Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

The average for 2023 based on 47 countries was 487236 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2023. Below is a chart for all countries where data are available.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

**🌍 World Countries Dataset This World Countries Dataset contains detailed information about countries across the globe, offering insights into their geographic, demographic, and economic characteristics.

It includes various features such as population, area, GDP, languages, and regional classifications. This dataset is ideal for projects related to data visualization, statistical analysis, geographical studies, or machine learning applications such as clustering or classification of countries.

This dataset was manually compiled/collected from reliable open data sources (e.g., Wikipedia, World Bank, or other governmental datasets).

**🔍 Sample Questions Explored Using Python: - Q. 1) Which countries have the highest and lowest population? - Q. 2) What is the average area (in sq. km) of countries in each region? - Q. 3) Which countries have more than 100 million population and GDP above $1 trillion? - Q. 4) Which languages are most commonly spoken across countries? - Q. 5) Show a bar graph comparing GDPs of G7 nations. - Q. 6) How many countries are there in each continent or region? - Q. 7) Which countries have both a high population density and low GDP per capita? - Q. 8) Create a world map visualization of population or GDP distribution. - Q. 9) What are the top 10 most densely populated countries? - Q. 10) How many landlocked countries are there in the world?

**🧾 Features / Columns in the Dataset: - Country: The name of the country (e.g., "Pakistan", "France").

• Capital: The capital city of the country.

• Region: Broad geographical region (e.g., "Asia", "Europe").

• Subregion: More specific geographical grouping (e.g., "Southern Asia").

• Population: Total population of the country.

• Area (sq. km): Total land area in square kilometers.

• Population Density: Number of people per square kilometer.

• GDP (USD): Gross Domestic Product (in U.S. dollars).

• GDP per Capita: GDP divided by the population.

• Official Languages: Officially recognized language(s) spoken.

• Currency: Name of the currency used.

• Timezones: Timezones in which the country falls.

• Borders: List of bordering countries (if any).

• Landlocked: Whether the country is landlocked (Yes/No).

• Latitude / Longitude: Coordinates for geographical plotting.

Based on land area, Brazil is the largest country in Latin America by far, with a total area of over 8.5 million square kilometers. Argentina follows with almost 2.8 million square kilometers. Cuba, whose surface area extends over almost 111,000 square kilometers, is the Caribbean country with the largest territory.

Brazil: a country with a lot to offer

Brazil's borders reach nearly half of the South American subcontinent, making it the fifth-largest country in the world and the third-largest country in the Western Hemisphere. Along with its landmass, Brazil also boasts the largest population and economy in the region. Although Brasília is the capital, the most significant portion of the country's population is concentrated along its coastline in the cities of São Paulo and Rio de Janeiro.

South America: a region of extreme geographic variation

With the Andes mountain range in the West, the Amazon Rainforest in the East, the Equator in the North, and Cape Horn as the Southern-most continental tip, South America has some of the most diverse climatic and ecological terrains in the world. At its core, its biodiversity can largely be attributed to the Amazon, the world's largest tropical rainforest, and the Amazon river, the world's largest river. However, with this incredible wealth of ecology also comes great responsibility. In the past decade, roughly 80,000 square kilometers of the Brazilian Amazon were destroyed. And, as of late 2019, there were at least 1,000 threatened species in Brazil alone.

This dataset provides a comprehensive list of countries and their respective land and water areas. It includes a detailed comparison of country sizes as depicted on the Mercator projection versus their true geographic proportions. This dataset is inspired by the limitations of traditional map projections and aims to provide a more accurate representation of global land masses.

File Information: - true-size-of-countries-2024.csv: A CSV file containing a list of countries, their land area, water area, total area, and percentage of water area.

The World Bank's data for 2021 on total land area by country provides detailed information on the size of land in square kilometers for various countries worldwide. Here are some key highlights from the dataset:

Russia is the largest country by land area, with approximately 16.38 million square kilometers. Canada follows with around 9.98 million square kilometers. China has a land area of about 9.42 million square kilometers, making it the third largest. The United States (excluding territories) covers around 9.14 million square kilometers. Smaller countries and regions include:

Vatican City, with an area of about 0.44 square kilometers. Monaco, with 2 square kilometers

THIS DATA WAS LAST UPDATED IN 2024 and it is owned by https://data.worldbank.org/indicator/AG.LND.TOTL.K2?end=2021&start=2021&view=map

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

The average for 2023 based on 194 countries was 31.6 percent. The highest value was in Suriname: 94.4 percent and the lowest value was in Egypt: 0 percent. The indicator is available from 1990 to 2023. Below is a chart for all countries where data are available.

Algeria is the biggest country in Africa, with an area exceeding 2.38 million square kilometers as of 2020. The Democratic Republic of the Congo and Sudan follow with a total area of around 2.34 million and 1.88 million square kilometers, respectively. On the other hand, Seychelles is the smallest country on the continent, with an area of only 460 square kilometers. Overall, Africa’s total area exceeds 30 million square kilometers, being the second largest continent in the world after Asia. Nigeria and Ethiopia lead the ranking of the most populated countries in Africa.

How have the African countries been formed?

The political geography of Africa has been influenced by its colonial history. Between the 19th and 20th Century, the European colonizers have divided up Africa. The partition of the territories was merely driven by strategic purposes: Borders between countries were artificially created in the absence of a geographic border. Following the decolonization, most countries gained their independence in the second half of the 1900s. The newest country in Africa is South Sudan, which became independent in 2011.

