From a9c88342eb873d1458c440d495851b7366f9c4f5 Mon Sep 17 00:00:00 2001 From: Christian Hill Date: Fri, 2 Aug 2019 14:20:36 +0200 Subject: [PATCH] Initial commit: India demographics Notebook --- .gitignore | 6 + india-demographics/html/india-crime.html | 683 ++ .../html/india-demographics.html | 8220 +++++++++++++++++ india-demographics/html/india-fertility.html | 871 ++ india-demographics/html/india-gdsp.html | 3104 +++++++ india-demographics/india-demographics.ipynb | 3016 ++++++ 6 files changed, 15900 insertions(+) create mode 100644 .gitignore create mode 100644 india-demographics/html/india-crime.html create mode 100644 india-demographics/html/india-demographics.html create mode 100644 india-demographics/html/india-fertility.html create mode 100644 india-demographics/html/india-gdsp.html create mode 100644 india-demographics/india-demographics.ipynb diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9f70cdb --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +*.swp +*.pyc +*.pyo +*.db +.DS_Store +.ipynb_checkpoints diff --git a/india-demographics/html/india-crime.html b/india-demographics/html/india-crime.html new file mode 100644 index 0000000..291edcc --- /dev/null +++ b/india-demographics/html/india-crime.html @@ -0,0 +1,683 @@ + + + + +List of states and union territories of India by crime rate - Wikipedia + + + + + + + + + + + + + + + + + + + + + + + + + +
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List of states and union territories of India by crime rate

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From Wikipedia, the free encyclopedia
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States and union
territories of India
ordered by
Flag of India.svg
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This is a list of States and Union Territories of India ranked by the recognizable Crime Rate as on 2012 and 2015, and represents the number of cognizable crimes occurred for every 100,000 persons. The list is compiled from the 2016 Crime in India Report published by National Crime Records Bureau (NCRB), Government of India.[1] +

As of 2016, Delhi has the highest cognizable crime rate of 974.9 (per 100,000 persons) and Uttar Pradesh has the highest incidence of crime based on percentage of share.[2] Lakshwadeep has the lowest crime rate of 43.9 (per 100,000 persons) as well as the lowest incidence of crime based on percentage of share. +

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S.NoState/UT201420152016Percentage Share of State/UT (2016)[3]Rank Based on Incidence / % share (2016)Rate of Cognizable Crimes (2016)Rank Based on Crime Rate (2016) +
1Andhra Pradesh1146041106931067743.613206.415 +
2Arunachal Pradesh2843296825340.129192.317 +
3Assam943371036161022503.414313.95 +
4Bihar1775951769731641635.59157.422 +
5Chhattisgarh5820056692550291.817211.714 +
6Goa4466307426920.128135.625 +
7Gujarat1313851269351471224.911233.211 +
8Haryana7994784466885273.015320.64 +
9Himachal Pradesh1416014007133860.421188.120 +
10Jammu and Kashmir2384823583245010.820196.616 +
11Jharkhand4533545050407101.418120.430 +
12Karnataka1373381388471484025.010237.210 +
13Kerala2067892570742600978.74727.62 +
14Madhya Pradesh2724232686142644188.92337.93 +
15Maharashtra2498342754142617148.83217.113 +
16Manipur3641384731700.126121.928 +
17Meghalaya3679407933660.125120.929 +
18Mizoram2140222824250.130227.312 +
19Nagaland1157130213760.03157.634 +
20Odisha7456983360814602.716191.318 +
21Punjab3716237983400071.319137.024 +
22Rajasthan2104181980801803986.16246.28 +
23Sikkim10657668090.032124.4727 +
24Tamil Nadu1932001875581798966.07258.87 +
25Telangana1068301062821089913.712295.76 +
26Tripura5499469239330.124102.431 +
27Uttar Pradesh2404752419202821719.51128.726 +
28Uttarakhand915610248108670.422101.832 +
29West Bengal1856721795011765695.98188.219 +
30Andaman and Nicobar Islands7468628020.033144.823 +
31Chandigarh3221324829960.127166.421 +
32Dadra and Nagar Haveli2772692440.03557.435 +
33Daman and Diu2333022710.03481.133 +
34Delhi1556541913772095197.05974.91 +
35Lakshwadeep8150360.03643.936 +
36Puducherry3584344040860.123242.89 +
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Note[edit]

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  1. ^ "Crime in India 2016" (PDF). National Crime Record Bureau. Retrieved 1 February 2018. +
    2. +
  2. ^ "Crime in India 2016" (PDF). National Crime Record Bureau. Retrieved 1 February 2018. +
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  3. ^ "Crime in India 2016" (PDF). National Crime Record Bureau. Retrieved 1 February 2018. +
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References[edit]

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Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_states_and_union_territories_of_India_by_crime_rate&oldid=906548268"
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Demographics of India

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From Wikipedia, the free encyclopedia
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This article is about the people from India. For other uses, see Indian.
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Demographics of India
India population density map en.svg
Map showing the population density of each district in India.
Population1,324,171,354 (2016 est.)[1]
Density382 people per.sq.km (2011 est.)
Growth rateIncrease 1.19% (2016) (96th)
Birth rate19.3 births/1,000 population (2016 est.)
Death rate7.3 deaths/1,000 population (2016 est.)
Life expectancy68.89 years (2009 est.)
 • male67.46 years (2009 est.)
 • female72.61 years (2009 est.)
Fertility rate2.2 children born/woman (2016 est.)[2]
Infant mortality rate41 deaths/1,000 live births (2016 est.)[citation needed]
Age structure
0–14 years28.6% (male 190,075,426/female 172,799,553)[2]
15–64 years63.6% (male 381,446,079/female 359,802,209) (2009 est.)
65 and over5.3% (male 29,364,920/female 32,591,030) (2009 est.)
Sex ratio
At birth1.10 male(s)/female (2013 est.)
Under 151.10 male(s)/female (2009 est.)
15–64 years1.06 male(s)/female (2009 est.)
65 and over0.90 male(s)/female (2009 est.)
Nationality
Major ethnicSee Ethnic Groups of India
Language
OfficialSee Languages of India
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India is the second most populated country in the world with nearly a fifth of the world's population. According to the 2017 revision of the World Population Prospects[1], the population stood at 1,324,171,354. +

During 1975–2010 the population doubled to 1.2 billion. The Indian population reached the billion mark in 1998. India is projected to be the world's most populous country by 2024,[3] surpassing the population of China. It is expected to become the first political entity in history to be home to more than 1.5 billion people by 2030, and its population is set to reach 1.7 billion by 2050.[4][5] Its population growth rate is 1.13%, ranking 112th in the world in 2017.[6] +

India has more than 50% of its population below the age of 25 and more than 65% below the age of 35. It is expected that, in 2020, the average age of an Indian will be 29 years, compared to 37 for China and 48 for Japan; and, by 2030, India's dependency ratio should be just over 0.4.[7] +

India has more than two thousand ethnic groups,[8] and every major religion is represented, as are four major families of languages (Indo-European, Dravidian, Austroasiatic and Sino-Tibetan languages) as well as two language isolates (the Nihali language[9] spoken in parts of Maharashtra and the Burushaski language spoken in parts of Jammu and Kashmir (Kashmir). +

Further complexity is lent by the great variation that occurs across this population on social parameters such as income and education. Only the continent of Africa exceeds the linguistic, genetic and cultural diversity of the nation of India.[10] +

The sex ratio is 944 females for 1000 males (2016) (940 per 1000 in 2011[11]) This ratio has been showing an upwards trend for the last two decades after a continuous decline in the last century.[12] +

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History[edit]

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See also: Economic history of India
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Prehistory to early 19th century[edit]

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The following table lists estimates for the population of India (including what are now Pakistan and Bangladesh) from prehistory up until 1820. It includes estimates and growth rates according to five different economic historians, along with interpolated estimates and overall aggregate averages derived from their estimates. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Year +Maddison (2001)[13] +Clark (1967)[14][15][16] +Biraben (1979)[15][17][18] +Durand (1974)[19][15] +McEvedy (1978)[20][15] +Aggregate average +Period +Average
 % growth
/ century +
Population +% growth
/ century +		Population +% growth
/ century +		Population +% growth
/ century +		Population +% growth
/ century +		Population +% growth
/ century +		Population +% growth
/ century +
10,000 BC +— +— +— +— +— +— +— +— +100,000 +— +100,000 +— +Stone Age +3.9 +
4000 BC +— +— +— +— +— +— +— +— +1,000,000 +3.9 +1,000,000 +3.9 +
2000 BC +— +— +— +— +— +— +— +— +6,000,000 +9.4 +6,000,000 +9.4 +Bronze Age +9.4 +
500 BC +— +— +— +— +— +— +— +— +25,000,000 +10 +25,000,000 +10 +Iron Age +10.2 +
400 BC +— +— +— +— +30,000,000 +— +— +— +26,600,000 +6.3 +28,300,000 +13.2 +
200 BC +— +— +— +— +55,000,000 +35.4 +— +— +30,000,000 +6.3 +42,500,000 +22.5 +Maurya era +22.5 +
1 AD +75,000,000 +— +70,000,000 +— +46,000,000 +–9.3 +75,000,000 +— +34,000,000 +6.5 +60,000,000 +18.8 +Classical
era +		5.3 +
200 +75,000,000 +0 +72,500,000 +1.7 +45,000,000 +–1.1 +75,000,000 +0 +39,000,000 +7.1 +61,300,000 +1.1 +
400 +75,000,000 +0 +75,000,000 +1.7 +32,000,000 +–18.6 +75,000,000 +0 +45,000,000 +7.4 +60,400,000 +–0.7 +
500 +75,000,000 +0 +75,000,000 +0 +33,000,000 +3.1 +75,000,000 +0 +48,000,000 +6.5 +61,200,000 +1.3 +
600 +75,000,000 +0 +75,000,000 +0 +37,000,000 +12.1 +75,000,000 +0 +51,000,000 +6.5 +62,600,000 +2.3 +Early
medieval
era +		1.9 +
700 +75,000,000 +0 +75,000,000 +0 +50,000,000 +35.1 +75,000,000 +0 +56,500,000 +10.3 +66,300,000 +5.9 +
800 +75,000,000 +0 +75,000,000 +0 +43,000,000 +–16.3 +75,000,000 +0 +62,000,000 +10.3 +66,000,000 +–0.5 +
900 +75,000,000 +0 +72,500,000 +–3.5 +38,000,000 +–13.2 +75,000,000 +0 +69,500,000 +11.4 +66,000,000 +0 +
1000 +75,000,000 +0 +70,000,000 +–3.5 +40,000,000 +5.3 +75,000,000 +0 +77,000,000 +11.4 +67,400,000 +2.1 +
1100 +81,000,000 +8 +72,500,000 +3.5 +51,000,000 +27.5 +81,300,000 +8.4 +80,000,000 +3.9 +73,200,000 +8.6 +Late
medieval
era +		8.1 +
1200 +87,500,000 +8 +75,000,000 +3.5 +65,100,000 +27.5 +88,200,000 +8.4 +83,000,000 +3.8 +79,800,000 +9 +
1300 +94,500,000 +8 +75,000,000 +0 +83,000,000 +27.5 +95,700,000 +8.4 +88,000,000 +6 +87,200,000 +9.3 +
1400 +102,000,000 +8 +77,000,000 +3.3 +88,800,000 +7 +103,700,000 +8.4 +94,000,000 +6.8 +92,900,000 +7 +
1500 +110,000,000 +8 +79,000,000 +3.3 +95,000,000 +7 +112,500,000 +8.4 +100,000,000 +6.4 +99,300,000 +7 +
1600 +135,000,000 +22.8 +100,000,000 +26.6 +145,000,000 +52.6 +135,800,000 +20.7 +130,000,000 +30 +129,200,000 +30.1 +Mughal era +31.9 +
1650 +150,000,000 +22.2 +150,000,000 +125 +160,000,000 +20.7 +149,100,000 +20.7 +145,000,000 +24.4 +150,800,000 +36.2 +
1700 +165,000,000 +22.2 +200,000,000 +77.8 +175,000,000 +20.7 +163,900,000 +20.7 +160,000,000 +21.8 +172,800,000 +31.3 +
1750 +182,100,000 +21.8 +200,000,000 +0 +182,700,000 +9 +180,000,000 +20.7 +170,000,000 +12.9 +183,000,000 +12.1 +Colonial
era +		12.2 +
1800 +200,900,000 +21.8 +190,000,000 +–10.8 +190,700,000 +9 +— +— +185,000,000 +18.4 +190,400,000 +8 +
1820 +209,000,000 +21.8 +190,000,000 +0 +194,000,000 +9 +— +— +200,000,000 +47.7 +198,300,000 +22 +
+

The population grew from the South Asian Stone Age in 10,000 BC to the Maurya Empire in 200 BC at a steadily increasing growth rate,[21] before population growth slowed down in the classical era up to 500 AD, and then became largely stagnant during the early medieval era up to 1000 AD.[13][15] The population growth rate then increased in the late medieval era (during the Delhi Sultanate) from 1000 to 1500.[13][15] +

India's population growth rate under the Mughal Empire (16th–18th centuries) was higher than during any previous period in Indian history.[21][22][15] Under the Mughal Empire, India experienced an unprecedented economic and demographic upsurge,[21] due to Mughal agrarian reforms that intensified agricultural production,[23] proto-industrialization[24] that established India as the most important centre of manufacturing in international trade,[25] and a relatively high degree of urbanisation for its time;[26] 15% of the population lived in urban centres, higher than the percentage of the population in 19th-century British India[26] and contemporary Europe[26] up until the 19th century.[27] +

Under the reign of Akbar the Great (reigned 1556–1605) in 1600, the Mughal Empire's urban population was up to 17 million people, larger than the urban population in Europe.[28] By 1700, Mughal India had an urban population of 23 million people, larger than British India's urban population of 22.3 million in 1871.[29] Nizamuddin Ahmad (1551–1621) reported that, under Akbar's reign, Mughal India had 120 large cities and 3,200 townships.[26] A number of cities in India had a population between a quarter-million and half-million people,[26] with larger cities including Agra (in Agra Subah) with up to 800,000 people[30] and Dhaka (in Bengal Subah) with over 1 million people.[31] Mughal India also had a large number of villages, with 455,698 villages by the time of Aurangzeb (reigned 1658–1707).[28] +

In the early 18th century, the average life expectancy in Mughal India was 35 years.[32] In comparison, the average life expectancy for several European nations in the 18th century were 34 years in early modern England, up to 30 years in France, and about 25 years in Prussia.[33] +

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Late 19th century to early 20th century[edit]

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The total fertility rate is the number of children born per woman. It is based on fairly good data for the entire years. Sources: Our World In Data and Gapminder Foundation.[34] +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Years188018811882188318841885188618871888188918901902[34] +
Total Fertility Rate in India5.955.925.895.865.825.794.385.765.765.755.755.75 +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
Years1921192219231924192519261927192819291930[34] +
Total Fertility Rate in India5.765.775.785.795.85.815.825.835.855.86 +
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Life expectancy from 1881 to 1950 +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Years +1881 +1891 +1901 +1905 +1911 +1915 +1921 +1925 +1931 +1935 +1941 +1950[35] +
Life expectancy in India +25.4 +24.3 +23.5 +24.0 +23.2 +24.0 +24.9 +27.6 +29.3 +31.0 +32.6 +35.4 +
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The population of India under the British Raj (including what are now Pakistan and Bangladesh) according to censuses: +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Census year +Population +Growth (%) +
1871[36] +238,830,958 +– +
1881[37] +253,896,330 +6.3 +
1891[36] +287,223,431 +13.1 +
1901[36] +293,550,310 +2.2 +
1911[38] +315,156,396 +7.4 +
1921[38] +318,942,480 +1.2 +
1931[38] +352,837,778 +10.6 +
1941[38] +388,997,955 +10.2 +
+

Studies of India's population since 1881 have focused on such topics as total population, birth and death rates, growth rates, geographic distribution, literacy, the rural and urban divide, cities of a million, and the three cities with populations over eight million: Delhi, Greater Mumbai (Bombay), and Kolkata (Calcutta).[39] +

Mortality rates fell in the period 1920–45, primarily due to biological immunisation. Other factors included rising incomes, better living conditions, improved nutrition, a safer and cleaner environment, and better official health policies and medical care.[40] +

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Salient features[edit]

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Crude birth rate trends in India
(per 1000 people, national average)[41][42][43]
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Infant mortality rate trends in India
(per 1000 births, under age 1, national average)
+

India occupies 2.41% of the world's land area but supports over 18% of the world's population. At the 2001 census 72.2% of the population[44] lived in about 638,000 villages[45] and the remaining 27.8%[44] lived in more than 5,100 towns and over 380 urban agglomerations.[46] +

India's population exceeded that of the entire continent of Africa by 200 million people in 2010.[47] However, because Africa's population growth is nearly double that of India, it is expected to surpass both China and India by 2025. +

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Comparative demographics[edit]

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Comparative demographics +
Category +Global ranking +References +
Area +7th +[48] +
Population +2nd +[48] +
Population growth rate +102nd of 212 +in 2010[49] +
Population density +24th of 212 +in 2010[49] +
Male to Female ratio, at birth +12th of 214 +in 2009[50] +
+

List of states and union territories by demographics[edit]

