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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.
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TwitterThe gender ratio in India was 900 between 2013 and 2015. This meant, for every 1,000 males, 900 females were present. Among its states, Chhattisgarh had the highest gender ratio at 961 in 2015 and 2016, while Haryana recorded the least at 833.
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Context
The dataset tabulates the population of Indian Village by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Indian Village. The dataset can be utilized to understand the population distribution of Indian Village by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Indian Village. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Indian Village.
Key observations
Largest age group (population): Male # 75-79 years (10) | Female # 10-14 years (11). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Indian Village Population by Gender. You can refer the same here
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TwitterIn 2011, the sex ratio of the general population in India was *** women to every one thousand men. On the other hand, the sex ratio of the elderly population in India stood at ***** women for every one thousand men, indicating an increase from the previous year. The sex ratio for both population types was forecasted to increase by 2031. After 2011, the sex ratio for the elderly population was estimated to be over ************, which indicates a higher number of elderly women than men.
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Actual value and historical data chart for India Population Female Percent Of Total
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TwitterIndia recorded a gender ratio of approximately *** in 2011. The ratio was expected to improve to *** in 2026. Rural areas are expected to have a lower gender ratio in comparison to urban areas, with around *** women for every 1,000 men.
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Sex Ratio at Birth: Female per 1000 Male: Uttar Pradesh data was reported at 905.000 NA in 2020. This records an increase from the previous number of 894.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: Uttar Pradesh data is updated yearly, averaging 878.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 905.000 NA in 2020 and a record low of 869.000 NA in 2014. Sex Ratio at Birth: Female per 1000 Male: Uttar Pradesh 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.GAJ001: Memo Items: Sex Ratio at Birth.
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TwitterFrom 2011 to 2036, the projected population among both, males and females in India is expected to see an increase. The percentage increase in the projected population for males was approximately ** percent from 2011 to 2036 while the percentage increase for the female population was roughly ** percent during the same time period.
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Actual value and historical data chart for India Sex Ratio At Birth Male Births Per Female Births
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Sex Ratio at Birth: Female per 1000 Male: Jharkhand data was reported at 914.000 NA in 2020. This records a decrease from the previous number of 916.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: Jharkhand data is updated yearly, averaging 916.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 927.000 NA in 2007 and a record low of 888.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: Jharkhand 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.GAJ001: Memo Items: Sex Ratio at Birth.
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Sex Ratio at Birth: Female per 1000 Male: West Bengal: Rural data was reported at 941.000 NA in 2020. This records a decrease from the previous number of 948.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: West Bengal: Rural data is updated yearly, averaging 940.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 953.000 NA in 2015 and a record low of 932.000 NA in 2007. Sex Ratio at Birth: Female per 1000 Male: West Bengal: Rural 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.GAJ001: Memo Items: Sex Ratio at Birth.
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This dataset provides a comprehensive overview of the demographic trends and population statistics of India. It includes various aspects of the population, such as total population figures, gender distribution, religious composition, linguistic diversity, and age group breakdowns. The dataset aims to facilitate research and analysis in the fields of sociology, economics, and public policy by offering valuable insights into the demographic dynamics of India.
Key Features: - Census Data: Detailed population statistics based on census years, including total population, male and female counts, and differences between genders. - Religious Demographics: Information on the population distribution among different religions, along with percentages. - Language Distribution: Data on the number of speakers for various languages in India and their corresponding percentages. - Vital Statistics: Key indicators such as live births, deaths, natural changes, crude birth rates, and total fertility rates. - Age Distribution: Breakdown of the population by age group, including gender-specific counts and percentages.
Purpose: This dataset serves as a valuable resource for researchers, policymakers, and educators interested in understanding the demographic landscape of India. It can be used for various analyses, including population growth trends, gender ratios, and the impact of cultural diversity on the social fabric of the nation.
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TwitterIn the year 2022, the sex ratio in Delhi, India increased to *** in 2022 from *** in 2001. Sex ratio refers to the number of females per **** males. The increase in sex ratio presents a positive trend towards balancing the male-to-female ratio in the national capital of Delhi.
