Converting location names into country names in pandas dataframe

I have a dataframe df such that:

print(df['user_location'].value_counts())

India                           3741
United States                   2455
New Delhi, India                1721
Mumbai, India                   1401
Washington, DC                  1354
                                ... 
SpaceCoast,Florida                 1
stuck in a book.                   1
Beirut , Lebanon                   1
Royston Vasey - Tralfamadore       1
Langham, Colchester                1
Name: user_location, Length: 26920, dtype: int64

I wanted to know the frequency of specific countries like USA, India from the user_location column. Then I wanted to plot the frequencies as USA, India, and Others.
So, I thought about applying some operation on that column such that the value_counts() would give the output as:

India     (sum of all frequencies of all the locations in India including cities, states, etc.)
USA       (sum of all frequencies of all the locations in the USA including cities, states, etc.)
Others    (sum of all frequencies of the other locations)                    

It seemed to me that I should merge the frequencies of rows containing the same countries and merge the rest of them together! But the complexity appeared while handling the rows that contain the names of cities, states, etc. instead of their country names.

The solution what I have come up with so far is given below (and also in stackoverflow):

Firstly, I have tried to get all the locations including cities, unions, states, districts, territories. Then I have made a function checkl() such that it can check if the location is India or USA and then convert it into its country name. Finally the function has been applied on the dataframe column df['user_location'] :

# Trying to get all the locations of USA and India

import pandas as pd

us_url = 'https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States'
us_states = pd.read_html(us_url)[0].iloc[:, 0].tolist()
us_cities = pd.read_html(us_url)[0].iloc[:, 1].tolist() + pd.read_html(us_url)[0].iloc[:, 2].tolist() + pd.read_html(us_url)[0].iloc[:, 3].tolist()
us_Federal_district = pd.read_html(us_url)[1].iloc[:, 0].tolist()
us_Inhabited_territories = pd.read_html(us_url)[2].iloc[:, 0].tolist()
us_Uninhabited_territories = pd.read_html(us_url)[3].iloc[:, 0].tolist()
us_Disputed_territories = pd.read_html(us_url)[4].iloc[:, 0].tolist()

us = us_states + us_cities + us_Federal_district + us_Inhabited_territories + us_Uninhabited_territories + us_Disputed_territories

in_url = 'https://en.wikipedia.org/wiki/States_and_union_territories_of_India#States_and_Union_territories'
in_states = pd.read_html(in_url)[3].iloc[:, 0].tolist() + pd.read_html(in_url)[3].iloc[:, 4].tolist() + pd.read_html(in_url)[3].iloc[:, 5].tolist()
in_unions = pd.read_html(in_url)[4].iloc[:, 0].tolist()
ind = in_states + in_unions

usToStr = ' '.join([str(elem) for elem in us])
indToStr = ' '.join([str(elem) for elem in ind]) 


# Country name checker function

def checkl(T): 
    TSplit_space = [x.lower().strip() for x in T.split()]
    TSplit_comma = [x.lower().strip() for x in T.split(',')]
    TSplit = list(set().union(TSplit_space, TSplit_comma))
    res_ind = [ele for ele in ind if(ele in T)]
    res_us = [ele for ele in us if(ele in T)]
  
    if 'india' in TSplit or 'hindustan' in TSplit or 'bharat' in TSplit or T.lower() in indToStr.lower() or bool(res_ind) == True :
        T = 'India'
    elif 'US' in T or 'USA' in T or 'United States' in T or 'usa' in TSplit or 'united state' in TSplit or T.lower() in usToStr.lower() or bool(res_us) == True:
        T = 'USA'
    elif len(T.split(','))>1 :
        if T.split(',')[0] in indToStr or  T.split(',')[1] in indToStr :
             T = 'India'
        elif T.split(',')[0] in usToStr or  T.split(',')[1] in usToStr :
             T = 'USA'
        else:
             T = "Others"
    else:
        T = "Others"
    return T

# Appling the function on the dataframe column

print(df['user_location'].dropna().apply(checkl).value_counts())

Others    74206
USA       47840
India     20291
Name: user_location, dtype: int64

I am quite new in python coding. I think this code can be written in a better and more compact form. Also, I think there are still a lot of edge cases left to deal with.

Any criticisms and suggestions to improve the efficiency & readability of my code would be greatly appreciated. Also, I want to know if there exist any dedicated python modules so that it can convert all the locations automatically into their country names!