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Main article: List of States and Union Territories by demographics
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population growth of India per decade[51] +
Census year +Population +Change (%) +
1951 +361,088,000 +– +
1961 +439,235,000 +21.6 +
1971 +548,160,000 +24.8 +
1981 +683,329,000 +24.7 +
1991 +846,387,888 +23.9 +
2001 +1,028,737,436 +21.5 +
2011 +1,210,726,932 +17.7 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population distribution by states/union territories (2011) +
Rank +State/UT +Population[52] +Percent (%) +Male +Female +Difference between male and female +Sex Ratio +Rural[53] +Urban[53] +Area[54] (km2) +Density (per km2) +
1 +Uttar Pradesh +199,812,341 +16.50 +104,480,510 +95,331,831 +9,148,679 +930 +155,111,022 +44,470,455 +240,928 +828 +
2 +Maharashtra +112,374,333 +9.28 +58,243,056 +54,131,277 +4,111,779 +929 +61,545,441 +50,827,531 +307,713 +365 +
3 +Bihar +104,099,452 +8.60 +54,278,157 +49,821,295 +4,456,862 +918 +92,075,028 +11,729,609 +94,163 +1,102 +
4 +West Bengal +91,276,115 +7.54 +46,809,027 +44,467,088 +2,341,939 +950 +62,213,676 +29,134,060 +88,752 +1,030 +
5 +Madhya Pradesh +72,626,809 +6.00 +37,612,306 +35,014,503 +2,597,803 +931 +52,537,899 +20,059,666 +308,245 +236 +
6 +Tamil Nadu +72,147,030 +5.96 +36,137,975 +36,009,055 +128,920 +996 +37,189,229 +34,949,729 +130,058 +555 +
7 +Rajasthan +68,548,437 +5.66 +35,550,997 +32,997,440 +2,553,557 +928 +51,540,236 +17,080,776 +342,239 +201 +
8 +Karnataka +61,095,297 +5.05 +30,966,657 +30,128,640 +838,017 +973 +37,552,529 +23,578,175 +191,791 +319 +
9 +Gujarat +60,439,692 +4.99 +31,491,260 +28,948,432 +2,542,828 +919 +34,670,817 +25,712,811 +196,024 +308 +
10 +Andhra Pradesh +49,386,799 +4.08 +24,738,068 +24,648,731 +89,337 +996 +34,776,389 +14,610,410 +160,205 +308 +
11 +Odisha +41,974,218 +3.47 +21,212,136 +20,762,082 +450,054 +979 +34,951,234 +6,996,124 +155,707 +269 +
12 +Telangana +35,193,978 +2.91 +17,704,078 +17,489,900 +214,178 +988 +21,585,313 +13,608,665 +114,840 +307 +
13 +Kerala +33,406,061 +2.76 +16,027,412 +17,378,649 +-1,351,237 +1084 +17,445,506 +15,932,171 +38,863 +859 +
14 +Jharkhand +32,988,134 +2.72 +16,930,315 +16,057,819 +872,496 +948 +25,036,946 +7,929,292 +79,714 +414 +
15 +Assam +31,205,576 +2.58 +15,939,443 +15,266,133 +673,310 +958 +26,780,526 +4,388,756 +78,438 +397 +
16 +Punjab +27,743,338 +2.29 +14,639,465 +13,103,873 +1,535,592 +895 +17,316,800 +10,387,436 +50,362 +550 +
17 +Chhattisgarh +25,545,198 +2.11 +12,832,895 +12,712,303 +120,592 +991 +19,603,658 +5,936,538 +135,191 +189 +
18 +Haryana +25,351,462 +2.09 +13,494,734 +11,856,728 +1,638,006 +879 +16,531,493 +8,821,588 +44,212 +573 +
19 +Delhi (UT) +16,787,941 +1.39 +8,887,326 +7,800,615 +1,086,711 +868 +944,727 +12,905,780 +1,484 +11,297 +
20 +Jammu and Kashmir +12,541,302 +1.04 +6,640,662 +5,900,640 +740,022 +889 +9,134,820 +3,414,106 +222,236 +56 +
21 +Uttarakhand +10,086,292 +0.83 +5,137,773 +4,948,519 +189,254 +963 +7,025,583 +3,091,169 +53,483 +189 +
22 +Himachal Pradesh +6,864,602 +0.57 +3,481,873 +3,382,729 +99,144 +972 +6,167,805 +688,704 +55,673 +123 +
23 +Tripura +3,673,917 +0.30 +1,874,376 +1,799,541 +74,835 +960 +2,710,051 +960,981 +10,486 +350 +
24 +Meghalaya +2,966,889 +0.25 +1,491,832 +1,475,057 +16,775 +989 +2,368,971 +595,036 +22,429 +132 +
25 +Manipur +2,855,794 +0.24 +1,438,687 +1,417,107 +21,580 +985 +1,899,624 +822,132 +22,327 +128 +
26 +Nagaland +1,978,502 +0.16 +1,024,649 +953,853 +70,796 +931 +1,406,861 +573,741 +16,579 +119 +
27 +Goa +1,458,545 +0.12 +739,140 +719,405 +19,735 +973 +551,414 +906,309 +3,702 +394 +
28 +Arunachal Pradesh +1,383,727 +0.11 +713,912 +669,815 +44,097 +938 +1,069,165 +313,446 +83,743 +17 +
29 +Puducherry (UT) +1,247,953 +0.10 +612,511 +635,442 +-22,931 +1037 +394,341 +850,123 +479 +2,598 +
30 +Mizoram +1,097,206 +0.09 +555,339 +541,867 +13,472 +976 +529,037 +561,997 +21,081 +52 +
31 +Chandigarh (UT) +1,055,450 +0.09 +580,663 +474,787 +105,876 +818 +29,004 +1,025,682 +114 +9,252 +
32 +Sikkim +610,577 +0.05 +323,070 +287,507 +35,563 +890 +455,962 +151,726 +7,096 +86 +
33 +Andaman and Nicobar Islands (UT) +380,581 +0.03 +202,871 +177,710 +25,161 +876 +244,411 +135,533 +8,249 +46 +
34 +Dadra and Nagar Haveli (UT) +343,709 +0.03 +193,760 +149,949 +43,811 +774 +183,024 +159,829 +491 +698 +
35 +Daman and Diu (UT) +243,247 +0.02 +150,301 +92,946 +57,355 +618 +60,331 +182,580 +112 +2,169 +
36 +Lakshadweep (UT) +64,473 +0.01 +33,123 +31,350 +1,773 +946 +14,121 +50,308 +32 +2,013 +
– +Total (India) +1,210,854,977 +100 +623,724,248 +586,469,174 +35,585,741 +943 +833,087,662 +377,105,760 +3,287,240 +382 +
+

Religious demographics[edit]

+
Main article: Religion in India
+

The table below summarises India's demographics (excluding the Mao-Maram, Paomata and Purul subdivisions of Senapati District of Manipur state due to cancellation of census results) according to religion at the 2011 census in per cent. The data is "unadjusted" (without excluding Assam and Jammu and Kashmir); the 1981 census was not conducted in Assam and the 1991 census was not conducted in Jammu and Kashmir. +

+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Religious populations' numbers (2011)[55] +
Religion +Population +Percent (%) +
All +1,210,854,977 +100.00 +
Hindus +966,378,868 +79.80 +
Muslims +172,245,158 +14.23 +
Christians +27,819,588 +2.30 +
Sikhs +20,833,116 +1.72 +
Buddhists +8,442,972 +0.70 +
Jains +4,451,753 +0.37 +
Others +7,937,734 +0.66 +
Not stated +2,867,303 +0.24 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Changes in religious demographics over time +
Religious
group +		Population
% 1951 +		Population
% 1961 +		Population
% 1971 +		Population
% 1981 +		Population
% 1991 +		Population
% 2001 +		Population
% 2011[56] +
Hinduism +84.1%Decrease83.45%Decrease82.73%Decrease82.30%Decrease81.53%Decrease80.46%Decrease79.80% +
Islam +9.8%Increase10.69%Increase11.21%Increase11.75%Increase12.61%Increase13.43%Increase14.23% +
Christianity +2.3%Increase2.44%Increase2.60%Decrease2.44%Decrease2.32%Increase2.34%Decrease2.30% +
Sikhism +1.79%Decrease1.79%Increase1.89%Increase1.92%Increase1.94%Decrease1.87%Decrease1.72% +
Buddhism +0.74%Decrease0.74%Decrease0.70%Steady0.70%Increase0.77%Steady0.77%Decrease0.70% +
Jainism +0.46%Decrease0.46%Increase0.48%Decrease0.47%Decrease0.40%Increase0.41%Decrease0.37% +
Zoroastrianism +0.13%Decrease0.09%Steady0.09%Steady0.09%Decrease0.08%Decrease0.06%n/a +
Others/Religion not specified +0.43%Steady0.43%Decrease0.41%Increase0.42%Increase0.44%Increase0.72%Increase0.9% +
+
+
Characteristics of religious groups[56]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Religious
group +		Population (2011)
% +		Growth
(2001–2011)[57][58] +		Sex ratio (2011)
(total)[59] +		Sex ratio (2011)
(rural) +		Sex ratio (2011)
(urban) +		Sex ratio (2011)
(child)[60] +		Literacy (2011)
(%)[61] +		Work participation (2011)
(%)[59][62] +
Hinduism +79.80%16.8%93994692191373.3%41.0% +
Islam +14.23%24.6%95195794194368.5%32.6% +
Christianity +2.30%15.5%10231008104695884.5%41.9% +
Sikhism +1.72%8.4%90390589882875.4%36.3% +
Buddhism +0.70%6.1%96596097393381.3%43.1% +
Jainism +0.37%5.4%95493595988994.9%35.5% +
Others/Religion not specified +0.90%n/a959947975974n/an/a +
+
+

Neonatal and infant demographics[edit]

+
Male to female sex ratio for India, based on its official census data, from 1941 through 2011.[63] The data suggests the existence of high sex ratios before and after the arrival of ultrasound-based prenatal care and sex screening technologies in India.
+

The table below represents the infant mortality rate trends in India, based on sex, over the last 15 years. In the urban areas of India, average male infant mortality rates are slightly higher than average female infant mortality rates.[64] +

+ + + + + + + + + + + + + + + + + +
Infant mortality rate trend (deaths per 1000) +
Year +Male +Female +
1998[65] +70 +74 +
2005[64] +56 +58 +
2009[66] +49 +52 +
+

Some activists believe India's 2011 census shows a serious decline in the number of girls under the age of seven – activists posit that eight million female fetuses may have been aborted between 2001 and 2011.[67] These claims are controversial. Scientists who study human sex ratios and demographic trends suggest that birth sex ratio between 1.08 and 1.12 can be because of natural factors, such as the age of mother at baby's birth, age of father at baby's birth, number of babies per couple, economic stress, endocrinological factors, etc.[68] The 2011 census birth sex ratio in India, of 917 girls to 1000 boys, is similar to 870–930 girls to 1000 boys birth sex ratios observed in Japanese, Chinese, Cuban, Filipino and Hawaiian ethnic groups in the United States between 1940 and 2005. They are also similar to birth sex ratios below 900 girls to 1000 boys observed in mothers of different age groups and gestation periods in the United States.[69][70] +

+

Population within the age group of 0–6[edit]

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population between age 0–6 by state/union territory[71] +
State or UT code +State or UT +Total +Male +Female +Difference +
1 +Jammu and Kashmir +2,008,670 +1,080,662 +927,982 +152,680 +
2 +Himachal Pradesh +763,864 +400,681 +363,183 +37,498 +
3 +Punjab +2,941,570 +1,593,262 +1,348,308 +244,954 +
4 +Chandigarh +117,953 +63,187 +54,766 +8,421 +
5 +Uttarakhand +1,328,844 +704,769 +624,075 +80,694 +
6 +Haryana +3,297,724 +1,802,047 +1,495,677 +306,370 +
7 +Delhi +1,970,510 +1,055,735 +914,775 +140,960 +
8 +Rajasthan +10,504,916 +5,580,212 +4,924,004 +656,208 +
9 +Uttar Pradesh +29,728,235 +15,653,175 +14,075,060 +1,578,115 +
10 +Bihar +18,582,229 +9,615,280 +8,966,949 +648,331 +
11 +Sikkim +61,077 +31,418 +29,659 +1,759 +
12 +Arunachal Pradesh +202,759 +103,430 +99,330 +4,100 +
13 +Nagaland +285,981 +147,111 +138,870 +8,241 +
14 +Manipur +353,237 +182,684 +170,553 +12,131 +
15 +Mizoram +165,536 +83,965 +81,571 +2,394 +
16 +Tripura +444,055 +227,354 +216,701 +10,653 +
17 +Meghalaya +555,822 +282,189 +273,633 +8,556 +
18 +Assam +4,511,307 +2,305,088 +2,206,219 +98,869 +
19 +West Bengal +10,112,599 +5,187,264 +4,925,335 +261,929 +
20 +Jharkhand +5,237,582 +2,695,921 +2,541,661 +154,260 +
21 +Odisha +5,035,650 +2,603,208 +2,432,442 +170,766 +
22 +Chhattisgarh +3,584,028 +1,824,987 +1,759,041 +65,946 +
23 +Madhya Pradesh +10,548,295 +5,516,957 +5,031,338 +485,619 +
24 +Gujarat +7,564,464 +3,974,286 +3,519,890 +454,396 +
25 +Daman and Diu +25,880 +13,556 +12,314 +1,242 +
26 +Dadra and Nagar Haveli +49,196 +25,575 +23,621 +1,954 +
27 +Maharashtra +12,848,375 +6,822,262 +6,026,113 +796,149 +
28 +Andhra Pradesh +8,642,686 +4,448,330 +4,194,356 +253,974 +
29 +Karnataka +6,855,801 +3,527,844 +3,327,957 +199,887 +
30 +Goa +139,495 +72,669 +66,826 +5,843 +
31 +Lakshadweep +7,088 +3,715 +3,373 +342 +
32 +Kerala +3,322,247 +1,695,889 +1,626,358 +69,531 +
33 +Tamil Nadu +6,894,821 +3,542,351 +3,352,470 +189,881 +
34 +Puducherry +127,610 +64,932 +62,678 +2,254 +
35 +Andaman and Nicobar Islands +39,497 +20,094 +19,403 +691 +
– +Total (India) +158,789,287 +82,952,135 +75,837,152 +7,114,983 +
+

Population above the age of 7[edit]

+
Life expectancy map of India, 2011–2016.[72]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population above the age of 7 by state/union territory[71] +
State or UT code +State or UT +Total +Male +Female +
1 +Jammu and Kashmir +– +– +– +
2 +Himachal Pradesh +– +– +– +
3 +Punjab +– +– +– +
4 +Chandigarh +– +– +– +
5 +Uttarakhand +– +– +– +
6 +Haryana +22,055,357 +11,703,083 +10,352,274 +
7 +Delhi +14,782,725 +7,920,675 +6,862,050 +
8 +Rajasthan +58,116,096 +30,039,874 +28,076,222 +
9 +Uttar Pradesh +169,853,242 +88,943,240 +80,910,002 +
10 +Bihar +85,222,408 +44,570,067 +40,652,341 +
11 +Sikkim +546,611 +290,243 +256,368 +
12 +Arunachal Pradesh +1,179,852 +616,802 +563,050 +
13 +Nagaland +1,694,621 +878,596 +816,025 +
14 +Manipur +2,368,519 +1,187,080 +1,181,439 +
15 +Mizoram +925,478 +468,374 +457,104 +
16 +Tripura +3,226,977 +1,644,513 +1,582,464 +
17 +Meghalaya +2,408,185 +1,210,479 +1,197,706 +
18 +Assam +26,657,965 +13,649,839 +13,008,126 +
19 +West Bengal +81,235,137 +41,740,125 +39,495,012 +
20 +Jharkhand +27,728,656 +14,235,767 +13,492,889 +
21 +Odisha +36,911,708 +18,598,470 +18,313,238 +
22 +Chhattisgarh +21,956,168 +11,002,928 +10,953,240 +
23 +Madhya Pradesh +62,049,270 +32,095,963 +29,953,307 +
24 +Gujarat +52,889,452 +27,507,996 +25,381,456 +
25 +Daman and Diu +217,031 +136,544 +80,487 +
26 +Dadra and Nagar Haveli +293,657 +167,603 +126,054 +
27 +Maharashtra +99,524,597 +51,539,135 +47,985,462 +
28 +Andhra Pradesh +76,022,847 +38,061,551 +37,961,296 +
29 +Karnataka +54,274,903 +27,529,898 +26,745,005 +
30 +Goa +1,318,228 +668,042 +650,186 +
31 +Lakshadweep +57,341 +29,391 +27,950 +
32 +Kerala +– +– +– +
33 +Tamil Nadu +65,244,137 +32,616,520 +32,627,617 +
34 +Puducherry +1,116,854 +545,553 +571,301 +
35 +Andaman and Nicobar Islands +340,447 +182,236 +158,211 +
– +Total (India) +1,051,404,135 +540,772,113 +510,632,022 +
+

Literacy rate[edit]

+
Literacy rate map of India, 2011.[73]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Literacy rate by state/union territory[71] +
State or UT code +State or UT +Overall (%) +Male (%) +Female (%) +
1 +Jammu and Kashmir +86.61 +87.26 +86.23 +
2 +Himachal Pradesh +83.78 +90.83 +76.60 +
3 +Punjab +76.60 +81.48 +71.34 +
4 +Chandigarh +86.43 +90.54 +81.38 +
5 +Uttarakhand +79.63 +88.33 +70.70 +
6 +Haryana +76.64 +85.38 +66.77 +
7 +Delhi +86.34 +91.03 +80.93 +
8 +Rajasthan +67.06 +80.51 +52.66 +
9 +Uttar Pradesh +69.72 +79.24 +59.26 +
10 +Bihar +63.82 +73.39 +53.33 +
11 +Sikkim +82.20 +87.29 +76.43 +
12 +Arunachal Pradesh +66.95 +73.69 +59.57 +
13 +Nagaland +80.11 +83.29 +76.69 +
14 +Manipur +79.85 +86.49 +73.17 +
15 +Mizoram +91.58 +93.72 +89.40 +
16 +Tripura +87.75 +92.18 +83.15 +
17 +Meghalaya +75.48 +77.17 +73.78 +
18 +Assam +73.18 +78.81 +67.27 +
19 +West Bengal +77.08 +82.67 +71.16 +
20 +Jharkhand +67.63 +78.45 +56.21 +
21 +Odisha +72.90 +82.40 +64.36 +
22 +Chhattisgarh +71.04 +81.45 +60.59 +
23 +Madhya Pradesh +70.63 +80.53 +60.02 +
24 +Gujarat +79.31 +87.23 +70.73 +
25 +Daman and Diu +87.07 +91.48 +79.59 +
26 +Dadra and Nagar Haveli +77.65 +86.46 +65.93 +
27 +Maharashtra +83.20 +89.82 +75.48 +
28 +Andhra Pradesh[74] +67.35 +74.77 +59.96 +
29 +Karnataka +75.60 +82.85 +68.13 +
30 +Goa +87.40 +92.81 +81.84 +
31 +Lakshadweep +92.28 +96.11 +88.25 +
32 +Kerala +93.91 +96.02 +91.98 +
33 +Tamil Nadu +80.33 +86.81 +73.86 +
34 +Puducherry +86.55 +92.12 +81.22 +
35 +Andaman and Nicobar Islands +86.27 +90.11 +81.84 +
– +Overall (India) +74.03 +82.14 +65.46 +
+

Linguistic demographics[edit]

+
Main article: List of languages by number of native speakers in India
+

41.03% of the Indians speak Hindi while the rest speak Assamese, Bengali, Gujarati, Maithili, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu, Urdu and a variety of other languages. There are a total of 122 languages and 234 mother tongues. The 22 languages are Languages specified in the Eighth Schedule of Indian Constitution and 100 non-specified languages. +