Africa's physical geography

Geographically, the African continent is mostly constituted by plains and tablelands. Inner plateaus are prevalent in the sub-Saharan region. In the center-north, the arid Sahara Desert extends for around nine million square kilometers, being the largest subtropical desert in the world. The continent also has some of the biggest water basins worldwide, namely the Nile, Congo, and Niger rivers. East Africa has, instead, the highest summit on the continent, the Kilimanjaro. Peaking at 5,895 meters, the mountain dominates Tanzania’s landscape and attracts thousands of climbers each year.

With 450,295 square kilometers, Sweden is the largest Nordic country by area size, followed by Finland and Norway. This makes it the fifth largest country in Europe. Meanwhile, Denmark is the smallest of the five Nordic countries with only 43,094 square kilometers, however, the Danish autonomous region of Greenland is significantly larger than any of the Nordic countries, and is almost double the size of the other five combined.

Population

Sweden is also the Nordic country with the largest population. 10.45 million people live in the country. Denmark, Finland, and Norway all have between five and six million inhabitants, whereas only 370,000 people live in Iceland. Meanwhile, Denmark has the highest population density of the five countries. Greenland is the most sparsely populated permanently-inhabited country in the world, followed by the regions of Svalbard and Jan Mayen.

Geography

The five Nordic countries vary geographically. While Denmark is mostly flat, its highest point only stretching around 170 meters above sea level, Norway's highest peak is nearly 2,500 meters high. Moreover, Finland is known for its many lakes and is often called the land of a thousand lakes, whereas Iceland is famous for its volcanoes.

Description

This Dataset contains details of World Population by country. According to the worldometer, the current population of the world is 8.2 billion people. Highest populated country is India followed by China and USA.

Attribute Information

• Rank : Country Rank by Population.
• Country : Name of the Country.
• Population(2024) : Current Population of each Country.
• Yearly Change : Percentage Yearly Change in Population.
• Net Change : Net change in the Population.
• Density (P/Km²) : Population density (population per square km)
• Land Area(Km²) : Total land area of the Country.
• Migrants (net) : Total number of migrants.
• Fertility Rate : Fertility rate
• Median Age : Median age of the population
• Urban Pop % : Percentage of urban population
• World Share : Share to the word with population.

Acknowledgements

https://www.worldometers.info/world-population/population-by-country/

Image by Gerd Altmann from Pixabay

Top five largest countries of Africa by land size are Algeria, Congo Democratic Republic of the, Sudan, Libya and Chad but the top five largest populations in Africa are in Nigeria, Ethiopia, Egypt, Democratic Republic of the Congo and Tanzania.

The average for 2023 based on 27 countries was 147715 sq. km. The highest value was in France: 538950 sq. km and the lowest value was in Malta: 320 sq. km. The indicator is available from 1961 to 2023. Below is a chart for all countries where data are available.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

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.

This dataset provides values for ARABLE LAND PERCENT OF LAND AREA WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

Many country data sets are available on kaggle which are either based on recent covid situation or contain less amount of informations. After going through some of them I feel the need of adding more informations which can be analysed by Machine Learning algorithms.

Content

There are total 3 country data frames are available and each one contains mostly numeric informaions and some textual informations on 250 countries in the world.

Country_data (API).csv: First data frame which is 'Country_data (API)', is created using API. It contains 250 rows and 11 columns. Each row in this data frame has information on a particular country. There are total 10 information regarding every country which are distributed in 10 columns (region, subregion, population, latlng (latitude and longitude), area, gini, timezones, numericCode, numberOfLanguages, regionalBlocs).

Country_data (Scraped): Second data frame which is 'Country_data (Scraped)', is created by scraping data from various websites. It has also 250 rows which contains information on same 250 countries from API data frame. There are total 37 columns (Including 'name') which has 36 more informations (e.g.: landArea, totalArea (Including water surface and land area), population, growthRate, birthRate, infantMortalityRate, lifeExpectancy, imports, majorTradingPartners, internetHostsNumber and much more) on each of the countries.

Country_data (Merged).csv: Both the scraped and API data frames which are mentioned above has one common column which is 'name' (Country names). In 'name' column there are total 250 country names, on which both the API and scraped data frames are meged which results in 'Country_data (Merged)' data frame. It has nothing but merged informations from first two data frames. It has total 250 rows for 250 countries and 47 unique columns (Including 'name' column). Unique 10 information column from API data frame, unique 36 information column from scraped data frame and one common 'name' column from both of them results in 47 unique columns in Merged data frame.

Hints

'landArea1' and 'landArea2' both the columns are in scraped and merged data frame. Those two contain the same information, one in square mile unit and another in square kilometer unit. Same goes for 'totalArea1' and 'totalArea2' columns.

In scraped and merged data frame, some textual columns are there like 'portsAndHarbors1', 'majorTradingPartners'. Those can be used by extracting only the number of ports and harbours from 'portsAndHarbors1' or number of majorTradingPartners in case of 'majorTradingPartners'. Without numbers if you have any suggestions how they can be used, let me know.

Inspiration

There are some columns like 'imports1', 'exports1' has textual informations followed by numerical informations. I want some suggestions if they can help in analysis, if so how they can be used. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting.