In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Real-time world population counter with births, deaths, and demographic breakdowns

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

Have you ever wondered how the population landscape of our planet looks in 2025? This dataset brings together the latest population statistics for 233 countries and territories, carefully collected from Worldometers.info — one of the most trusted global data sources.

📊 It reveals how countries are growing, shrinking, and evolving demographically. From population density to fertility rate, from migration trends to urbanization, every number tells a story about humanity’s future.

🌆 You can explore which nations are rapidly expanding, which are aging, and how urban populations are transforming global living patterns. This dataset includes key metrics like yearly population change, net migration, land area, fertility rate, and each country’s share of the world population.

🧠 Ideal for data analysis, visualization, and machine learning, it can be used to study global trends, forecast population growth, or build engaging dashboards in Python, R, or Tableau. It’s also perfect for students and researchers exploring geography, demographics, or development studies.

📈 Whether you’re analyzing Asia’s population boom, Europe’s aging curve, or Africa’s youthful surge — this dataset gives you a complete view of the world’s demographic balance in 2025. 🌎 With 233 rows and 12 insightful columns, it’s ready for your next EDA, visualization, or predictive modeling project.

🚀 Dive in, explore the data, and uncover what the world looks like — one country at a time.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Actual value and historical data chart for World Population Female Percent Of Total

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

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.

The average for 2023 based on 196 countries was 0.51 percent. The highest value was in India: 17.94 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

This dataset was obtained through web scraping from Worldometer, a website that provides real-time global statistics. The data was collected in September 2025.

Column Description

• Population: The total number of inhabitants of a country in a given year.
• Yearly Change (%): The percentage growth in population per year compared to the previous year.
• Net Change: The difference in the number of inhabitants added each year (in numbers, not percentages).
• Density (P/Km²): Population density, calculated as the number of people per square kilometer (people per km²).
• Land Area (Km²): The land area of a country in square kilometers (excluding water areas).
• Migrants (net): Net migration figures (immigrants minus emigrants). Positive → more people entering, Negative → more people leaving.
• Fertility Rate: The average number of children born to a woman throughout her lifetime.
• Median Age: The middle age of the population (half are younger than this number, half are older).
• Urban Population (%): The percentage of the population living in urban areas.
• World Share (%): The percentage of a country's population compared to the total world population.

The population rating shows how many people currently live in a particular country. This rating helps not only to compare countries by the number of inhabitants and population density, but also to predict the further dynamics of growth, stagnation and population decline.

Actual value and historical data chart for World Population Male

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Actual value and historical data chart for World Population Female

Over the past 24 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2024, 4.09 billion were men and 4.05 billion were women. One-quarter of the world's total population in 2024 was below 15 years.

Dataset Description: Worldometer Data Introduction This dataset contains detailed information on the population statistics of various countries, compiled from Worldometer. It includes demographic data such as yearly population changes, migration numbers, fertility rates, and urbanization metrics over multiple years.

Dataset Overview Total Entries: 4,104 Total Columns: 14 Columns Description country (object):

The name of the country. Example: 'India', 'China'. year (float64):

The year for which the data is recorded. Example: 2024, 2023. population (object):

The total population for the given year. Example: '1,441,719,852', '1,428,627,663'. yearly_change_pct (object):

The percentage change in population from the previous year. Example: '0.92%', '0.81%'. yearly_change (object):

The absolute change in population from the previous year. Example: '13,092,189', '11,454,490'. migrants (object):

The net number of migrants for the given year. Example: '-486,784', '-486,136'. median_age (object):

The median age of the population. Example: '28.6', '28.2'. fertility_rate (object):

The fertility rate for the given year. Example: '1.98', '2.00'. density_p_km2 (object):

The population density per square kilometer. Example: '485', '481'. urban_pop_pct (object):

The percentage of the population living in urban areas. Example: '36.8%', '36.3%'. urban_pop (object):

The total urban population for the given year. Example: '530,387,142', '518,239,122'. share_of_world_pop_pct (object):

The country's share of the world's population as a percentage. Example: '17.76%', '17.77%'. world_pop (object):

The total world population for the given year. Example: '8,118,835,999', '8,045,311,447'. global_rank (float64):

The global population rank of the country for the given year. Example: '1.0', '2.0'. Data Quality Missing Values:

Some columns have missing values which need to be handled before analysis. Columns with significant missing data: year, population, yearly_change_pct, yearly_change, migrants, median_age, fertility_rate, density_p_km2, urban_pop_pct, urban_pop, share_of_world_pop_pct, world_pop, global_rank. Data Types:

Most columns are of type object due to the presence of commas and percentage signs. Conversion to appropriate numeric types (e.g., integers, floats) is required for analysis. Potential Uses Demographic Analysis: Study population growth trends, migration patterns, and changes in fertility rates. Urbanization Studies: Analyze urban population growth and density changes over time. Global Ranking: Evaluate and compare the population statistics of different countries. Conclusion This dataset provides a comprehensive view of the world population trends over the years. Cleaning and preprocessing steps, including handling missing values and converting data types, will be necessary to prepare the data for analysis. This dataset can be valuable for researchers, demographers, and data scientists interested in population studies and demographic trends.

