Of countries that contain megacities (urbanized areas >= 10M), what percentage of their population lives in such areas? Uses 2012 estimates from Demographia World Urban Areas and Wikipedia. Output in comments toward the bottom.

megacities.py

#!/usr/bin/python
from collections import defaultdict

def main():
	#From http://www.newgeography.com/content/002808-world-urban-areas-population-and-density-a-2012-update
	citydata = """
1 	Japan 	Tokyo-Yokohama 	37,126,000 	3,300 	11,300 	8,547 	4,300
2 	Indonesia 	Jakarta 	26,063,000 	1,075 	24,200 	2,784 	9,400
3 	South Korea 	Seoul-Incheon 	22,547,000 	835 	27,000 	2,163 	10,400
4 	India 	Delhi, DL-HR-UP 	22,242,000 	750 	29,700 	1,943 	11,500
5 	Philippines 	Manila 	21,951,000 	550 	39,900 	1,425 	15,400
6 	China 	Shanghai, SHG 	20,860,000 	1,350 	15,500 	3,497 	6,000
7 	United States 	New York, NY-NJ-CT 	20,464,000 	4,495 	4,600 	11,642 	1,800
8 	Brazil 	Sao Paulo 	20,186,000 	1,225 	16,500 	3,173 	6,400
9 	Mexico 	Mexico City 	19,463,000 	790 	24,600 	2,046 	9,500
10 	Egypt 	Cairo 	17,816,000 	660 	27,000 	1,709 	10,400
11 	China 	Beijing, BJ 	17,311,000 	1,350 	12,800 	3,497 	5,000
12 	Japan 	Osaka-Kobe-Kyoto 	17,011,000 	1,240 	13,700 	3,212 	5,300
13 	India 	Mumbai, MAH 	16,910,000 	211 	80,100 	546 	30,900
14 	China 	Guangzhou-Foshan, GD 	16,827,000 	1,225 	13,700 	3,173 	5,300
15 	Russia 	Moscow 	15,512,000 	1,700 	9,100 	4,403 	3,500
16 	Bangladesh 	Dhaka 	15,414,000 	134 	115,000 	347 	44,400
17 	United States 	Los Angeles, CA 	14,900,000 	2,432 	6,100 	6,299 	2,400
18 	India 	Kolkota, WB 	14,374,000 	465 	30,900 	1,204 	11,900
19 	Pakistan 	Karachi 	14,198,000 	300 	47,300 	777 	18,300
20 	Argentina 	Buenos Aires 	13,639,000 	1,020 	13,400 	2,642 	5,200
21 	Turkey 	Istanbul 	13,576,000 	540 	25,100 	1,399 	9,700
22 	Brazil 	Rio de Janeiro 	12,043,000 	780 	15,400 	2,020 	6,000
23 	China 	Shenzhen, GD 	11,885,000 	675 	17,600 	1,748 	6,800
24 	Nigeria 	Lagos 	11,547,000 	350 	33,000 	907 	12,700
25 	France 	Paris 	10,755,000 	1,098 	9,800 	2,844 	3,800
26 	Japan 	Nagoya 	10,027,000 	1,475 	6,800 	3,820 	2,600
"""
	#From http://en.wikipedia.org/wiki/List_of_countries_by_population
	countrydata = """
1 	 China[7] 	1,354,040,000 	December 31, 2012 	19.17% 	Official estimate
2 	 India 	1,210,193,422 	March 1, 2011 	17.14% 	2011 census
3 	 United States 	315,224,000 	January 27, 2013 	4.46% 	Official population clock
4 	 Indonesia 	237,641,326 	May 1, 2010 	3.36% 	2010 census
5 	 Brazil 	193,946,886 	July 1, 2012 	2.75% 	Official estimate
6 	 Pakistan 	182,063,000 	January 27, 2013 	2.58% 	Official population clock
7 	 Nigeria 	166,629,000 	July 1, 2012 	2.36% 	UN estimate
8 	 Bangladesh 	152,518,015 	July 16, 2012 	2.16% 	Official estimate
9 	 Russia 	143,369,806 	January 1, 2013 	2.03% 	Official estimate
10 	 Japan 	127,520,000 	December 1, 2012 	1.81% 	Monthly official estimate
11 	 Mexico 	112,336,538 	June 12, 2010 	1.59% 	2010 census
12 	 Philippines 	92,337,852 	May 1, 2010 	1.31% 	2010 census
13 	 Vietnam 	87,840,000 	July 1, 2011 	1.24% 	Official estimate
14 	 Ethiopia 	84,320,987 	July 1, 2012 	1.19% 	Official estimate
15 	 Egypt 	83,819,000 	January 27, 2013 	1.19% 	Official population clock
16 	 Germany 	81,923,000 	July 31, 2012 	1.16% 	Monthly official estimate
17 	 Iran 	77,105,000 	January 27, 2013 	1.09% 	Official population clock
18 	 Turkey 	74,724,269 	December 31, 2011 	1.06% 	Official estimate
19 	 Democratic Republic of the Congo 	69,575,000 	July 1, 2012 	0.99% 	UN estimate
20 	 Thailand 	65,926,261 	September 1, 2010 	0.93% 	2010 census
21 	 France[8] 	65,635,000 	November 1, 2012 	0.93% 	Monthly official estimate
22 	 United Kingdom 	63,181,775 	March 27, 2011 	0.89% 	2011 census
23 	 Italy 	60,870,745 	June 30, 2012 	0.86% 	Monthly official estimate
24 	 South Africa 	51,770,560 	October 9, 2011 	0.73% 	2011 census