The table immediately below excludes Mao-Maram, Paomata and Purul subdivisions of Senapati District of Manipur state due to cancellation of census results. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Languages of India by number of native speakers at the 2001 census[75] +
Rank +Language +Speakers +Percentage (%) +
1 +Hindi[76] +422,048,642 +41.030 +
2 +Bengali +83,369,769 +8.110 +
3 +Telugu +74,002,856 +7.190 +
4 +Marathi +71,936,894 +6.990 +
5 +Tamil +60,793,814 +5.910 +
6 +Urdu +51,536,111 +5.010 +
7 +Gujarati +46,091,617 +4.480 +
8 +Kannada +37,924,011 +3.690 +
9 +Malayalam +33,066,392 +3.210 +
10 +Odia +33,017,446 +3.210 +
11 +Punjabi +29,102,477 +2.830 +
12 +Assamese +13,168,484 +1.280 +
13 +Maithili +12,179,122 +1.180 +
14 +Bhili/Bhilodi +9,582,957 +0.930 +
15 +Santali +6,469,600 +0.630 +
16 +Kashmiri +5,527,698 +0.540 +
17 +Nepali +2,871,749 +0.280 +
18 +Gondi +2,713,790 +0.260 +
19 +Sindhi +2,535,485 +0.250 +
20 +Konkani +2,489,015 +0.240 +
21 +Dogri +2,282,589 +0.220 +
22 +Khandeshi +2,075,258 +0.200 +
23 +Kurukh +1,751,489 +0.170 +
24 +Tulu +1,722,768 +0.170 +
25 +Meitei (Manipuri) +1,466,705 +0.140 +
26 +Bodo +1,350,478 +0.130 +
27 +Khasi – Garo +1,128,575 +0.112 +
28 +Mundari +1,061,352 +0.105 +
29 +Ho +1,042,724 +0.103 +
+

Largest cities[edit]

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
Largest cities or towns in India
+
Rank +Name +State/UT +Pop. +Rank +Name +State/UT +Pop. +
Mumbai
Mumbai
Delhi
Delhi +		1MumbaiMaharashtra12,478,44711KanpurUttar Pradesh2,920,067 +Bangalore
Bangalore
Hyderabad
Hyderabad +
2DelhiDelhi11,007,83512LucknowUttar Pradesh2,901,474 +
3BangaloreKarnataka8,425,97013NagpurMaharashtra2,405,421 +
4HyderabadTelangana6,809,97014IndoreMadhya Pradesh1,960 521 +
5AhmedabadGujarat5,570,58515ThaneMaharastra1,818,872 +
6ChennaiTamil Nadu4,681,08716BhopalMadhya Pradesh1,795,648 +
7KolkataWest Bengal4,486,67917VisakhapatnamAndhra Pradesh1,730,320 +
8SuratGujarat4,462,00218Pimpri-ChinchwadMaharashtra1,729,359 +
9PuneMaharashtra3,115,43119PatnaBihar1,683,200 +
10JaipurRajasthan3,073,35020LudhianaPunjab1,613,878 +
+


+

+

Vital statistics[edit]

+

UN estimates[edit]

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
United Nations, World Population Prospects: The 2015 revision – India[78] +
Period +Births per year +Deaths per year +Natural change per year +CBR1 +CDR1 +NC1 +TFR1 +IMR1 +
1950–1955 +16,832,000 +9,928,000 +6,904,000 +43.3 +25.5 +17.7 +5.90 +165.0 +
1955–1960 +17,981,000 +9,686,000 +8,295,000 +42.1 +22.7 +19.4 +5.90 +153.1 +
1960–1965 +19,086,000 +9,358,000 +9,728,000 +40.4 +19.8 +20.6 +5.82 +140.1 +
1965–1970 +20,611,000 +9,057,000 +11,554,000 +39.2 +17.2 +22.0 +5.69 +128.5 +
1970–1975 +22,022,000 +8,821,000 +13,201,000 +37.5 +15.0 +22.5 +5.26 +118.0 +
1975–1980 +24,003,000 +8,584,000 +15,419,000 +36.3 +13.0 +23.3 +4.89 +106.4 +
1980–1985 +25,577,000 +8,763,000 +16,814,000 +34.5 +11.8 +22.7 +4.47 +95.0 +
1985–1990 +26,935,000 +9,073,000 +17,862,000 +32.5 +10.9 +21.5 +4.11 +85.1 +
1990–1995 +27,566,000 +9,400,000 +18,166,000 +30.0 +10.2 +19.8 +3.72 +76.4 +
1995–2000 +27,443,000 +9,458,000 +17,985,000 +27.2 +9.4 +17.8 +3.31 +68.9 +
2000–2005 +27,158,000 +9,545,000 +17,614,000 +25.3 +8.4 +16.9 +3.14 +60.7 +
2005–2010 +27,271,000 +9,757,000 +17,514,000 +22.9 +7.9 +15.0 +2.80 +52.9 +
2010–2015 +27,243,000 +– +– +20.4 +7.4 +14.0 +2.48 +– +
1 CBR = crude birth rate (per 1000); CDR = crude death rate (per 1000); NC = natural change (per 1000); TFR = total fertility rate (number of children per woman); IMR = infant mortality rate per 1000 births +
+

Census of India: sample registration system[edit]

+
Total fertility rate map: average births per woman by states and union territories, 2012[79]
+
Total fertility rate map: average births per woman by districts, 2011
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Census of India: sample registration system[80][81][82][83] +
Year +Average population
(x 1000) +		Live births1 +Deaths1 +Natural change +Crude birth rate
(per 1000) +		Crude death rate
(per 1000) +		Natural change
(per 1000) +		Total fertility rate +
1981 +716,493 +24,289,000 +8,956,000 +15,333,000 +33.9 +12.5 +21.4 +– +
1982 +733,152 +24,781,000 +8,725,000 +16,056,000 +33.8 +11.9 +21.9 +– +
1983 +750,034 +25,276,000 +8,925,000 +16,351,000 +33.7 +11.9 +21.8 +– +
1984 +767,147 +26,006,000 +9,666,000 +16,340,000 +33.9 +12.6 +21.3 +– +
1985 +784,491 +25,810,000 +9,257,000 +16,553,000 +32.9 +11.8 +21.1 +– +
1986 +802,052 +26,147,000 +8,903,000 +17,244,000 +32.6 +11.1 +21.5 +– +
1987 +819,800 +26,316,000 +8,936,000 +17,380,000 +32.1 +10.9 +21.2 +– +
1988 +837,700 +26,388,000 +9,215,000 +17,173,000 +31.5 +11.0 +20.5 +– +
1989 +855,707 +26,185,000 +8,814,000 +17,371,000 +30.6 +10.3 +20.3 +– +
1990 +873,785 +26,388,000 +8,476,000 +17,912,000 +30.2 +9.7 +20.5 +3.80 +
1991 +891,910 +26,133,000 +8,741,000 +17,392,000 +29.3 +9.8 +19.5 +– +
1992 +910,065 +26,392,000 +9,192,000 +17,200,000 +29.0 +10.1 +18.9 +– +
1993 +928,226 +26,640,000 +8,633,000 +18,007,000 +28.7 +9.3 +19.4 +– +
1994 +946,373 +27,161,000 +8,801,000 +18,360,000 +28.7 +9.3 +19.4 +– +
1995 +964,486 +27,295,000 +8,680,000 +18,615,000 +28.3 +9.0 +19.3 +3.50 +
1996 +982,553 +26,824,000 +8,745,000 +18,079,000 +27.3 +8.9 +18.4 +– +
1997 +1,000,558 +27,215,000 +8,905,000 +18,310,000 +27.2 +8.9 +18.3 +– +
1998 +1,018,471 +26,989,000 +9,166,000 +17,823,000 +26.5 +9.0 +17.5 +– +
1999 +1,036,259 +26,943,000 +9,015,000 +17,928,000 +26.0 +8.7 +17.3 +– +
2000 +1,053,898 +27,191,000 +8,958,000 +18,233,000 +25.8 +8.5 +17.3 +3.20 +
2001 +1,071,374 +27,213,000 +9,000,000 +18,213,000 +25.4 +8.4 +17.0 +– +
2002 +1,088,694 +27,217,000 +8,818,000 +18,399,000 +25.0 +8.1 +16.9 +– +
2003 +1,105,886 +27,426,000 +8,847,000 +18,579,000 +24.8 +8.0 +16.8 +– +
2004 +1,122,991 +27,064,000 +8,422,000 +18,642,000 +24.1 +7.5 +16.6 +– +
2005 +1,140,043 +27,133,000 +8,664,000 +18,469,000 +23.8 +7.6 +16.2 +2.90 +
2006 +1,157,039 +27,190,000 +8,678,000 +18,512,000 +23.5 +7.5 +16.0 +– +
2007 +1,134,024 +26,195,954 +8,391,778 +17,804,176 +23.1 +7.4 +15.7 +– +
2008 +1,150,196 +26,224,469 +8,511,450 +17,713,019 +22.8 +7.4 +15.4 +2.60 +
2009 +1,166,228 +26,240,130 +8,513,464 +17,726,666 +22.5 +7.3 +15.2 +2.60 +
2010 +1,182,108 +26,124,587 +8,511,178 +17,613,409 +22.1 +7.2 +14.9 +2.50 +
2011 +1,197,658 +26,108,944 +8,503,372 +17,605,572 +21.8 +7.1 +14.7 +2.44 +
2012 +1,212,827 +26,197,063 +8,489,789 +17,707,274 +21.6 +7.0 +14.6 +~ 2.4 +
2013 +1,227,012 +26,258,057 +8,589,084 +17,668,973 +21.4 +7.0 +14.4 +~ 2.3 +
2014 +1,243,542 +25,904,377 +8,264,730 +17,639,647 +21.0 +6.7 +14.3 +~ 2.3 +
2015 +1,259,108 +26,189,446 +8,184,202 +18,005,244 +20.8 +6.5 +14.3 +~ 2.2 +
2016 +1,273,986 +25,989,314 +8,153,510 +17,835,804 +20.4 +6.4 +14.0 +~ 2.2 +
1 The numbers of births and deaths were calculated from the birth and death rates and the average population. +
+

Life expectancy[edit]

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Period +Life expectancy in
Years +
1950–1955 +36.6 +
1955–1960 +39.7 +
1960–1965 +42.7 +
1965–1970 +46.0 +
1970–1975 +49.4 +
1975–1980 +52.5 +
1980–1985 +54.9 +
1985–1990 +56.7 +
1990–1995 +59.1 +
1995–2000 +61.5 +
2000–2005 +63.5 +
2005–2010 +65.6 +
2010–2015 +67.6 +
+

Source: UN World Population Prospects[84] +

+

Structure of the population[edit]

+

Structure of the population (9 February 2011) (Census) (Includes data for the Indian-administered part of Jammu and Kashmir):[85] +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population by age group +
Age group +Male +Female +Total +Percentage (%) +
0–4 +58,632,074 +54,174,704 +112,806,778 +9.32 +
5–9 +66,300,466 +60,627,660 +126,928,126 +10.48 +
10–14 +69,418,835 +63,290,377 +132,709,212 +10.96 +
15–19 +63,982,396 +56,544,053 +120,526,449 +9.95 +
20–24 +57,584,693 +53,839,529 +111,424,222 +9.20 +
25–29 +51,344,208 +50,069,757 +101,413,965 +8.38 +
30–34 +44,660,674 +43,934,277 +88,594,951 +7.32 +
35–39 +42,919,381 +42,221,303 +85,140,684 +7.03 +
40–44 +37,545,386 +34,892,726 +72,438,112 +5.98 +
45–49 +32,138,114 +30,180,213 +62,318,327 +5.15 +
50–54 +25,843,266 +23,225,988 +49,069,254 +4.05 +
55–59 +19,456,012 +19,690,043 +39,146,055 +3.23 +
60–64 +18,701,749 +18,961,958 +37,663,707 +3.11 +
65–69 +12,944,326 +13,510,657 +26,454,983 +2.18 +
70–74 +9,651,499 +9,557,343 +19,208,842 +1.59 +
75–79 +4,490,603 +4,741,900 +9,232,503 +0.76 +
80–84 +2,927,040 +3,293,189 +6,220,229 +0.51 +
85–89 +1,120,106 +1,263,061 +2,383,167 +0.20 +
90–94 +652,465 +794,069 +1,446,534 +0.12 +
95–99 +294,759 +338,538 +633,297 +0.05 +
100+ +289,325 +316,453 +605,778 +0.05 +
Unknown +2,372,881 +2,116,921 +4,489,802 +0.37 +
Total +623,270,258 +587,584,719 +1,210,854,977 +100.00 +
+

Population pyramid 2016 (estimates)[86] +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Population by age group +
Age group +Male +Female +Total +
0–4 +8.7 +8.2 +8.5 +
5–9 +9.1 +8.8 +8.9 +
10–14 +9.8 +9.4 +9.6 +
15–19 +10.4 +9.9 +10.1 +
20–24 +10.2 +10.7 +10.4 +
25–29 +9.5 +9.8 +9.7 +
30–34 +8.1 +8.0 +8.1 +
35–39 +7.0 +7.2 +7.1 +
40–44 +6.1 +6.1 +6.1 +
45–49 +5.3 +5.4 +5.3 +
50–54 +4.4 +4.3 +4.3 +
55–59 +3.5 +3.7 +3.6 +
60–64 +3.0 +3.1 +3.1 +
65–69 +2.1 +2.2 +2.2 +
70–74 +1.4 +1.5 +1.5 +
75–79 +0.8 +0.9 +0.9 +
80–84 +0.4 +0.5 +0.5 +
85+ +0.2 +0.3 +0.3 +
0–14 +27.6 +26.4 +27.0 +
15–64 +67.5 +68.2 +67.8 +
65+ +4.9 +5.4 +5.4 +
+

Fertility rate[edit]

+

From the Demographic Health Survey:[87] +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Crude birth rate and total fertility rate (wanted fertility rate) +
Year +CBR – Total +TFR – Total1 +CBR – Urban +TFR – Urban1 +CBR – Rural +TFR – Rural1 +
1992–1993 +28.7 +3.39 (2.64) +24.1 +2.70 (2.09) +30.4 +3.67 (2.86) +
1998–1999 +24.8 +2.85 (2.13) +20.9 +2.27 (1.73) +26.2 +3.07 (2.28) +
2005–2006 +23.1 +2.68 (1.90) +18.8 +2.06 (1.60) +25.0 +2.98 (2.10) +
2015–2016 +19.0 +2.18 (1.8) +15.8 +1.75 (1.5) +20.7 +2.41 (1.9) +
CBR = crude birth rate (per 1000); TFR = total fertility rate (number of children per woman). 1Number in parenthesis represents the wanted fertility rate. +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Crude birth rate and total fertility rate (wanted fertility rate) 2015–2016 +
State (Population 2011) +CBR – Total +TFR – Total1 +CBR – Urban +TFR – Urban1 +CBR – Rural +TFR – Rural1 +
Uttar Pradesh (199 812 341) +22.6 +2.74 (2.06) +18.6 +2.08 (1.62) +24.0 +2.99 (2.22) +
Maharashtra (112 374 333) +16.6 +1.87 (1.57) +15.5 +1.68 (1.41) +17.5 +2.06 (1.73) +
Bihar (104 099 452) +27.1 +3.41 (2.48) +20.4 +2.42 (1.83) +28.0 +3.56 (2.58) +
West Bengal (91 276 115) +16.6 +1.77 (1.53) +14.0 +1.57 (1.38) +18.0 +1.85 (1.58) +
Madhya Pradesh (72 626 809) +20.2 +2.32 (1.82) +17.7 +1.95 (1.61) +21.3 +2.48 (1.91) +
Tamil Nadu (72 147 030) +15.5 +1.70 (1.51) +13.9 +1.54 (1.38) +17.2 +1.86 (1.63) +
Rajasthan (68 548 437) +20.8 +2.40 (1.81) +17.5 +1.94 (1.52) +22.0 +2.56 (1.91) +
Karnataka (61 095 297) +15.9 +1.81 (1.42) +15.2 +1.65 (1.30) +16.5 +1.92 (1.50) +
Gujarat (60 439 692) +16.7 +2.03 (1.54) +15.3 +1.82 (1.39) +17.9 +2.19 (1.64) +
Andhra Pradesh (49 386 799) +16.1 +1.83 (1.64) +13.9 +1.53 (1.39) +17.0 +1.96 (1.75) +
Odisha (41 974 218) +18.1 +2.05 (1.69) +15.6 +1.73 (1.50) +18.7 +2.12 (1.72) +
Telangana (35 193 978) +17.1 +1.79 (1.59) +17.1 +1.67 (1.53) +17.2 +1.88 (1.64) +
Kerala (33 406 061) +11.2 +1.56 (1.47) +11.4 +1.57 (1.47) +11.0 +1.55 (1.46) +
Jharkhand (32 988 134) +21.7 +2.55 (2.06) +16.3 +1.78 (1.47) +23.5 +2.83 (2.27) +
Assam (31 205 576) +19.5 +2.21 (1.78) +13.2 +1.45 (1.25) +20.5 +2.34 (1.87) +
Punjab (27 743 338) +13.8 +1.62 (1.37) +13.5 +1.59 (1.32) +14.0 +1.63 (1.39) +
Chhattisgarh (25 545 198) +20.7 +2.23 (1.88) +17.9 +1.78 (1.58) +21.5 +2.37 (1.97) +
Haryana (25 351 462) +18.7 +2.05 (1.63) +16.3 +1.78 (1.44) +20.2 +2.22 (1.75) +
Jammu and Kashmir (12 541 302) +17.7 +2.01 (1.67) +13.9 +1.58 (1.39) +19.4 +2.18 (1.77) +
Uttarakhand (10 086 292) +19.0 +2.07 (1.60) +17.1 +1.80 (1.43) +20.0 +2.24 (1.71) +
Himachal Pradesh (6 864 602) +15.3 +1.88 (1.55) +12.0 +1.43 (1.15) +15.7 +1.92 (1.59) +
Tripura (3 673 917) +15.3 +1.69 (1.55) +12.7 +1.40 (1.34) +16.4 +1.80 (1.62) +
Meghalaya (2 966 889) +24.6 +3.04 (2.79) +16.1 +1.67 (1.57) +26.7 +3.47 (3.18) +
Manipur (2 855 794) +21.2 +2.61 (2.33) +17.5 +2.14 (1.96) +23.7 +2.92 (2.57) +
Nagaland (1 978 502) +21.4 +2.74 (2.35) +16.3 +1.78 (1.58) +24.1 +3.38 (2.86) +
Goa (1 458 545) +12.8 +1.66 (1.37) +13.4 +1.72 (1.37) +11.7 +1.55 (1.37) +
Arunachal Pradesh (1 383 727) +17.9 +2.12 (1.64) +17.0 +1.69 (1.26) +18.2 +2.29 (1.79) +
Mizoram (1 097 206) +18.7 +2.26 (2.15) +16.9 +1.97 (1.89) +21.2 +2.71 (2.54) +
Sikkim (610 577) +11.4 +1.17 (0.88) +12.1 +1.11 (0.82) +11.1 +1.21 (0.91) +
+CBR = crude birth rate (per 1000); TFR = total fertility rate (number of children per woman). 1Number in parenthesis represents the wanted fertility rate. +
+

