'M'),
 ({'last three letters': 'iya'}, 'F'),
 ({'last three letters': 'yah'}, 'F'),
 ({'last three letters': 'yan'}, 'M'),
 ({'last three letters': 'ana'}, 'F'),
 ({'last three letters': 'nna'}, 'F'),
 ({'last three letters': 'ona'}, 'F'),
 ({'last three letters': 'nna'}, 'F'),
 ({'last three letters': 'riz'}, 'M'),
 ({'last three letters': 'jav'}, 'M'),
 ({'last three letters': 'arn'}, 'M'),
 ({'last three letters': 'rna'}, 'F'),
 ({'last three letters': 'nav'}, 'M'),
 ({'last three letters': 'avi'}, 'F'),
 ({'last three letters': 'rne'}, 'M'),
 ({'last three letters': 'nik'}, 'M'),
 ({'last three letters': 'ika'}, 'F'),
 ({'last three letters': 'aro'}, 'M'),
 ({'last three letters': 'roh'}, 'M'),
 ({'last three letters': 'han'}, 'M'),
 ({'last three letters': 'ohi'}, 'F'),
 ({'last three letters': 'lyn'}, 'F'),
 ({'last three letters': 'ron'}, 'F'),
 ({'last three letters': 'ron'}, 'M'),
 ({'last three letters': 'ona'}, 'F'),
 ({'last three letters': 'nae'}, 'F'),
 ({'last three letters': 'nda'}, 'F'),
 ({'last three letters': 'tte'}, 'F'),
 ({'last three letters': 'sha'}, 'F'),
 ({'last three letters': 'cob'}, 'M'),
 ({'last three letters': 'mes'}, 'M'),
 ({'last three letters': 'ohn'}, 'M'),
 ({'last three letters': 'eph'}, 'M'),
 ({'last three letters': 'osh'}, 'M'),
 ({'last three letters': 'hua'}, 'M'),
 ({'last three letters': 'lee'}, 'M'),
 ({'last three letters': 'ael'}, 'M'),
 ({'last three letters': 'onn'}, 'M'),
 ({'last three letters': 'osh'}, 'M'),
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 ({'last three letters': 'buk'}, 'F'),
 ({'last three letters': 'kar'}, 'M'),
 ({'last three letters': 'dio'}, 'M'),
 ({'last three letters': 'zar'}, 'M'),
 ({'last three letters': 'Aby'}, 'F'),
 ({'last three letters': 'Aby'}, 'M'),
 ({'last three letters': 'ade'}, 'F'),
 ({'last three letters': 'yan'}, 'F'),
 ({'last three letters': 'yan'}, 'M'),
 ({'last three letters': 'ael'}, 'F'),
 ({'last three letters': 'ail'}, 'F'),
 ({'last three letters': 'ile'}, 'F'),
 ({'last three letters': 'ale'}, 'F'),
 ({'last three letters': 'yle'}, 'F'),
 ({'last three letters': 'nia'}, 'F'),
 ({'last three letters': 'Ac'}, 'M'),
 ({'last three letters': 'cia'}, 'F'),
 ({'last three letters': 'ius'}, 'M'),
 ({'last three letters': 'dia'}, 'F'),
 ({'last three letters': 'cai'}, 'F'),
 ({'last three letters': 'cai'}, 'M'),
 ({'last three letters': 'mas'}, 'M'),
 ({'last three letters': 'ari'}, 'F'),
 ({'last three letters': 'ari'}, 'M'),
 ({'last three letters': 'sia'}, 'F'),
 ({'last three letters': 'cia'}, 'F'),
 ({'last three letters': 'lia'}, 'F'),
 ({'last three letters': 'ess'}, 'M'),
 ({'last three letters': 'cie'}, 'M'),
 ({'last three letters': 'sio'}, 'M'),
 ({'last three letters': 'Ace'}, 'F'),
 ({'last three letters': 'Ace'}, 'M'),
 ({'last three letters': 'cea'}, 'F'),
 ({'last three letters': 'cea'}, 'M'),
 ({'last three letters': 'cee'}, 'M'),
 ({'last three letters': 'ein'}, 'M'),
 ({'last three letters': 'ion'}, 'M'),
 ({'last three letters': 'cel'}, 'M'),
 ({'last three letters': 'lee'}, 'F'),
 ({'last three letters': 'lia'}, 'F'),
 ({'last three letters': 'lin'}, 'F'),
 ({'last three letters': 'lin'}, 'M'),
 ({'last three letters': 'ino'}, 'M'),
 ({'last three letters': 'lyn'}, 'F'),
 ({'last three letters': 'lyn'}, 'M'),
 ({'last three letters': 'ynn'}, 'F'),
 ({'last three letters': 'cen'}, 'M'),
 ({'last three letters': 'ion'}, 'M'),
 ({'last three letters': 'eon'}, 'M'),
 ({'last three letters': 'cer'}, 'M'),
 ({'last three letters': 'sen'}, 'M'),
 ({'last three letters': 'son'}, 'M'),
 ({'last three letters': 'ton'}, 'M'),
 ({'last three letters': 'syn'}, 'M'),
 ({'last three letters': 'ton'}, 'M'),
 ({'last three letters': 'tyn'}, 'M'),
 ({'last three letters': 'cey'}, 'F'),
 ({'last three letters': 'cey'}, 'M'),
 ({'last three letters': 'eyn'}, 'M'),
 ({'last three letters': 'yon'}, 'M'),
 ({'last three letters': 'hai'}, 'M'),
 ({'last three letters': 'aia'}, 'F'),
 ({'last three letters': 'ius'}, 'M'),
 ({'last three letters': 'han'}, 'F'),
 ({'last three letters': 'nte'}, 'F'),
 ({'last three letters': 'nti'}, 'F'),
 ({'last three letters': 'ary'}, 'M'),
 ({'last three letters': 'zia'}, 'F'),
 ({'last three letters': 'hel'}, 'F'),
 ({'last three letters': 'ron'}, 'M'),
 ({'last three letters': 'lle'}, 'M'),
 ({'last three letters': 'eas'}, 'M'),
 ({'last three letters': 'les'}, 'M'),
 ({'last three letters': 'eus'}, 'M'),
 ({'last three letters': 'ies'}, 'M'),
 ({'last three letters': 'him'}, 'M'),
 ({'last three letters': 'tya'}, 'M'),
 ({'last three letters': 'hol'}, 'F'),
 ({'last three letters': 'raf'}, 'M'),
 ({'last three letters': 'hsa'}, 'F'),
 ({'last three letters': 'sah'}, 'F'),
 ({'last three letters': 'yut'}, 'M'),
 ({'last three letters': 'uth'}, 'M'),
 ({'last three letters': 'cia'}, 'F'),
 ({'last three letters': 'ano'}, 'M'),
 ({'last three letters': 'cie'}, 'F'),
 ({'last three letters': 'cie'}, 'M'),
 ({'last three letters': 'iel'}, 'M'),
 ({'last three letters': 'cil'}, 'M'),
 ({'last three letters': 'ire'}, 'F'),
 ({'last three letters': 'ire'}, 'M'),
 ({'last three letters': 'eem'}, 'M'),
 ({'last three letters': 'ley'}, 'F'),
 ({'last three letters': 'ley'}, 'M'),
 ({'last three letters': 'cob'}, 'M'),
 ({'last three letters': 'tta'}, 'F'),
 ({'last three letters': 'tte'}, 'F'),
 ({'last three letters': 'cre'}, 'M'),
 ({'last three letters': 'csa'}, 'F'),
 ...]


# Instantiating empty list to store output from loop below
u_list = []

# Extracting all names that were given "U" (unknown) or "EV" (Even) gender prediction from first model and storing them in 'u_list' list object
for name in sunshine_first_pass.itertuples():
    if (name.gender == "U"):
        u_list.append(name)
    elif (name.gender =="EV"):
        u_list.append(name)