25 	 South Korea 	50,004,441 	July 1, 2012 	0.71% 	Official estimate
26 	 Myanmar 	48,724,000 	July 1, 2012 	0.69% 	UN estimate
27 	 Colombia 	46,894,000 	January 27, 2013 	0.66% 	Official population clock
28 	 Spain 	46,815,916 	November 1, 2011 	0.66% 	2011 census
29 	 Ukraine 	45,560,255 	November 1, 2012 	0.65% 	Monthly official estimate
30 	 Tanzania 	44,929,002 	August 26, 2012 	0.64% 	2012 census
31 	 Argentina 	40,117,096 	October 27, 2010 	0.57% 	2010 census
32 	 Kenya 	38,610,097 	August 24, 2009 	0.55% 	2009 census
33 	 Poland 	38,317,000 	July 1, 2012 	0.54% 	Official estimate
34 	 Algeria 	37,800,000 	January 1, 2013 	0.54% 	Official estimate
35 	 Canada 	35,002,447 	October 1, 2012 	0.5% 	Official estimate
36 	 Uganda 	34,131,400 	July 1, 2012 	0.48% 	Official estimate
37 	 Iraq 	33,330,000 	July 1, 2011 	0.47% 	Official estimate
38 	 Morocco 	32,800,100 	January 27, 2013 	0.46% 	Official population clock
39 	 Sudan 	30,894,000 	April 22, 2008 	0.44% 	2008 census
40 	 Peru 	30,135,875 	June 30, 2012 	0.43% 	Official estimate
41 	 Malaysia 	29,584,000 	January 27, 2013 	0.42% 	Official population clock
42 	 Uzbekistan 	29,559,100 	January 1, 2012 	0.42% 	Official estimate
43 	 Venezuela 	28,946,101 	October 30, 2011 	0.41% 	2011 census
44 	 Saudi Arabia 	28,376,355 	July 1, 2011 	0.4% 	Official estimate
45 	 Nepal 	26,494,504 	June 22, 2011 	0.38% 	2011 census
46 	 Afghanistan 	25,500,100 	January 1, 2013 	0.36% 	Official estimate
47 	 Ghana 	24,658,823 	September 26, 2010 	0.35% 	2010 census
48 	 North Korea 	24,554,000 	July 1, 2012 	0.35% 	Official estimate
49 	 Yemen 	24,527,000 	July 1, 2012 	0.35% 	Official estimate
50 	 Mozambique 	23,700,715 	July 1, 2012 	0.34% 	Official estimate
51 	 Taiwan[9] 	23,305,021 	November 30, 2012 	0.33% 	Monthly official estimate
52 	 Australia 	22,885,049 	January 27, 2013 	0.32% 	Official population clock
53 	 Syria 	21,941,000 	January 27, 2013 	0.31% 	Official population clock
54 	 Ivory Coast 	21,395,000 	July 1, 2009 	0.3% 	Official estimate
55 	 Madagascar 	20,696,070 	July 1, 2011 	0.29% 	Official estimate
56 	 Angola 	20,609,300 	July 1, 2012 	0.29% 	Official estimate
57 	 Sri Lanka 	20,277,597 	March 20, 2012 	0.29% 	2012 census
58 	 Cameroon 	19,406,100 	January 1, 2010 	0.27% 	Official estimate
59 	 Romania 	19,043,767 	October 20, 2011 	0.27% 	2011 census
60 	 Kazakhstan 	16,897,000 	December 1, 2012 	0.24% 	Monthly official estimate
61 	 Netherlands 	16,775,273 	November 30, 2012 	0.24% 	Monthly official estimate
62 	 Chile 	16,572,475 	April 9, 2012 	0.23% 	2012 census
63 	 Niger 	16,274,738 	July 1, 2012 	0.23% 	Official estimate
64 	 Malawi 	15,883,000 	July 1, 2012 	0.22% 	UN estimate
65 	 Burkina Faso 	15,730,977 	July 1, 2010 	0.22% 	Official estimate
66 	 Ecuador 	15,410,700 	January 27, 2013 	0.22% 	Official population clock
67 	 Guatemala 	14,713,763 	July 1, 2011 	0.21% 	Official estimate
68 	 Mali 	14,528,662 	April 1, 2009 	0.21% 	2009 census
69 	 Cambodia 	14,478,000 	July 1, 2012 	0.21% 	UN estimate
70 	 Zambia 	13,092,666 	October 16, 2010 	0.19% 	2010 census
71 	 Zimbabwe 	12,973,808 	August 17, 2012 	0.18% 	2012 census
72 	 Senegal 	12,855,153 	July 1, 2011 	0.18% 	Official estimate
73 	 Chad 	11,274,106 	May 20, 2009 	0.16% 	2009 census
74 	 Cuba 	11,163,934 	September 15, 2012 	0.16% 	2012 census
75 	 Belgium 	11,139,292 	November 1, 2012 	0.16% 	Official estimate
76 	 Guinea 	10,824,200 	July 1, 2010 	0.15% 	Official estimate
77 	 Greece 	10,815,197 	May 24, 2011 	0.15% 	2011 census
78 	 Tunisia 	10,777,500 	July 1, 2012 	0.15% 	Official estimate
79 	 Portugal 	10,562,178 	March 21, 2011 	0.15% 	2011 census
80 	 Rwanda 	10,537,222 	August 15, 2012 	0.15% 	2012 census
81 	 Czech Republic 	10,513,209 	September 30, 2012 	0.15% 	Official estimate
82 	 Bolivia 	10,426,155 	July 1, 2010 	0.15% 	Official estimate
83 	 Haiti 	10,085,214 	July 1, 2010 	0.14% 	Official estimate
"""
	
