The statistic displays the main states and union territories with the highest number of people living in urban areas in India in 2011. In that year, the state of Maharashtra had the highest population with over 50 million people living in urban areas. The population density in India from 2004 to 2014 can be seen here.

In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

India is the most populous country in the world with one-sixth of the world's population. According to official estimates in 2022, India's population stood at over 1.42 billion.

This dataset contains the population distribution by state, gender, sex & region.

The file is in .csv format thus it is accessible everywhere.

📊 India States & Union Territories Dataset 🇮🇳

This dataset provides a comprehensive overview of India's States and Union Territories, offering detailed information about their administrative structure, demographics, and linguistic diversity. It serves as a valuable resource for understanding the vastness and complexity of India's regional distribution.

📑 Dataset Contents:

• Name: Name of the State or Union Territory 🏙️
• Type: Administrative type (State or Union Territory) 🏛️
• ISO: Official ISO Code 🌍
• Vehiclecode: Unique Vehicle Registration Code 🚗
• Zone: Geographical Zone 🌏
• Capital: Administrative Capital 🏛️
• Largest City: The most populous city within the state 🏙️
• Statehood: Year the state gained full statehood 🎉
• Population: Total population of the state or UT 👥
• Area: Geographic area in square kilometers 🌍
• Official Languages: Primary official languages of the state 🗣️
• Additional Official Languages: Secondary official languages 📚

🔍 Purpose:

This dataset is designed to provide an in-depth look at the various States and Union Territories across India. It combines vital information on governance, geography, population, and language diversity, making it an essential tool for researchers, analysts, and anyone interested in India's regional landscape.

🚀 Applications:

• Data Analysis: Understand regional distribution and demographics 📊
• Geography Research: Explore geographical zones and area details 🌍
• Linguistic Studies: Study the diversity of languages spoken across India 🗣️
• Governance Comparison: Compare States and Union Territories based on statehood and type ⚖️

📝 Contributing:

Feel free to fork this repository and contribute! 💡 Pull requests are welcome for improvements or additional information.

📬 Contact:

For any questions or suggestions, raise suggestions! 📧

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

Title: Population Data of Indian Cities (2011 and 2001)

Description: This dataset contains population information for various cities in India, categorized by rank, city name, and population figures for the years 2011 and 2001. Additionally, it includes the corresponding state or union territory to which each city belongs. The dataset provides insights into population changes over a decade in different cities across India.

Columns:

Rank: This column represents the rank of each city based on its population in the year 2011. Cities are typically ranked in descending order of population, with the most populous city having the rank 1.

City: This column contains the names of the cities for which population data is recorded.

Population (2011): This column displays the population count of each city as of the year 2011. The population figures are likely to be recorded in thousands or millions

Population (2001): This column provides the population count of each city as of the year 2001. Comparing this data with the 2011 population figures allows for an analysis of population growth or decline over the decade.

State or Union Territory: This column indicates the administrative division to which each city belongs. In India, cities are typically grouped into states or union territories, and this column helps identify the geographical context of each city.

According to wikipedia, Maharashtra is the second-most populous state in India and the second-most populous country subdivision globally. the state is divided into 36 districts. Information for the Dataset is sourced from the government website of Maharashtra State. It contains information on 3 Census years - 1991,2001 and 2011.

Content

The dataset has 29 columns and 1.32 Lac rows.

Column headers are self-explanatory. Below is the list of columns - District, Taluka, Town/Village, No. of households, Total population, Total male population, Total female population, Total 0 to 6-year children, Male 0 to 6-year children, Female 0 to 6-year children, Total SC population, Male SC population, Female SC population, Total ST population, Male ST population, Female ST population, Total literates, Male literates, Female literates, Total illiterates, Male illiterates, Female illiterates, Total main workers, Male main workers, Female main workers, Total non-workers, Male non-workers, Female non-workers

Abbreviations - SC - Scheduled Caste ST - Scheduled Tribes

The statistic gives the share of aging population in India across selected states and union territories in India in 2011. The regions with the highest share of elderly people were in the state of Kerala, with some 12.6 percent of the population living there were 60 years or older, followed by the state of Goa with 11.2 percent. The share of aging population in the whole country that year was 8.6 percent.

According to the 2011 census, the population density in the Indian state of Maharashtra was *** individuals per square kilometer. Located on the Deccan Plateau, it is the second-most populous state in the country. A steady increase in the population of the state can be attributed to growing urban districts such as Mumbai and Pune, with diverse employment opportunities in several sectors.