# Displaying Output
u_list

[Pandas(Index=0, first_name='Aaan', gender='U'),
 Pandas(Index=2, first_name='Aadu', gender='U'),
 Pandas(Index=3, first_name='Aaida', gender='U'),
 Pandas(Index=6, first_name='Aal', gender='U'),
 Pandas(Index=7, first_name='Aalaa', gender='U'),
 Pandas(Index=8, first_name='Aalla', gender='U'),
 Pandas(Index=9, first_name='Aaltje', gender='U'),
 Pandas(Index=13, first_name='Aan', gender='U'),
 Pandas(Index=15, first_name='Aane', gender='U'),
 Pandas(Index=16, first_name='Aanhoward', gender='U'),
 Pandas(Index=17, first_name='Aanna', gender='U'),
 Pandas(Index=18, first_name='Aanne', gender='U'),
 Pandas(Index=19, first_name='Aanno', gender='U'),
 Pandas(Index=20, first_name='Aano', gender='U'),
 Pandas(Index=21, first_name='Aanus', gender='U'),
 Pandas(Index=24, first_name='Aaranan', gender='U'),
 Pandas(Index=26, first_name='Aareni', gender='U'),
 Pandas(Index=35, first_name='Aasif', gender='U'),
 Pandas(Index=38, first_name='Aatesh', gender='U'),
 Pandas(Index=40, first_name='Aatika', gender='U'),
 Pandas(Index=41, first_name='Aatos', gender='U'),
 Pandas(Index=42, first_name='Aazar', gender='U'),
 Pandas(Index=44, first_name='Abana', gender='U'),
 Pandas(Index=48, first_name='Abbass', gender='U'),
 Pandas(Index=50, first_name='Abbeyjoyce', gender='U'),
 Pandas(Index=57, first_name='Abdelatif', gender='U'),
 Pandas(Index=59, first_name='Abdelbaset', gender='U'),
 Pandas(Index=60, first_name='Abdelfettah', gender='U'),
 Pandas(Index=62, first_name='Abdelhakim', gender='U'),
 Pandas(Index=63, first_name='Abdelhalim', gender='U'),
 Pandas(Index=65, first_name='Abdeljalil', gender='U'),
 Pandas(Index=68, first_name='Abdelkhalig', gender='U'),
 Pandas(Index=69, first_name='Abdelkrim', gender='U'),
 Pandas(Index=71, first_name='Abdellatif', gender='U'),
 Pandas(Index=72, first_name='Abdelmajid', gender='U'),
 Pandas(Index=73, first_name='Abdelmonem', gender='U'),
 Pandas(Index=74, first_name='Abdelouahid', gender='U'),
 Pandas(Index=76, first_name='Abdelsamie', gender='U'),
 Pandas(Index=77, first_name='Abdelwahab', gender='U'),
 Pandas(Index=78, first_name='Abdemusa', gender='U'),
 Pandas(Index=80, first_name='Abderrahmane', gender='U'),
 Pandas(Index=81, first_name='Abderrazak', gender='U'),
 Pandas(Index=83, first_name='Abdifitah', gender='U'),
 Pandas(Index=84, first_name='Abdigafar', gender='U'),
 Pandas(Index=87, first_name='Abdilhalim', gender='U'),
 Pandas(Index=88, first_name='Abdillahi', gender='U'),
 Pandas(Index=97, first_name='Abdol', gender='U'),
 Pandas(Index=98, first_name='Abdoladel', gender='U'),
 Pandas(Index=99, first_name='Abdolhossein', gender='U'),
 Pandas(Index=100, first_name='Abdollah', gender='U'),
 Pandas(Index=101, first_name='Abdolmajid', gender='U'),
 Pandas(Index=102, first_name='Abdolmanaf', gender='U'),
 Pandas(Index=103, first_name='Abdolrasool', gender='U'),
 Pandas(Index=104, first_name='Abdolreza', gender='U'),
 Pandas(Index=105, first_name='Abdol–Reza', gender='U'),
 Pandas(Index=106, first_name='Abdool', gender='U'),
 Pandas(Index=108, first_name='Abdoulka', gender='U'),
 Pandas(Index=109, first_name='Abdubois', gender='U'),
 Pandas(Index=116, first_name='Abdulfattah', gender='U'),
 Pandas(Index=121, first_name='Abdulhaq', gender='U'),
 Pandas(Index=122, first_name='Abdulkader', gender='U'),
 Pandas(Index=125, first_name='Abdulkerim', gender='U'),
 Pandas(Index=126, first_name='Abdulkhalik', gender='U'),
 Pandas(Index=131, first_name='Abdulle', gender='U'),
 Pandas(Index=132, first_name='Abdulmotaleb', gender='U'),
 Pandas(Index=135, first_name='Abdulrazzak', gender='U'),
 Pandas(Index=136, first_name='Abdulreza', gender='U'),
 Pandas(Index=137, first_name='Abdulrezak', gender='U'),
 Pandas(Index=139, first_name='Abdulwasie', gender='U'),
 Pandas(Index=140, first_name='Abdul–Fattah', gender='U'),
 Pandas(Index=142, first_name='Abdurraouf', gender='U'),
 Pandas(Index=143, first_name='Abdus', gender='U'),
 Pandas(Index=145, first_name='Abeba', gender='U'),
 Pandas(Index=146, first_name='Abebaye', gender='U'),
 Pandas(Index=147, first_name='Abebe', gender='U'),
 Pandas(Index=149, first_name='Abedakhatun', gender='U'),
 Pandas(Index=150, first_name='Abedeen', gender='U'),
 Pandas(Index=151, first_name='Abedia', gender='U'),
 Pandas(Index=155, first_name='Abeir', gender='U'),
 Pandas(Index=157, first_name='Abelardino', gender='U'),
 Pandas(Index=159, first_name='Abenna', gender='U'),
 Pandas(Index=160, first_name='Aberdine', gender='U'),
 Pandas(Index=161, first_name='Abere', gender='U'),
 Pandas(Index=162, first_name='Aberto', gender='U'),
 Pandas(Index=170, first_name='Abhilasha', gender='U'),
 Pandas(Index=173, first_name='Abhiramy', gender='U'),
 Pandas(Index=180, first_name='Abidemi', gender='U'),
 Pandas(Index=181, first_name='Abidin', gender='U'),
 Pandas(Index=182, first_name='Abieyuwa', gender='U'),
 Pandas(Index=185, first_name='Abigailvera', gender='U'),
 Pandas(Index=187, first_name='Abigayel', gender='U'),
 Pandas(Index=188, first_name='Abike', gender='U'),
 Pandas(Index=189, first_name='Abilashen', gender='U'),
 Pandas(Index=193, first_name='Abina', gender='U'),
 Pandas(Index=194, first_name='Abinash', gender='U'),
 Pandas(Index=195, first_name='Abinath', gender='U'),
 Pandas(Index=198, first_name='Abioye', gender='U'),
 Pandas(Index=201, first_name='Abiramy', gender='U'),
 Pandas(Index=203, first_name='Ablert', gender='U'),
 Pandas(Index=204, first_name='Abolaji', gender='U'),
 Pandas(Index=205, first_name='Aboobaker', gender='U'),
 Pandas(Index=207, first_name='Aboulfazl', gender='U'),
 Pandas(Index=208, first_name='Abouzar', gender='U'),
 Pandas(Index=212, first_name='Absera', gender='U'),
 Pandas(Index=218, first_name='Abuchi', gender='U'),
 Pandas(Index=220, first_name='Abuturab', gender='U'),
 Pandas(Index=221, first_name='Abuyusuf', gender='U'),
 Pandas(Index=222, first_name='Abuzafar', gender='U'),
 Pandas(Index=223, first_name='Acacio', gender='U'),
 Pandas(Index=225, first_name='Achal', gender='U'),
 Pandas(Index=228, first_name='Achuil', gender='U'),
 Pandas(Index=229, first_name='Achuthan', gender='U'),
 Pandas(Index=230, first_name='Ackert', gender='U'),
 Pandas(Index=231, first_name='Acrifa', gender='U'),
 Pandas(Index=232, first_name='Acrive', gender='U'),
 Pandas(Index=236, first_name='Adalaura', gender='U'),
 Pandas(Index=238, first_name='Adaleta', gender='U'),
 Pandas(Index=239, first_name='Adalgisa', gender='U'),
 Pandas(Index=240, first_name='Adalgisio', gender='U'),
 Pandas(Index=241, first_name='Adalsteinn', gender='U'),
 Pandas(Index=244, first_name='Adamadia', gender='U'),
 Pandas(Index=246, first_name='Adamira', gender='U'),
 Pandas(Index=249, first_name='Adampercival', gender='U'),
 Pandas(Index=251, first_name='Adanah', gender='U'),
 Pandas(Index=254, first_name='Adaoma', gender='U'),
 Pandas(Index=257, first_name='Addasonne', gender='U'),
 Pandas(Index=259, first_name='Addiegail', gender='U'),
 Pandas(Index=261, first_name='Addo', gender='U'),
 Pandas(Index=273, first_name='Adeela', gender='U'),
 Pandas(Index=276, first_name='Adeesha', gender='U'),
 Pandas(Index=277, first_name='Adefemi', gender='U'),
 Pandas(Index=278, first_name='Adefumilola', gender='U'),
 Pandas(Index=279, first_name='Adejoke', gender='U'),
 Pandas(Index=292, first_name='Adelio', gender='U'),
 Pandas(Index=298, first_name='Adeluta', gender='U'),
 Pandas(Index=305, first_name='Adenola', gender='U'),
 Pandas(Index=308, first_name='Aderito', gender='U'),
 Pandas(Index=310, first_name='Adesayo', gender='U'),
 Pandas(Index=314, first_name='Adetumbi', gender='U'),
 Pandas(Index=316, first_name='Adewumi', gender='U'),
 Pandas(Index=317, first_name='Adeye', gender='U'),
 Pandas(Index=319, first_name='Adeyinka', gender='EV'),
 Pandas(Index=328, first_name='Adino', gender='U'),
 Pandas(Index=332, first_name='Aditha', gender='U'),
 Pandas(Index=335, first_name='Adjei', gender='U'),
 Pandas(Index=336, first_name='Adkin', gender='U'),
 Pandas(Index=341, first_name='Admira', gender='U'),
 Pandas(Index=342, first_name='Admon', gender='U'),
 Pandas(Index=345, first_name='Adnun', gender='U'),
 Pandas(Index=352, first_name='Adoracion', gender='U'),
 Pandas(Index=353, first_name='Adorata', gender='U'),
 Pandas(Index=358, first_name='Adèle', gender='U'),
 Pandas(Index=359, first_name='Adèphe', gender='U'),
 Pandas(Index=360, first_name='Aeana', gender='U'),
 Pandas(Index=361, first_name='Aebe', gender='U'),
 Pandas(Index=363, first_name='Aen', gender='U'),
 Pandas(Index=364, first_name='Aene', gender='U'),
 Pandas(Index=365, first_name='Aenil', gender='U'),
 Pandas(Index=366, first_name='Aenne', gender='U'),
 Pandas(Index=367, first_name='Aephraim', gender='U'),
 Pandas(Index=368, first_name='Aercio', gender='U'),
 Pandas(Index=369, first_name='Aerhan', gender='U'),
 Pandas(Index=375, first_name='Afaq', gender='U'),
 Pandas(Index=376, first_name='Afework', gender='U'),
 Pandas(Index=378, first_name='Afkham', gender='U'),
 Pandas(Index=379, first_name='Aflora', gender='U'),
 Pandas(Index=385, first_name='Afroditi', gender='U'),
 Pandas(Index=386, first_name='Afrood', gender='U'),
 Pandas(Index=387, first_name='Afrouz', gender='U'),
 Pandas(Index=388, first_name='Afroz', gender='U'),
 Pandas(Index=389, first_name='Afroza', gender='U'),
 Pandas(Index=390, first_name='Afroze', gender='U'),
 Pandas(Index=393, first_name='Afsha', gender='U'),
 Pandas(Index=394, first_name='Afshaan', gender='U'),
 Pandas(Index=399, first_name='Aftim', gender='U'),
 Pandas(Index=403, first_name='Aga', gender='U'),
 Pandas(Index=405, first_name='Agapi', gender='U'),
 Pandas(Index=408, first_name='Agathajulia', gender='U'),
 Pandas(Index=409, first_name='Agatha–Julia', gender='U'),
 Pandas(Index=411, first_name='Agathea', gender='U'),
 Pandas(Index=412, first_name='Agatina', gender='U'),
 Pandas(Index=413, first_name='Agaton', gender='U'),
 Pandas(Index=414, first_name='Agavni', gender='U'),
 Pandas(Index=415, first_name='Agelio', gender='U'),
 Pandas(Index=416, first_name='Agerico', gender='U'),
 Pandas(Index=417, first_name='Agert', gender='U'),
 Pandas(Index=418, first_name='Agessan', gender='U'),
 Pandas(Index=420, first_name='Aggery', gender='U'),
 Pandas(Index=421, first_name='Aggey', gender='U'),
 Pandas(Index=422, first_name='Aggrey', gender='U'),
 Pandas(Index=424, first_name='Aghdas', gender='U'),
 Pandas(Index=425, first_name='Agi', gender='U'),
 Pandas(Index=427, first_name='Aglaya', gender='U'),
 Pandas(Index=431, first_name='Agniesz', gender='U'),
 Pandas(Index=434, first_name='Agnès', gender='U'),
 Pandas(Index=435, first_name='Agoostino', gender='U'),
 Pandas(Index=436, first_name='Agoritsa', gender='U'),
 Pandas(Index=438, first_name='Agostinho', gender='U'),
 Pandas(Index=440, first_name='Agoua', gender='U'),
 Pandas(Index=442, first_name='Agris', gender='U'),
 Pandas(Index=445, first_name='Ahailya', gender='U'),
 Pandas(Index=448, first_name='Ahamparam', gender='U'),
 Pandas(Index=451, first_name='Ahcene', gender='U'),
 Pandas(Index=453, first_name='Ahid', gender='U'),
 Pandas(Index=454, first_name='Ahilesan', gender='U'),
 Pandas(Index=456, first_name='Ahlek', gender='U'),
 Pandas(Index=458, first_name='Ahma', gender='U'),
 Pandas(Index=460, first_name='Ahmadali', gender='U'),
 Pandas(Index=461, first_name='Ahmadullah', gender='U'),
 Pandas(Index=463, first_name='Ahmaza', gender='U'),
 Pandas(Index=465, first_name='Ahmedkhador', gender='U'),
 Pandas(Index=466, first_name='Ahmedshafiqul', gender='U'),
 Pandas(Index=467, first_name='Ahmedur', gender='U'),
 Pandas(Index=468, first_name='Ahmereen', gender='U'),
 Pandas(Index=470, first_name='Ahmett', gender='U'),
 Pandas(Index=472, first_name='Ahmiel', gender='U'),
 Pandas(Index=473, first_name='Ahn', gender='U'),
 Pandas(Index=474, first_name='Ahnie', gender='U'),
 Pandas(Index=475, first_name='Ahouva', gender='U'),
 Pandas(Index=476, first_name='Ahrend', gender='U'),
 Pandas(Index=477, first_name='Ahreum', gender='U'),
 Pandas(Index=479, first_name='Ahrong', gender='U'),
 Pandas(Index=481, first_name='Ahsun', gender='U'),
 Pandas(Index=482, first_name='Ahtashamuddi', gender='U'),
 Pandas(Index=483, first_name='Ahtashamuddin', gender='U'),
 Pandas(Index=484, first_name='Ahtesham', gender='U'),
 Pandas(Index=485, first_name='Ahyoung', gender='U'),
 Pandas(Index=486, first_name='Aicam', gender='U'),
 Pandas(Index=488, first_name='Aichah', gender='U'),
 Pandas(Index=489, first_name='Aicheng', gender='U'),
 Pandas(Index=492, first_name='Aidarus', gender='U'),
 Pandas(Index=498, first_name='Aigbonosimua', gender='U'),
 Pandas(Index=499, first_name='Aihui', gender='U'),
 Pandas(Index=500, first_name='Aiisha', gender='U'),
 Pandas(Index=502, first_name='Aijaz', gender='U'),
 Pandas(Index=503, first_name='Aijun', gender='U'),
 Pandas(Index=504, first_name='Aikaterini', gender='U'),
 Pandas(Index=505, first_name='Aikateriny', gender='U'),
 Pandas(Index=506, first_name='Aikens', gender='U'),
 Pandas(Index=513, first_name='Ailynne', gender='U'),
 Pandas(Index=517, first_name='Aimeerose', gender='U'),
 Pandas(Index=518, first_name='Aimey', gender='U'),
 Pandas(Index=520, first_name='Aiming', gender='U'),
 Pandas(Index=521, first_name='Aimmie', gender='U'),
 Pandas(Index=523, first_name='Aimé', gender='U'),
 Pandas(Index=524, first_name='Aimée', gender='U'),
 Pandas(Index=528, first_name='Ainee', gender='U'),
 Pandas(Index=533, first_name='Ainul', gender='U'),
 Pandas(Index=534, first_name='Ainy', gender='U'),
 Pandas(Index=535, first_name='Aiping', gender='U'),
 Pandas(Index=538, first_name='Airein', gender='U'),
 Pandas(Index=543, first_name='Aishajiang', gender='U'),
 Pandas(Index=545, first_name='Aishvarya', gender='U'),