Regional vital statistics[edit]

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Birth rate, death rate, natural growth rate, and infant mortality rate, by state or UT(2010)[88] +
State or UT +Birth rate +Death rate +Natural growth rate +Infant mortality rate +
Total +Rural +Urban +Total +Rural +Urban +Total +Rural +Urban +Total +Rural +Urban +
Andaman and Nicobar Islands +15.6 +15.5 +15.8 +4.3 +4.8 +3.3 +11.3 +10.7 +12.6 +25 +29 +18 +
Andhra Pradesh +17.9 +18.3 +16.7 +7.6 +8.6 +5.4 +10.2 +9.7 +11.3 +46 +51 +33 +
Arunachal Pradesh +20.5 +22.1 +14.6 +5.9 +6.9 +2.3 +14.6 +15.2 +12.3 +31 +34 +12 +
Assam +23.2 +24.4 +15.8 +8.2 +8.6 +5.8 +14.9 +15.8 +10.1 +58 +60 +36 +
Bihar +28.1 +28.8 +22.0 +6.8 +7.0 +5.6 +21.3 +21.8 +16.4 +48 +49 +38 +
Chandigarh +15.6 +21.6 +15.0 +3.9 +3.7 +3.9 +11.6 +17.9 +11.0 +22 +20 +23 +
Chhattisgarh +25.3 +26.8 +18.6 +8.0 +8.4 +6.2 +17.3 +18.4 +12.4 +51 +52 +44 +
Dadra and Nagar Haveli +26.6 +26.0 +28.6 +4.7 +5.1 +3.3 +21.9 +20.9 +25.3 +38 +43 +22 +
Daman and Diu +18.8 +19.1 +18.3 +4.9 +4.9 +4.8 +13.9 +14.2 +13.6 +23 +19 +29 +
Delhi +17.8 +19.7 +17.5 +4.2 +4.6 +4.1 +13.6 +15.0 +13.4 +30 +37 +29 +
Goa +13.2 +12.6 +13.7 +6.6 +8.1 +5.7 +6.6 +4.5 +8.0 +10 +10 +10 +
Gujarat +21.8 +23.3 +19.4 +6.7 +7.5 +5.5 +15.1 +15.8 +14.0 +44 +51 +30 +
Haryana +22.3 +23.3 +19.8 +6.6 +7.0 +5.6 +15.7 +16.3 +14.3 +48 +51 +38 +
Himachal Pradesh +16.9 +17.5 +11.5 +6.9 +7.2 +4.2 +10.0 +10.3 +7.3 +40 +41 +29 +
Jammu and Kashmir +18.3 +19.5 +13.5 +5.7 +5.9 +4.7 +12.6 +13.6 +8.8 +43 +45 +32 +
Jharkhand +25.3 +26.7 +19.3 +7.0 +7.4 +5.4 +18.3 +19.3 +13.9 +42 +44 +30 +
Karnataka +19.2 +20.2 +17.5 +7.1 +8.1 +5.4 +12.1 +12.1 +12.1 +38 +43 +28 +
Kerala +14.8 +14.8 +14.8 +7.0 +7.1 +6.7 +7.8 +7.7 +8.1 +13 +14 +10 +
Lakshadweep +14.3 +15.5 +13.2 +6.4 +6.1 +6.7 +8.0 +9.5 +6.5 +25 +23 +27 +
Madhya Pradesh +27.3 +29.2 +20.5 +8.3 +9.0 +6.0 +18.9 +20.2 +14.5 +62 +67 +42 +
Maharashtra +17.1 +17.6 +16.4 +6.5 +7.5 +5.3 +10.6 +10.2 +11.1 +28 +34 +20 +
Manipur +14.9 +14.8 +15.3 +4.2 +4.3 +4.0 +10.7 +10.5 +11.3 +14 +15 +9 +
Meghalaya +24.5 +26.6 +14.8 +7.9 +8.4 +5.6 +16.6 +18.2 +9.2 +55 +58 +37 +
Mizoram +17.1 +21.1 +13.0 +4.5 +5.4 +3.7 +12.5 +15.7 +9.3 +37 +47 +21 +
Nagaland +16.8 +17.0 +16.0 +3.6 +3.7 +3.3 +13.2 +13.3 +12.7 +23 +24 +20 +
Odisha +20.5 +21.4 +15.2 +8.6 +9.0 +6.6 +11.9 +12.4 +8.6 +61 +63 +43 +
Puducherry +16.7 +16.7 +16.7 +7.4 +8.2 +7.0 +9.3 +8.5 +9.6 +22 +25 +21 +
Punjab +16.6 +17.2 +15.6 +7.0 +7.7 +5.8 +9.6 +9.5 +9.8 +34 +37 +28 +
Rajasthan +26.7 +27.9 +22.9 +6.7 +6.9 +6.0 +20.0 +20.9 +16.9 +55 +61 +31 +
Sikkim +17.8 +18.1 +16.1 +5.6 +5.9 +3.8 +12.3 +12.3 +12.3 +30 +31 +19 +
Tamil Nadu +15.9 +16.0 +15.8 +7.6 +8.2 +6.9 +8.3 +7.8 +8.9 +24 +25 +22 +
Tripura +14.9 +15.6 +11.5 +5.0 +4.8 +5.7 +9.9 +10.8 +5.8 +27 +29 +19 +
Uttar Pradesh +28.3 +29.2 +24.2 +8.1 +8.5 +6.3 +20.2 +20.7 +17.9 +61 +64 +44 +
Uttarakhand +19.3 +20.2 +16.2 +6.3 +6.7 +5.1 +13.0 +13.5 +11.1 +38 +41 +25 +
West Bengal +16.8 +18.6 +11.9 +6.0 +6.0 +6.3 +10.7 +12.6 +5.6 +31 +32 +25 +
+

CIA World Factbook demographic statistics[edit]

+
Map showing the population density in India, per 2011 Census.[89]
+

The following demographic statistics are from the CIA World Factbook, unless otherwise indicated. +

+
Total population
+
+

1,166,079,217 (July 2009 est. CIA),[90] 1,210 million (2011 census),[91] 1,281,935,911 (July 2017 est.) +

+
Rural population
+
+

72.2%; male: 381,668,992, female: 360,948,755 (2001 census) +

+
Age structure
+
+

0–14 years: 27.34% (male 186,087,665/female 164,398,204) +
15-24 years: 17.9% (male 121,879,786/female 107,583,437) +
25-54 years: 41.08% (male 271,744,709/female 254,834,569) +
55-64 years: 7.45% (male 47,846,122/female 47,632,532) +
65+ years: 6.24% (male 37,837,801/female 42,091,086) (2017 est.) +

+
Median age
+
+

27.9 years +

+
Population growth rate
+
+

1.17% (2017 est.) +

+
Literacy rate
+
+

74% (age 7 and above, in 2011)[92] +
81.4% (total population, age 15–25, in 2006)[93] +

+
Per cent of population below poverty line
+
+

22% (2006 est.) +

+
Unemployment rate
+
+

7.8% +

+
Net migration rate
+
+

−0.05 migrant(s)/1,000 population (2007 est.) +

+
Sex ratio
+
+

At birth: +1.12 male(s)/female +
Under 10 years: +1.13 male(s)/female +
15–24 years: +1.13 male(s)/female +
24–64 years: +1.06 male(s)/female +
65 years and over: +0.9 male(s)/female +
Total population: +1.08 male(s)/female (2017 est.) +

+
Life expectancy at birth
+
+

Total population: 68.8 years +
Male: 67.6 years +
Female: 70.1 years (2017 est.) +

+
Total fertility rate
+
+

2.43 (2017 est.)[94][95] +

The TFR (total number of children born per women) by religion in 2005–2006 was: Hindus, 2.7; Muslims, 3.1; Christians, 2.4; and Sikhs, 2.0.[96] +

+
Religions
+
+

Hindu 80.5%, Muslim 13.4%, Christian 2.3%, Sikh 1.8%, Buddhists 0.8%, Jains 0.4%, others 0.7%, unspecified 0.1% (2001 census)[97][98][99][100] +

+
Scheduled castes and tribes
+
+

Scheduled castes: 16.6% (2011 census);[101][102] +scheduled tribes: 8.6% (2011 census) +

+
Languages
+
+

See Languages of India and List of Indian languages by total speakers. There are 216 languages with more than 10,000 native speakers in India. The largest of these is Hindi with some 337 million, and the second largest is Bengali with 238 million. 22 languages are recognised as official languages. In India, there are 1,652 languages and dialects in total.[103][104] +

+

Population projections[edit]

+

India is projected to overtake China as the world's most populous nation by 2030. India's population growth has raised concerns that it would lead to widespread unemployment and political instability.[105][106] Note that these projections make assumptions about future fertility and death rates which may not turn out to be correct in the event. Fertility rates also vary from region to region, with some higher than the national average and some lower. +

+

2020 estimate[edit]

+

In millions +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Future projections[107] +
YearUnder 1515–6465+Total +
2000361604451010 +
2005368673511093 +
2010370747581175 +
2015372819651256 +
2020373882761332 +
+

Ethnic groups[edit]

+
See also: South Asian ethnic groups, Ethnic relations in India, Caste system in India, Genetics and archaeogenetics of South Asia, and Languages of India
+

The national Census of India does not recognise racial or ethnic groups within India,[108] but recognises many of the tribal groups as Scheduled Castes and Tribes (see list of Scheduled Tribes in India). +

According to a 2009 study published by Reich et al., the modern Indian population is composed of two genetically divergent and heterogeneous populations which mixed in ancient times (about 1,200–3,500 BC), known as Ancestral North Indians (ANI) and Ancestral South Indians (ASI). ASI corresponds to the Dravidian-speaking population of southern India, whereas ANI corresponds to the Indo-Aryan-speaking population of northern India.[109][110] +

For a list of ethnic groups in the Republic of India (as well as neighbouring countries) see ethnic groups of the Indian subcontinent. +

+
+
+
+
+
+
+
+
+
+

Linguistic groups in India chart[111][112] +

+
  Indo-Aryan (75%)
  Dravidian (20%)
  Austroasiatic, Sino-Tibetan, Tai-Kadai and others (5%)
+
+
+

Genetics[edit]

+
See also: Genetics and archaeogenetics of South Asia
+

Y-chromosome DNA[edit]

+

[113] +

Y-Chromosome DNA Y-DNA represents the male lineage, The Indian Y-chromosome pool may be summarised as follows where haplogroups R-M420, H, R2, L and NOP comprise generally more than 80% of the total chromosomes.[114] +

+
  • H ~ 30%
    • +
  • R1a ~ 34%
    • +
  • R2 ~ 15%
    • +
  • L ~ 10%
    • +
  • NOP ~ 10% (Excluding R)
    • +
  • Other Haplogroups 15%
+

Mitochondrial DNA[edit]

+

[115] +

Mitochondrial DNA mtDNA represents the female lineage. The Indian mitochondrial DNA is primarily made up of Haplogroup M[116] +

+ +

Autosomal DNA[edit]

+
Tripuri children preparing for a dance performance. The Tripuris, are a Tibeto-Burman ethnic group.
+

Numerous genomic studies have been conducted in the last 15 years to seek insights into India's demographic and cultural diversity. These studies paint a complex and conflicting picture. +

+
  • In a 2003 study, Basu, Majumder et al. have concluded on the basis of results obtained from mtDNA, Y-chromosome and autosomal markers that "(1) there is an underlying unity of female lineages in India, indicating that the initial number of female settlers may have been small; (2) the tribal and the caste populations are highly differentiated; (3) the Austroasiatic tribals are the earliest settlers in India, providing support to one anthropological hypothesis while refuting some others; (4) a major wave of humans entered India through the northeast; (5) the Tibeto-Burman tribals share considerable genetic commonalities with the Austroasiatic tribals, supporting the hypothesis that they may have shared a common habitat in southern China, but the two groups of tribals can be differentiated on the basis of Y-chromosomal haplotypes; (6) the Dravidian speaking populations were possibly widespread throughout India but are regulated to South India now ; (7) formation of populations by fission that resulted in founder and drift effects have left their imprints on the genetic structures of contemporary populations; (8) the upper castes show closer genetic affinities with Central Asian populations, although those of southern India are more distant than those of northern India; (9) historical gene flow into India has contributed to a considerable obliteration of genetic histories of contemporary populations so that there is at present no clear congruence of genetic and geographical or sociocultural affinities."[117]
    • +
  • In a later 2010 review article, Majumder affirms some of these conclusions, introduces and revises some other. The ongoing studies, concludes Majumder, suggest India has served as the major early corridor for geographical dispersal of modern humans from out-of-Africa. The archaeological and genetic traces of the earliest settlers in India has not provided any conclusive evidence. The tribal populations of India are older than the non-tribal populations. The autosomal differentiation and genetic diversity within India's caste populations at 0.04 is significantly lower than 0.14 for continental populations and 0.09 for 31 world population sets studied by Watkins et al., suggesting that while tribal populations were differentiated, the differentiation effects within India's caste population was less than previously thought. Majumder also concludes that recent studies suggest India has been a major contributor to the gene pool of southeast Asia.[118][119]
    • +
  • Another study covering a large sample of Indian populations allowed Watkins et al. to examine eight Indian caste groups and four endogamous south Indian tribal populations. The Indian castes data show low between-group differences, while the tribal Indian groups show relatively high between-group differentiation. This suggests that people between Indian castes were not reproductively isolated, while Indian tribal populations experienced reproductive isolation and drift. Furthermore, the genetic fixation index data shows historical genetic differentiation and segregation between Indian castes population is much smaller than those found in east Asia, Africa and other continental populations; while being similar to the genetic differentiation and segregation observed in European populations.[119]
    • +
  • In 2006, Sahoo et al. reported their analysis of genomic data on 936 Y-chromosomes representing 32 tribal and 45 caste groups from different regions of India. These scientists find that the haplogroup frequency distribution across the country, between different caste groups, was found to be predominantly driven by geographical, rather than cultural determinants. They conclude there is clear evidence for both large-scale immigration into ancient India of Sino-Tibetan speakers and language change of former Austroasiatic speakers, in the northeast Indian region.[120][121]
    • +
  • The genome studies conducted up until 2010 have been on relatively small population sets. Many are from just one southeastern state of Andhra Pradesh (including Telangana, which was part of the state until June 2014). Thus, any conclusions on demographic history of India must be interpreted with caution. A larger national genome study with demographic growth and sex ratio balances may offer further insights on the extent of genetic differentiation and segregation in India over the millenniums.[118]
+

See also[edit]

+
+ +
+

Government[edit]

+ +

Lists[edit]

+ +

References[edit]

+
+
    +
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  96. ^ Haub, Carl (17 November 2009). "Future Fertility Prospects for India" (PDF). Retrieved 27 January 2017. +
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  97. ^ Religious Composition Census of India: Census Data 2001: India at a glance >> Religious Composition. Office of the Registrar General and Census Commissioner, India. Retrieved 26 November 2008. +
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  98. ^ International Religious Freedom Report 2007 — India International Religious Freedom Report 2007. U.S. Department of State. +
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  99. ^ "CIA's The World Factbook – India". Cia.gov. Retrieved 24 September 2011. +
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  100. ^ "India". +
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  101. ^ "2011 Census Primary Census Abstract" (PDF). +
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  102. ^ "Half of India's dalit population lives in 4 states". +
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  103. ^ "Mother Tongues of India According to the 1961 Census". Languageinindia.com. Retrieved 26 September 2011. +
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  104. ^ Rupert Goodwins.Smashing India's language barriers. ZDNet UK +
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  105. ^ "Soutik Biswas's India: India's census: The good and bad news". BBC. 31 March 2011. Retrieved 24 September 2011. +
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  106. ^ "India set to overtake China as world's most populated country after adding 180 m people in a decade". Daily Mail. London. 31 March 2011. +
    107. +
  107. ^ Based on P.N. Mari Bhat, "Indian Demographic Scenario 2025", Institute of Economic Growth, New Delhi, Discussion Paper No. 27/2001. +
    108. +
  108. ^ Kumar, Jayant. Census of India. 2001. 4 September 2006. Indian Census +
    109. +
  109. ^ Nature (2009). "Reconstructing Indian population history : Abstract". Nature. 461 (7263): 489–494. doi:10.1038/nature08365. PMC 2842210. PMID 19779445. +
    110. +
  110. ^ "Abstract/Presentation". Ichg2011.org. 12 October 2011. Archived from the original on 24 April 2012. Retrieved 16 June 2013. +
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  111. ^ "Indo-Aryan languages". Encyclopædia Britannica Online. Retrieved 10 December 2014. +
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  112. ^ "Dravidian languages". Encyclopædia Britannica Online. Retrieved 10 December 2014. +
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  113. ^ Sahoo S, Singh A, Himabindu G, et al. (January 2006). "A prehistory of Indian Y chromosomes: Evaluating demic diffusion scenarios". Proc. Natl. Acad. Sci. U.S.A. 103 (4): 843–8. doi:10.1073/pnas.0507714103. PMC 1347984. PMID 16415161. Retrieved 24 September 2011. +
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  114. ^ Hammer et al. 2005, S. Sahoo et al. 2006, R. Trivedi et al. 2007, Zhao et al. 2008 +
    115. +
  115. ^ "1471-2148-5-26.fm" (PDF). Retrieved 16 June 2013. +
    116. +
  116. ^ Semino et al. 2000, Kivisild et al. 2003, Metspalu et al. 2004, Rajkumar et al. 2005, Chandrasekar et al. 2007, Gonzalez et al. 2007 +
    117. +
  117. ^ "Ethnic India: A Genomic View, With Special Reference to Peopling and Structure". Genome.cshlp.org. Retrieved 16 June 2013. +
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  118. ^ a b Majumder (23 February 2010). "The Human Genetic History of South Asia: A Review". Current Biology. 20 (4): R184–7. doi:10.1016/j.cub.2009.11.053. PMID 20178765. +
    119. +
  119. ^ a b Watkins; et al. (July 2003). "Genetic variation among world populations: inferences from 100 Alu insertion polymorphisms". Genome Res. 13 (7): 1607–18. doi:10.1101/gr.894603. PMC 403734. PMID 12805277. +
    120. +
  120. ^ Sahoo; et al. (2006). "A prehistory of Indian Y-chromosomes: evaluating demic diffusion scenarios". Proc. Natl. Acad. Sci. USA. 103 (4): 843–848. doi:10.1073/pnas.0507714103. PMC 1347984. PMID 16415161. +
    121. +
  121. ^ Artis Zelmenis (2014). "Immigration for Indians to Europe; history & law". Immigration World Guru. 1 (1): 10–24. +
    122. +
+

Bibliography[edit]