India's economic powerhouse

With a contribution of over ** trillion Indian rupees in the financial year 2017, the state of Maharashtra had the highest gross state domestic product in the country. A per capita income of over *** thousand Indian rupees was estimated across the state for the preceding year. Based on its economic model, the state was a highly preferred destination for domestic and foreign investments.

The most populous Indian state

Mumbai, the capital city of Maharashtra, was the most populous city after Delhi. As the country's economic core, it serves as the financial and commercial capital while providing numerous job opportunities. Many are attracted to this dream city in search of a lucrative career and to make it big in the world-famous Bollywood film industry.

Census: Population: Maharashtra data was reported at 112,374,333.000 Person in 03-01-2011. This records an increase from the previous number of 96,878,627.000 Person for 03-01-2001. Census: Population: Maharashtra data is updated decadal, averaging 35,778,141.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 112,374,333.000 Person in 03-01-2011 and a record low of 19,391,643.000 Person in 03-01-1901. Census: Population: Maharashtra data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

Census: Population: Goa data was reported at 1,458,545.000 Person in 03-01-2011. This records an increase from the previous number of 1,347,668.000 Person for 03-01-2001. Census: Population: Goa data is updated decadal, averaging 568,722.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 1,458,545.000 Person in 03-01-2011 and a record low of 469,494.000 Person in 03-01-1921. Census: Population: Goa data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

India Census: Population: by Religion: Muslim: Urban data was reported at 68,740,419.000 Person in 2011. This records an increase from the previous number of 49,393,496.000 Person for 2001. India Census: Population: by Religion: Muslim: Urban data is updated yearly, averaging 59,066,957.500 Person from Mar 2001 (Median) to 2011, with 2 observations. The data reached an all-time high of 68,740,419.000 Person in 2011 and a record low of 49,393,496.000 Person in 2001. India Census: Population: by Religion: Muslim: Urban data remains active status in CEIC and is reported by Census of India. The data is categorized under India Premium Database’s Demographic – Table IN.GAE001: Census: Population: by Religion.

Census: Population: Punjab data was reported at 27,743,338.000 Person in 03-01-2011. This records an increase from the previous number of 24,358,999.000 Person for 03-01-2001. Census: Population: Punjab data is updated decadal, averaging 10,367,652.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 27,743,338.000 Person in 03-01-2011 and a record low of 6,731,510.000 Person in 03-01-1911. Census: Population: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

india - Population Growth for India was 0.88329 % Chg. at Annual Rate in January of 2023, according to the United States Federal Reserve. Historically, india - Population Growth for India reached a record high of 0.88329 in January of 2023 and a record low of 0.79020 in January of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for india - Population Growth for India - last updated from the United States Federal Reserve on November of 2025.

Census: Population: Delhi data was reported at 16,787,941.000 Person in 03-01-2011. This records an increase from the previous number of 13,850,507.000 Person for 03-01-2001. Census: Population: Delhi data is updated decadal, averaging 2,201,342.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 16,787,941.000 Person in 03-01-2011 and a record low of 405,819.000 Person in 03-01-1901. Census: Population: Delhi data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

Census: Population: Uttarakhand data was reported at 10,086,292.000 Person in 03-01-2011. This records an increase from the previous number of 8,489,349.000 Person for 03-01-2001. Census: Population: Uttarakhand data is updated decadal, averaging 3,278,433.500 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 10,086,292.000 Person in 03-01-2011 and a record low of 1,979,866.000 Person in 03-01-1901. Census: Population: Uttarakhand data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAB002: Census: Population: by States.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

The statistic shows the total population of India from 2020 to 2030. In 2024, the estimated total population in India amounted to approximately 1.44 billion people. Total population in India India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population. With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year. As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

Abstract

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children.

A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples.

NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files.

The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

Vital Statistics: Natural Growth Rate: per 1000 Population: Maharashtra: Urban data was reported at 10.100 NA in 2020. This records a decrease from the previous number of 10.500 NA for 2019. Vital Statistics: Natural Growth Rate: per 1000 Population: Maharashtra: Urban data is updated yearly, averaging 11.400 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 15.000 NA in 1998 and a record low of 10.100 NA in 2020. Vital Statistics: Natural Growth Rate: per 1000 Population: Maharashtra: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH004: Vital Statistics: Natural Growth Rate: by States.