 Pandas(Index=551, first_name='Ait', gender='U'),
 Pandas(Index=553, first_name='Aivars', gender='U'),
 Pandas(Index=556, first_name='Aiying', gender='U'),
 Pandas(Index=557, first_name='Aizhen', gender='U'),
 Pandas(Index=558, first_name='Ai–Hua', gender='U'),
 Pandas(Index=560, first_name='Ajaib', gender='U'),
 Pandas(Index=565, first_name='Ajanth', gender='U'),
 Pandas(Index=566, first_name='Ajantha', gender='U'),
 Pandas(Index=567, first_name='Ajanthan', gender='U'),
 Pandas(Index=571, first_name='Ajazuddin', gender='U'),
 Pandas(Index=573, first_name='Ajeetha', gender='U'),
 Pandas(Index=574, first_name='Ajeetpal', gender='U'),
 Pandas(Index=575, first_name='Ajesh', gender='U'),
 Pandas(Index=576, first_name='Ajibola', gender='U'),
 Pandas(Index=579, first_name='Ajithan', gender='U'),
 Pandas(Index=580, first_name='Ajitkumar', gender='U'),
 Pandas(Index=581, first_name='Ajitpal', gender='U'),
 Pandas(Index=583, first_name='Ajmer', gender='U'),
 Pandas(Index=584, first_name='Ajneet', gender='U'),
 Pandas(Index=585, first_name='Ajoy', gender='U'),
 Pandas(Index=586, first_name='Ajoye', gender='U'),
 Pandas(Index=588, first_name='Ajunim', gender='U'),
 Pandas(Index=589, first_name='Ajwaid', gender='U'),
 Pandas(Index=590, first_name='Akamaladdin', gender='U'),
 Pandas(Index=592, first_name='Akaran', gender='U'),
 Pandas(Index=595, first_name='Akashjot', gender='U'),
 Pandas(Index=596, first_name='Akaterina', gender='U'),
 Pandas(Index=598, first_name='Akber', gender='U'),
 Pandas(Index=599, first_name='Akberet', gender='U'),
 Pandas(Index=600, first_name='Akbinder', gender='U'),
 Pandas(Index=601, first_name='Ake', gender='U'),
 Pandas(Index=605, first_name='Akeliki', gender='U'),
 Pandas(Index=608, first_name='Akhtar', gender='U'),
 Pandas(Index=609, first_name='Akhter', gender='U'),
 Pandas(Index=617, first_name='Akinbowale', gender='U'),
 Pandas(Index=618, first_name='Akinsola', gender='U'),
 Pandas(Index=622, first_name='Akli', gender='U'),
 Pandas(Index=623, first_name='Aklilu', gender='U'),
 Pandas(Index=624, first_name='Akm', gender='U'),
 Pandas(Index=629, first_name='Akramul', gender='U'),
 Pandas(Index=630, first_name='Akrivi', gender='U'),
 Pandas(Index=631, first_name='Aksal', gender='U'),
 Pandas(Index=634, first_name='Akshaikumar', gender='U'),
 Pandas(Index=638, first_name='Akter', gender='U'),
 Pandas(Index=641, first_name='Akwatu', gender='U'),
 Pandas(Index=642, first_name='Akyva', gender='U'),
 Pandas(Index=647, first_name='Alaeric', gender='U'),
 Pandas(Index=648, first_name='Alagan', gender='U'),
 Pandas(Index=651, first_name='Alaindesire', gender='U'),
 Pandas(Index=652, first_name='Alain–Desire', gender='U'),
 Pandas(Index=653, first_name='Alaisdar', gender='U'),
 Pandas(Index=654, first_name='Alal', gender='U'),
 Pandas(Index=655, first_name='Alaleh', gender='U'),
 Pandas(Index=657, first_name='Alamgir', gender='U'),
 Pandas(Index=664, first_name='Alanjeffrey', gender='U'),
 Pandas(Index=667, first_name='Alanpeter', gender='U'),
 Pandas(Index=668, first_name='Alar', gender='U'),
 Pandas(Index=675, first_name='Albalina', gender='U'),
 Pandas(Index=677, first_name='Albarosa', gender='U'),
 Pandas(Index=678, first_name='Albeer', gender='U'),
 Pandas(Index=681, first_name='Alberino', gender='U'),
 Pandas(Index=684, first_name='Albertajean', gender='U'),
 Pandas(Index=687, first_name='Alberts', gender='U'),
 Pandas(Index=689, first_name='Albertyne', gender='U'),
 Pandas(Index=692, first_name='Albrecht', gender='U'),
 Pandas(Index=697, first_name='Alcindo', gender='U'),
 Pandas(Index=699, first_name='Alcombe', gender='U'),
 Pandas(Index=700, first_name='Ald', gender='U'),
 Pandas(Index=702, first_name='Aldaine', gender='U'),
 Pandas(Index=704, first_name='Aldert', gender='U'),
 Pandas(Index=705, first_name='Aldevino', gender='U'),
 Pandas(Index=706, first_name='Aldge', gender='U'),
 Pandas(Index=707, first_name='Aldith', gender='U'),
 Pandas(Index=722, first_name='Aled', gender='U'),
 Pandas(Index=726, first_name='Aleempasha', gender='U'),
 Pandas(Index=728, first_name='Alefiya', gender='U'),
 Pandas(Index=730, first_name='Alejan', gender='U'),
 Pandas(Index=736, first_name='Aleksan', gender='U'),
 Pandas(Index=740, first_name='Aleksanja', gender='U'),
 Pandas(Index=745, first_name='Alemka', gender='U'),
 Pandas(Index=746, first_name='Alemseged', gender='U'),
 Pandas(Index=747, first_name='Alemu', gender='U'),
 Pandas(Index=754, first_name='Alesan', gender='U'),
 Pandas(Index=757, first_name='Alessan', gender='U'),
 Pandas(Index=765, first_name='Alewyn', gender='U'),
 Pandas(Index=773, first_name='Alexanderkeith', gender='U'),
 Pandas(Index=775, first_name='Alexane', gender='U'),
 Pandas(Index=777, first_name='Alexans', gender='U'),
 Pandas(Index=784, first_name='Alexisevelyn', gender='U'),
 Pandas(Index=785, first_name='Alexisglenn', gender='U'),
 Pandas(Index=786, first_name='Alexlander', gender='U'),
 Pandas(Index=787, first_name='Alexzan', gender='U'),
 Pandas(Index=790, first_name='Aleyamma', gender='U'),
 Pandas(Index=793, first_name='Alfiero', gender='U'),
 Pandas(Index=801, first_name='Alfreds', gender='U'),
 Pandas(Index=803, first_name='Alge', gender='U'),
 Pandas(Index=806, first_name='Algimantas', gender='U'),
 Pandas(Index=809, first_name='Alharith', gender='U'),
 Pandas(Index=810, first_name='Alhusein', gender='U'),
 Pandas(Index=815, first_name='Aliahmad', gender='U'),
 Pandas(Index=817, first_name='Aliakber', gender='U'),
 Pandas(Index=818, first_name='Aliaksan', gender='U'),
 Pandas(Index=819, first_name='Aliaksandr', gender='U'),
 Pandas(Index=820, first_name='Aliasger', gender='U'),
 Pandas(Index=821, first_name='Aliazam', gender='U'),
 Pandas(Index=825, first_name='Aliciaanne', gender='U'),
 Pandas(Index=826, first_name='Alicie', gender='U'),
 Pandas(Index=827, first_name='Alicija', gender='U'),
 Pandas(Index=832, first_name='Alidad', gender='U'),
 Pandas(Index=833, first_name='Alieh', gender='U'),
 Pandas(Index=836, first_name='Alieta', gender='U'),
 Pandas(Index=843, first_name='Alimatu', gender='U'),
 Pandas(Index=844, first_name='Alimohammad', gender='U'),
 Pandas(Index=845, first_name='Alimul', gender='U'),
 Pandas(Index=849, first_name='Alineh', gender='U'),
 Pandas(Index=850, first_name='Alinoor', gender='U'),
 Pandas(Index=851, first_name='Aliomid', gender='U'),
 Pandas(Index=853, first_name='Alioska', gender='U'),
 Pandas(Index=859, first_name='Alisdair', gender='U'),
 Pandas(Index=863, first_name='Alision', gender='U'),
 Pandas(Index=868, first_name='Alit', gender='U'),
 Pandas(Index=878, first_name='Ali–Reza', gender='U'),
 Pandas(Index=880, first_name='Alkarim', gender='U'),
 Pandas(Index=881, first_name='Alket', gender='U'),
 Pandas(Index=882, first_name='Alkis', gender='U'),
 Pandas(Index=883, first_name='Alkiviadis', gender='U'),
 Pandas(Index=886, first_name='Allain', gender='U'),
 Pandas(Index=890, first_name='Allane', gender='U'),
 Pandas(Index=891, first_name='Allangordon', gender='U'),
 Pandas(Index=893, first_name='Allar', gender='U'),
 Pandas(Index=895, first_name='Allaudin', gender='U'),
 Pandas(Index=896, first_name='Alleem', gender='U'),
 Pandas(Index=897, first_name='Alleke', gender='U'),
 Pandas(Index=899, first_name='Alleth', gender='U'),
 Pandas(Index=900, first_name='Alliemuthu', gender='U'),
 Pandas(Index=901, first_name='Alliette', gender='U'),
 Pandas(Index=908, first_name='Alliza', gender='U'),
 Pandas(Index=909, first_name='Allman', gender='U'),
 Pandas(Index=910, first_name='Allott', gender='U'),
 Pandas(Index=911, first_name='Allworth', gender='U'),
 Pandas(Index=919, first_name='Almeera', gender='U'),
 Pandas(Index=923, first_name='Almisa', gender='U'),
 Pandas(Index=926, first_name='Almut', gender='U'),
 Pandas(Index=927, first_name='Aln', gender='U'),
 Pandas(Index=928, first_name='Alnoor', gender='U'),
 Pandas(Index=932, first_name='Aloka', gender='U'),
 Pandas(Index=933, first_name='Alokendu', gender='U'),
 Pandas(Index=938, first_name='Alourdes', gender='U'),
 Pandas(Index=943, first_name='Alpeshkumar', gender='U'),
 Pandas(Index=945, first_name='Alphanso', gender='U'),
 Pandas(Index=952, first_name='Alpna', gender='U'),
 Pandas(Index=953, first_name='Alqa', gender='U'),
 Pandas(Index=955, first_name='Alrie', gender='U'),
 Pandas(Index=956, first_name='Als', gender='U'),
 Pandas(Index=957, first_name='Alsajad', gender='U'),
 Pandas(Index=958, first_name='Alshad', gender='U'),
 Pandas(Index=959, first_name='Alshah', gender='U'),
 Pandas(Index=965, first_name='Althaf', gender='U'),
 Pandas(Index=970, first_name='Altug', gender='U'),
 Pandas(Index=973, first_name='Alun', gender='U'),
 Pandas(Index=975, first_name='Alvajoye', gender='U'),
 Pandas(Index=977, first_name='Alvano', gender='U'),
 Pandas(Index=978, first_name='Alvapillai', gender='U'),
 Pandas(Index=980, first_name='Alva–Joye', gender='U'),
 Pandas(Index=986, first_name='Alvinder', gender='U'),
 Pandas(Index=987, first_name='Alvinwarren', gender='U'),
 Pandas(Index=988, first_name='Alvise', gender='U'),
 Pandas(Index=989, first_name='Alvisia', gender='U'),
 Pandas(Index=997, first_name='Alykhan', gender='U'),
 Pandas(Index=998, first_name='Alym', gender='U'),
 Pandas(Index=1005, first_name='Alyshah', gender='U'),
 Pandas(Index=1012, first_name='Alyxan', gender='U'),
 Pandas(Index=1013, first_name='Al–Amin', gender='U'),
 Pandas(Index=1014, first_name='Al–Karim', gender='U'),
 Pandas(Index=1015, first_name='Al–Noor', gender='U'),
 Pandas(Index=1027, first_name='Amale', gender='U'),
 Pandas(Index=1029, first_name='Amaliakaili', gender='U'),
 Pandas(Index=1032, first_name='Amanbir', gender='U'),
 Pandas(Index=1037, first_name='Amandeepkaur', gender='U'),
 Pandas(Index=1038, first_name='Amandi', gender='U'),
 Pandas(Index=1039, first_name='Amandina', gender='U'),
 Pandas(Index=1041, first_name='Amaneh', gender='U'),
 Pandas(Index=1047, first_name='Amanulla', gender='U'),
 Pandas(Index=1050, first_name='Amarbir', gender='U'),
 Pandas(Index=1054, first_name='Amaresh', gender='U'),
 Pandas(Index=1057, first_name='Amarinder', gender='U'),
 Pandas(Index=1059, first_name='Amarjeet', gender='U'),
 Pandas(Index=1060, first_name='Amarjit', gender='U'),
 Pandas(Index=1061, first_name='Amarjot', gender='U'),
 Pandas(Index=1062, first_name='Amarkumar', gender='U'),
 Pandas(Index=1063, first_name='Amarnath', gender='U'),
 Pandas(Index=1064, first_name='Amarpal', gender='U'),
 Pandas(Index=1068, first_name='Amatul', gender='U'),
 Pandas(Index=1070, first_name='Amaz', gender='U'),
 Pandas(Index=1071, first_name='Ambah', gender='U'),
 Pandas(Index=1072, first_name='Ambalangoda', gender='U'),
 Pandas(Index=1073, first_name='Ambalavana', gender='U'),
 Pandas(Index=1074, first_name='Ambalavanar', gender='U'),
 Pandas(Index=1076, first_name='Ambareen', gender='U'),
 Pandas(Index=1077, first_name='Ambarish', gender='U'),
 Pandas(Index=1078, first_name='Ambaye', gender='U'),
 Pandas(Index=1079, first_name='Ambe', gender='U'),
 Pandas(Index=1080, first_name='Ambeika', gender='U'),
 Pandas(Index=1082, first_name='Amberin', gender='U'),
 Pandas(Index=1088, first_name='Ambermaryanne', gender='U'),
 Pandas(Index=1090, first_name='Ambily', gender='U'),
 Pandas(Index=1092, first_name='Ambrogio', gender='U'),
 Pandas(Index=1095, first_name='Ambrus', gender='U'),
 Pandas(Index=1096, first_name='Amde', gender='U'),
 Pandas(Index=1098, first_name='Amede', gender='U'),
 Pandas(Index=1100, first_name='Ameed', gender='U'),
 Pandas(Index=1104, first_name='Ameeta', gender='U'),
 Pandas(Index=1105, first_name='Ameetkumar', gender='U'),
 Pandas(Index=1115, first_name='Ameneh', gender='U'),
 Pandas(Index=1116, first_name='Amephilia', gender='U'),
 Pandas(Index=1119, first_name='Amerina', gender='U'),
 Pandas(Index=1120, first_name='Amerino', gender='U'),
 Pandas(Index=1121, first_name='Amerjit', gender='U'),
 Pandas(Index=1122, first_name='Ameur', gender='U'),
 Pandas(Index=1123, first_name='Ameurfina', gender='U'),
 Pandas(Index=1126, first_name='Amgad', gender='U'),
 Pandas(Index=1130, first_name='Amiha', gender='U'),
 Pandas(Index=1140, first_name='Amineh', gender='U'),
 Pandas(Index=1141, first_name='Aminmohamed', gender='U'),
 Pandas(Index=1144, first_name='Amirabbas', gender='U'),
 Pandas(Index=1146, first_name='Amiram', gender='U'),
 Pandas(Index=1147, first_name='Amiramjacob', gender='U'),
 Pandas(Index=1148, first_name='Amirhassan', gender='U'),
 Pandas(Index=1149, first_name='Amirhossein', gender='U'),
 Pandas(Index=1150, first_name='Amirkianoosh', gender='U'),
 Pandas(Index=1151, first_name='Amirmassoud', gender='U'),
 Pandas(Index=1152, first_name='Amirmohamed', gender='U'),
 Pandas(Index=1153, first_name='Amirmohsen', gender='U'),
 Pandas(Index=1154, first_name='Amirnaser', gender='U'),
 Pandas(Index=1155, first_name='Amirreza', gender='U'),
 Pandas(Index=1156, first_name='Amirul', gender='U'),
 Pandas(Index=1159, first_name='Amishkumar', gender='U'),
 Pandas(Index=1162, first_name='Amitabha', gender='U'),
 Pandas(Index=1163, first_name='Amitava', gender='U'),
 Pandas(Index=1164, first_name='Amitavikram', gender='U'),
 Pandas(Index=1165, first_name='Amitesh', gender='U'),
 Pandas(Index=1167, first_name='Amitkumar', gender='U'),
 Pandas(Index=1170, first_name='Amjadkhan', gender='U'),
 Pandas(Index=1172, first_name='Amjid', gender='U'),
 Pandas(Index=1173, first_name='Amlyn', gender='U'),
 Pandas(Index=1180, first_name='Ammer', gender='EV'),
 Pandas(Index=1182, first_name='Ammini', gender='U'),
 Pandas(Index=1186, first_name='Amninder', gender='U'),
 Pandas(Index=1187, first_name='Amnon', gender='U'),
 Pandas(Index=1188, first_name='Amodio', gender='U'),
 Pandas(Index=1190, first_name='Amolak', gender='U'),
 Pandas(Index=1191, first_name='Amonulla', gender='U'),
 Pandas(Index=1194, first_name='Amorell', gender='U'),
 Pandas(Index=1195, first_name='Amormio', gender='U'),
 Pandas(Index=1198, first_name='Amparito', gender='U'),
 Pandas(Index=1202, first_name='Amrapali', gender='U'),
 Pandas(Index=1203, first_name='Amrat', gender='U'),
 Pandas(Index=1204, first_name='Amraz', gender='U'),
 Pandas(Index=1205, first_name='Amresh', gender='U'),
 Pandas(Index=1207, first_name='Amrinder', gender='U'),
 Pandas(Index=1212, first_name='Amsale', gender='U'),
 Pandas(Index=1213, first_name='Amtul', gender='U'),
 Pandas(Index=1214, first_name='Amudha', gender='U'),
 Pandas(Index=1215, first_name='Amul', gender='U'),
 Pandas(Index=1216, first_name='Amund', gender='U'),
 Pandas(Index=1217, first_name='Amuthakaran', gender='U'),
 Pandas(Index=1219, first_name='Amy`', gender='U'),
 Pandas(Index=1220, first_name='Amyjane', gender='U'),
 Pandas(Index=1224, first_name='Amyn', gender='U'),
 Pandas(Index=1225, first_name='Amzad', gender='U'),
 Pandas(Index=1226, first_name='Amzy', gender='U'),
 Pandas(Index=1227, first_name='Amédomé', gender='U'),
 Pandas(Index=1228, first_name='Amélie', gender='U'),
 Pandas(Index=1238, first_name='Anacleta', gender='U'),
 Pandas(Index=1240, first_name='Anagaile', gender='U'),
 Pandas(Index=1253, first_name='Anamarija', gender='U'),
 Pandas(Index=1255, first_name='Anamitra', gender='U'),
 Pandas(Index=1260, first_name='Anandadevan', gender='U'),
 Pandas(Index=1261, first_name='Anandhi', gender='U'),
 Pandas(Index=1263, first_name='Anandvir', gender='U'),
 Pandas(Index=1264, first_name='Anane', gender='U'),
 Pandas(Index=1265, first_name='Ananna', gender='U'),
 Pandas(Index=1266, first_name='Ananne', gender='U'),
 Pandas(Index=1268, first_name='Ananta', gender='U'),
 Pandas(Index=1270, first_name='Ananthan', gender='U'),
 Pandas(Index=1271, first_name='Ananthanaray', gender='U'),
 Pandas(Index=1272, first_name='Anantharaman', gender='U'),
 Pandas(Index=1275, first_name='Anaregina', gender='U'),
 Pandas(Index=1276, first_name='Anarita', gender='U'),
 Pandas(Index=1278, first_name='Anast', gender='U'),
 Pandas(Index=1279, first_name='Anastas', gender='U'),
 Pandas(Index=1280, first_name='Anastase', gender='U'),
 Pandas(Index=1284, first_name='Anastasis', gender='U'),
 Pandas(Index=1286, first_name='Anastassios', gender='U'),
 Pandas(Index=1293, first_name='Anaîs', gender='U'),
 Pandas(Index=1294, first_name='Anaïs', gender='U'),
 Pandas(Index=1295, first_name='Anbang', gender='U'),
 Pandas(Index=1296, first_name='Anca', gender='U'),
 Pandas(Index=1298, first_name='Ances', gender='U'),
 Pandas(Index=1299, first_name='Ancesca', gender='U'),
 Pandas(Index=1300, first_name='Ancesco', gender='U'),
 Pandas(Index=1301, first_name='Anchalee', gender='U'),
 Pandas(Index=1302, first_name='Anchang', gender='U'),
 Pandas(Index=1303, first_name='Ancharles', gender='U'),
 Pandas(Index=1304, first_name='Anchel', gender='U'),
 Pandas(Index=1307, first_name='Ancile', gender='U'),
 Pandas(Index=1308, first_name='Ancilla', gender='U'),
 Pandas(Index=1309, first_name='Ancis', gender='U'),
 Pandas(Index=1312, first_name='Andalee', gender='U'),
 Pandas(Index=1313, first_name='Andalib', gender='U'),
 Pandas(Index=1314, first_name='Andaluza', gender='U'),
 Pandas(Index=1315, first_name='Andbuty', gender='U'),
 Pandas(Index=1316, first_name='Andenye', gender='U'),
 Pandas(Index=1322, first_name='Andil', gender='U'),
 Pandas(Index=1323, first_name='Andisheh', gender='U'),
 Pandas(Index=1324, first_name='Andjelka', gender='U'),
 Pandas(Index=1325, first_name='Andjelko', gender='U'),
 Pandas(Index=1326, first_name='Andla', gender='U'),
 Pandas(Index=1327, first_name='Andleeb', gender='U'),
 Pandas(Index=1328, first_name='Andna', gender='U'),
 Pandas(Index=1329, first_name='Ando', gender='U'),
 Pandas(Index=1331, first_name='Andor', gender='U'),
 Pandas(Index=1332, first_name='Anduela', gender='U'),
 Pandas(Index=1334, first_name='Andée', gender='U'),
 Pandas(Index=1337, first_name='Aneanne', gender='U'),
 Pandas(Index=1338, first_name='Anecita', gender='U'),
 Pandas(Index=1339, first_name='Anecito', gender='U'),
 Pandas(Index=1346, first_name='Anegret', gender='U'),
 Pandas(Index=1349, first_name='Anej', gender='U'),
 Pandas(Index=1353, first_name='Anelfa', gender='U'),
 Pandas(Index=1355, first_name='Aneliya', gender='U'),
 Pandas(Index=1356, first_name='Anen', gender='U'),
 Pandas(Index=1357, first_name='Anenthan', gender='U'),
 Pandas(Index=1360, first_name='Anesti', gender='EV'),
 Pandas(Index=1361, first_name='Anestis', gender='U'),
 Pandas(Index=1362, first_name='Anesttis', gender='U'),
 Pandas(Index=1366, first_name='Aneurin', gender='U'),
 Pandas(Index=1367, first_name='Aneyamma', gender='U'),
 Pandas(Index=1368, first_name='Anfeng', gender='U'),
 Pandas(Index=1369, first_name='Anfrançois', gender='U'),
 Pandas(Index=1370, first_name='Angadvir', gender='U'),
 Pandas(Index=1371, first_name='Angana', gender='U'),
 Pandas(Index=1375, first_name='Angelakim', gender='U'),
 Pandas(Index=1396, first_name='Angelta', gender='U'),
 Pandas(Index=1399, first_name='Angen', gender='U'),
 Pandas(Index=1400, first_name='Angers', gender='U'),
 Pandas(Index=1403, first_name='Angshu', gender='U'),
 Pandas(Index=1404, first_name='Angshuman', gender='U'),
 Pandas(Index=1405, first_name='Angue', gender='U'),
 Pandas(Index=1407, first_name='Angèle', gender='U'),
 Pandas(Index=1408, first_name='Angéla', gender='U'),
 Pandas(Index=1409, first_name='Angéle', gender='U'),
 Pandas(Index=1410, first_name='Angélique', gender='U'),
 Pandas(Index=1415, first_name='Aniamma', gender='U'),
 Pandas(Index=1417, first_name='Anic', gender='U'),
 Pandas(Index=1420, first_name='Anick', gender='U'),
 Pandas(Index=1431, first_name='Anilkumar', gender='U'),
 Pandas(Index=1432, first_name='Anill', gender='U'),
 Pandas(Index=1433, first_name='Animah', gender='U'),
 Pandas(Index=1434, first_name='Animary', gender='U'),
 Pandas(Index=1436, first_name='Anindya', gender='U'),
 Pandas(Index=1437, first_name='Anio', gender='U'),
 Pandas(Index=1439, first_name='Anirude', gender='U'),
 Pandas(Index=1440, first_name='Anirudhra', gender='U'),
 Pandas(Index=1446, first_name='Anisoara', gender='U'),
 Pandas(Index=1448, first_name='Anisur', gender='U'),
 Pandas(Index=1453, first_name='Anj', gender='U'),
 Pandas(Index=1459, first_name='Anjanee', gender='U'),
 Pandas(Index=1463, first_name='Anjhela', gender='U'),
 Pandas(Index=1466, first_name='Anjili', gender='U'),
 Pandas(Index=1467, first_name='Anjlee', gender='U'),
 Pandas(Index=1469, first_name='Anjna', gender='U'),
 Pandas(Index=1470, first_name='Anjs', gender='U'),
 Pandas(Index=1474, first_name='Anjus', gender='U'),
 Pandas(Index=1475, first_name='Ank', gender='U'),
 Pandas(Index=1479, first_name='Anklin', gender='U'),
 Pandas(Index=1480, first_name='Ankou', gender='U'),
 Pandas(Index=1483, first_name='Anliya', gender='U'),
 Pandas(Index=1484, first_name='Anluc', gender='U'),
 Pandas(Index=1485, first_name='Anluise', gender='U'),
 Pandas(Index=1486, first_name='Anmache', gender='U'),
 Pandas(Index=1488, first_name='Anmeola', gender='U'),
 Pandas(Index=1495, first_name='Annadar', gender='U'),
 Pandas(Index=1496, first_name='Annahid', gender='U'),
 Pandas(Index=1508, first_name='Annalucy', gender='U'),
 Pandas(Index=1513, first_name='Annamarria', gender='U'),
 Pandas(Index=1514, first_name='Annamma', gender='U'),
 Pandas(Index=1515, first_name='Annapurani', gender='U'),
 Pandas(Index=1517, first_name='Annarita', gender='U'),
 Pandas(Index=1518, first_name='Annariza', gender='U'),
 Pandas(Index=1519, first_name='Anna–Lisa', gender='U'),
 Pandas(Index=1520, first_name='Anna–Marie', gender='U'),
 Pandas(Index=1521, first_name='Anna–Marria', gender='U'),
 Pandas(Index=1522, first_name='Annbarbara', gender='U'),
 Pandas(Index=1523, first_name='Anncarol', gender='U'),
 Pandas(Index=1524, first_name='Annchristina', gender='U'),
 Pandas(Index=1525, first_name='Anndia', gender='U'),
 Pandas(Index=1527, first_name='Anneane', gender='U'),
 Pandas(Index=1528, first_name='Annecarol', gender='U'),
 Pandas(Index=1529, first_name='Annechantal', gender='U'),
 Pandas(Index=1530, first_name='Annechristi', gender='U'),
 Pandas(Index=1531, first_name='Annechristine', gender='U'),
 Pandas(Index=1532, first_name='Anneclaude', gender='U'),
 Pandas(Index=1533, first_name='Annedésirée', gender='U'),
 Pandas(Index=1534, first_name='Anneemanuel', gender='U'),
 Pandas(Index=1535, first_name='Anneemanuelle', gender='U'),
 Pandas(Index=1536, first_name='Annefrançoi', gender='U'),
 Pandas(Index=1537, first_name='Annefrançois', gender='U'),
 Pandas(Index=1538, first_name='Annefrançoise', gender='U'),
 Pandas(Index=1539, first_name='Annegaëlle', gender='U'),
 Pandas(Index=1540, first_name='Annejulie', gender='U'),
 Pandas(Index=1541, first_name='Annekatrin', gender='U'),
 Pandas(Index=1545, first_name='Annelia', gender='U'),
 Pandas(Index=1549, first_name='Anneliisa', gender='U'),
 Pandas(Index=1553, first_name='Annellie', gender='U'),
 Pandas(Index=1555, first_name='Annelsie', gender='U'),
 Pandas(Index=1556, first_name='Anneluise', gender='U'),
 Pandas(Index=1560, first_name='Annemaria', gender='U'),
 Pandas(Index=1562, first_name='Annemarise', gender='U'),
 Pandas(Index=1563, first_name='Annemieke', gender='U'),
 Pandas(Index=1564, first_name='Annemilia', gender='U'),
 Pandas(Index=1565, first_name='Annepascale', gender='U'),
 Pandas(Index=1566, first_name='Annerose', gender='U'),
 Pandas(Index=1571, first_name='Annethelma', gender='U'),
 Pandas(Index=1575, first_name='Annettebarb', gender='U'),
 Pandas(Index=1576, first_name='Annettebarbara', gender='U'),
 Pandas(Index=1577, first_name='Annette–Barb', gender='U'),
 Pandas(Index=1578, first_name='Annevalerie', gender='U'),
 Pandas(Index=1579, first_name='Anneviviane', gender='U'),
 Pandas(Index=1582, first_name='Anne–Emanuelle', gender='U'),
 Pandas(Index=1583, first_name='Anne–Françoise', gender='U'),
 Pandas(Index=1584, first_name='Anne–Gaëlle', gender='U'),
 Pandas(Index=1585, first_name='Anne–Louise', gender='U'),
 Pandas(Index=1586, first_name='Anne–Luise', gender='U'),
 Pandas(Index=1587, first_name='Anne–Maria', gender='U'),
 Pandas(Index=1588, first_name='Anne–Marie', gender='U'),
 Pandas(Index=1589, first_name='Anne–Thelma', gender='U'),
 Pandas(Index=1591, first_name='Anngelee', gender='U'),
 Pandas(Index=1592, first_name='Annhelen', gender='U'),
 Pandas(Index=1593, first_name='Annhien', gender='U'),
 Pandas(Index=1595, first_name='Annic', gender='U'),
 Pandas(Index=1599, first_name='Anniedoreen', gender='U'),
 Pandas(Index=1600, first_name='Annielyn', gender='U'),
 Pandas(Index=1601, first_name='Annik', gender='U'),
 Pandas(Index=1604, first_name='Anning', gender='U'),
 Pandas(Index=1605, first_name='Annino', gender='U'),
 Pandas(Index=1610, first_name='Anniz', gender='U'),
 Pandas(Index=1612, first_name='Annmar', gender='U'),
 Pandas(Index=1615, first_name='Annmichelle', gender='U'),
 Pandas(Index=1616, first_name='Annne', gender='U'),
 Pandas(Index=1618, first_name='Annouse', gender='U'),
 Pandas(Index=1619, first_name='Annu', gender='U'),
 Pandas(Index=1621, first_name='Annupam', gender='U'),
 Pandas(Index=1622, first_name='Annuziatina', gender='U'),
 Pandas(Index=1624, first_name='Annyck', gender='U'),
 Pandas(Index=1625, first_name='Ann–Barbara', gender='U'),
 Pandas(Index=1626, first_name='Ann–Elizabeth', gender='U'),
 Pandas(Index=1627, first_name='Ann–Elsie', gender='U'),
 Pandas(Index=1628, first_name='Ann–Helen', gender='U'),
 Pandas(Index=1629, first_name='Ann–Louise', gender='U'),
 Pandas(Index=1630, first_name='Ann–Mari', gender='U'),
 Pandas(Index=1631, first_name='Ann–Marie', gender='U'),
 Pandas(Index=1632, first_name='Ano', gender='U'),
 Pandas(Index=1633, first_name='Anokhsingh', gender='U'),
 Pandas(Index=1635, first_name='Anontz', gender='U'),
 Pandas(Index=1637, first_name='Anos', gender='U'),
 Pandas(Index=1638, first_name='Anouar', gender='U'),
 Pandas(Index=1639, first_name='Anouchka', gender='U'),
 Pandas(Index=1641, first_name='Anouma', gender='U'),
 Pandas(Index=1642, first_name='Anouschka', gender='U'),
 Pandas(Index=1645, first_name='Ans', gender='U'),
 Pandas(Index=1647, first_name='Ansari', gender='U'),
 Pandas(Index=1648, first_name='Ansberta', gender='U'),
 Pandas(Index=1649, first_name='Ansberth', gender='U'),
 Pandas(Index=1652, first_name='Anshoo', gender='U'),
 Pandas(Index=1655, first_name='Anshula', gender='U'),
 Pandas(Index=1656, first_name='Anshuyla', gender='U'),
 Pandas(Index=1659, first_name='Anssi', gender='U'),
 Pandas(Index=1661, first_name='Ansuya', gender='U'),
 Pandas(Index=1663, first_name='Antalene', gender='U'),
 Pandas(Index=1664, first_name='Antanas', gender='U'),
 Pandas(Index=1666, first_name='Anteneh', gender='U'),
 Pandas(Index=1667, first_name='Anter', gender='U'),
 Pandas(Index=1670, first_name='Anthi', gender='U'),
 Pandas(Index=1676, first_name='Anthonygeorge', gender='U'),
 Pandas(Index=1677, first_name='Anthonyj', gender='U'),
 Pandas(Index=1679, first_name='Anthousa', gender='U'),
 Pandas(Index=1680, first_name='Anthy', gender='U'),
 Pandas(Index=1682, first_name='Antigoni', gender='U'),
 Pandas(Index=1684, first_name='Antin', gender='U'),
 Pandas(Index=1688, first_name='Anto', gender='U'),
 Pandas(Index=1690, first_name='Antoanela', gender='U'),
 Pandas(Index=1691, first_name='Antoaneta', gender='U'),
 Pandas(Index=1692, first_name='Antoe', gender='U'),
 Pandas(Index=1693, first_name='Antoiinette', gender='U'),
 Pandas(Index=1702, first_name='Antonet', gender='U'),
 Pandas(Index=1703, first_name='Antoneta', gender='U'),