	#Work with the megacity pop data
	parsedrows = [[item.strip() for item in row.split('\t')] for row in citydata.splitlines()[1:]]
	urbanpoppair = [(row[1], float(row[3].replace(',',''))) for row in parsedrows]
	country, urbanpop = zip(*urbanpoppair)

	#Get unique countries under consideration
	countryset = set(country)

	#Add together megacities for the same country
	totalurbanpop = defaultdict(float)
	for c,u in urbanpoppair:
		totalurbanpop[c] += u
	totalurbanpop = dict(totalurbanpop)

	#Work with the total country population data
	parsedrows = [[item.strip() for item in row.split('\t')] for row in countrydata.splitlines()[1:]]
	countrypoppair = [(row[1].partition('[')[0], float(row[2].replace(',',''))) for row in parsedrows]
	countrypopdict = dict(countrypoppair)

	percurban = [(totalurbanpop[country] / countrypopdict[country],country) for country in countryset if country in countrypopdict]
	percurban = sorted(percurban)
	percurban.reverse()

	for perc, city in percurban:
		print "{:.2%} of the population of {} is in a megacity.".format(perc,city)

	#Output:
# 50.32% of the population of Japan is in a megacity.
# 45.09% of the population of South Korea is in a megacity.
# 34.00% of the population of Argentina is in a megacity.
# 23.77% of the population of Philippines is in a megacity.
# 21.26% of the population of Egypt is in a megacity.
# 18.17% of the population of Turkey is in a megacity.
# 17.33% of the population of Mexico is in a megacity.
# 16.62% of the population of Brazil is in a megacity.
# 16.39% of the population of France is in a megacity.
# 11.22% of the population of United States is in a megacity.
# 10.97% of the population of Indonesia is in a megacity.
# 10.82% of the population of Russia is in a megacity.
# 10.11% of the population of Bangladesh is in a megacity.
# 7.80% of the population of Pakistan is in a megacity.
# 6.93% of the population of Nigeria is in a megacity.
# 4.94% of the population of China is in a megacity.
# 4.42% of the population of India is in a megacity.

if __name__ == "__main__":
	main()