+ +
Historical
+
  • Lal, K. S. (1978). Growth of Muslim population in medieval India (A.D. 1000–1800). Delhi, Research Publications.
    • +
  • Lal, K. S. (1995). Growth of scheduled tribes and castes in medieval India. New Delhi: Aditya Prakashan.
+

External links[edit]

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List of states and union territories of India by fertility rate

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From Wikipedia, the free encyclopedia
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This is a list of the States and union territories of India of India ranked in order of number of children born for each woman. Recent surveys show that majority of Indian states fertility rate has fallen well below the replacement level of 2.1 and the country is fast approaching the replacement level itself.[1] The total fertility rate of India stands at 2.2 as of 2017.[2] +

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RankState/UT +Fertility rate 1981[2]Fertility rate 1991[2]Fertility rate 1999[3]Fertility rate 2009 [2]Fertility rate 2016[4]Change (1981-99)Change (1999-16) +
1Sikkim +--2.52.11.2Positive decreasePositive decrease +
2Manipur +--2.41.51.4Positive decreasePositive decrease +
1Andaman & Nicobar +--1.91.51.5Positive decreasePositive decrease +
3Goa +--1.01.61.5Positive decreasePositive decrease +
4Lakshadweep +--2.82.11.5Positive decreasePositive decrease +
5Chandigarh +---1.81.6Positive decreasePositive decrease +
6Punjab +4.63.12.51.91.6Positive decreasePositive decrease +
7West Bengal +4.23.22.41.91.6Positive decreasePositive decrease +
8Puduchery +--1.81.61.7Positive decreasePositive decrease +
9Himachal Pradesh +3.83.12.41.91.7Positive decreasePositive decrease +
10Andhra Pradesh +4.0*3.0*2.4*1.8*1.7Positive decreasePositive decrease +
12Jammu and Kashmir +4.5--2.21.7Positive decreasePositive decrease +
13Tamil Nadu +3.42.22.01.71.7Positive decreasePositive decrease +
14Delhi +-2.11.61.91.7Positive decreasePositive decrease +
15Tripura +--3.91.71.7Positive decreasePositive decrease +
16Daman & Diu +--2.51.91.7Positive decreasePositive decrease +
17Kerala +2.81.81.71.71.8Positive decreasePositive decrease +
18Telangana +4.0*3.0*2.4*1.8*1.8Positive decreasePositive decrease +
19Karnataka +3.63.12.51.91.8Positive decreasePositive decrease +
20Maharashtra +3.63.02.71.91.8Positive decreasePositive decrease +
21Odisha +4.33.32.72.42.0Positive decreasePositive decrease +
22Uttarakhand +---2.61.9Positive decreasePositive decrease +
23Arunachal Pradesh +---2.72.1Positive decreasePositive decrease +
24Gujarat +4.33.13.02.52.2Positive decreasePositive decrease +
25Haryana +5.04.03.22.52.2Positive decreasePositive decrease +
– India +4.53.83.22.62.2 Positive decrease 0.14 Positive decrease 0.74 +
26Assam +4.13.53.22.62.3Positive decreasePositive decrease +
27Dadra Nagar Haveli +--3.53.32.3Positive decreasePositive decrease +
28Mizoram +---2.02.3Positive decreasePositive decrease +
29Chhattisgarh +--3.73.02.5Positive decreasePositive decrease +
30Jharkhand +--3.73.22.6Positive decreasePositive decrease +
31Nagaland +--1.52.02.6Positive decreasePositive decrease +
32Madhya Pradesh +5.24.63.93.32.7Positive decreasePositive decrease +
33Rajasthan +5.24.64.23.32.7Positive decreasePositive decrease +
34Meghalaya +---3.12.9Positive decreasePositive decrease +
35Uttar Pradesh +5.85.14.73.73.1Positive decreasePositive decrease +
36Bihar +5.74.44.53.93.3Positive decreasePositive decrease +
+

Country comparisons use data from the Population Reference Bureau.[4] +


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Total Fertility Rate Yearwise [5] +
Rank in 2015State/UT201320142015 +
1J&K1.91.71.6 +
2Tamil Nadu1.71.71.6 +
3West Bengal1.61.61.6 +
4Andhra Pr.1.81.81.7 +
5Delhi1.71.71.7 +
6Himachal Pr.1.71.71.7 +
7Punjab1.71.71.7 +
8Karnataka1.91.81.8 +
9Kerala1.81.91.8 +
10Maharashtra1.81.81.8 +
11Odisha2.12.12 +
12Gujarat2.32.32.2 +
13Haryana2.22.32.2 +
*All India2.32.32.3 +
14Assam2.32.32.3 +
15Chhattisgarh2.62.62.5 +
16Jharkhand2.72.82.7 +
17Rajasthan2.82.82.7 +
18Madhya Pr.2.92.82.8 +
19Uttar Pr.3.13.23.1 +
20Bihar3.43.23.2 +
21Telangana1.81.8 +
22Uttarakhand2.62.62.6 +
+

Visualisation[edit]

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Google Chart TFR vs Area vs Population +

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Notes[edit]

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+
    +
  1. ^ "Three states hold the key". The Indian Express. 15 July 2016. Retrieved 1 June 2017. +
    2. +
  2. ^ a b c d Table in [1] Fourth +National Family Health Survey of TFR, Department of Health and Family Welfare, Ministry of Health and Family Welfare, Government of India +
    3. +
  3. ^ Table in [2] SRS Report (1999), Census Commission of India +
    4. +
  4. ^ Table in [3] +
    5. +
  5. ^ Table in Population Control Measures for States with High TFR Rajya Sabha Unstarred Question 2958, 2017 Department of Health and Family Welfare, Ministry of Health and Family Welfare, Government of India +
    6. +
+ + + + +
+ +
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List of Indian states and union territories by GDP

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From Wikipedia, the free encyclopedia
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This article is about GDP. For Gross Domestic Product per capita, see List of Indian states and union territories by GDP per capita.
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States and union
territories of India
ordered by
Flag of India.svg
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These are lists of Indian states and union territories by their nominal gross state domestic product (GSDP). GSDP is the sum of all value added by industries within each state or union territory and serves as a counterpart to the national gross domestic product (GDP).[1] +

In India, the Government accounts for around 21% of the GDP, Agriculture accounts for 21%, the corporate sector accounts for 12% and the balance 48% of the GDP is sourced from small proprietorship and partnership companies, the so-called unorganized sector and households.[2] +

+
GDP of Indian states and union territories in 2019–20
+ + +

GSDP

+

The following list gives the latest available gross state domestic product (GSDP) figures for all Indian States and Union Territories at current prices in crores (units of 10 million) of Indian rupees. No data is available for the union territories of Dadra and Nagar Haveli, and Lakshadweep. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
List of Indian states and union territories by GDP +
RankState/Union territoryNominal GDP
(INR, ₹)		Data year +
1Maharashtra₹24.96 lakh crore2017–18[3] +
2Tamil Nadu₹17.25 lakh crore2018–19[3] +
3Uttar Pradesh₹15.42 lakh crore2018–19[3] +
4Karnataka₹15.10 lakh crore2018–19[3] +
5Gujarat₹11.62 lakh crore2016–17[3] +
6West Bengal₹10.20 lakh crore2017–18[3] +
7Rajasthan₹9.24 lakh crore2018–19[3] +
8Telangana₹8.67 lakh crore2018–19[3] +
9Andhra Pradesh₹8.10 lakh crore2017–18[3] +
10Madhya Pradesh₹8.09 lakh crore2018–19[3] +
11Delhi₹7.80 lakh crore2018–19[3] +
12Haryana₹7.07 lakh crore2018–19[4] +
13Kerala₹6.87 lakh crore2017–18[5] +
14Punjab₹5.18 lakh crore2018–19[3] +
15Bihar₹4.88 lakh crore2017–18[3] +
16Odisha₹4.16 lakh crore2017–18[3] +
17Chhattisgarh₹3.12 lakh crore2018–19[3] +
18Jharkhand₹2.55 lakh crore2017–18[3] +
19Assam₹2.54 lakh crore2016–17[3] +
20Uttarakhand₹2.14 lakh crore2017–18[3] +
21Himachal Pradesh₹1.52 lakh crore2018–19[3] +
22Jammu and Kashmir₹1.27 lakh crore2016–17[3] +
23Goa₹62,667 crore2016–17[3] +
24Tripura₹46,133 crore2017–18.[3] +
25Puducherry₹35,859 crore2018–19.[3] +
26Chandigarh₹31,823 crore2016–17[3] +
27Meghalaya₹32,972 crore2018–19[3] +
28Arunachal Pradesh₹23,437 crore2017–18[3] +
29Manipur₹23,048 crore2017–18[3] +
30Sikkim₹22,248 crore2017–18[3] +
31Nagaland₹21,488 crore2016–17[3] +
32Mizoram₹17,620 crore2017–18[3] +
33Andaman and Nicobar Islands₹6,649 crore2016–17[6][3] +
—
India₹200.87 lakh crore2018 or 2017 est.[7] +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
List of Indian administrative regions by GDP +
RankZonal councilsNominal GDP
(INR, ₹)		Nominal GDP
(USD, $)		Data yearPopulation(as per 2011)Comparable Country +
1Southern₹62.2 lakh crore$890 billion252,922,172
 Netherlands
+
2Western₹43.7 lakh crore$625 billion174,859,526
 Turkey
+
3Northern₹31.2 lakh crore$500 billion158,892,532
 Thailand
+
4Central₹25.9 lakh crore$400 billion308,070,640
 Nigeria
+
5Eastern₹22.6 lakh crore$365 billion270,337,919
 South Africa
+
6North-Eastern₹5.0 lakh crore$80 billion45,486,784
 Guatemala
+
—
India₹172 lakh crore$2.65 trillion2017 est.[8]1,210,854,977 +
+

|} +List of Indian administrative regions by GDP +

+

Nominal GSDP from 2011–12 to 2020–21

+

The following table shows the annual growth in nominal GSDP for the financial years 2011–12 to 2020–21, from the Ministry of Statistics and Programme Implementation.[9][10] Revised data for the past years differ from the tables below. +Figures are in crores (units of 10 million) of Indian rupees at current prices.[1] No data is available for the union territories of Dadra and Nagar Haveli, Daman and Diu and Lakshadweep. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
State/Union territory +2011–12
in ₹ Crore +		2012–13
in ₹ Crore +		2013–14
in ₹ Crore +		2014–15
in ₹ Crore +		2015–16
in ₹ Crore +		2016–17
in ₹ Crore +		2017–18
in ₹ Crore +		2018–19
in ₹ Crore +		2019–20
in ₹ Crore +		2020–21
in ₹ Crore +
 India +87,36,039 +99,46,636 +1,12,36,635 +1,24,33,749 +1,36,75,331 +1,52,51,028 +– +– +– +– +
Andhra Pradesh +3,79,402 +4,11,404 +4,64,272 +5,24,976 +6,04,229 +6,97,508 +8,09,547 +– +– +
Arunachal Pradesh +11,063 +12,547 +14,581 +17,959 +18,534 +20,207 +23,437 +– +– +– +
Assam +1,43,175 +1,56,864 +1,77,745 +1,98,098 +2,24,234 +2,54,341 +– +– +3,74,096
+		– +
Bihar +2,47,144 +2,82,368 +3,17,101 +3,42,950 +3,69,469 +4,25,887 +4,87,628 +– +– +– +
Chhattisgarh +1,58,074 +1,77,511 +2,06,690 +2,21,142 +2,34,212 +2,62,263 +2,91,680 +3,11,660 +– +– +
Goa +42,367 +38,120 +35,921 +47,814 +55,053 +62,661 +– +– +– +– +
Gujarat +6,15,606 +7,24,495 +8,07,623 +9,21,773 +10,29,010 +11,62,287 +13,10,788 +14,99,013 +17,01,495 +– +
Haryana +2,97,539 +3,47,032 +4,00,662 +4,37,462 +4,85,184 +5,47,396 +– +– +– +– +
Himachal Pradesh +72,720 +82,820 +94,764 +1,03,772 +1,14,239 +1,26,020 +1,36,198 +– +– +– +
Jammu and Kashmir +78,254 +87,105 +95,893 +98,370 +1,17,186 +1,26,846 +– +– +– +– +
Jharkhand +1,50,918 +1,74,724 +1,88,567 +2,18,525 +2,06,613 +2,35,560 +2,55,270 +– +– +– +
Karnataka +6,06,010 +6,95,413 +8,16,666 +9,13,323 +10,45,182 +11,55,912 +13,01,443 +14,10,250 +15,88,751 +– +
Kerala +3,64,048 +4,12,313 +4,65,041 +5,12,564 +5,61,545 +6,21,700 +– +– +– +– +
Madhya Pradesh +3,15,561 +3,80,924 +4,39,483 +4,79,939 +5,42,750 +6,47,303 +7,28,242 +8,09,327 +– +– +
Maharashtra +12,80,369 +14,59,628 +16,49,695 +17,80,721 +1986,721 +22,57,032 +24,96,505 +26,56,551 +– +– +
Manipur +12,915 +13,748 +16,198 +18,043 +19,530 +21,065 +– +– +– +– +
Meghalaya +19,918 +21,872 +22,938 +23,234 +25,117 +27,228 +– +– +– +– +
Mizoram +7,259 +8,362 +10,293 +13,509 +15,138 +17,613 +– +– +– +– +
Nagaland +12,176 +14,121 +16612 +18,401 +19,523 +21,487 +– +– +– +– +
Odisha +2,30,987 +2,61,700 +2,96,475 +3,14,267 +3,30,873 +3,77,201 +4,15,981 +– +– +– +
Punjab +2,66,628 +2,97,734 +3,34,714 +3,55,101 +3,90,087 +4,28,340 +4,75,554 +5,18,291 +– +– +
Rajasthan +4,36,465 +4,94,004 +5,49,701 +6,15,694 +6,83,758 +7,59,234 +8,23,941 +9,24,251 +– +– +
Sikkim +11,165 +12,338 +13,862 +15,209 +18,033 +20,020 +22,247 +– +– +– +
Tamil Nadu +7,51,485 +8,55,481 +9,71,090 +10,72,677 +11,76,500 +12,70,490 +14,27,073 +16,05,893 +– +– +
Telangana +3,59,434 +4,01,594 +4,51,580 +5,05,849 +5,77,902 +6,59,676 +7,53,804 +8,66,875 +– +– +
Tripura +19,208 +21,663 +25,593 +29,533 +35,938 +39,612 +46,133 +– +– +– +
Uttar Pradesh +7,24,049 +8,22,903 +9,44,146 +10,11,789 +11,37,210 +12,50,213 +13,76,324 +15,42,432 +– +– +
Uttarakhand +1,15,523 +1,31,835 +1,49,817 +1,61,985 +1,77,163 +1,91,866 +2,14,033 +– +– +– +
West Bengal +5,20,485 +5,91,464 +6,76,848 +7,18,081 +7,97,299 +8,79,167 +10,20,857 +– +– +– +
Andaman and Nicobar Islands +3,979 +4,421 +5,023 +5,477 +6,032 +6,649 +– +– +– +– +
Chandigarh +18,768 +21,609 +24,787 +26,548 +29,300 +31,823 +– +– +– +– +
Delhi +3,43,767 +3,91,071 +4,43,783 +4,92,424 +5,48,081 +6,16,825 +6,90,098 +7,79,652 +– +– +
Puducherry +16,818 +18,875 +21,870 +22,573 +26,643 +29,279 +32,215 +35859 +– +– +
+

Nominal GDP from 2001–02 to 2010–11

+

The following table shows the annual growth in nominal GSDP for the financial years 2001–2 to 2010–11, released by Planning Commission of India, in local currency.[1] +

+
Revised data for the past years differ from the tables below.  
+
+

Figures are in crores (units of 10 million) of Indian rupees at current prices.[1] No data is available for the union territories of Dadra and Nagar Haveli, Daman and Diu and Lakshadweep. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
State/Union territory +2001–02
in ₹ Crore +		2002–03
in ₹ Crore +		2003–04
in ₹ Crore +		2004–05
in ₹ Crore +		2005–06
in ₹ Crore +		2006–07
in ₹ Crore +		2007–08
in ₹ Crore +		2008–09
in ₹ Crore +		2009–10
in ₹ Crore +		2010–11
in ₹ Crore +
 India +2,097,726 +2,261,415 +2,538,170 +2,971,464 +3,390,503 +3,953,276 +4,582,086 +5,303,567 +6,108,903 +7,248,860 +
Andhra Pradesh +156,711 +167,096 +190,017 +224,713 +255,941 +301,035 +364,813 +426,765 +476,835 +583,762 +
Arunachal Pradesh +2,104 +2,071 +2,368 +3,488 +3,755 +4,108 +4,810 +5,687 +7,474 +9,013 +
Assam +38,313 +43,407 +47,305 +53,398 +59,385 +64,692 +71,076 +81,074 +95,975 +112,688 +
Bihar +57,657 +64,965 +66,174 +77,781 +82,490 +100,737 +113,680 +142,279 +162,923 +204,289 +
Chhattisgarh +29,539 +32,493 +38,802 +47,862 +53,381 +66,875 +80,255 +96,972 +99,364 +119,420 +
Goa +7,097 +8,100 +9,301 +12,713 +14,327 +16,523 +19,565 +25,414 +29,126 +33,605 +
Gujarat +123,573 +141,534 +168,080 +203,373 +244,736 +283,693 +329,285 +367,912 +431,262 +521,519 +
Haryana +65,505 +72,528 +82,862 +95,795 +108,885 +128,732 +151,596 +182,522 +223,600 +260,621 +
Himachal Pradesh +17,148 +18,905 +20,721 +24,077 +27,127 +30,274 +33,963 +41,483 +48,189 +57,452 +
Jammu and Kashmir +18,039 +20,326 +22,194 +27,305 +29,920 +33,230 +37,099 +42,315 +48,385 +58,073 +
Jharkhand +35,069 +37,967 +42,449 +59,758 +60,901 +66,935 +83,950 +87,794 +100,621 +127,281 +
Karnataka +112,847 +120,889 +130,990 +166,747 +195,904 +227,237 +270,629 +310,312 +337,559 +410,703 +
Kerala +77,924 +86,895 +96,698 +119,264 +136,842 +153,758 +175,141 +202,783 +231,999 +263,773 +
Madhya Pradesh +86,745 +86,832 +102,839 +112,927 +124,276 +144,577 +161,479 +197,276 +227,984 +263,396 +
Maharashtra +273,188 +299,479 +340,600 +415,480 +486,766 +584,498 +684,817 +753,969 +855,751 +1,035,086 +
Manipur +3,369 +3,506 +3,979 +5,133 +5,718 +6,137 +6,783 +7,399 +8,254 +9,137 +
Meghalaya +4,478 +4,763 +5,280 +6,559 +7,265 +8,625 +9,735 +11,617 +12,709 +14,583 +
Mizoram +1,947 +2,166 +2,325 +2,682 +2,971 +3,290 +3,816 +4,577 +5,260 +6,388 +
Nagaland +3,972 +4,467 +4,812 +5,839 +6,588 +7,257 +8,075 +9,436 +10,527 +11,759 +
Odisha +46,756 +49,713 +61,008 +77,729 +85,096 +101,839 +129,274 +148,491 +162,946 +197,530 +
Punjab +79,611 +82,249 +90,089 +96,839 +108,637 +127,123 +152,245 +174,039 +197,500 +226,204 +
Rajasthan +91,771 +88,550 +111,606 +127,746 +142,236 +171,043 +194,822 +230,949 +265,825 +338,348 +
Sikkim +1,136 +1,276 +1,430 +1,739 +1,993 +2,161 +2,506 +3,229 +6,133 +7,412 +
Tamil Nadu +148,861 +158,155 +175,371 +219,003 +257,833 +310,526 +350,819 +401,336 +479,733 +584,896 +
Telangana +– +– +– +– +– +– +– +– +– +– +
Tripura +6,370 +6,733 +7,551 +8,904 +9,826 +10,914 +11,797 +13,573 +15,403 +17,868 +
Uttar Pradesh +190,269 +206,855 +226,972 +260,841 +293,172 +336,317 +383,026 +444,685 +523,394 +600,164 +
Uttarakhand +15,144 +18,473 +20,439 +24,786 +29,968 +36,795 +45,856 +56,025 +70,730 +83,969 +
West Bengal +157,144 +168,000 +189,259 +208,656 +230,245 +261,682 +299,483 +341,942 +398,880 +460,959 +
Andaman and Nicobar Islands +1,082 +1,228 +1,392 +1,813 +2,044 +2,538 +2,990 +3,480 +4,120 +4,345 +
Chandigarh +5,490 +6,453 +7,419 +8,504 +10,185 +12,276 +13,669 +15,334 +17,717 +20,017 +
Delhi +65,027 +71,361 +79,468 +100,325 +115,374 +135,584 +157,947 +189,553 +219,753 +252,753 +
Puducherry +4,259 +4,931 +5,438 +5,754 +7,977 +8,335 +9,251 +10,050 +12,304 +13,092 +
+