 Pandas(Index=1708, first_name='Antonide', gender='U'),
 Pandas(Index=1710, first_name='Antoniella', gender='U'),
 Pandas(Index=1713, first_name='Antonija', gender='U'),
 Pandas(Index=1718, first_name='Antoniorocco', gender='U'),
 Pandas(Index=1723, first_name='Antonyos', gender='U'),
 Pandas(Index=1724, first_name='Antranik', gender='U'),
 Pandas(Index=1725, first_name='Antti', gender='U'),
 Pandas(Index=1727, first_name='Antun', gender='U'),
 Pandas(Index=1730, first_name='Anubha', gender='U'),
 Pandas(Index=1734, first_name='Anujkumar', gender='U'),
 Pandas(Index=1735, first_name='Anukta', gender='U'),
 Pandas(Index=1736, first_name='Anula', gender='U'),
 Pandas(Index=1738, first_name='Anumol', gender='U'),
 Pandas(Index=1742, first_name='Anupma', gender='U'),
 Pandas(Index=1743, first_name='Anuppiriya', gender='U'),
 Pandas(Index=1749, first_name='Anuraj', gender='U'),
 Pandas(Index=1750, first_name='Anurita', gender='U'),
 Pandas(Index=1751, first_name='Anus', gender='U'),
 Pandas(Index=1753, first_name='Anushervon', gender='U'),
 Pandas(Index=1754, first_name='Anushia', gender='U'),
 Pandas(Index=1756, first_name='Anushya', gender='U'),
 Pandas(Index=1757, first_name='Anusiya', gender='U'),
 Pandas(Index=1758, first_name='Anver', gender='U'),
 Pandas(Index=1759, first_name='Anw', gender='U'),
 Pandas(Index=1761, first_name='Anwarul', gender='U'),
 Pandas(Index=1762, first_name='Anwdr', gender='U'),
 Pandas(Index=1763, first_name='Anwroy', gender='U'),
 Pandas(Index=1764, first_name='Anws', gender='U'),
 Pandas(Index=1765, first_name='Anx', gender='U'),
 Pandas(Index=1766, first_name='Anxhela', gender='U'),
 Pandas(Index=1769, first_name='Anyes', gender='U'),
 Pandas(Index=1770, first_name='Anyk', gender='U'),
 Pandas(Index=1772, first_name='Anyta', gender='U'),
 Pandas(Index=1773, first_name='Anyu', gender='U'),
 Pandas(Index=1774, first_name='Anzalene', gender='U'),
 Pandas(Index=1775, first_name='Anzalina', gender='U'),
 Pandas(Index=1776, first_name='Anzette', gender='U'),
 Pandas(Index=1777, first_name='Anú', gender='U'),
 Pandas(Index=1778, first_name='An–Chang', gender='U'),
 Pandas(Index=1779, first_name='Aon', gender='U'),
 Pandas(Index=1780, first_name='Aouni', gender='U'),
 Pandas(Index=1781, first_name='Aoussine', gender='U'),
 Pandas(Index=1783, first_name='Apdias', gender='U'),
 Pandas(Index=1785, first_name='Apichart', gender='U'),
 Pandas(Index=1787, first_name='Apollinaire', gender='U'),
 Pandas(Index=1788, first_name='Apolone', gender='U'),
 Pandas(Index=1791, first_name='Apparao', gender='U'),
 Pandas(Index=1792, first_name='Apparjit', gender='U'),
 Pandas(Index=1794, first_name='Appolin', gender='U'),
 Pandas(Index=1796, first_name='Aprildawn', gender='U'),
 Pandas(Index=1797, first_name='Aprillee', gender='U'),
 Pandas(Index=1801, first_name='Apurv', gender='U'),
 Pandas(Index=1804, first_name='Aquilas', gender='U'),
 Pandas(Index=1809, first_name='Araby', gender='U'),
 Pandas(Index=1815, first_name='Arain', gender='U'),
 Pandas(Index=1817, first_name='Aranka', gender='U'),
 Pandas(Index=1821, first_name='Aravapalli', gender='U'),
 Pandas(Index=1823, first_name='Aravinthan', gender='U'),
 Pandas(Index=1825, first_name='Arbe', gender='U'),
 Pandas(Index=1829, first_name='Arbinder', gender='U'),
 Pandas(Index=1831, first_name='Arcady', gender='U'),
 Pandas(Index=1832, first_name='Arcan', gender='U'),
 Pandas(Index=1833, first_name='Arcangelina', gender='U'),
 Pandas(Index=1837, first_name='Arcelita', gender='U'),
 Pandas(Index=1841, first_name='Archimede', gender='U'),
 Pandas(Index=1843, first_name='Archishman', gender='U'),
 Pandas(Index=1845, first_name='Archna', gender='U'),
 Pandas(Index=1846, first_name='Arcilen', gender='U'),
 Pandas(Index=1847, first_name='Arcot', gender='U'),
 Pandas(Index=1852, first_name='Ardeshir', gender='U'),
 Pandas(Index=1858, first_name='Arej', gender='U'),
 Pandas(Index=1861, first_name='Aremanda', gender='U'),
 Pandas(Index=1864, first_name='Areski', gender='U'),
 Pandas(Index=1865, first_name='Aretadawn', gender='U'),
 Pandas(Index=1869, first_name='Arezoo', gender='U'),
 Pandas(Index=1870, first_name='Arezou', gender='U'),
 Pandas(Index=1872, first_name='Arfona', gender='U'),
 Pandas(Index=1875, first_name='Argeo', gender='U'),
 Pandas(Index=1876, first_name='Argero', gender='U'),
 Pandas(Index=1877, first_name='Arghya', gender='U'),
 Pandas(Index=1879, first_name='Argilio', gender='U'),
 Pandas(Index=1881, first_name='Argyri', gender='U'),
 Pandas(Index=1882, first_name='Argyrios', gender='U'),
 Pandas(Index=1884, first_name='Arialdo', gender='U'),
 Pandas(Index=1890, first_name='Arianwen', gender='U'),
 Pandas(Index=1900, first_name='Ariff', gender='U'),
 Pandas(Index=1901, first_name='Arig', gender='U'),
 Pandas(Index=1916, first_name='Aristocles', gender='U'),
 Pandas(Index=1918, first_name='Ariyadasa', gender='U'),
 Pandas(Index=1920, first_name='Ariyanayagam', gender='U'),
 Pandas(Index=1922, first_name='Arja', gender='U'),
 Pandas(Index=1927, first_name='Arjinder', gender='U'),
 Pandas(Index=1928, first_name='Arjumand', gender='U'),
 Pandas(Index=1932, first_name='Arkadii', gender='U'),
 Pandas(Index=1942, first_name='Arlenejoy', gender='U'),
 Pandas(Index=1946, first_name='Arlindo', gender='U'),
 Pandas(Index=1952, first_name='Arlène', gender='U'),
 Pandas(Index=1953, first_name='Armagan', gender='U'),
 Pandas(Index=1954, first_name='Armaghan', gender='U'),
 Pandas(Index=1955, first_name='Armah', gender='U'),
 Pandas(Index=1956, first_name='Armaine', gender='U'),
 Pandas(Index=1963, first_name='Armi', gender='U'),
 Pandas(Index=1966, first_name='Armig', gender='U'),
 Pandas(Index=1967, first_name='Armila', gender='U'),
 Pandas(Index=1968, first_name='Armilou', gender='U'),
 Pandas(Index=1971, first_name='Arminder', gender='U'),
 Pandas(Index=1972, first_name='Armindo', gender='U'),
 Pandas(Index=1974, first_name='Armiss', gender='U'),
 Pandas(Index=1977, first_name='Armta', gender='U'),
 Pandas(Index=1980, first_name='Arnanie', gender='U'),
 Pandas(Index=1982, first_name='Arnd', gender='U'),
 Pandas(Index=1983, first_name='Arndis', gender='U'),
 Pandas(Index=1984, first_name='Arndt', gender='U'),
 Pandas(Index=1986, first_name='Arneil', gender='U'),
 Pandas(Index=1988, first_name='Arnelsita', gender='U'),
 Pandas(Index=1989, first_name='Arnelson', gender='U'),
 Pandas(Index=1997, first_name='Arnoldas', gender='U'),
 Pandas(Index=2000, first_name='Arnost', gender='U'),
 Pandas(Index=2001, first_name='Arnould', gender='U'),
 Pandas(Index=2005, first_name='Arokia', gender='U'),
 Pandas(Index=2007, first_name='Aronela', gender='U'),
 Pandas(Index=2008, first_name='Aronienens', gender='U'),
 Pandas(Index=2009, first_name='Aroniennens', gender='U'),
 Pandas(Index=2010, first_name='Arosha', gender='U'),
 Pandas(Index=2016, first_name='Arqile', gender='U'),
 Pandas(Index=2019, first_name='Arrlynn', gender='U'),
 Pandas(Index=2031, first_name='Arshaq', gender='U'),
 Pandas(Index=2041, first_name='Arthurg', gender='U'),
 Pandas(Index=2043, first_name='Arthy', gender='U'),
 Pandas(Index=2045, first_name='Artimis', gender='U'),
 Pandas(Index=2048, first_name='Artsiom', gender='U'),
 Pandas(Index=2050, first_name='Arturas', gender='U'),
 Pandas(Index=2053, first_name='Arubah', gender='U'),
 Pandas(Index=2054, first_name='Aruchana', gender='U'),
 Pandas(Index=2055, first_name='Arujunan', gender='U'),
 Pandas(Index=2057, first_name='Aruljothi', gender='U'),
 Pandas(Index=2058, first_name='Arulnayagam', gender='U'),
 Pandas(Index=2059, first_name='Arulrajan', gender='U'),
 Pandas(Index=2060, first_name='Arumuga', gender='U'),
 Pandas(Index=2061, first_name='Arumugam', gender='U'),
 Pandas(Index=2062, first_name='Arumugan', gender='U'),
 Pandas(Index=2063, first_name='Arumugaraj', gender='U'),
 Pandas(Index=2066, first_name='Arunangshu', gender='U'),
 Pandas(Index=2067, first_name='Arunanthy', gender='U'),
 Pandas(Index=2069, first_name='Arunasis', gender='U'),
 Pandas(Index=2071, first_name='Arunie', gender='U'),
 Pandas(Index=2073, first_name='Arunita', gender='U'),
 Pandas(Index=2074, first_name='Arunkumar', gender='U'),
 Pandas(Index=2075, first_name='Arup', gender='U'),
 Pandas(Index=2076, first_name='Aruran', gender='U'),
 Pandas(Index=2077, first_name='Arushdeepkaur', gender='U'),
 Pandas(Index=2078, first_name='Arv', gender='U'),
 Pandas(Index=2083, first_name='Arvinder', gender='U'),
 Pandas(Index=2084, first_name='Arvindi', gender='U'),
 Pandas(Index=2088, first_name='Arwinder', gender='U'),
 Pandas(Index=2094, first_name='Aryta', gender='U'),
 Pandas(Index=2095, first_name='Arzina', gender='U'),
 Pandas(Index=2097, first_name='As Ad', gender='U'),
 Pandas(Index=2100, first_name='Asadollah', gender='U'),
 Pandas(Index=2104, first_name='Asanga', gender='U'),
 Pandas(Index=2106, first_name='Asare', gender='U'),
 Pandas(Index=2107, first_name='Asas', gender='U'),
 Pandas(Index=2108, first_name='Asbjorn', gender='U'),
 Pandas(Index=2109, first_name='Ascanio', gender='U'),
 Pandas(Index=2110, first_name='Asdghig', gender='U'),
 Pandas(Index=2111, first_name='Aseefa', gender='U'),
 Pandas(Index=2115, first_name='Aseni', gender='U'),
 Pandas(Index=2116, first_name='Asfa', gender='U'),
 Pandas(Index=2117, first_name='Asfaw', gender='U'),
 Pandas(Index=2119, first_name='Asfrah', gender='U'),
 Pandas(Index=2120, first_name='Asgarali', gender='U'),
 Pandas(Index=2121, first_name='Asghar', gender='U'),
 Pandas(Index=2125, first_name='Ashalea', gender='U'),
 Pandas(Index=2128, first_name='Asheet', gender='U'),
 Pandas(Index=2131, first_name='Ashfak', gender='U'),
 Pandas(Index=2132, first_name='Ashfaq', gender='U'),
 Pandas(Index=2134, first_name='Ashif', gender='U'),
 Pandas(Index=2135, first_name='Ashifa', gender='U'),
 Pandas(Index=2136, first_name='Ashik', gender='U'),
 Pandas(Index=2139, first_name='Ashishkuma', gender='U'),
 Pandas(Index=2140, first_name='Ashishkumar', gender='U'),
 Pandas(Index=2150, first_name='Ashma', gender='U'),
 Pandas(Index=2151, first_name='Ashmeed', gender='U'),
 Pandas(Index=2152, first_name='Ashnand', gender='U'),
 Pandas(Index=2153, first_name='Ashnoor', gender='U'),
 Pandas(Index=2154, first_name='Ashoak', gender='U'),
 Pandas(Index=2156, first_name='Ashoke', gender='U'),
 Pandas(Index=2157, first_name='Ashokkumar', gender='U'),
 Pandas(Index=2158, first_name='Ashook', gender='U'),
 Pandas(Index=2160, first_name='Ashour', gender='U'),
 Pandas(Index=2162, first_name='Ashrafa', gender='U'),
 Pandas(Index=2163, first_name='Ashrafali', gender='U'),
 Pandas(Index=2164, first_name='Ashraff', gender='U'),
 Pandas(Index=2165, first_name='Ashrafhusein', gender='U'),
 Pandas(Index=2167, first_name='Ashu', gender='U'),
 Pandas(Index=2171, first_name='Ashwani', gender='U'),
 Pandas(Index=2173, first_name='Ashwinder', gender='U'),
 Pandas(Index=2175, first_name='Ashwyn', gender='U'),
 Pandas(Index=2177, first_name='Asiat', gender='U'),
 Pandas(Index=2178, first_name='Asiedu', gender='U'),
 Pandas(Index=2180, first_name='Asifa', gender='U'),
 Pandas(Index=2181, first_name='Asifhusain', gender='U'),
 Pandas(Index=2185, first_name='Asish', gender='U'),
 Pandas(Index=2186, first_name='Asiye', gender='U'),
 Pandas(Index=2187, first_name='Askin', gender='U'),
 Pandas(Index=2188, first_name='Askold', gender='U'),
 Pandas(Index=2191, first_name='Aslim', gender='U'),
 Pandas(Index=2194, first_name="Asma'A", gender='U'),
 Pandas(Index=2197, first_name='Asmus', gender='U'),
 Pandas(Index=2198, first_name='Asoka', gender='U'),
 Pandas(Index=2199, first_name='Asokan', gender='U'),
 Pandas(Index=2200, first_name='Asokkumar', gender='U'),
 Pandas(Index=2201, first_name='Aspa', gender='U'),
 Pandas(Index=2205, first_name='Assefa', gender='U'),
 Pandas(Index=2206, first_name='Assen', gender='U'),
 Pandas(Index=2207, first_name='Assine', gender='U'),
 Pandas(Index=2209, first_name='Assuntina', gender='U'),
 Pandas(Index=2210, first_name='Assuntino', gender='U'),
 Pandas(Index=2213, first_name='Astero', gender='U'),
 Pandas(Index=2214, first_name='Asthagiri', gender='U'),
 Pandas(Index=2219, first_name='Astrida', gender='U'),
 Pandas(Index=2220, first_name='Astrit', gender='U'),
 Pandas(Index=2222, first_name='Asuf', gender='U'),
 Pandas(Index=2224, first_name='Aswini', gender='U'),
 Pandas(Index=2226, first_name='Ataharul', gender='U'),
 Pandas(Index=2227, first_name='Atala', gender='U'),
 Pandas(Index=2228, first_name='Atallah', gender='U'),
 Pandas(Index=2229, first_name='Atam', gender='U'),
 Pandas(Index=2230, first_name='Ataollah', gender='U'),
 Pandas(Index=2231, first_name='Ataul', gender='U'),
 Pandas(Index=2232, first_name='Ataulmajeed', gender='U'),
 Pandas(Index=2233, first_name='Ataur', gender='U'),
 Pandas(Index=2235, first_name='Atefeh', gender='U'),
 Pandas(Index=2238, first_name='Athanase', gender='U'),
 Pandas(Index=2241, first_name='Athar', gender='U'),
 Pandas(Index=2242, first_name='Atheia', gender='U'),
 Pandas(Index=2243, first_name='Atheling', gender='U'),
 Pandas(Index=2245, first_name='Athenie', gender='U'),
 Pandas(Index=2248, first_name='Athill', gender='U'),
 Pandas(Index=2250, first_name='Athinoula', gender='U'),
 Pandas(Index=2251, first_name='Athiththan', gender='U'),
 Pandas(Index=2254, first_name='Athula', gender='U'),
 Pandas(Index=2255, first_name='Athur', gender='U'),
 Pandas(Index=2259, first_name='Atin', gender='U'),
 Pandas(Index=2260, first_name='Atinderpal', gender='U'),
 Pandas(Index=2261, first_name='Atipol', gender='U'),
 Pandas(Index=2262, first_name='Atiqa', gender='U'),
 Pandas(Index=2263, first_name='Atique', gender='U'),
 Pandas(Index=2264, first_name='Atiqur', gender='U'),
 Pandas(Index=2265, first_name='Atishay', gender='U'),
 ...]