File Details Filename: worldometer_data.csv Size: 4104 rows x 14 columns Format: CSV Source Website: Worldometer Scraped Using: Scrapy

Context

2021 World Population dataset which gets updated daily.

Content

2021_population.csv: File contains data for only live 2021 population count which gets updated daily. Also contains more information about the country's growth rate, area, etc. timeseries_population_count.csv: File contains data for live population count which gets updated daily but it contains last updated data also. Data in this file is managed day-wise.

Inspiration

This type of data can be used for population-related use cases. Like, my own dataset COVID Vaccination in World (updated daily), which requires population data. I believe there are more use cases that I didn't explore yet but might other Kaggler needs this. Time-series related use-case can be implemented on this data but I know it will take time to compile that amount of data. So stay tuned.

Cuba CU: Birth Rate: Crude: per 1000 People data was reported at 8.706 Ratio in 2023. This records an increase from the previous number of 8.662 Ratio for 2022. Cuba CU: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 14.015 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 35.367 Ratio in 1963 and a record low of 8.662 Ratio in 2022. Cuba CU: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cuba – Table CU.World Bank.WDI: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.;(1) United Nations Population Division. World Population Prospects: 2024 Revision; (2) Statistical databases and publications from national statistical offices; (3) Eurostat: Demographic Statistics; (4) United Nations Statistics Division. Population and Vital Statistics Reprot (various years).;Weighted average;

This Dataset provides comprehensive demographic information on global populations from 1950 to the present. It offers insights into various aspects of population dynamics, including population counts, gender ratios, birth and death rates, life expectancy, and migration patterns.

Column Descriptions:

SortOrder: Numeric identifier for sorting.

LocID: Location identifier.

Notes: Additional notes or comments (blank in this dataset).

ISO3_code: ISO 3-character country code.

ISO2_code: ISO 2-character country code.

SDMX_code: Statistical Data and Metadata Exchange code.

LocTypeID: Location type identifier.

LocTypeName: Location type name.

ParentID: Identifier for the parent location.

Location: Name of the location.

VarID: Identifier for the variant.

Variant: Type of population variant.

Time: Year or time period.

TPopulation1Jan: Total population on January 1st.

TPopulation1July: Total population on July 1st.

TPopulationMale1July: Total male population on July 1st.

TPopulationFemale1July: Total female population on July 1st.

PopDensity: Population density (people per square kilometer).

PopSexRatio: Population sex ratio (male/female).

MedianAgePop: Median age of the population.

NatChange: Natural change in population.

NatChangeRT: Natural change rate (per 1,000 people).

PopChange: Population change.

PopGrowthRate: Population growth rate (percentage).

DoublingTime: Time for population to double (in years).

Births: Total number of births.

Births1519: Births to mothers aged 15-19.

CBR: Crude birth rate (per 1,000 people).

TFR: Total fertility rate (average number of children per woman).

NRR: Net reproduction rate.

MAC: Mean age at childbearing.

SRB: Sex ratio at birth (male/female).

Deaths: Total number of deaths.

DeathsMale: Total male deaths.

DeathsFemale: Total female deaths.

CDR: Crude death rate (per 1,000 people).

LEx: Life expectancy at birth.

LExMale: Life expectancy for males at birth.

LExFemale: Life expectancy for females at birth.

LE15: Life expectancy at age 15.

LE15Male: Life expectancy for males at age 15.

LE15Female: Life expectancy for females at age 15.

LE65: Life expectancy at age 65.

LE65Male: Life expectancy for males at age 65.

LE65Female: Life expectancy for females at age 65.

LE80: Life expectancy at age 80.

LE80Male: Life expectancy for males at age 80.

LE80Female: Life expectancy for females at age 80.

InfantDeaths: Number of infant deaths.

IMR: Infant mortality rate (per 1,000 live births).

LBsurvivingAge1: Children surviving to age 1.

Under5Deaths: Number of deaths under age 5.

NetMigrations: Net migration rate (per 1,000 people).

CNMR: Crude net migration rate.

How to Use the Dataset:

1. Researchers can analyze demographic trends, birth and death rates, and population growth over time.
2. Policymakers can use population data to inform decisions on healthcare, education, and social services.
3. Data scientists can visualize and model population dynamics for various regions.
4. Journalists can use the dataset to report on global population trends and disparities.
5. Educators can incorporate real-world population data into lessons and research.

Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!