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.
+
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]
+
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]
+
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.
+
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]
+
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]
+
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.
+
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.
+
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]
+
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]
+
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]
+
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
+
CBR = crude birth rate (per 1000); TFR = total fertility rate (number of children per woman). 1Number in parenthesis represents the wanted fertility rate.
+
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.
+
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]
+
Y-Chromosome DNA Y-DNA represents the male lineage, The Indian Y-chromosome pool may be summarised as follows where haplogroupsR-M420, H, R2, L and NOP comprise generally more than 80% of the total chromosomes.[114]
+
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]
^Guillaume Wunsch, Graziella Caselli, Jacques Vallin (2005). "Population in Time and Space". Demography: Analysis and Synthesis. Academic Press. p. 34.CS1 maint: Multiple names: authors list (link)
+
^Parthasarathi, Prasannan (2011), Why Europe Grew Rich and Asia Did Not: Global Economic Divergence, 1600–1850, Cambridge University Press, p. 2, ISBN978-1-139-49889-0
+
^ abRural-Urban distributionCensus of India: Census Data 2001: India at a glance >> Rural-Urban Distribution. Office of the Registrar General and Census Commissioner, India. Retrieved 26 November 2008.
+
+
^Number of VillagesCensus of India: Number of Villages Office of the Registrar General and Census Commissioner, India. Retrieved 26 November 2008.
+
+
^Urban Agglomerations and TownsCensus of India: Urban Agglomerations and Towns. Office of the Registrar General and Census Commissioner, India. Retrieved 26 November 2008.
+
^"Census Population"(PDF). Census of India. Ministry of Finance India. Archived from the original(PDF) on 19 December 2008. Retrieved 1 January 2014.
+
^"Sex Composition of the Population", Office of Registrar General and Census Commissioner of India, Ministry of Home Affairs, Government of India (2013)
+
^(a) Ponnapalli et al. (2013), Aging and the Demographic Transition in India and Its States: A Comparative Perspective, International Journal of Asian Social Science, 3(1), pp. 171–193; (b) The Future Population of India Population Research Bureau and Population Fund of India.
+
+
^"Literacy Rate – 7+years (%)". NITI Aayog, (National Institution for Transforming India), Government of India. Retrieved 8 June 2019.
+
^(a) Census 2011 Final, Ministry of Home Affairs, Government of India (may need subscription); (b) The data is mirrored here: Density of Population, Chapter 7, Census of India (2013)
+
^Religious CompositionCensus of India: Census Data 2001: India at a glance >> Religious Composition. Office of the Registrar General and Census Commissioner, India. Retrieved 26 November 2008.
+
This tree diagram depicts the relationships of the major ethnic, linguistic and religious groups in India. For example, an H under Gujarati implies a Hindu, Gujarati-speaking Indian of Indo-Aryan ancestry. This list excludes caste groups like the Dalits which is a socio-political identity across linguistic, religious and racial lines. In addition, it should be noted that the terms 'Indo-Aryan' and 'Dravidian' refer to linguistic differences that exist between both groups.
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]
+
^ abcdTable in [1] Fourth
+National Family Health Survey of TFR, Department of Health and Family Welfare, Ministry of Health and Family Welfare, Government of India
+
These are lists of Indianstates 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
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
+
|}
+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.
+
+
+
+
+
+
+
+
+
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": [
+ "
+
+
+
+
+
+
+ 
1.19% (2016) (
83.45%
0.70%
.jpg)