# Instantiating empty list to store output from loop below
u_names_list = []

# Extracting only the 'first_name' value from the above list of tuples
for name in u_list:
    u_names_list.append(name[1])

# Displaying output
u_names_list

['Aaan',
 'Aadu',
 'Aaida',
 'Aal',
 'Aalaa',
 'Aalla',
 'Aaltje',
 'Aan',
 'Aane',
 'Aanhoward',
 'Aanna',
 'Aanne',
 'Aanno',
 'Aano',
 'Aanus',
 'Aaranan',
 'Aareni',
 'Aasif',
 'Aatesh',
 'Aatika',
 'Aatos',
 'Aazar',
 'Abana',
 'Abbass',
 'Abbeyjoyce',
 'Abdelatif',
 'Abdelbaset',
 'Abdelfettah',
 'Abdelhakim',
 'Abdelhalim',
 'Abdeljalil',
 'Abdelkhalig',
 'Abdelkrim',
 'Abdellatif',
 'Abdelmajid',
 'Abdelmonem',
 'Abdelouahid',
 'Abdelsamie',
 'Abdelwahab',
 'Abdemusa',
 'Abderrahmane',
 'Abderrazak',
 'Abdifitah',
 'Abdigafar',
 'Abdilhalim',
 'Abdillahi',
 'Abdol',
 'Abdoladel',
 'Abdolhossein',
 'Abdollah',
 'Abdolmajid',
 'Abdolmanaf',
 'Abdolrasool',
 'Abdolreza',
 'Abdol–Reza',
 'Abdool',
 'Abdoulka',
 'Abdubois',
 'Abdulfattah',
 'Abdulhaq',
 'Abdulkader',
 'Abdulkerim',
 'Abdulkhalik',
 'Abdulle',
 'Abdulmotaleb',
 'Abdulrazzak',
 'Abdulreza',
 'Abdulrezak',
 'Abdulwasie',
 'Abdul–Fattah',
 'Abdurraouf',
 'Abdus',
 'Abeba',
 'Abebaye',
 'Abebe',
 'Abedakhatun',
 'Abedeen',
 'Abedia',
 'Abeir',
 'Abelardino',
 'Abenna',
 'Aberdine',
 'Abere',
 'Aberto',
 'Abhilasha',
 'Abhiramy',
 'Abidemi',
 'Abidin',
 'Abieyuwa',
 'Abigailvera',
 'Abigayel',
 'Abike',
 'Abilashen',
 'Abina',
 'Abinash',
 'Abinath',
 'Abioye',
 'Abiramy',
 'Ablert',
 'Abolaji',
 'Aboobaker',
 'Aboulfazl',
 'Abouzar',
 'Absera',
 'Abuchi',
 'Abuturab',
 'Abuyusuf',
 'Abuzafar',
 'Acacio',
 'Achal',
 'Achuil',
 'Achuthan',
 'Ackert',
 'Acrifa',
 'Acrive',
 'Adalaura',
 'Adaleta',
 'Adalgisa',
 'Adalgisio',
 'Adalsteinn',
 'Adamadia',
 'Adamira',
 'Adampercival',
 'Adanah',
 'Adaoma',
 'Addasonne',
 'Addiegail',
 'Addo',
 'Adeela',
 'Adeesha',
 'Adefemi',
 'Adefumilola',
 'Adejoke',
 'Adelio',
 'Adeluta',
 'Adenola',
 'Aderito',
 'Adesayo',
 'Adetumbi',
 'Adewumi',
 'Adeye',
 'Adeyinka',
 'Adino',
 'Aditha',
 'Adjei',
 'Adkin',
 'Admira',
 'Admon',
 'Adnun',
 'Adoracion',
 'Adorata',
 'Adèle',
 'Adèphe',
 'Aeana',
 'Aebe',
 'Aen',
 'Aene',
 'Aenil',
 'Aenne',
 'Aephraim',
 'Aercio',
 'Aerhan',
 'Afaq',
 'Afework',
 'Afkham',
 'Aflora',
 'Afroditi',
 'Afrood',
 'Afrouz',
 'Afroz',
 'Afroza',
 'Afroze',
 'Afsha',
 'Afshaan',
 'Aftim',
 'Aga',
 'Agapi',
 'Agathajulia',
 'Agatha–Julia',
 'Agathea',
 'Agatina',
 'Agaton',
 'Agavni',
 'Agelio',
 'Agerico',
 'Agert',
 'Agessan',
 'Aggery',
 'Aggey',
 'Aggrey',
 'Aghdas',
 'Agi',
 'Aglaya',
 'Agniesz',
 'Agnès',
 'Agoostino',
 'Agoritsa',
 'Agostinho',
 'Agoua',
 'Agris',
 'Ahailya',
 'Ahamparam',
 'Ahcene',
 'Ahid',
 'Ahilesan',
 'Ahlek',
 'Ahma',
 'Ahmadali',
 'Ahmadullah',
 'Ahmaza',
 'Ahmedkhador',
 'Ahmedshafiqul',
 'Ahmedur',
 'Ahmereen',
 'Ahmett',
 'Ahmiel',
 'Ahn',
 'Ahnie',
 'Ahouva',
 'Ahrend',
 'Ahreum',
 'Ahrong',
 'Ahsun',
 'Ahtashamuddi',
 'Ahtashamuddin',
 'Ahtesham',
 'Ahyoung',
 'Aicam',
 'Aichah',
 'Aicheng',
 'Aidarus',
 'Aigbonosimua',
 'Aihui',
 'Aiisha',
 'Aijaz',
 'Aijun',
 'Aikaterini',
 'Aikateriny',
 'Aikens',
 'Ailynne',
 'Aimeerose',
 'Aimey',
 'Aiming',
 'Aimmie',
 'Aimé',
 'Aimée',
 'Ainee',
 'Ainul',
 'Ainy',
 'Aiping',
 'Airein',
 'Aishajiang',
 'Aishvarya',
 'Ait',
 'Aivars',
 'Aiying',
 'Aizhen',
 'Ai–Hua',
 'Ajaib',
 'Ajanth',
 'Ajantha',
 'Ajanthan',
 'Ajazuddin',
 'Ajeetha',
 'Ajeetpal',
 'Ajesh',
 'Ajibola',
 'Ajithan',
 'Ajitkumar',
 'Ajitpal',
 'Ajmer',
 'Ajneet',
 'Ajoy',
 'Ajoye',
 'Ajunim',
 'Ajwaid',
 'Akamaladdin',
 'Akaran',
 'Akashjot',
 'Akaterina',
 'Akber',
 'Akberet',
 'Akbinder',
 'Ake',
 'Akeliki',
 'Akhtar',
 'Akhter',
 'Akinbowale',
 'Akinsola',
 'Akli',
 'Aklilu',
 'Akm',
 'Akramul',
 'Akrivi',
 'Aksal',
 'Akshaikumar',
 'Akter',
 'Akwatu',
 'Akyva',
 'Alaeric',
 'Alagan',
 'Alaindesire',
 'Alain–Desire',
 'Alaisdar',
 'Alal',
 'Alaleh',
 'Alamgir',
 'Alanjeffrey',
 'Alanpeter',
 'Alar',
 'Albalina',
 'Albarosa',
 'Albeer',
 'Alberino',
 'Albertajean',
 'Alberts',
 'Albertyne',
 'Albrecht',
 'Alcindo',
 'Alcombe',
 'Ald',
 'Aldaine',
 'Aldert',
 'Aldevino',
 'Aldge',
 'Aldith',
 'Aled',
 'Aleempasha',
 'Alefiya',
 'Alejan',
 'Aleksan',
 'Aleksanja',
 'Alemka',
 'Alemseged',
 'Alemu',
 'Alesan',
 'Alessan',
 'Alewyn',
 'Alexanderkeith',
 'Alexane',
 'Alexans',
 'Alexisevelyn',
 'Alexisglenn',
 'Alexlander',
 'Alexzan',
 'Aleyamma',
 'Alfiero',
 'Alfreds',
 'Alge',
 'Algimantas',
 'Alharith',
 'Alhusein',
 'Aliahmad',
 'Aliakber',
 'Aliaksan',
 'Aliaksandr',
 'Aliasger',
 'Aliazam',
 'Aliciaanne',
 'Alicie',
 'Alicija',
 'Alidad',
 'Alieh',
 'Alieta',
 'Alimatu',
 'Alimohammad',
 'Alimul',
 'Alineh',
 'Alinoor',
 'Aliomid',
 'Alioska',
 'Alisdair',
 'Alision',
 'Alit',
 'Ali–Reza',
 'Alkarim',
 'Alket',
 'Alkis',
 'Alkiviadis',
 'Allain',
 'Allane',
 'Allangordon',
 'Allar',
 'Allaudin',
 'Alleem',
 'Alleke',
 'Alleth',
 'Alliemuthu',
 'Alliette',
 'Alliza',
 'Allman',
 'Allott',
 'Allworth',
 'Almeera',
 'Almisa',
 'Almut',
 'Aln',
 'Alnoor',
 'Aloka',
 'Alokendu',
 'Alourdes',
 'Alpeshkumar',
 'Alphanso',
 'Alpna',
 'Alqa',
 'Alrie',
 'Als',
 'Alsajad',
 'Alshad',
 'Alshah',
 'Althaf',
 'Altug',
 'Alun',
 'Alvajoye',
 'Alvano',
 'Alvapillai',
 'Alva–Joye',
 'Alvinder',
 'Alvinwarren',
 'Alvise',
 'Alvisia',
 'Alykhan',
 'Alym',
 'Alyshah',
 'Alyxan',
 'Al–Amin',
 'Al–Karim',
 'Al–Noor',
 'Amale',
 'Amaliakaili',
 'Amanbir',
 'Amandeepkaur',
 'Amandi',
 'Amandina',
 'Amaneh',
 'Amanulla',
 'Amarbir',
 'Amaresh',
 'Amarinder',
 'Amarjeet',
 'Amarjit',
 'Amarjot',
 'Amarkumar',
 'Amarnath',
 'Amarpal',
 'Amatul',
 'Amaz',
 'Ambah',
 'Ambalangoda',
 'Ambalavana',
 'Ambalavanar',
 'Ambareen',
 'Ambarish',
 'Ambaye',
 'Ambe',
 'Ambeika',
 'Amberin',
 'Ambermaryanne',
 'Ambily',
 'Ambrogio',
 'Ambrus',
 'Amde',
 'Amede',
 'Ameed',
 'Ameeta',
 'Ameetkumar',
 'Ameneh',
 'Amephilia',
 'Amerina',
 'Amerino',
 'Amerjit',
 'Ameur',
 'Ameurfina',
 'Amgad',
 'Amiha',
 'Amineh',
 'Aminmohamed',
 'Amirabbas',
 'Amiram',
 'Amiramjacob',
 'Amirhassan',
 'Amirhossein',
 'Amirkianoosh',
 'Amirmassoud',
 'Amirmohamed',
 'Amirmohsen',
 'Amirnaser',
 'Amirreza',
 'Amirul',
 'Amishkumar',
 'Amitabha',
 'Amitava',
 'Amitavikram',
 'Amitesh',
 'Amitkumar',
 'Amjadkhan',
 'Amjid',
 'Amlyn',
 'Ammer',
 'Ammini',
 'Amninder',
 'Amnon',
 'Amodio',
 'Amolak',
 'Amonulla',
 'Amorell',
 'Amormio',
 'Amparito',
 'Amrapali',
 'Amrat',
 'Amraz',
 'Amresh',
 'Amrinder',
 'Amsale',
 'Amtul',
 'Amudha',
 'Amul',
 'Amund',
 'Amuthakaran',
 'Amy`',
 'Amyjane',
 'Amyn',
 'Amzad',
 'Amzy',
 'Amédomé',
 'Amélie',
 'Anacleta',
 'Anagaile',
 'Anamarija',
 'Anamitra',
 'Anandadevan',
 'Anandhi',
 'Anandvir',
 'Anane',
 'Ananna',
 'Ananne',
 'Ananta',
 'Ananthan',
 'Ananthanaray',
 'Anantharaman',
 'Anaregina',
 'Anarita',
 'Anast',
 'Anastas',
 'Anastase',
 'Anastasis',
 'Anastassios',
 'Anaîs',
 'Anaïs',
 'Anbang',
 'Anca',
 'Ances',
 'Ancesca',
 'Ancesco',
 'Anchalee',
 'Anchang',
 'Ancharles',
 'Anchel',
 'Ancile',
 'Ancilla',
 'Ancis',
 'Andalee',
 'Andalib',
 'Andaluza',
 'Andbuty',
 'Andenye',
 'Andil',
 'Andisheh',
 'Andjelka',
 'Andjelko',
 'Andla',
 'Andleeb',
 'Andna',
 'Ando',
 'Andor',
 'Anduela',
 'Andée',
 'Aneanne',
 'Anecita',
 'Anecito',
 'Anegret',
 'Anej',
 'Anelfa',
 'Aneliya',
 'Anen',
 'Anenthan',
 'Anesti',
 'Anestis',
 'Anesttis',
 'Aneurin',
 'Aneyamma',
 'Anfeng',
 'Anfrançois',
 'Angadvir',
 'Angana',
 'Angelakim',
 'Angelta',
 'Angen',
 'Angers',
 'Angshu',
 'Angshuman',
 'Angue',
 'Angèle',
 'Angéla',
 'Angéle',
 'Angélique',
 'Aniamma',
 'Anic',
 'Anick',
 'Anilkumar',
 'Anill',
 'Animah',
 'Animary',
 'Anindya',
 'Anio',
 'Anirude',
 'Anirudhra',
 'Anisoara',
 'Anisur',
 'Anj',
 'Anjanee',
 'Anjhela',
 'Anjili',
 'Anjlee',
 'Anjna',
 'Anjs',
 'Anjus',
 'Ank',
 'Anklin',
 'Ankou',
 'Anliya',
 'Anluc',
 'Anluise',
 'Anmache',
 'Anmeola',
 'Annadar',
 'Annahid',
 'Annalucy',
 'Annamarria',
 'Annamma',
 'Annapurani',
 'Annarita',
 'Annariza',
 'Anna–Lisa',
 'Anna–Marie',
 'Anna–Marria',
 'Annbarbara',
 'Anncarol',
 'Annchristina',
 'Anndia',
 'Anneane',
 'Annecarol',
 'Annechantal',
 'Annechristi',
 'Annechristine',
 'Anneclaude',
 'Annedésirée',
 'Anneemanuel',
 'Anneemanuelle',
 'Annefrançoi',
 'Annefrançois',
 'Annefrançoise',
 'Annegaëlle',
 'Annejulie',
 'Annekatrin',
 'Annelia',
 'Anneliisa',
 'Annellie',
 'Annelsie',
 'Anneluise',
 'Annemaria',
 'Annemarise',
 'Annemieke',
 'Annemilia',
 'Annepascale',
 'Annerose',
 'Annethelma',
 'Annettebarb',
 'Annettebarbara',
 'Annette–Barb',
 'Annevalerie',
 'Anneviviane',
 'Anne–Emanuelle',
 'Anne–Françoise',
 'Anne–Gaëlle',
 'Anne–Louise',
 'Anne–Luise',
 'Anne–Maria',
 'Anne–Marie',
 'Anne–Thelma',
 'Anngelee',
 'Annhelen',
 'Annhien',
 'Annic',
 'Anniedoreen',
 'Annielyn',
 'Annik',
 'Anning',
 'Annino',
 'Anniz',
 'Annmar',
 'Annmichelle',
 'Annne',
 'Annouse',
 'Annu',
 'Annupam',
 'Annuziatina',
 'Annyck',
 'Ann–Barbara',
 'Ann–Elizabeth',
 'Ann–Elsie',
 'Ann–Helen',
 'Ann–Louise',
 'Ann–Mari',
 'Ann–Marie',
 'Ano',
 'Anokhsingh',
 'Anontz',
 'Anos',
 'Anouar',
 'Anouchka',
 'Anouma',
 'Anouschka',
 'Ans',
 'Ansari',
 'Ansberta',
 'Ansberth',
 'Anshoo',
 'Anshula',
 'Anshuyla',
 'Anssi',
 'Ansuya',
 'Antalene',
 'Antanas',
 'Anteneh',
 'Anter',
 'Anthi',
 'Anthonygeorge',
 'Anthonyj',
 'Anthousa',
 'Anthy',
 'Antigoni',
 'Antin',
 'Anto',
 'Antoanela',
 'Antoaneta',
 'Antoe',
 'Antoiinette',
 'Antonet',
 'Antoneta',
 'Antonide',
 'Antoniella',
 'Antonija',
 'Antoniorocco',
 'Antonyos',
 'Antranik',
 'Antti',
 'Antun',
 'Anubha',
 'Anujkumar',
 'Anukta',
 'Anula',
 'Anumol',
 'Anupma',
 'Anuppiriya',
 'Anuraj',
 'Anurita',
 'Anus',
 'Anushervon',
 'Anushia',
 'Anushya',
 'Anusiya',
 'Anver',
 'Anw',
 'Anwarul',
 'Anwdr',
 'Anwroy',
 'Anws',
 'Anx',
 'Anxhela',
 'Anyes',
 'Anyk',
 'Anyta',
 'Anyu',
 'Anzalene',
 'Anzalina',
 'Anzette',
 'Anú',
 'An–Chang',
 'Aon',
 'Aouni',
 'Aoussine',
 'Apdias',
 'Apichart',
 'Apollinaire',
 'Apolone',
 'Apparao',
 'Apparjit',
 'Appolin',
 'Aprildawn',
 'Aprillee',
 'Apurv',
 'Aquilas',
 'Araby',
 'Arain',
 'Aranka',
 'Aravapalli',
 'Aravinthan',
 'Arbe',
 'Arbinder',
 'Arcady',
 'Arcan',
 'Arcangelina',
 'Arcelita',
 'Archimede',
 'Archishman',
 'Archna',
 'Arcilen',
 'Arcot',
 'Ardeshir',
 'Arej',
 'Aremanda',
 'Areski',
 'Aretadawn',
 'Arezoo',
 'Arezou',
 'Arfona',
 'Argeo',
 'Argero',
 'Arghya',
 'Argilio',
 'Argyri',
 'Argyrios',
 'Arialdo',
 'Arianwen',
 'Ariff',
 'Arig',
 'Aristocles',
 'Ariyadasa',
 'Ariyanayagam',
 'Arja',
 'Arjinder',
 'Arjumand',
 'Arkadii',
 'Arlenejoy',
 'Arlindo',
 'Arlène',
 'Armagan',
 'Armaghan',
 'Armah',
 'Armaine',
 'Armi',
 'Armig',
 'Armila',
 'Armilou',
 'Arminder',
 'Armindo',
 'Armiss',
 'Armta',
 'Arnanie',
 'Arnd',
 'Arndis',
 'Arndt',
 'Arneil',
 'Arnelsita',
 'Arnelson',
 'Arnoldas',
 'Arnost',
 'Arnould',
 'Arokia',
 'Aronela',
 'Aronienens',
 'Aroniennens',
 'Arosha',
 'Arqile',
 'Arrlynn',
 'Arshaq',
 'Arthurg',
 'Arthy',
 'Artimis',
 'Artsiom',
 'Arturas',
 'Arubah',
 'Aruchana',
 'Arujunan',
 'Aruljothi',
 'Arulnayagam',
 'Arulrajan',
 'Arumuga',
 'Arumugam',
 'Arumugan',
 'Arumugaraj',
 'Arunangshu',
 'Arunanthy',
 'Arunasis',
 'Arunie',
 'Arunita',
 'Arunkumar',
 'Arup',
 'Aruran',
 'Arushdeepkaur',
 'Arv',
 'Arvinder',
 'Arvindi',
 'Arwinder',
 'Aryta',
 'Arzina',
 'As Ad',
 'Asadollah',
 'Asanga',
 'Asare',
 'Asas',
 'Asbjorn',
 'Ascanio',
 'Asdghig',
 'Aseefa',
 'Aseni',
 'Asfa',
 'Asfaw',
 'Asfrah',
 'Asgarali',
 'Asghar',
 'Ashalea',
 'Asheet',
 'Ashfak',
 'Ashfaq',
 'Ashif',
 'Ashifa',
 'Ashik',
 'Ashishkuma',
 'Ashishkumar',
 'Ashma',
 'Ashmeed',
 'Ashnand',
 'Ashnoor',
 'Ashoak',
 'Ashoke',
 'Ashokkumar',
 'Ashook',
 'Ashour',
 'Ashrafa',
 'Ashrafali',
 'Ashraff',
 'Ashrafhusein',
 'Ashu',
 'Ashwani',
 'Ashwinder',
 'Ashwyn',
 'Asiat',
 'Asiedu',
 'Asifa',
 'Asifhusain',
 'Asish',
 'Asiye',
 'Askin',
 'Askold',
 'Aslim',
 "Asma'A",
 'Asmus',
 'Asoka',
 'Asokan',
 'Asokkumar',
 'Aspa',
 'Assefa',
 'Assen',
 'Assine',
 'Assuntina',
 'Assuntino',
 'Astero',
 'Asthagiri',
 'Astrida',
 'Astrit',
 'Asuf',
 'Aswini',
 'Ataharul',
 'Atala',
 'Atallah',
 'Atam',
 'Ataollah',
 'Ataul',
 'Ataulmajeed',
 'Ataur',
 'Atefeh',
 'Athanase',
 'Athar',
 'Atheia',
 'Atheling',
 'Athenie',
 'Athill',
 'Athinoula',
 'Athiththan',
 'Athula',
 'Athur',
 'Atin',
 'Atinderpal',
 'Atipol',
 'Atiqa',
 'Atique',
 'Atiqur',
 'Atishay',
 ...]


# Using list comprehension + the function created when processing the training data in step #1 to return a new list that includes the last three letters of each name and the gender from each item in 'u_names_list' created in the above cell
u_names_input = [(gender_features(n))
               for (n)in u_names_list]

