Code Compiles

	import pandas as pd
	import enchant
	import re
	from collections import Counter

	word_list = []
	email_dataframe = pd.read_csv("../input/Emails.csv")
	raw_text_list = email_dataframe['RawText'].tolist()
	for raw_text in raw_text_list:
	word_list = word_list + re.findall('\w+', raw_text.lower())
	english_checker = enchant.Dict("en_US")
	high_frequency_words = Counter(x for x in word_list if not english_checker.check(x)).most_common(100)

	import pandas as pd
	import re
	import csv
	import pycountry
	from collections import Counter

	# Reading all the text in raw email body and partitioning to words.
	word_list = []
	email_dataframe = pd.read_csv("../input/Emails.csv")
	raw_text_list = email_dataframe['RawText'].tolist()
	for raw_text in raw_text_list:
	word_list = word_list + re.findall('\w+', raw_text.lower())

	# Using pyCountry module to list down all the countries. We consider shorten forms of the countries as well.
	# Example: We consider all 3 formats of pakistan. (pakistan, pk, pak).
	# But if it is a language specific word such as 'us', we ignore those items from our search.
	country_list = []
	english_checker = enchant.Dict("en_US")
	country_list = []
	for country in pycountry.countries:
	country_list.append(country.name.strip().lower())
	if not english_checker.check(country.alpha2.lower()):
	country_list.append(country.alpha2.lower())
	if not english_checker.check(country.alpha3.lower()):
	country_list.append(country.alpha3.lower())

	high_frequency_countries = Counter(x for x in word_list if x in country_list).most_common(100)