Nominal GSDP growth rate

+

This table shows annual growth in each state's nominal GSDP growth in percentage.[1][10] No data is available for the union territories of Dadra and Nagar Haveli, Daman and Diu and Lakshadweep. +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
State/Union territory +2010–11 +2011–12 +2012–13 +2013–14 +2014–15 +2015–16 +2016–17 +2017–18 +
 India +18.66 +15.77 +13.80 +13.0 +11.0 +10.4 +10.8 +10.0 +
Andhra Pradesh +17.03 +13.25 +13.20 +12.90 +13.40 +15.90 +14.70 +– +
Arunachal Pradesh +20.59 +17.82 +13.86 +16.20 +23.2 +3.3 +9.2 +– +
Assam +17.41 +11.65 +9.60 +13.30 +10.10 +16.50 +11.60 +– +
Bihar +25.39 +21.06 +14.30 +12.30 +8.20 +7.70 +15.30 +14.50 +
Chhattisgarh +20.18 +11.26 +12.30 +16.50 +6.90 +5.90 +12.90 +11.20 +
Goa +15.38 +7.20 +−10.00 +-5.80 +33.10 +15.10 +13.80 +– +
Gujarat +20.93 +14.01 +17.70 +11.50 +14.10 +11.60 +13.00 +– +
Haryana +16.56 +15.86 +16.60 +15.50 +9.20 +10.90 +12.80 +– +
Himachal Pradesh +19.22 +13.06 +13.90 +14.40 +9.50 +10.10 +10.30 +8.10 +
Jammu and Kashmir +20.02 +13.24 +11.40 +9.70 +2.90 +19.10 +8.20 +– +
Jharkhand +26.50 +13.05 +15.80 +7.90 +15.90 +-5.50 +14.00 +8.40 +
Karnataka +21.7 +10.8 +14.80 +17.40 +11.80 +11.00 +11.80 +12.10 +
Kerala +13.70 +16.73 +13.30 +12.80 +10.20 +9.60 +10.70 +– +
Madhya Pradesh +15.53 +18.33 +20.70 +15.40 +9.20 +13.10 +19.30 +– +
Maharashtra +20.96 +15.89 +14.00 +13.00 +7.90 +11.60 +13.60 +10.6 +
Manipur +10.70 +14.96 +6.40 +17.70 +12.00 +7.70 +7.90 +– +
Meghalaya +14.75 +12.54 +9.80 +4.90 +1.30 +8.10 +8.40 +– +
Mizoram +21.44 +12.68 +15.20 +23.10 +31.20 +12.10 +16.30 +– +
Nagaland +11.70 +12.28 +16.00 +17.60 +10.80 +6.10 +10.10 +– +
Odisha +21.22 +8.63 +13.30 +13.30 +6.00 +5.30 +14.00 +10.30 +
Punjab +14.53 +13.36 +11.70 +11.60 +6.90 +9.90 +9.80 +– +
Rajasthan +27.28 +19.23 +13.50 +11.60 +11.70 +11.10 +11.00 +10.70 +
Sikkim +20.85 +16.24 +10.5 +12.30 +11.10 +17.10 +11.00 +11.10 +
Tamil Nadu +21.92 +13.75 +13.80 +13.30 +10.80 +9.70 +8.00 +12.30 +
Telangana +29.78 +11.99 +11.70 +12.40 +12.00 +14.20 +14.00 +14.10 +
Tripura +16.00 +17.43 +12.80 +18.10 +7.10 +25.30 +– +– +
Uttar Pradesh +14.67 +13.14 +13.40 +14.30 +7.60 +12.40 +9.90 +10.00 +
Uttarakhand +18.72 +16.35 +14.10 +13.30 +8.30 +8.90 +11.30 +11.20 +
West Bengal +15.56 +16.76 +13.60 +14.40 +6.10 +11.00 +10.30 +16.10 +
Andaman and Nicobar Islands +5.46 +9.23 +11.10 +13.60 +9.10 +10.10 +10.20 +– +
Chandigarh +12.98 +15.96 +15.10 +14.90 +7.00 +10.40 +8.60 +– +
Delhi +15.20 +17.49 +13.80 +13.40 +11.50 +10.70 +12.50 +11.20 +
Puducherry +6.40 +11.75 +12.20 +15.90 +3.20 +18.00 +9.90 +10.00 +
+

See also

+ +

References

+
+
    +
  1. ^ a b c d e "Gross State Domestic Product (GSDP) at Current Prices (as on 31-05-2014)" (PDF). Planning Commission Government of India. Archived from the original (PDF) on 15 July 2014. +
    2. +
  2. ^ "National economic debate – Stock markets or rigged casinos – talk by Professor Dr. R. Vaidyanathan (IIM Bangalore) – 21 Jan 2011, Mumbai". National Economic Debates. Retrieved 1 November 2016. +
    3. +
  3. ^ a b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae "MOSPI Gross State Domestic Product". Ministry of Statistics and Programme Implementation, Government of India. 1 March 2019. Retrieved 9 June 2019. +
    4. +
  4. ^ "Economic Survey of Haryana 2018-19: Gross State Domestic Product" (PDF). Department of Economic and Statistical Analysis, Haryana. p. 7. Retrieved 9 June 2019. +
    5. +
  5. ^ "Gross State Domestic Product of Kerala". Department of Economics and Statistics, Government of Kerala. Retrieved 9 June 2019. +
    6. +
  6. ^ "Handbook of Statistics on Indian States". Reserve Bank of Inda. Retrieved 20 February 2017. +
    7. +
  7. ^ "Report for Selected Countries and Subjects". IMF. Retrieved 1 February 2018. +
    8. +
  8. ^ "Report for Selected Countries and Subjects". IMF. Retrieved 17 February 2017. +
    9. +
  9. ^ "State Domestic Product and other aggregates, 2004–05 series". Ministry of Statistics and Programme Implementation. 27 February 2015. Archived from the original on 23 March 2015. Retrieved 18 June 2015. +
    10. +
  10. ^ a b "MOSPI GSDP". MOSPI. +
    11. +
+


+

+

External links

+ + + + + + + +
+ +
Retrieved from "https://en.wikipedia.org/w/index.php?title=List_of_Indian_states_and_union_territories_by_GDP&oldid=908551932"
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Navigation menu