u_names_input

[{'last three letters': 'aan'},
 {'last three letters': 'adu'},
 {'last three letters': 'ida'},
 {'last three letters': 'Aal'},
 {'last three letters': 'laa'},
 {'last three letters': 'lla'},
 {'last three letters': 'tje'},
 {'last three letters': 'Aan'},
 {'last three letters': 'ane'},
 {'last three letters': 'ard'},
 {'last three letters': 'nna'},
 {'last three letters': 'nne'},
 {'last three letters': 'nno'},
 {'last three letters': 'ano'},
 {'last three letters': 'nus'},
 {'last three letters': 'nan'},
 {'last three letters': 'eni'},
 {'last three letters': 'sif'},
 {'last three letters': 'esh'},
 {'last three letters': 'ika'},
 {'last three letters': 'tos'},
 {'last three letters': 'zar'},
 {'last three letters': 'ana'},
 {'last three letters': 'ass'},
 {'last three letters': 'yce'},
 {'last three letters': 'tif'},
 {'last three letters': 'set'},
 {'last three letters': 'tah'},
 {'last three letters': 'kim'},
 {'last three letters': 'lim'},
 {'last three letters': 'lil'},
 {'last three letters': 'lig'},
 {'last three letters': 'rim'},
 {'last three letters': 'tif'},
 {'last three letters': 'jid'},
 {'last three letters': 'nem'},
 {'last three letters': 'hid'},
 {'last three letters': 'mie'},
 {'last three letters': 'hab'},
 {'last three letters': 'usa'},
 {'last three letters': 'ane'},
 {'last three letters': 'zak'},
 {'last three letters': 'tah'},
 {'last three letters': 'far'},
 {'last three letters': 'lim'},
 {'last three letters': 'ahi'},
 {'last three letters': 'dol'},
 {'last three letters': 'del'},
 {'last three letters': 'ein'},
 {'last three letters': 'lah'},
 {'last three letters': 'jid'},
 {'last three letters': 'naf'},
 {'last three letters': 'ool'},
 {'last three letters': 'eza'},
 {'last three letters': 'eza'},
 {'last three letters': 'ool'},
 {'last three letters': 'lka'},
 {'last three letters': 'ois'},
 {'last three letters': 'tah'},
 {'last three letters': 'haq'},
 {'last three letters': 'der'},
 {'last three letters': 'rim'},
 {'last three letters': 'lik'},
 {'last three letters': 'lle'},
 {'last three letters': 'leb'},
 {'last three letters': 'zak'},
 {'last three letters': 'eza'},
 {'last three letters': 'zak'},
 {'last three letters': 'sie'},
 {'last three letters': 'tah'},
 {'last three letters': 'ouf'},
 {'last three letters': 'dus'},
 {'last three letters': 'eba'},
 {'last three letters': 'aye'},
 {'last three letters': 'ebe'},
 {'last three letters': 'tun'},
 {'last three letters': 'een'},
 {'last three letters': 'dia'},
 {'last three letters': 'eir'},
 {'last three letters': 'ino'},
 {'last three letters': 'nna'},
 {'last three letters': 'ine'},
 {'last three letters': 'ere'},
 {'last three letters': 'rto'},
 {'last three letters': 'sha'},
 {'last three letters': 'amy'},
 {'last three letters': 'emi'},
 {'last three letters': 'din'},
 {'last three letters': 'uwa'},
 {'last three letters': 'era'},
 {'last three letters': 'yel'},
 {'last three letters': 'ike'},
 {'last three letters': 'hen'},
 {'last three letters': 'ina'},
 {'last three letters': 'ash'},
 {'last three letters': 'ath'},
 {'last three letters': 'oye'},
 {'last three letters': 'amy'},
 {'last three letters': 'ert'},
 {'last three letters': 'aji'},
 {'last three letters': 'ker'},
 {'last three letters': 'azl'},
 {'last three letters': 'zar'},
 {'last three letters': 'era'},
 {'last three letters': 'chi'},
 {'last three letters': 'rab'},
 {'last three letters': 'suf'},
 {'last three letters': 'far'},
 {'last three letters': 'cio'},
 {'last three letters': 'hal'},
 {'last three letters': 'uil'},
 {'last three letters': 'han'},
 {'last three letters': 'ert'},
 {'last three letters': 'ifa'},
 {'last three letters': 'ive'},
 {'last three letters': 'ura'},
 {'last three letters': 'eta'},
 {'last three letters': 'isa'},
 {'last three letters': 'sio'},
 {'last three letters': 'inn'},
 {'last three letters': 'dia'},
 {'last three letters': 'ira'},
 {'last three letters': 'val'},
 {'last three letters': 'nah'},
 {'last three letters': 'oma'},
 {'last three letters': 'nne'},
 {'last three letters': 'ail'},
 {'last three letters': 'ddo'},
 {'last three letters': 'ela'},
 {'last three letters': 'sha'},
 {'last three letters': 'emi'},
 {'last three letters': 'ola'},
 {'last three letters': 'oke'},
 {'last three letters': 'lio'},
 {'last three letters': 'uta'},
 {'last three letters': 'ola'},
 {'last three letters': 'ito'},
 {'last three letters': 'ayo'},
 {'last three letters': 'mbi'},
 {'last three letters': 'umi'},
 {'last three letters': 'eye'},
 {'last three letters': 'nka'},
 {'last three letters': 'ino'},
 {'last three letters': 'tha'},
 {'last three letters': 'jei'},
 {'last three letters': 'kin'},
 {'last three letters': 'ira'},
 {'last three letters': 'mon'},
 {'last three letters': 'nun'},
 {'last three letters': 'ion'},
 {'last three letters': 'ata'},
 {'last three letters': 'èle'},
 {'last three letters': 'phe'},
 {'last three letters': 'ana'},
 {'last three letters': 'ebe'},
 {'last three letters': 'Aen'},
 {'last three letters': 'ene'},
 {'last three letters': 'nil'},
 {'last three letters': 'nne'},
 {'last three letters': 'aim'},
 {'last three letters': 'cio'},
 {'last three letters': 'han'},
 {'last three letters': 'faq'},
 {'last three letters': 'ork'},
 {'last three letters': 'ham'},
 {'last three letters': 'ora'},
 {'last three letters': 'iti'},
 {'last three letters': 'ood'},
 {'last three letters': 'ouz'},
 {'last three letters': 'roz'},
 {'last three letters': 'oza'},
 {'last three letters': 'oze'},
 {'last three letters': 'sha'},
 {'last three letters': 'aan'},
 {'last three letters': 'tim'},
 {'last three letters': 'Aga'},
 {'last three letters': 'api'},
 {'last three letters': 'lia'},
 {'last three letters': 'lia'},
 {'last three letters': 'hea'},
 {'last three letters': 'ina'},
 {'last three letters': 'ton'},
 {'last three letters': 'vni'},
 {'last three letters': 'lio'},
 {'last three letters': 'ico'},
 {'last three letters': 'ert'},
 {'last three letters': 'san'},
 {'last three letters': 'ery'},
 {'last three letters': 'gey'},
 {'last three letters': 'rey'},
 {'last three letters': 'das'},
 {'last three letters': 'Agi'},
 {'last three letters': 'aya'},
 {'last three letters': 'esz'},
 {'last three letters': 'nès'},
 {'last three letters': 'ino'},
 {'last three letters': 'tsa'},
 {'last three letters': 'nho'},
 {'last three letters': 'oua'},
 {'last three letters': 'ris'},
 {'last three letters': 'lya'},
 {'last three letters': 'ram'},
 {'last three letters': 'ene'},
 {'last three letters': 'hid'},
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 {'last three letters': 'Ahn'},
 {'last three letters': 'nie'},
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 {'last three letters': 'end'},
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 {'last three letters': 'ong'},
 {'last three letters': 'sun'},
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 {'last three letters': 'din'},
 {'last three letters': 'ham'},
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 {'last three letters': 'cam'},
 {'last three letters': 'hah'},
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 {'last three letters': 'iny'},
 {'last three letters': 'ing'},
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 {'last three letters': 'ing'},
 {'last three letters': 'hen'},
 {'last three letters': 'Hua'},
 {'last three letters': 'aib'},
 {'last three letters': 'nth'},
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 {'last three letters': 'pal'},
 {'last three letters': 'esh'},
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 {'last three letters': 'han'},
 {'last three letters': 'mar'},
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 {'last three letters': 'mer'},
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 {'last three letters': 'oye'},
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 {'last three letters': 'aid'},
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 {'last three letters': 'ran'},
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 {'last three letters': 'ret'},
 {'last three letters': 'der'},
 {'last three letters': 'Ake'},
 {'last three letters': 'iki'},
 {'last three letters': 'tar'},
 {'last three letters': 'ter'},
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 {'last three letters': 'ola'},
 {'last three letters': 'kli'},
 {'last three letters': 'ilu'},
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 {'last three letters': 'ivi'},
 {'last three letters': 'sal'},
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 {'last three letters': 'atu'},
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 {'last three letters': 'gan'},
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 {'last three letters': 'ire'},
 {'last three letters': 'dar'},
 {'last three letters': 'lal'},
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 {'last three letters': 'lar'},
 {'last three letters': 'ina'},
 {'last three letters': 'osa'},
 {'last three letters': 'eer'},
 {'last three letters': 'ino'},
 {'last three letters': 'ean'},
 {'last three letters': 'rts'},
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 {'last three letters': 'cht'},
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 {'last three letters': 'ine'},
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 {'last three letters': 'dge'},
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 {'last three letters': 'led'},
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 {'last three letters': 'iya'},
 {'last three letters': 'jan'},
 {'last three letters': 'san'},
 {'last three letters': 'nja'},
 {'last three letters': 'mka'},
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 {'last three letters': 'emu'},
 {'last three letters': 'san'},
 {'last three letters': 'san'},
 {'last three letters': 'wyn'},
 {'last three letters': 'ith'},
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 {'last three letters': 'ans'},
 {'last three letters': 'lyn'},
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 {'last three letters': 'der'},
 {'last three letters': 'zan'},
 {'last three letters': 'mma'},
 {'last three letters': 'ero'},
 {'last three letters': 'eds'},
 {'last three letters': 'lge'},
 {'last three letters': 'tas'},
 {'last three letters': 'ith'},
 {'last three letters': 'ein'},
 {'last three letters': 'mad'},
 {'last three letters': 'ber'},
 {'last three letters': 'san'},
 {'last three letters': 'ndr'},
 {'last three letters': 'ger'},
 {'last three letters': 'zam'},
 {'last three letters': 'nne'},
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 {'last three letters': 'ija'},
 {'last three letters': 'dad'},
 {'last three letters': 'ieh'},
 {'last three letters': 'eta'},
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 {'last three letters': 'mad'},
 {'last three letters': 'mul'},
 {'last three letters': 'neh'},
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 {'last three letters': 'ska'},
 {'last three letters': 'air'},
 {'last three letters': 'ion'},
 {'last three letters': 'lit'},
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 {'last three letters': 'ain'},
 {'last three letters': 'ane'},
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 {'last three letters': 'lar'},
 {'last three letters': 'din'},
 {'last three letters': 'eem'},
 {'last three letters': 'eke'},
 {'last three letters': 'eth'},
 {'last three letters': 'thu'},
 {'last three letters': 'tte'},
 {'last three letters': 'iza'},
 {'last three letters': 'man'},
 {'last three letters': 'ott'},
 {'last three letters': 'rth'},
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 {'last three letters': 'isa'},
 {'last three letters': 'mut'},
 {'last three letters': 'Aln'},
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 {'last three letters': 'oka'},
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 {'last three letters': 'des'},
 {'last three letters': 'mar'},
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 {'last three letters': 'pna'},
 {'last three letters': 'lqa'},
 {'last three letters': 'rie'},
 {'last three letters': 'Als'},
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 {'last three letters': 'had'},
 {'last three letters': 'hah'},
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 {'last three letters': 'tug'},
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 {'last three letters': 'oye'},
 {'last three letters': 'der'},
 {'last three letters': 'ren'},
 {'last three letters': 'ise'},
 {'last three letters': 'sia'},
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 {'last three letters': 'lym'},
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 {'last three letters': 'ale'},
 {'last three letters': 'ili'},
 {'last three letters': 'bir'},
 {'last three letters': 'aur'},
 {'last three letters': 'ndi'},
 {'last three letters': 'ina'},
 {'last three letters': 'neh'},
 {'last three letters': 'lla'},
 {'last three letters': 'bir'},
 {'last three letters': 'esh'},
 {'last three letters': 'der'},
 {'last three letters': 'eet'},
 {'last three letters': 'jit'},
 {'last three letters': 'jot'},
 {'last three letters': 'mar'},
 {'last three letters': 'ath'},
 {'last three letters': 'pal'},
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 {'last three letters': 'maz'},
 {'last three letters': 'bah'},
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 {'last three letters': 'ana'},
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 {'last three letters': 'een'},
 {'last three letters': 'ish'},
 {'last three letters': 'aye'},
 {'last three letters': 'mbe'},
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 {'last three letters': 'rin'},
 {'last three letters': 'nne'},
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 {'last three letters': 'gio'},
 {'last three letters': 'rus'},
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 {'last three letters': 'eed'},
 {'last three letters': 'eta'},
 {'last three letters': 'mar'},
 {'last three letters': 'neh'},
 {'last three letters': 'lia'},
 {'last three letters': 'ina'},
 {'last three letters': 'ino'},
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 {'last three letters': 'ina'},
 {'last three letters': 'gad'},
 {'last three letters': 'iha'},
 {'last three letters': 'neh'},
 {'last three letters': 'med'},
 {'last three letters': 'bas'},
 {'last three letters': 'ram'},
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 {'last three letters': 'san'},
 {'last three letters': 'ein'},
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 {'last three letters': 'oud'},
 {'last three letters': 'med'},
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 {'last three letters': 'ser'},
 {'last three letters': 'eza'},
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 {'last three letters': 'mar'},
 {'last three letters': 'bha'},
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 {'last three letters': 'ram'},
 {'last three letters': 'esh'},
 {'last three letters': 'mar'},
 {'last three letters': 'han'},
 {'last three letters': 'jid'},
 {'last three letters': 'lyn'},
 {'last three letters': 'mer'},
 {'last three letters': 'ini'},
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 {'last three letters': 'non'},
 {'last three letters': 'dio'},
 {'last three letters': 'lak'},
 {'last three letters': 'lla'},
 {'last three letters': 'ell'},
 {'last three letters': 'mio'},
 {'last three letters': 'ito'},
 {'last three letters': 'ali'},
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 {'last three letters': 'raz'},
 {'last three letters': 'esh'},
 {'last three letters': 'der'},
 {'last three letters': 'ale'},
 {'last three letters': 'tul'},
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 {'last three letters': 'mul'},
 {'last three letters': 'und'},
 {'last three letters': 'ran'},
 {'last three letters': 'my`'},
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 {'last three letters': 'mzy'},
 {'last three letters': 'omé'},
 {'last three letters': 'lie'},
 {'last three letters': 'eta'},
 {'last three letters': 'ile'},
 {'last three letters': 'ija'},
 {'last three letters': 'tra'},
 {'last three letters': 'van'},
 {'last three letters': 'dhi'},
 {'last three letters': 'vir'},
 {'last three letters': 'ane'},
 {'last three letters': 'nna'},
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 {'last three letters': 'nta'},
 {'last three letters': 'han'},
 {'last three letters': 'ray'},
 {'last three letters': 'man'},
 {'last three letters': 'ina'},
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 {'last three letters': 'ast'},
 {'last three letters': 'tas'},
 {'last three letters': 'ase'},
 {'last three letters': 'sis'},
 {'last three letters': 'ios'},
 {'last three letters': 'aîs'},
 {'last three letters': 'aïs'},
 {'last three letters': 'ang'},
 {'last three letters': 'nca'},
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 {'last three letters': 'sca'},
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 {'last three letters': 'lee'},
 {'last three letters': 'ang'},
 {'last three letters': 'les'},
 {'last three letters': 'hel'},
 {'last three letters': 'ile'},
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 {'last three letters': 'cis'},
 {'last three letters': 'lee'},
 {'last three letters': 'lib'},
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 {'last three letters': 'uty'},
 {'last three letters': 'nye'},
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 {'last three letters': 'heh'},
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 {'last three letters': 'ndo'},
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 {'last three letters': 'ela'},
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 {'last three letters': 'tis'},
 {'last three letters': 'tis'},
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 {'last three letters': 'mma'},
 {'last three letters': 'eng'},
 {'last three letters': 'ois'},
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 {'last three letters': 'ana'},
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 {'last three letters': 'ers'},
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 {'last three letters': 'éla'},
 {'last three letters': 'éle'},
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 {'last three letters': 'mma'},
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 {'last three letters': 'mar'},
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 {'last three letters': 'ude'},
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 {'last three letters': 'Anj'},
 {'last three letters': 'nee'},
 {'last three letters': 'ela'},
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 {'last three letters': 'lin'},
 {'last three letters': 'kou'},
 {'last three letters': 'iya'},
 {'last three letters': 'luc'},
 {'last three letters': 'ise'},
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 {'last three letters': 'iza'},
 {'last three letters': 'isa'},
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 {'last three letters': 'ria'},
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 {'last three letters': 'ine'},
 {'last three letters': 'ude'},
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 {'last three letters': 'lle'},
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 {'last three letters': 'ise'},
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 {'last three letters': 'lie'},
 {'last three letters': 'rin'},
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 {'last three letters': 'isa'},
 {'last three letters': 'lie'},
 {'last three letters': 'sie'},
 {'last three letters': 'ise'},
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 {'last three letters': 'ale'},
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 {'last three letters': 'ara'},
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 {'last three letters': 'rie'},
 {'last three letters': 'lma'},
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 {'last three letters': 'een'},
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 {'last three letters': 'ing'},
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 {'last three letters': 'ina'},
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 {'last three letters': 'Ano'},
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 {'last three letters': 'rth'},
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 {'last three letters': 'ula'},
 {'last three letters': 'yla'},
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 {'last three letters': 'neh'},
 {'last three letters': 'ter'},
 {'last three letters': 'thi'},
 {'last three letters': 'rge'},
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 {'last three letters': 'oni'},
 {'last three letters': 'tin'},
 {'last three letters': 'nto'},
 {'last three letters': 'ela'},
 {'last three letters': 'eta'},
 {'last three letters': 'toe'},
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 {'last three letters': 'eta'},
 {'last three letters': 'ide'},
 {'last three letters': 'lla'},
 {'last three letters': 'ija'},
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 {'last three letters': 'yos'},
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 {'last three letters': 'mar'},
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 {'last three letters': 'pma'},
 {'last three letters': 'iya'},
 {'last three letters': 'raj'},
 {'last three letters': 'ita'},
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 {'last three letters': 'hia'},
 {'last three letters': 'hya'},
 {'last three letters': 'iya'},
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 {'last three letters': 'Anw'},
 {'last three letters': 'rul'},
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 {'last three letters': 'roy'},
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 {'last three letters': 'Anx'},
 {'last three letters': 'ela'},
 {'last three letters': 'yes'},
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 {'last three letters': 'yta'},
 {'last three letters': 'nyu'},
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 {'last three letters': 'ina'},
 {'last three letters': 'tte'},
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 {'last three letters': 'ang'},
 {'last three letters': 'Aon'},
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 {'last three letters': 'ine'},
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 {'last three letters': 'ire'},
 {'last three letters': 'one'},
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 {'last three letters': 'lee'},
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 {'last three letters': 'aby'},
 {'last three letters': 'ain'},
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 {'last three letters': 'lli'},
 {'last three letters': 'han'},
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 {'last three letters': 'ady'},
 {'last three letters': 'can'},
 {'last three letters': 'ina'},
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 {'last three letters': 'ede'},
 {'last three letters': 'man'},
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 {'last three letters': 'cot'},
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 {'last three letters': 'ero'},
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 {'last three letters': 'yri'},
 {'last three letters': 'ios'},
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 {'last three letters': 'iff'},
 {'last three letters': 'rig'},
 {'last three letters': 'les'},
 {'last three letters': 'asa'},
 {'last three letters': 'gam'},
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 {'last three letters': 'joy'},
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 {'last three letters': 'mah'},
 {'last three letters': 'ine'},
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 {'last three letters': 'eil'},
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 {'last three letters': 'das'},
 {'last three letters': 'ost'},
 {'last three letters': 'uld'},
 {'last three letters': 'kia'},
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 {'last three letters': 'ens'},
 {'last three letters': 'sha'},
 {'last three letters': 'ile'},
 {'last three letters': 'ynn'},
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 {'last three letters': 'thi'},
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 {'last three letters': 'uga'},
 {'last three letters': 'gam'},
 {'last three letters': 'gan'},
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 {'last three letters': 'rup'},
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 {'last three letters': 'Arv'},
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 {'last three letters': 'ndi'},
 {'last three letters': 'der'},
 {'last three letters': 'yta'},
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 {'last three letters': ' Ad'},
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 {'last three letters': 'hig'},
 {'last three letters': 'efa'},
 {'last three letters': 'eni'},
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 {'last three letters': 'faw'},
 {'last three letters': 'rah'},
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 {'last three letters': 'har'},
 {'last three letters': 'lea'},
 {'last three letters': 'eet'},
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 {'last three letters': 'faq'},
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 {'last three letters': 'mar'},
 {'last three letters': 'hma'},
 {'last three letters': 'eed'},
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 {'last three letters': 'oor'},
 {'last three letters': 'oak'},
 {'last three letters': 'oke'},
 {'last three letters': 'mar'},
 {'last three letters': 'ook'},
 {'last three letters': 'our'},
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 {'last three letters': 'ali'},
 {'last three letters': 'aff'},
 {'last three letters': 'ein'},
 {'last three letters': 'shu'},
 {'last three letters': 'ani'},
 {'last three letters': 'der'},
 {'last three letters': 'wyn'},
 {'last three letters': 'iat'},
 {'last three letters': 'edu'},
 {'last three letters': 'ifa'},
 {'last three letters': 'ain'},
 {'last three letters': 'ish'},
 {'last three letters': 'iye'},
 {'last three letters': 'kin'},
 {'last three letters': 'old'},
 {'last three letters': 'lim'},
 {'last three letters': "a'A"},
 {'last three letters': 'mus'},
 {'last three letters': 'oka'},
 {'last three letters': 'kan'},
 {'last three letters': 'mar'},
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 {'last three letters': 'ine'},
 {'last three letters': 'ina'},
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 {'last three letters': 'ida'},
 {'last three letters': 'rit'},
 {'last three letters': 'suf'},
 {'last three letters': 'ini'},
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 {'last three letters': 'ala'},
 {'last three letters': 'lah'},
 {'last three letters': 'tam'},
 {'last three letters': 'lah'},
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 {'last three letters': 'eed'},
 {'last three letters': 'aur'},
 {'last three letters': 'feh'},
 {'last three letters': 'ase'},
 {'last three letters': 'har'},
 {'last three letters': 'eia'},
 {'last three letters': 'ing'},
 {'last three letters': 'nie'},
 {'last three letters': 'ill'},
 {'last three letters': 'ula'},
 {'last three letters': 'han'},
 {'last three letters': 'ula'},
 {'last three letters': 'hur'},
 {'last three letters': 'tin'},
 {'last three letters': 'pal'},
 {'last three letters': 'pol'},
 {'last three letters': 'iqa'},
 {'last three letters': 'que'},
 {'last three letters': 'qur'},
 {'last three letters': 'hay'},
 ...]