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This dataset provides comprehensive census data at the district level for India. It includes detailed demographic, religious, educational, and workforce-related attributes, making it a rich resource for socio-economic analysis.
District_code: A unique numeric code for each district. State_name: Name of the state to which the district belongs. District_name: Name of the district.
Population: Total population of the district. Male: Total male population in the district. Female: Total female population in the district.
Literate: Total number of literate individuals in the district.
Workers: Total number of workers in the district. Male_Workers: Total number of male workers in the district. Female_Workers: Total number of female workers in the district. Cultivator_Workers: Number of workers engaged as cultivators. Agricultural_Workers: Number of workers engaged in agricultural labor. Household_Workers: Number of workers engaged in household industries.
Hindus: Total number of Hindus in the district. Muslims: Total number of Muslims in the district. Christians: Total number of Christians in the district. Sikhs: Total number of Sikhs in the district. Buddhists: Total number of Buddhists in the district. Jains: Total number of Jains in the district.
Secondary_Education: Number of individuals with secondary education. Higher_Education: Number of individuals with higher education qualifications. Graduate_Education: Number of individuals with graduate-level education.
Age_Group_0_29: Population in the age group 0–29 years. Age_Group_30_49: Population in the age group 30–49 years. Age_Group_50: Population aged 50 years and above.
Number of Districts: 640 Number of Columns: 25 Non-null Values: All columns are complete with no missing data. Detailed breakdown of population by gender, age group, literacy levels, and workforce distribution. Religious composition and education statistics are also included for each district.
Data Analysis and Visualization:
Explore patterns in population distribution, literacy rates, workforce composition, and religious demographics. Machine Learning Applications:
Build predictive models to classify districts or forecast demographic trends. Social Research:
Investigate correlations between education levels, workforce participation, and religion. Policy Planning:
Help policymakers target specific demographics or regions for intervention. Educational Insights:
Analyze the impact of education levels on workforce participation or literacy.
Total Rows: 640 Total Columns: 25 This dataset provides a unique opportunity to understand India's socio-economic and demographic composition at a granular district level.
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TwitterThe growth in India's overall population is driven by its young population. Nearly ** percent of the country's population was between the ages of 15 and 64 years old in 2020. With over *** million people between 18 and 35 years old, India had the largest number of millennials and Gen Zs globally.
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This dataset provides comprehensive demographic information for various districts in India. It includes data on population, growth rates, sex ratios, and literacy rates. The dataset offers insights into the socio-economic landscape of Indian districts, making it valuable for demographic analyses, regional studies, and decision-making processes.
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India Population: Census: Female: Age: 20 to 24 Year data was reported at 53,839.529 Person th in 2011. This records an increase from the previous number of 43,443.000 Person th for 2001. India Population: Census: Female: Age: 20 to 24 Year data is updated yearly, averaging 43,443.000 Person th from Mar 1991 (Median) to 2011, with 3 observations. The data reached an all-time high of 53,839.529 Person th in 2011 and a record low of 36,959.000 Person th in 1991. India Population: Census: Female: Age: 20 to 24 Year 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.GAD001: Census: Population: by Age Group.
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Actual value and historical data chart for India Ratio Of Female To Male Labor Force Participation Rate Percent National Estimate
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This dataset contains the number of females per 100 males, i.e. gender ratio, for the elderly population and general population.
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Sex Ratio at Birth: Female per 1000 Male: Rajasthan data was reported at 911.000 NA in 2020. This records an increase from the previous number of 879.000 NA for 2019. Sex Ratio at Birth: Female per 1000 Male: Rajasthan data is updated yearly, averaging 875.000 NA from Dec 2006 (Median) to 2020, with 15 observations. The data reached an all-time high of 911.000 NA in 2020 and a record low of 855.000 NA in 2006. Sex Ratio at Birth: Female per 1000 Male: Rajasthan 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.GAJ001: Memo Items: Sex Ratio at Birth.
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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.