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+ + + + + + + + diff --git a/india-demographics/india-demographics.ipynb b/india-demographics/india-demographics.ipynb new file mode 100644 index 0000000..9c6817d --- /dev/null +++ b/india-demographics/india-demographics.ipynb @@ -0,0 +1,3016 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Some analysis of the demographics of India\n", + "\n", + "In this Notebook we will analyse some of the tables of data at https://en.wikipedia.org/wiki/Demographics_of_India relating to the demographics of India.\n", + "First, we will obtain a local copy of the HTML file so we don't have to keep fetching it from the internet. We will save it in a subdirectory specified by the variable `HTML_DIR`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import urllib\n", + "\n", + "# The directory we're going to save local copies of the HTML files into.\n", + "HTML_DIR = 'html'\n", + "if not os.path.exists(HTML_DIR):\n", + " os.mkdir(HTML_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_htmlpath(filename):\n", + " \"\"\"Get qualified path to local HTML file filename.\"\"\"\n", + " return os.path.join(HTML_DIR, filename)\n", + "\n", + "def fetch_html(url, filename):\n", + " \"\"\"Fetch HTML file for continent from internet and save as filename.\"\"\"\n", + "\n", + " print('Fetching HTML file from', url, '...')\n", + " req = urllib.request.urlopen(url)\n", + " html = req.read().decode()\n", + " filepath = get_htmlpath(filename)\n", + " print('Saving as', filepath, '...')\n", + " with open(filepath, 'w') as fo:\n", + " fo.write(html)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching HTML file from https://en.wikipedia.org/wiki/Demographics_of_India ...\n", + "Saving as html/india-demographics.html ...\n" + ] + } + ], + "source": [ + "url = 'https://en.wikipedia.org/wiki/Demographics_of_India'\n", + "filename = 'india-demographics.html'\n", + "fetch_html(url, filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 1008\r\n", + "-rw-r--r-- 1 christian staff 513046 2 Aug 14:10 india-demographics.html\r\n" + ] + } + ], + "source": [ + "# Check the file is there:\n", + "!ls -l $HTML_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're in business. First some imports and configuration for our Jupyter session:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# I want to always display warnings\n", + "import warnings\n", + "np.seterr(all='warn')\n", + "warnings.simplefilter(\"always\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now read the data tables into a list of pandas `DataFrames`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in 36 HTML tables.\n" + ] + } + ], + "source": [ + "data = pd.read_html(os.path.join(HTML_DIR, filename))\n", + "print(f'Read in {len(data)} HTML tables.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Population statistics\n", + "\n", + "The first one I'm interested in lists the some population distribution by states and union territories and turns out to be at index 8. We'll make a copy into the object `df` so that we can always check it against the original if we need to. We'll also do some tidying in the following lines:\n", + "\n", + "* set the state / UT name as the index;\n", + "* strip out \"(UT\") from the union territory names;\n", + "* drop some of the redundant columns (we will re-create some of these);\n", + "* rename some of the columns to get rid of the footnote numbers; and\n", + "* drop the last row, which contains the totals / summary statistics of the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Population distribution by states / union territories (2011)\n", + "df = data[8].copy()\n", + "df.set_index('State/UT', inplace=True)\n", + "# Match index labels against strings that end in \" (UT)\" and strip this part:\n", + "df.rename({e: re.sub(r'(.+)\\s\\(UT\\)$',r'\\1', e) for e in df.index}, inplace=True)\n", + "df.drop(['Rank', 'Percent (%)', 'Population[52]', 'Difference between male and female',\n", + " 'Sex Ratio', 'Density (per km2)'], axis='columns', inplace=True)\n", + "df.rename({'Rural[53]': 'Rural', 'Urban[53]': 'Urban', 'Area[54] (km2)': 'Area (km2)',\n", + " 'Male': 'Male Population', 'Female': 'Female Population'},\n", + " axis='columns', inplace=True)\n", + "df.drop('Total (India)', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Male PopulationFemale PopulationRuralUrbanArea (km2)
State/UT
Uttar Pradesh1044805109533183115511102244470455240928
Maharashtra58243056541312776154544150827531307713
Bihar5427815749821295920750281172960994163
West Bengal4680902744467088622136762913406088752
Madhya Pradesh37612306350145035253789920059666308245
Tamil Nadu36137975360090553718922934949729130058
Rajasthan35550997329974405154023617080776342239
Karnataka30966657301286403755252923578175191791
Gujarat31491260289484323467081725712811196024
Andhra Pradesh24738068246487313477638914610410160205
Odisha2121213620762082349512346996124155707
Telangana17704078174899002158531313608665114840
Kerala1602741217378649174455061593217138863
Jharkhand169303151605781925036946792929279714
Assam159394431526613326780526438875678438
Punjab1463946513103873173168001038743650362
Chhattisgarh1283289512712303196036585936538135191
Haryana134947341185672816531493882158844212
Delhi88873267800615944727129057801484
Jammu and Kashmir6640662590064091348203414106222236
Uttarakhand513777349485197025583309116953483
Himachal Pradesh34818733382729616780568870455673
Tripura18743761799541271005196098110486
Meghalaya14918321475057236897159503622429
Manipur14386871417107189962482213222327
Nagaland1024649953853140686157374116579
Goa7391407194055514149063093702
Arunachal Pradesh713912669815106916531344683743
Puducherry612511635442394341850123479
Mizoram55533954186752903756199721081
Chandigarh580663474787290041025682114
Sikkim3230702875074559621517267096
Andaman and Nicobar Islands2028711777102444111355338249
Dadra and Nagar Haveli193760149949183024159829491
Daman and Diu1503019294660331182580112
Lakshadweep3312331350141215030832
\n", + "
" + ], + "text/plain": [ + " Male Population Female Population Rural \\\n", + "State/UT \n", + "Uttar Pradesh 104480510 95331831 155111022 \n", + "Maharashtra 58243056 54131277 61545441 \n", + "Bihar 54278157 49821295 92075028 \n", + "West Bengal 46809027 44467088 62213676 \n", + "Madhya Pradesh 37612306 35014503 52537899 \n", + "Tamil Nadu 36137975 36009055 37189229 \n", + "Rajasthan 35550997 32997440 51540236 \n", + "Karnataka 30966657 30128640 37552529 \n", + "Gujarat 31491260 28948432 34670817 \n", + "Andhra Pradesh 24738068 24648731 34776389 \n", + "Odisha 21212136 20762082 34951234 \n", + "Telangana 17704078 17489900 21585313 \n", + "Kerala 16027412 17378649 17445506 \n", + "Jharkhand 16930315 16057819 25036946 \n", + "Assam 15939443 15266133 26780526 \n", + "Punjab 14639465 13103873 17316800 \n", + "Chhattisgarh 12832895 12712303 19603658 \n", + "Haryana 13494734 11856728 16531493 \n", + "Delhi 8887326 7800615 944727 \n", + "Jammu and Kashmir 6640662 5900640 9134820 \n", + "Uttarakhand 5137773 4948519 7025583 \n", + "Himachal Pradesh 3481873 3382729 6167805 \n", + "Tripura 1874376 1799541 2710051 \n", + "Meghalaya 1491832 1475057 2368971 \n", + "Manipur 1438687 1417107 1899624 \n", + "Nagaland 1024649 953853 1406861 \n", + "Goa 739140 719405 551414 \n", + "Arunachal Pradesh 713912 669815 1069165 \n", + "Puducherry 612511 635442 394341 \n", + "Mizoram 555339 541867 529037 \n", + "Chandigarh 580663 474787 29004 \n", + "Sikkim 323070 287507 455962 \n", + "Andaman and Nicobar Islands 202871 177710 244411 \n", + "Dadra and Nagar Haveli 193760 149949 183024 \n", + "Daman and Diu 150301 92946 60331 \n", + "Lakshadweep 33123 31350 14121 \n", + "\n", + " Urban Area (km2) \n", + "State/UT \n", + "Uttar Pradesh 44470455 240928 \n", + "Maharashtra 50827531 307713 \n", + "Bihar 11729609 94163 \n", + "West Bengal 29134060 88752 \n", + "Madhya Pradesh 20059666 308245 \n", + "Tamil Nadu 34949729 130058 \n", + "Rajasthan 17080776 342239 \n", + "Karnataka 23578175 191791 \n", + "Gujarat 25712811 196024 \n", + "Andhra Pradesh 14610410 160205 \n", + "Odisha 6996124 155707 \n", + "Telangana 13608665 114840 \n", + "Kerala 15932171 38863 \n", + "Jharkhand 7929292 79714 \n", + "Assam 4388756 78438 \n", + "Punjab 10387436 50362 \n", + "Chhattisgarh 5936538 135191 \n", + "Haryana 8821588 44212 \n", + "Delhi 12905780 1484 \n", + "Jammu and Kashmir 3414106 222236 \n", + "Uttarakhand 3091169 53483 \n", + "Himachal Pradesh 688704 55673 \n", + "Tripura 960981 10486 \n", + "Meghalaya 595036 22429 \n", + "Manipur 822132 22327 \n", + "Nagaland 573741 16579 \n", + "Goa 906309 3702 \n", + "Arunachal Pradesh 313446 83743 \n", + "Puducherry 850123 479 \n", + "Mizoram 561997 21081 \n", + "Chandigarh 1025682 114 \n", + "Sikkim 151726 7096 \n", + "Andaman and Nicobar Islands 135533 8249 \n", + "Dadra and Nagar Haveli 159829 491 \n", + "Daman and Diu 182580 112 \n", + "Lakshadweep 50308 32 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's recreate columns for each state for the total population, sex ratio (number of women per 1000 men) and population density and produce some summary statistics:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df['Population'] = df['Male Population'] + df['Female Population']\n", + "df['Sex Ratio'] = df['Female Population'] / df['Male Population'] * 1000\n", + "df['Population density (km-2)'] = df['Population'] / df['Area (km2)']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total population of India (2011): 1,210,754,977\n", + "Lowest sex ratio: F:M * 1000 = 618.4 (Daman and Diu)\n", + "Highest sex ratio: F:M * 1000 = 1084.3 (Kerala)\n", + "Lowest population density: 16.5 km-2 (Arunachal Pradesh)\n", + "Aveage population density of India: 368.3 km-2\n" + ] + } + ], + "source": [ + "total_pop = df['Population'].sum()\n", + "print('Total population of India (2011): {:,}'.format(total_pop))\n", + "state_sr_min = df['Sex Ratio'].idxmin()\n", + "print('Lowest sex ratio: F:M * 1000 = {:.1f} ({})'.format(df.loc[state_sr_min]['Sex Ratio'], state_sr_min))\n", + "state_sr_max = df['Sex Ratio'].idxmax()\n", + "print('Highest sex ratio: F:M * 1000 = {:.1f} ({})'.format(df.loc[state_sr_max]['Sex Ratio'], state_sr_max))\n", + "state_least_dense = df['Population density (km-2)'].idxmin()\n", + "print('Lowest population density: {:.1f} km-2 ({})'.format(\n", + " df.loc[state_least_dense]['Population density (km-2)'], state_least_dense))\n", + "\n", + "mean_pop_density = total_pop / df['Area (km2)'].sum()\n", + "print('Aveage population density of India: {:.1f} km-2'.format(mean_pop_density))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Literacy\n", + "\n", + "Next, let's look at the table on literacy rates, which turns out to be the one indexed at 16. We'll do some more cleaning after obtaining a copy of the `DataFrame` first:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Overall (%)Male (%)Female (%)
State or UT
Jammu and Kashmir86.6187.2686.23
Himachal Pradesh83.7890.8376.60
Punjab76.6081.4871.34
Chandigarh86.4390.5481.38
Uttarakhand79.6388.3370.70
Haryana76.6485.3866.77
Delhi86.3491.0380.93
Rajasthan67.0680.5152.66
Uttar Pradesh69.7279.2459.26
Bihar63.8273.3953.33
Sikkim82.2087.2976.43
Arunachal Pradesh66.9573.6959.57
Nagaland80.1183.2976.69
Manipur79.8586.4973.17
Mizoram91.5893.7289.40
Tripura87.7592.1883.15
Meghalaya75.4877.1773.78
Assam73.1878.8167.27
West Bengal77.0882.6771.16
Jharkhand67.6378.4556.21
Odisha72.9082.4064.36
Chhattisgarh71.0481.4560.59
Madhya Pradesh70.6380.5360.02
Gujarat79.3187.2370.73
Daman and Diu87.0791.4879.59
Dadra and Nagar Haveli77.6586.4665.93
Maharashtra83.2089.8275.48
Andhra Pradesh67.3574.7759.96
Karnataka75.6082.8568.13
Goa87.4092.8181.84
Lakshadweep92.2896.1188.25
Kerala93.9196.0291.98
Tamil Nadu80.3386.8173.86
Puducherry86.5592.1281.22
Andaman and Nicobar Islands86.2790.1181.84
\n", + "
" + ], + "text/plain": [ + " Overall (%) Male (%) Female (%)\n", + "State or UT \n", + "Jammu and Kashmir 86.61 87.26 86.23\n", + "Himachal Pradesh 83.78 90.83 76.60\n", + "Punjab 76.60 81.48 71.34\n", + "Chandigarh 86.43 90.54 81.38\n", + "Uttarakhand 79.63 88.33 70.70\n", + "Haryana 76.64 85.38 66.77\n", + "Delhi 86.34 91.03 80.93\n", + "Rajasthan 67.06 80.51 52.66\n", + "Uttar Pradesh 69.72 79.24 59.26\n", + "Bihar 63.82 73.39 53.33\n", + "Sikkim 82.20 87.29 76.43\n", + "Arunachal Pradesh 66.95 73.69 59.57\n", + "Nagaland 80.11 83.29 76.69\n", + "Manipur 79.85 86.49 73.17\n", + "Mizoram 91.58 93.72 89.40\n", + "Tripura 87.75 92.18 83.15\n", + "Meghalaya 75.48 77.17 73.78\n", + "Assam 73.18 78.81 67.27\n", + "West Bengal 77.08 82.67 71.16\n", + "Jharkhand 67.63 78.45 56.21\n", + "Odisha 72.90 82.40 64.36\n", + "Chhattisgarh 71.04 81.45 60.59\n", + "Madhya Pradesh 70.63 80.53 60.02\n", + "Gujarat 79.31 87.23 70.73\n", + "Daman and Diu 87.07 91.48 79.59\n", + "Dadra and Nagar Haveli 77.65 86.46 65.93\n", + "Maharashtra 83.20 89.82 75.48\n", + "Andhra Pradesh 67.35 74.77 59.96\n", + "Karnataka 75.60 82.85 68.13\n", + "Goa 87.40 92.81 81.84\n", + "Lakshadweep 92.28 96.11 88.25\n", + "Kerala 93.91 96.02 91.98\n", + "Tamil Nadu 80.33 86.81 73.86\n", + "Puducherry 86.55 92.12 81.22\n", + "Andaman and Nicobar Islands 86.27 90.11 81.84" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lit = data[16][:-1].copy()\n", + "df_lit.set_index('State or UT', inplace=True)\n", + "df_lit.rename({e: re.sub(r'(.+)\\s\\(UT\\)',r'\\1', e) for e in df.index}, inplace=True)\n", + "df_lit.rename(index={'Andhra Pradesh[74]': 'Andhra Pradesh'}, inplace=True)\n", + "df_lit.drop('State or UT code', axis='columns', inplace=True)\n", + "df_lit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'd like to combine this table with the existing one, `df`, which is also indexed by state / UT name:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Male PopulationFemale PopulationRuralUrbanArea (km2)PopulationSex RatioPopulation density (km-2)Male Literacy (%)Female Literacy (%)
State/UT
Uttar Pradesh1044805109533183115511102244470455240928199812341912.436501829.34462279.2459.26
Maharashtra58243056541312776154544150827531307713112374333929.403103365.19202389.8275.48
Bihar5427815749821295920750281172960994163104099452917.8884801105.52395373.3953.33
West Bengal468090274446708862213676291340608875291276115949.9682191028.44009182.6771.16
Madhya Pradesh3761230635014503525378992005966630824572626809930.932100235.61390880.5360.02
Tamil Nadu3613797536009055371892293494972913005872147030996.432562554.72965986.8173.86
Rajasthan3555099732997440515402361708077634223968548437928.172000200.29405580.5152.66
Karnataka3096665730128640375525292357817519179161095297972.938086318.55142882.8568.13
Gujarat3149126028948432346708172571281119602460439692919.252897308.32802187.2370.73
Andhra Pradesh2473806824648731347763891461041016020549386799996.388683308.27252074.7759.96
Odisha212121362076208234951234699612415570741974218978.783183269.57181182.4064.36
Telangana1770407817489900215853131360866511484035193978987.902335306.460972NaNNaN
Kerala1602741217378649174455061593217138863334060611084.307872859.58523596.0291.98
Jharkhand16930315160578192503694679292927971432988134948.465460413.83112178.4556.21
Assam15939443152661332678052643887567843831205576957.758248397.83747778.8167.27
Punjab146394651310387317316800103874365036227743338895.106003550.87840081.4871.34
Chhattisgarh128328951271230319603658593653813519125545198990.602900188.95635181.4560.59
Haryana13494734118567281653149388215884421225351462878.618875573.40681385.3866.77
Delhi8887326780061594472712905780148416687941877.72351311245.24326191.0380.93
Jammu and Kashmir664066259006409134820341410622223612541302888.56201456.43236087.2686.23
Uttarakhand51377734948519702558330911695348310086292963.164196188.58874888.3370.70
Himachal Pradesh348187333827296167805688704556736864602971.525670123.30217590.8376.60
Tripura187437617995412710051960981104863673917960.074713350.36400992.1883.15
Meghalaya149183214750572368971595036224292966889988.755436132.27914877.1773.78
Manipur143868714171071899624822132223272855794985.000212127.90764586.4973.17
Nagaland10246499538531406861573741165791978502930.907072119.33783783.2976.69
Goa73914071940555141490630937021458545973.300051393.98838592.8181.84
Arunachal Pradesh7139126698151069165313446837431383727938.23188316.52349573.6959.57
Puducherry61251163544239434185012347912479531037.4376952605.32985492.1281.22
Mizoram555339541867529037561997210811097206975.74094452.04715193.7289.40
Chandigarh5806634747872900410256821141055450817.6636029258.33333390.5481.38
Sikkim3230702875074559621517267096610577889.92168986.04523787.2976.43
Andaman and Nicobar Islands2028711777102444111355338249380581875.97537446.13662390.1181.84
Dadra and Nagar Haveli193760149949183024159829491343709773.890380700.01833086.4665.93
Daman and Diu1503019294660331182580112243247618.3990792171.84821491.4879.59
Lakshadweep331233135014121503083264473946.4722402014.78125096.1188.25
\n", + "
" + ], + "text/plain": [ + " Male Population Female Population Rural \\\n", + "State/UT \n", + "Uttar Pradesh 104480510 95331831 155111022 \n", + "Maharashtra 58243056 54131277 61545441 \n", + "Bihar 54278157 49821295 92075028 \n", + "West Bengal 46809027 44467088 62213676 \n", + "Madhya Pradesh 37612306 35014503 52537899 \n", + "Tamil Nadu 36137975 36009055 37189229 \n", + "Rajasthan 35550997 32997440 51540236 \n", + "Karnataka 30966657 30128640 37552529 \n", + "Gujarat 31491260 28948432 34670817 \n", + "Andhra Pradesh 24738068 24648731 34776389 \n", + "Odisha 21212136 20762082 34951234 \n", + "Telangana 17704078 17489900 21585313 \n", + "Kerala 16027412 17378649 17445506 \n", + "Jharkhand 16930315 16057819 25036946 \n", + "Assam 15939443 15266133 26780526 \n", + "Punjab 14639465 13103873 17316800 \n", + "Chhattisgarh 12832895 12712303 19603658 \n", + "Haryana 13494734 11856728 16531493 \n", + "Delhi 8887326 7800615 944727 \n", + "Jammu and Kashmir 6640662 5900640 9134820 \n", + "Uttarakhand 5137773 4948519 7025583 \n", + "Himachal Pradesh 3481873 3382729 6167805 \n", + "Tripura 1874376 1799541 2710051 \n", + "Meghalaya 1491832 1475057 2368971 \n", + "Manipur 1438687 1417107 1899624 \n", + "Nagaland 1024649 953853 1406861 \n", + "Goa 739140 719405 551414 \n", + "Arunachal Pradesh 713912 669815 1069165 \n", + "Puducherry 612511 635442 394341 \n", + "Mizoram 555339 541867 529037 \n", + "Chandigarh 580663 474787 29004 \n", + "Sikkim 323070 287507 455962 \n", + "Andaman and Nicobar Islands 202871 177710 244411 \n", + "Dadra and Nagar Haveli 193760 149949 183024 \n", + "Daman and Diu 150301 92946 60331 \n", + "Lakshadweep 33123 31350 14121 \n", + "\n", + " Urban Area (km2) Population Sex Ratio \\\n", + "State/UT \n", + "Uttar Pradesh 44470455 240928 199812341 912.436501 \n", + "Maharashtra 50827531 307713 112374333 929.403103 \n", + "Bihar 11729609 94163 104099452 917.888480 \n", + "West Bengal 29134060 88752 91276115 949.968219 \n", + "Madhya Pradesh 20059666 308245 72626809 930.932100 \n", + "Tamil Nadu 34949729 130058 72147030 996.432562 \n", + "Rajasthan 17080776 342239 68548437 928.172000 \n", + "Karnataka 23578175 191791 61095297 972.938086 \n", + "Gujarat 25712811 196024 60439692 919.252897 \n", + "Andhra Pradesh 14610410 160205 49386799 996.388683 \n", + "Odisha 6996124 155707 41974218 978.783183 \n", + "Telangana 13608665 114840 35193978 987.902335 \n", + "Kerala 15932171 38863 33406061 1084.307872 \n", + "Jharkhand 7929292 79714 32988134 948.465460 \n", + "Assam 4388756 78438 31205576 957.758248 \n", + "Punjab 10387436 50362 27743338 895.106003 \n", + "Chhattisgarh 5936538 135191 25545198 990.602900 \n", + "Haryana 8821588 44212 25351462 878.618875 \n", + "Delhi 12905780 1484 16687941 877.723513 \n", + "Jammu and Kashmir 3414106 222236 12541302 888.562014 \n", + "Uttarakhand 3091169 53483 10086292 963.164196 \n", + "Himachal Pradesh 688704 55673 6864602 971.525670 \n", + "Tripura 960981 10486 3673917 960.074713 \n", + "Meghalaya 595036 22429 2966889 988.755436 \n", + "Manipur 822132 22327 2855794 985.000212 \n", + "Nagaland 573741 16579 1978502 930.907072 \n", + "Goa 906309 3702 1458545 973.300051 \n", + "Arunachal Pradesh 313446 83743 1383727 938.231883 \n", + "Puducherry 850123 479 1247953 1037.437695 \n", + "Mizoram 561997 21081 1097206 975.740944 \n", + "Chandigarh 1025682 114 1055450 817.663602 \n", + "Sikkim 151726 7096 610577 889.921689 \n", + "Andaman and Nicobar Islands 135533 8249 380581 875.975374 \n", + "Dadra and Nagar Haveli 159829 491 343709 773.890380 \n", + "Daman and Diu 182580 112 243247 618.399079 \n", + "Lakshadweep 50308 32 64473 946.472240 \n", + "\n", + " Population density (km-2) Male Literacy (%) \\\n", + "State/UT \n", + "Uttar Pradesh 829.344622 79.24 \n", + "Maharashtra 365.192023 89.82 \n", + "Bihar 1105.523953 73.39 \n", + "West Bengal 1028.440091 82.67 \n", + "Madhya Pradesh 235.613908 80.53 \n", + "Tamil Nadu 554.729659 86.81 \n", + "Rajasthan 200.294055 80.51 \n", + "Karnataka 318.551428 82.85 \n", + "Gujarat 308.328021 87.23 \n", + "Andhra Pradesh 308.272520 74.77 \n", + "Odisha 269.571811 82.40 \n", + "Telangana 306.460972 NaN \n", + "Kerala 859.585235 96.02 \n", + "Jharkhand 413.831121 78.45 \n", + "Assam 397.837477 78.81 \n", + "Punjab 550.878400 81.48 \n", + "Chhattisgarh 188.956351 81.45 \n", + "Haryana 573.406813 85.38 \n", + "Delhi 11245.243261 91.03 \n", + "Jammu and Kashmir 56.432360 87.26 \n", + "Uttarakhand 188.588748 88.33 \n", + "Himachal Pradesh 123.302175 90.83 \n", + "Tripura 350.364009 92.18 \n", + "Meghalaya 132.279148 77.17 \n", + "Manipur 127.907645 86.49 \n", + "Nagaland 119.337837 83.29 \n", + "Goa 393.988385 92.81 \n", + "Arunachal Pradesh 16.523495 73.69 \n", + "Puducherry 2605.329854 92.12 \n", + "Mizoram 52.047151 93.72 \n", + "Chandigarh 9258.333333 90.54 \n", + "Sikkim 86.045237 87.29 \n", + "Andaman and Nicobar Islands 46.136623 90.11 \n", + "Dadra and Nagar Haveli 700.018330 86.46 \n", + "Daman and Diu 2171.848214 91.48 \n", + "Lakshadweep 2014.781250 96.11 \n", + "\n", + " Female Literacy (%) \n", + "State/UT \n", + "Uttar Pradesh 59.26 \n", + "Maharashtra 75.48 \n", + "Bihar 53.33 \n", + "West Bengal 71.16 \n", + "Madhya Pradesh 60.02 \n", + "Tamil Nadu 73.86 \n", + "Rajasthan 52.66 \n", + "Karnataka 68.13 \n", + "Gujarat 70.73 \n", + "Andhra Pradesh 59.96 \n", + "Odisha 64.36 \n", + "Telangana NaN \n", + "Kerala 91.98 \n", + "Jharkhand 56.21 \n", + "Assam 67.27 \n", + "Punjab 71.34 \n", + "Chhattisgarh 60.59 \n", + "Haryana 66.77 \n", + "Delhi 80.93 \n", + "Jammu and Kashmir 86.23 \n", + "Uttarakhand 70.70 \n", + "Himachal Pradesh 76.60 \n", + "Tripura 83.15 \n", + "Meghalaya 73.78 \n", + "Manipur 73.17 \n", + "Nagaland 76.69 \n", + "Goa 81.84 \n", + "Arunachal Pradesh 59.57 \n", + "Puducherry 81.22 \n", + "Mizoram 89.40 \n", + "Chandigarh 81.38 \n", + "Sikkim 76.43 \n", + "Andaman and Nicobar Islands 81.84 \n", + "Dadra and Nagar Haveli 65.93 \n", + "Daman and Diu 79.59 \n", + "Lakshadweep 88.25 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['Male Literacy (%)', 'Female Literacy (%)']] = df_lit[['Male (%)', 'Female (%)']]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see we have a problem: there's no literacy data in our table for the state of Telangana. If we want the total literacy rate for the whole of India, we cannot therefore simply take a population-weighted sum of the literacy columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "79.96883256461174" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights = df['Male Population'] / df['Male Population'].sum()\n", + "(df['Male Literacy (%)'] * weights).sum() # wrong" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead, we need to form the weighted sum over only the population rows which are not null in the literacy column. This gives:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total male literacy rate: 82.3 %\n", + "Total female literacy rate: 65.7 %\n" + ] + } + ], + "source": [ + "male_weights = df['Male Population'][df['Male Literacy (%)'].notnull()].sum()\n", + "total_male_literacy = (df['Male Literacy (%)'] * df['Male Population'] / male_weights).sum()\n", + "female_weights = df['Female Population'][df['Female Literacy (%)'].notnull()].sum()\n", + "total_female_literacy = (df['Female Literacy (%)'] * df['Female Population'] / female_weights).sum()\n", + "print('Total male literacy rate: {:.1f} %'.format(total_male_literacy))\n", + "print('Total female literacy rate: {:.1f} %'.format(total_female_literacy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with per capita GDP\n", + "\n", + "The Wikipedia page at https://en.wikipedia.org/wiki/List_of_Indian_states_and_union_territories_by_GDP provides a table of nominal GSDP (gross state domestic product) for the years since 2011.\n", + "\n", + "Again, we'll start by taking a local copy:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching HTML file from https://en.wikipedia.org/wiki/List_of_Indian_states_and_union_territories_by_GDP ...\n", + "Saving as html/india-gdsp.html ...\n" + ] + } + ], + "source": [ + "url = 'https://en.wikipedia.org/wiki/List_of_Indian_states_and_union_territories_by_GDP'\n", + "gdsp_filename = 'india-gdsp.html'\n", + "fetch_html(url, gdsp_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 1288\r\n", + "-rw-r--r-- 1 christian staff 513046 2 Aug 14:10 india-demographics.html\r\n", + "-rw-r--r-- 1 christian staff 141104 2 Aug 14:10 india-gdsp.html\r\n" + ] + } + ], + "source": [ + "!ls -l $HTML_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in 8 HTML tables.\n" + ] + } + ], + "source": [ + "# NB missing values appear in the tables as '–'\n", + "gdsp_data = pd.read_html(os.path.join(HTML_DIR, gdsp_filename), na_values='–')\n", + "print(f'Read in {len(gdsp_data)} HTML tables.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More tidying:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gdsp_data[3]\n", + "gdf.set_index('State/Union territory', inplace=True)\n", + "gdf.index.name = 'State/UT'\n", + "gdf.drop('India', inplace=True)\n", + "gdf.dropna(axis='columns', inplace=True)\n", + "df['GSDP (₹ crore)'] = gdf.iloc[:,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A more useful measure would be the per capita GSDP:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "df['GSDP per head (₹)'] = df['GSDP (₹ crore)'] / df['Population'] * 1.e7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot some statistics:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.scatter('Female Literacy (%)', 'GSDP per head (₹)')\n", + "df.plot.scatter('Sex Ratio', 'GSDP per head (₹)')\n", + "df.plot.scatter('Sex Ratio', 'Female Literacy (%)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Crime Rates" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching HTML file from https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_crime_rate ...\n", + "Saving as html/india-crime.html ...\n" + ] + } + ], + "source": [ + "url = 'https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_crime_rate'\n", + "crime_filename = 'india-crime.html'\n", + "fetch_html(url, crime_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in 2 HTML tables.\n" + ] + } + ], + "source": [ + "crime_data = pd.read_html(os.path.join(HTML_DIR, crime_filename), na_values='–')\n", + "print(f'Read in {len(crime_data)} HTML tables.')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "cdf = crime_data[1]\n", + "cdf.set_index('State/UT', inplace=True)\n", + "df['Crime Rate'] = cdf['Rate of Cognizable Crimes (2016)']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.scatter('GSDP per head (₹)', 'Crime Rate')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fertility Rates" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching HTML file from https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_fertility_rate ...\n", + "Saving as html/india-fertility.html ...\n" + ] + } + ], + "source": [ + "url = 'https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_fertility_rate'\n", + "fertility_filename = 'india-fertility.html'\n", + "fetch_html(url, fertility_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in 2 HTML tables.\n" + ] + } + ], + "source": [ + "fertility_data = pd.read_html(os.path.join(HTML_DIR, fertility_filename), na_values='-')\n", + "print(f'Read in {len(fertility_data)} HTML tables.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one requires quite a bit of cleaning and sorting." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "fdf = fertility_data[0].copy()\n", + "fdf.set_index('State/UT', inplace=True)\n", + "fdf.drop('India', inplace=True)\n", + "fdf.rename({'Andaman & Nicobar': 'Andaman and Nicobar Islands', 'Daman & Diu': 'Daman and Diu',\n", + " 'Puduchery': 'Puducherry', 'Dadra Nagar Haveli': 'Dadra and Nagar Haveli'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Male PopulationFemale PopulationRuralUrbanArea (km2)PopulationSex RatioPopulation density (km-2)Male Literacy (%)Female Literacy (%)GSDP (₹ crore)GSDP per head (₹)Crime RateFertility Rate
State/UT
Uttar Pradesh1044805109533183115511102244470455240928199812341912.436501829.34462279.2459.26724049.036236.450480128.703.7
Maharashtra58243056541312776154544150827531307713112374333929.403103365.19202389.8275.481280369.0113937.850915217.101.9
Bihar5427815749821295920750281172960994163104099452917.8884801105.52395373.3953.33247144.023741.143229157.403.9
West Bengal468090274446708862213676291340608875291276115949.9682191028.44009182.6771.16520485.057023.132503188.201.9
Madhya Pradesh3761230635014503525378992005966630824572626809930.932100235.61390880.5360.02315561.043449.657825337.903.3
Tamil Nadu3613797536009055371892293494972913005872147030996.432562554.72965986.8173.86751485.0104160.212832258.801.7
Rajasthan3555099732997440515402361708077634223968548437928.172000200.29405580.5152.66436465.063672.494823246.203.3
Karnataka3096665730128640375525292357817519179161095297972.938086318.55142882.8568.13606010.099190.940998237.201.9
Gujarat3149126028948432346708172571281119602460439692919.252897308.32802187.2370.73615606.0101854.589199233.202.5
Andhra Pradesh2473806824648731347763891461041016020549386799996.388683308.27252074.7759.96379402.076822.553330206.401.8
Odisha212121362076208234951234699612415570741974218978.783183269.57181182.4064.36230987.055030.685741191.302.4
Telangana1770407817489900215853131360866511484035193978987.902335306.460972NaNNaN359434.0102129.404070295.701.8
Kerala1602741217378649174455061593217138863334060611084.307872859.58523596.0291.98364048.0108976.631516727.601.7
Jharkhand16930315160578192503694679292927971432988134948.465460413.83112178.4556.21150918.045749.177568120.403.2
Assam15939443152661332678052643887567843831205576957.758248397.83747778.8167.27143175.045881.223279313.902.6
Punjab146394651310387317316800103874365036227743338895.106003550.87840081.4871.34266628.096105.234345137.001.9
Chhattisgarh128328951271230319603658593653813519125545198990.602900188.95635181.4560.59158074.061880.123223211.703.0
Haryana13494734118567281653149388215884421225351462878.618875573.40681385.3866.77297539.0117365.617809320.602.5
Delhi8887326780061594472712905780148416687941877.72351311245.24326191.0380.93343767.0205997.252747974.901.9
Jammu and Kashmir664066259006409134820341410622223612541302888.56201456.43236087.2686.2378254.062397.030229196.602.2
Uttarakhand51377734948519702558330911695348310086292963.164196188.58874888.3370.70115523.0114534.657533101.802.6
Himachal Pradesh348187333827296167805688704556736864602971.525670123.30217590.8376.6072720.0105934.765045188.101.9
Tripura187437617995412710051960981104863673917960.074713350.36400992.1883.1519208.052282.073874102.401.7
Meghalaya149183214750572368971595036224292966889988.755436132.27914877.1773.7819918.067134.294542120.903.1
Manipur143868714171071899624822132223272855794985.000212127.90764586.4973.1712915.045223.850180121.901.5
Nagaland10246499538531406861573741165791978502930.907072119.33783783.2976.6912176.061541.50968857.602.0
Goa73914071940555141490630937021458545973.300051393.98838592.8181.8442367.0290474.411143135.601.6
Arunachal Pradesh7139126698151069165313446837431383727938.23188316.52349573.6959.5711063.079950.741729192.302.7
Puducherry61251163544239434185012347912479531037.4376952605.32985492.1281.2216818.0134764.690657242.801.6
Mizoram555339541867529037561997210811097206975.74094452.04715193.7289.407259.066158.952831227.302.0
Chandigarh5806634747872900410256821141055450817.6636029258.33333390.5481.3818768.0177819.887252166.401.8
Sikkim3230702875074559621517267096610577889.92168986.04523787.2976.4311165.0182859.819482124.472.1
Andaman and Nicobar Islands2028711777102444111355338249380581875.97537446.13662390.1181.843979.0104550.673838144.801.5
Dadra and Nagar Haveli193760149949183024159829491343709773.890380700.01833086.4665.93NaNNaN57.403.3
Daman and Diu1503019294660331182580112243247618.3990792171.84821491.4879.59NaNNaN81.101.9
Lakshadweep331233135014121503083264473946.4722402014.78125096.1188.25NaNNaNNaN2.1
\n", + "
" + ], + "text/plain": [ + " Male Population Female Population Rural \\\n", + "State/UT \n", + "Uttar Pradesh 104480510 95331831 155111022 \n", + "Maharashtra 58243056 54131277 61545441 \n", + "Bihar 54278157 49821295 92075028 \n", + "West Bengal 46809027 44467088 62213676 \n", + "Madhya Pradesh 37612306 35014503 52537899 \n", + "Tamil Nadu 36137975 36009055 37189229 \n", + "Rajasthan 35550997 32997440 51540236 \n", + "Karnataka 30966657 30128640 37552529 \n", + "Gujarat 31491260 28948432 34670817 \n", + "Andhra Pradesh 24738068 24648731 34776389 \n", + "Odisha 21212136 20762082 34951234 \n", + "Telangana 17704078 17489900 21585313 \n", + "Kerala 16027412 17378649 17445506 \n", + "Jharkhand 16930315 16057819 25036946 \n", + "Assam 15939443 15266133 26780526 \n", + "Punjab 14639465 13103873 17316800 \n", + "Chhattisgarh 12832895 12712303 19603658 \n", + "Haryana 13494734 11856728 16531493 \n", + "Delhi 8887326 7800615 944727 \n", + "Jammu and Kashmir 6640662 5900640 9134820 \n", + "Uttarakhand 5137773 4948519 7025583 \n", + "Himachal Pradesh 3481873 3382729 6167805 \n", + "Tripura 1874376 1799541 2710051 \n", + "Meghalaya 1491832 1475057 2368971 \n", + "Manipur 1438687 1417107 1899624 \n", + "Nagaland 1024649 953853 1406861 \n", + "Goa 739140 719405 551414 \n", + "Arunachal Pradesh 713912 669815 1069165 \n", + "Puducherry 612511 635442 394341 \n", + "Mizoram 555339 541867 529037 \n", + "Chandigarh 580663 474787 29004 \n", + "Sikkim 323070 287507 455962 \n", + "Andaman and Nicobar Islands 202871 177710 244411 \n", + "Dadra and Nagar Haveli 193760 149949 183024 \n", + "Daman and Diu 150301 92946 60331 \n", + "Lakshadweep 33123 31350 14121 \n", + "\n", + " Urban Area (km2) Population Sex Ratio \\\n", + "State/UT \n", + "Uttar Pradesh 44470455 240928 199812341 912.436501 \n", + "Maharashtra 50827531 307713 112374333 929.403103 \n", + "Bihar 11729609 94163 104099452 917.888480 \n", + "West Bengal 29134060 88752 91276115 949.968219 \n", + "Madhya Pradesh 20059666 308245 72626809 930.932100 \n", + "Tamil Nadu 34949729 130058 72147030 996.432562 \n", + "Rajasthan 17080776 342239 68548437 928.172000 \n", + "Karnataka 23578175 191791 61095297 972.938086 \n", + "Gujarat 25712811 196024 60439692 919.252897 \n", + "Andhra Pradesh 14610410 160205 49386799 996.388683 \n", + "Odisha 6996124 155707 41974218 978.783183 \n", + "Telangana 13608665 114840 35193978 987.902335 \n", + "Kerala 15932171 38863 33406061 1084.307872 \n", + "Jharkhand 7929292 79714 32988134 948.465460 \n", + "Assam 4388756 78438 31205576 957.758248 \n", + "Punjab 10387436 50362 27743338 895.106003 \n", + "Chhattisgarh 5936538 135191 25545198 990.602900 \n", + "Haryana 8821588 44212 25351462 878.618875 \n", + "Delhi 12905780 1484 16687941 877.723513 \n", + "Jammu and Kashmir 3414106 222236 12541302 888.562014 \n", + "Uttarakhand 3091169 53483 10086292 963.164196 \n", + "Himachal Pradesh 688704 55673 6864602 971.525670 \n", + "Tripura 960981 10486 3673917 960.074713 \n", + "Meghalaya 595036 22429 2966889 988.755436 \n", + "Manipur 822132 22327 2855794 985.000212 \n", + "Nagaland 573741 16579 1978502 930.907072 \n", + "Goa 906309 3702 1458545 973.300051 \n", + "Arunachal Pradesh 313446 83743 1383727 938.231883 \n", + "Puducherry 850123 479 1247953 1037.437695 \n", + "Mizoram 561997 21081 1097206 975.740944 \n", + "Chandigarh 1025682 114 1055450 817.663602 \n", + "Sikkim 151726 7096 610577 889.921689 \n", + "Andaman and Nicobar Islands 135533 8249 380581 875.975374 \n", + "Dadra and Nagar Haveli 159829 491 343709 773.890380 \n", + "Daman and Diu 182580 112 243247 618.399079 \n", + "Lakshadweep 50308 32 64473 946.472240 \n", + "\n", + " Population density (km-2) Male Literacy (%) \\\n", + "State/UT \n", + "Uttar Pradesh 829.344622 79.24 \n", + "Maharashtra 365.192023 89.82 \n", + "Bihar 1105.523953 73.39 \n", + "West Bengal 1028.440091 82.67 \n", + "Madhya Pradesh 235.613908 80.53 \n", + "Tamil Nadu 554.729659 86.81 \n", + "Rajasthan 200.294055 80.51 \n", + "Karnataka 318.551428 82.85 \n", + "Gujarat 308.328021 87.23 \n", + "Andhra Pradesh 308.272520 74.77 \n", + "Odisha 269.571811 82.40 \n", + "Telangana 306.460972 NaN \n", + "Kerala 859.585235 96.02 \n", + "Jharkhand 413.831121 78.45 \n", + "Assam 397.837477 78.81 \n", + "Punjab 550.878400 81.48 \n", + "Chhattisgarh 188.956351 81.45 \n", + "Haryana 573.406813 85.38 \n", + "Delhi 11245.243261 91.03 \n", + "Jammu and Kashmir 56.432360 87.26 \n", + "Uttarakhand 188.588748 88.33 \n", + "Himachal Pradesh 123.302175 90.83 \n", + "Tripura 350.364009 92.18 \n", + "Meghalaya 132.279148 77.17 \n", + "Manipur 127.907645 86.49 \n", + "Nagaland 119.337837 83.29 \n", + "Goa 393.988385 92.81 \n", + "Arunachal Pradesh 16.523495 73.69 \n", + "Puducherry 2605.329854 92.12 \n", + "Mizoram 52.047151 93.72 \n", + "Chandigarh 9258.333333 90.54 \n", + "Sikkim 86.045237 87.29 \n", + "Andaman and Nicobar Islands 46.136623 90.11 \n", + "Dadra and Nagar Haveli 700.018330 86.46 \n", + "Daman and Diu 2171.848214 91.48 \n", + "Lakshadweep 2014.781250 96.11 \n", + "\n", + " Female Literacy (%) GSDP (₹ crore) \\\n", + "State/UT \n", + "Uttar Pradesh 59.26 724049.0 \n", + "Maharashtra 75.48 1280369.0 \n", + "Bihar 53.33 247144.0 \n", + "West Bengal 71.16 520485.0 \n", + "Madhya Pradesh 60.02 315561.0 \n", + "Tamil Nadu 73.86 751485.0 \n", + "Rajasthan 52.66 436465.0 \n", + "Karnataka 68.13 606010.0 \n", + "Gujarat 70.73 615606.0 \n", + "Andhra Pradesh 59.96 379402.0 \n", + "Odisha 64.36 230987.0 \n", + "Telangana NaN 359434.0 \n", + "Kerala 91.98 364048.0 \n", + "Jharkhand 56.21 150918.0 \n", + "Assam 67.27 143175.0 \n", + "Punjab 71.34 266628.0 \n", + "Chhattisgarh 60.59 158074.0 \n", + "Haryana 66.77 297539.0 \n", + "Delhi 80.93 343767.0 \n", + "Jammu and Kashmir 86.23 78254.0 \n", + "Uttarakhand 70.70 115523.0 \n", + "Himachal Pradesh 76.60 72720.0 \n", + "Tripura 83.15 19208.0 \n", + "Meghalaya 73.78 19918.0 \n", + "Manipur 73.17 12915.0 \n", + "Nagaland 76.69 12176.0 \n", + "Goa 81.84 42367.0 \n", + "Arunachal Pradesh 59.57 11063.0 \n", + "Puducherry 81.22 16818.0 \n", + "Mizoram 89.40 7259.0 \n", + "Chandigarh 81.38 18768.0 \n", + "Sikkim 76.43 11165.0 \n", + "Andaman and Nicobar Islands 81.84 3979.0 \n", + "Dadra and Nagar Haveli 65.93 NaN \n", + "Daman and Diu 79.59 NaN \n", + "Lakshadweep 88.25 NaN \n", + "\n", + " GSDP per head (₹) Crime Rate Fertility Rate \n", + "State/UT \n", + "Uttar Pradesh 36236.450480 128.70 3.7 \n", + "Maharashtra 113937.850915 217.10 1.9 \n", + "Bihar 23741.143229 157.40 3.9 \n", + "West Bengal 57023.132503 188.20 1.9 \n", + "Madhya Pradesh 43449.657825 337.90 3.3 \n", + "Tamil Nadu 104160.212832 258.80 1.7 \n", + "Rajasthan 63672.494823 246.20 3.3 \n", + "Karnataka 99190.940998 237.20 1.9 \n", + "Gujarat 101854.589199 233.20 2.5 \n", + "Andhra Pradesh 76822.553330 206.40 1.8 \n", + "Odisha 55030.685741 191.30 2.4 \n", + "Telangana 102129.404070 295.70 1.8 \n", + "Kerala 108976.631516 727.60 1.7 \n", + "Jharkhand 45749.177568 120.40 3.2 \n", + "Assam 45881.223279 313.90 2.6 \n", + "Punjab 96105.234345 137.00 1.9 \n", + "Chhattisgarh 61880.123223 211.70 3.0 \n", + "Haryana 117365.617809 320.60 2.5 \n", + "Delhi 205997.252747 974.90 1.9 \n", + "Jammu and Kashmir 62397.030229 196.60 2.2 \n", + "Uttarakhand 114534.657533 101.80 2.6 \n", + "Himachal Pradesh 105934.765045 188.10 1.9 \n", + "Tripura 52282.073874 102.40 1.7 \n", + "Meghalaya 67134.294542 120.90 3.1 \n", + "Manipur 45223.850180 121.90 1.5 \n", + "Nagaland 61541.509688 57.60 2.0 \n", + "Goa 290474.411143 135.60 1.6 \n", + "Arunachal Pradesh 79950.741729 192.30 2.7 \n", + "Puducherry 134764.690657 242.80 1.6 \n", + "Mizoram 66158.952831 227.30 2.0 \n", + "Chandigarh 177819.887252 166.40 1.8 \n", + "Sikkim 182859.819482 124.47 2.1 \n", + "Andaman and Nicobar Islands 104550.673838 144.80 1.5 \n", + "Dadra and Nagar Haveli NaN 57.40 3.3 \n", + "Daman and Diu NaN 81.10 1.9 \n", + "Lakshadweep NaN NaN 2.1 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Fertility Rate'] = pd.to_numeric(fdf['Fertility rate 2009 [2]'].replace(r'\\*', '', regex=True))\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is there an (inverse) correlation between female literacy and ferility?" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.7361949271996956" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Female Literacy (%)'].corr(df['Fertility Rate'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.scatter('Female Literacy (%)', 'Fertility Rate')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, there is a strong anti-correlation of fertility rate with female literacy. _Note_: there is no reason to expect the relationship to be linear, however." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}