# Instantiating an NLTK Naive Bayes Classification Model instance and training it with 'featuresets' which is the list created in paer 2, step 1 to hold all the dictionaries containing the last three letters of each of the training names (US Social Security names data) and their associated gender
classifier = nltk.NaiveBayesClassifier.train(featuresets)


# Instantiating empty list to store output from loop below
u_names_pred = []

# Looping through all items in 'u_names_input' (last three letters of each unique first name from Sunshine List) and using the trained NLTK Naive Bayes Classifier to output a gender prediction for each name and storing output in list created above
for value in u_names_input:
    pred = classifier.classify(value)
    u_names_pred.append(pred)

# Displaying Output
u_names_pred

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# Creating a dictionary that contains two key value pairs--the first being all of the unique Sunshine List first names from 'u_names_list'--and the second being all of the gender predictions output by this model from 'u_names_pred'
u_pred_dict = {'first_name': u_names_list, 'gender': u_names_pred}
u_pred_dict

{'first_name': ['Aaan',
  'Aadu',
  'Aaida',
  'Aal',
  'Aalaa',
  'Aalla',
  'Aaltje',
  'Aan',
  'Aane',
  'Aanhoward',
  'Aanna',
  'Aanne',
  'Aanno',
  'Aano',
  'Aanus',
  'Aaranan',
  'Aareni',
  'Aasif',
  'Aatesh',
  'Aatika',
  'Aatos',
  'Aazar',
  'Abana',
  'Abbass',
  'Abbeyjoyce',
  'Abdelatif',
  'Abdelbaset',
  'Abdelfettah',
  'Abdelhakim',
  'Abdelhalim',
  'Abdeljalil',
  'Abdelkhalig',
  'Abdelkrim',
  'Abdellatif',
  'Abdelmajid',
  'Abdelmonem',
  'Abdelouahid',
  'Abdelsamie',
  'Abdelwahab',
  'Abdemusa',
  'Abderrahmane',
  'Abderrazak',
  'Abdifitah',
  'Abdigafar',
  'Abdilhalim',
  'Abdillahi',
  'Abdol',
  'Abdoladel',
  'Abdolhossein',
  'Abdollah',
  'Abdolmajid',
  'Abdolmanaf',
  'Abdolrasool',
  'Abdolreza',
  'Abdol–Reza',
  'Abdool',
  'Abdoulka',
  'Abdubois',
  'Abdulfattah',
  'Abdulhaq',
  'Abdulkader',
  'Abdulkerim',
  'Abdulkhalik',
  'Abdulle',
  'Abdulmotaleb',
  'Abdulrazzak',
  'Abdulreza',
  'Abdulrezak',
  'Abdulwasie',
  'Abdul–Fattah',
  'Abdurraouf',
  'Abdus',
  'Abeba',
  'Abebaye',
  'Abebe',
  'Abedakhatun',
  'Abedeen',
  'Abedia',
  'Abeir',
  'Abelardino',
  'Abenna',
  'Aberdine',
  'Abere',
  'Aberto',
  'Abhilasha',
  'Abhiramy',
  'Abidemi',
  'Abidin',
  'Abieyuwa',
  'Abigailvera',
  'Abigayel',
  'Abike',
  'Abilashen',
  'Abina',
  'Abinash',
  'Abinath',
  'Abioye',
  'Abiramy',
  'Ablert',
  'Abolaji',
  'Aboobaker',
  'Aboulfazl',
  'Abouzar',
  'Absera',
  'Abuchi',
  'Abuturab',
  'Abuyusuf',
  'Abuzafar',
  'Acacio',
  'Achal',
  'Achuil',
  'Achuthan',
  'Ackert',
  'Acrifa',
  'Acrive',
  'Adalaura',
  'Adaleta',
  'Adalgisa',
  'Adalgisio',
  'Adalsteinn',
  'Adamadia',
  'Adamira',
  'Adampercival',
  'Adanah',
  'Adaoma',
  'Addasonne',
  'Addiegail',
  'Addo',
  'Adeela',
  'Adeesha',
  'Adefemi',
  'Adefumilola',
  'Adejoke',
  'Adelio',
  'Adeluta',
  'Adenola',
  'Aderito',
  'Adesayo',
  'Adetumbi',
  'Adewumi',
  'Adeye',
  'Adeyinka',
  'Adino',
  'Aditha',
  'Adjei',
  'Adkin',
  'Admira',
  'Admon',
  'Adnun',
  'Adoracion',
  'Adorata',
  'Adèle',
  'Adèphe',
  'Aeana',
  'Aebe',
  'Aen',
  'Aene',
  'Aenil',
  'Aenne',
  'Aephraim',
  'Aercio',
  'Aerhan',
  'Afaq',
  'Afework',
  'Afkham',
  'Aflora',
  'Afroditi',
  'Afrood',
  'Afrouz',
  'Afroz',
  'Afroza',
  'Afroze',
  'Afsha',
  'Afshaan',
  'Aftim',
  'Aga',
  'Agapi',
  'Agathajulia',
  'Agatha–Julia',
  'Agathea',
  'Agatina',
  'Agaton',
  'Agavni',
  'Agelio',
  'Agerico',
  'Agert',
  'Agessan',
  'Aggery',
  'Aggey',
  'Aggrey',
  'Aghdas',
  'Agi',
  'Aglaya',
  'Agniesz',
  'Agnès',
  'Agoostino',
  'Agoritsa',
  'Agostinho',
  'Agoua',
  'Agris',
  'Ahailya',
  'Ahamparam',
  'Ahcene',
  'Ahid',
  'Ahilesan',
  'Ahlek',
  'Ahma',
  'Ahmadali',
  'Ahmadullah',
  'Ahmaza',
  'Ahmedkhador',
  'Ahmedshafiqul',
  'Ahmedur',
  'Ahmereen',
  'Ahmett',
  'Ahmiel',
  'Ahn',
  'Ahnie',
  'Ahouva',
  'Ahrend',
  'Ahreum',
  'Ahrong',
  'Ahsun',
  'Ahtashamuddi',
  'Ahtashamuddin',
  'Ahtesham',
  'Ahyoung',
  'Aicam',
  'Aichah',
  'Aicheng',
  'Aidarus',
  'Aigbonosimua',
  'Aihui',
  'Aiisha',
  'Aijaz',
  'Aijun',
  'Aikaterini',
  'Aikateriny',
  'Aikens',
  'Ailynne',
  'Aimeerose',
  'Aimey',
  'Aiming',
  'Aimmie',
  'Aimé',
  'Aimée',
  'Ainee',
  'Ainul',
  'Ainy',
  'Aiping',
  'Airein',
  'Aishajiang',
  'Aishvarya',
  'Ait',
  'Aivars',
  'Aiying',
  'Aizhen',
  'Ai–Hua',
  'Ajaib',
  'Ajanth',
  'Ajantha',
  'Ajanthan',
  'Ajazuddin',
  'Ajeetha',
  'Ajeetpal',
  'Ajesh',
  'Ajibola',
  'Ajithan',
  'Ajitkumar',
  'Ajitpal',
  'Ajmer',
  'Ajneet',
  'Ajoy',
  'Ajoye',
  'Ajunim',
  'Ajwaid',
  'Akamaladdin',
  'Akaran',
  'Akashjot',
  'Akaterina',
  'Akber',
  'Akberet',
  'Akbinder',
  'Ake',
  'Akeliki',
  'Akhtar',
  'Akhter',
  'Akinbowale',
  'Akinsola',
  'Akli',
  'Aklilu',
  'Akm',
  'Akramul',
  'Akrivi',
  'Aksal',
  'Akshaikumar',
  'Akter',
  'Akwatu',
  'Akyva',
  'Alaeric',
  'Alagan',
  'Alaindesire',
  'Alain–Desire',
  'Alaisdar',
  'Alal',
  'Alaleh',
  'Alamgir',
  'Alanjeffrey',
  'Alanpeter',
  'Alar',
  'Albalina',
  'Albarosa',
  'Albeer',
  'Alberino',
  'Albertajean',
  'Alberts',
  'Albertyne',
  'Albrecht',
  'Alcindo',
  'Alcombe',
  'Ald',
  'Aldaine',
  'Aldert',
  'Aldevino',
  'Aldge',
  'Aldith',
  'Aled',
  'Aleempasha',
  'Alefiya',
  'Alejan',
  'Aleksan',
  'Aleksanja',
  'Alemka',
  'Alemseged',
  'Alemu',
  'Alesan',
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# Creating a DF from the dictionary above
u_names_df = pd.DataFrame(u_pred_dict)

# Displaying output
u_names_df


# Running value_counts() method on "gender" column from DF above to ensure all rows receieved a gender prediction
u_names_df.value_counts("gender")

gender
F    7538
M    7020
dtype: int64


# Creating a new DF to be used in the cell below because if 'sunshine_first_pass' was used in the concatenation below, the 9 EV values would be added, but we don't want this since the 9 EV values have already been assigned a gender within the 'u_names_df' DF, so it is to avoid adding these names twice
# *NOTE TO SELF: If I can figure out a way to add another condition to the loc within the pd.concatenate line in the cell below then I can take this cell out
sunshine_first_pass_updated = sunshine_first_pass.loc[sunshine_first_pass["gender"] != "EV"]
sunshine_first_pass_updated


# Concatenating the 'u_names_df' DF with the 'sunshine_first_pass_gender' DF but using loc to only include the 'sunshine_first_pass_gender' DF 'gender' columns that do NOT == 'U' or 'EV' to get our final DF product
final_sunshine_pred_df = pd.concat([sunshine_first_pass_updated.loc[sunshine_first_pass_updated.gender != 'U'], u_names_df]).reset_index(drop=True).sort_values(by='first_name').reset_index(drop=True)
final_sunshine_pred_df


# # db_password = 'db_password'
# db_password = getpass ('Enter in Password')


# connect to local server
db_string = f"postgresql://postgres:{db_password}@127.0.0.1:5432/Final_Project_SunshineList"


# Create database engine
engine = create_engine(db_string)


# Import final DF into SQL table
final_sunshine_pred_df.to_sql(name='sunshine_unique_first_name', con=engine)


# Running 'value_counts()' method on the final DF's gender column to ensure the model has predicted a gender for all names in the final DF
final_sunshine_pred_df.value_counts("gender")

	first_name	F	M
0	Aaban	0.0	120.0
1	Aabha	46.0	0.0
2	Aabid	0.0	16.0
3	Aabidah	5.0	0.0
4	Aabir	0.0	10.0
...	...	...	...
89251	Zyvion	0.0	5.0
89252	Zyvon	0.0	7.0
89253	Zyyanna	6.0	0.0
89254	Zyyon	0.0	6.0
89255	Zzyzx	0.0	10.0

	first_name	gender
0	Aaan	U
1	Aadil	M
2	Aadu	U
3	Aaida	U
4	Aaisha	F
...	...	...
28769	Éloise	U
28770	Élyse	U
28771	Émilie	U
28772	Éric	U
28773	Étienne	U

	first_name	gender	frequency
0	Aaban	M	120
1	Aabha	F	46
2	Aabid	M	16
3	Aabidah	F	5
4	Aabir	M	10
...	...	...	...
111467	Zyvion	M	5
111468	Zyvon	M	7
111469	Zyyanna	F	6
111470	Zyyon	M	6
111471	Zzyzx	M	10

	first_name	gender
0	Aaan	U
1	Aadil	M
2	Aadu	U
3	Aaida	U
4	Aaisha	F
...	...	...
28769	Éloise	U
28770	Élyse	U
28771	Émilie	U
28772	Éric	U
28773	Étienne	U

	first_name	gender
0	Aaan	M
1	Aadil	M
2	Aadu	M
3	Aaida	F
4	Aaisha	F
...	...	...
28769	Éloise	F
28770	Élyse	F
28771	Émilie	F
28772	Éric	M
28773	Étienne	F

Machine Learning Model Part 1: Relative Name Frequency Function¶

Step 2: Loading in List of Unique First Names from Sunshine List¶

Step 3: Create Function¶

Step 4: Run Function¶

Machine Learning Model 2: Naive Bayes Classifier via NLTK Library¶

(To be used as training data for the NLTK classifier)¶

Step 2: Preprocessing Sunshine List Unique First Names Data to be Used in NLTK Model¶

Only the names that receieved a 'U' or 'EV' gender prediction from the relative name frequency model will be passed to the NLTK model.¶

Step 3: Instantiating and Training NLTK Naive Bayes Classification Model Instance¶

Step 4: Naive Bayes Classification Model Gender Predictions¶

Notes:¶

Step 4a: Preprocessing Output from Above¶

Note:¶

Step 5: Concatenation¶

Notes:¶

Step 6: Gender Predictions to Database¶

Notes:¶

	first_name	gender	frequency	year
0	Mary	F	7065	1880
1	Anna	F	2604	1880
2	Emma	F	2003	1880
3	Elizabeth	F	1939	1880
4	Minnie	F	1746	1880
...	...	...	...	...
1995	Woodie	M	5	1880
1996	Worthy	M	5	1880
1997	Wright	M	5	1880
1998	York	M	5	1880
1999	Zachariah	M	5	1880

	first_name	F	M
0	Aaban	0	120
1	Aabha	46	0
2	Aabid	0	16
3	Aabidah	5	0
4	Aabir	0	10
5	Aabriella	51	0
6	Aada	13	0
7	Aadam	0	308
8	Aadan	0	130
9	Aadarsh	0	233

Machine Learning Model Part 1: Relative Name Frequency Function¶

Step 1: Preprocessing U.S. Social Security Names Data¶

Step 2: Loading in List of Unique First Names from Sunshine List¶

Step 3: Create Function¶

High-Level Explanation: Function will use the unique name instances with respective male/female gender frequency from preprocessed US Social Security name data above to calculate probability of each name from input (second parameter) being male/female.¶

Step 4: Run Function¶

Machine Learning Model 2: Naive Bayes Classifier via NLTK Library¶

Step 1: Preprocessing US Social Security Names Data¶

(To be used as training data for the NLTK classifier)¶

Step 2: Preprocessing Sunshine List Unique First Names Data to be Used in NLTK Model¶

Only the names that receieved a 'U' or 'EV' gender prediction from the relative name frequency model will be passed to the NLTK model.¶

Step 3: Instantiating and Training NLTK Naive Bayes Classification Model Instance¶

Step 4: Naive Bayes Classification Model Gender Predictions¶

Notes:¶

Step 4a: Preprocessing Output from Above¶

Note:¶

Step 5: Concatenation¶

Notes:¶

Step 6: Gender Predictions to Database¶

Notes:¶