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

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.

Explore the World Bank Population dataset to access rankings and insights on global population statistics. Click here for extensive data on various countries.

Rankings

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, San Marino, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, ZimbabweFollow data.kapsarc.org for timely data to advance energy economics research..

🌍 Countries and Dependencies by Population (2025)

This dataset provides a comprehensive list of countries and dependent territories worldwide, along with their most recent population estimates.The data is sourced from the Wikipedia page List of countries and dependencies by population, which compiles figures from national statistical offices and the United Nations Population Division

📄 Dataset Overview

• Country/Territory Name Includes sovereign states, dependent territories, and regions with limited recognition.
• Population Latest available estimates, primarily from national censuses or UN projection.
• Percentage of World Population Each country's population as a percentage of the global total.
• Date of Estimate The reference date for the population figure.
• Notes Additional information, such as inclusion or exclusion of certain region.

🧠 Potential Use Cases

• Analyzing global population distribution and trends.- Creating visualizations like choropleth maps.- Normalizing other datasets by population for per capita analysis.- Educational purposes in demographics and geography.

📌 Notes

• The dataset includes territories and regions with limited recognition to provide a complete global perspective.
• Population figures are based on the most recent estimates available as of 225.
• Data may be subject to revisions as new census information becomes available.

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

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Explore population projections for China on this dataset webpage. Get valuable insights into the future demographic trends of one of the world's most populous countries.

Population, China, projections ChinaFollow data.kapsarc.org for timely data to advance energy economics research..Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimatesSource: (1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.

The Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 is a raster representation of nation-states in GPWv4 for use in aggregating population data. This data set was produced from the input census units which were used to create a raster surface where pixels that cover the same census data source (most often a country or territory) have the same value. Note that these data are not official representations of country boundaries; rather, they represent the area covered by the input data. In cases where multiple countries overlapped a given pixel (e.g. on national borders), the pixels were assigned the country code of the input data set which made up the majority of the land area. The data file was produced as a global raster at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions. Each level of aggregation results in the loss of one or more countries with areas smaller than the cell size of the final raster. Rasters of all resolutions were also converted to polygon shapefiles. To provide a raster representation of nation-states in GPWv4 for use in aggregating population data.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.


Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App.
The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively):

- Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020.
- Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019)
-Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019).
-Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets.
-Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020.
-Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019).

Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

The resilience of population growth in countries along the Belt and Road reflects the level of resilience of population growth in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of population growth in the countries along the Belt and Road. The World Bank's statistical database was used to prepare the Resilience to Population Growth data product, which uses year-on-year data on the population of countries along the Belt and Road from 2000 to 2019. The Resilience to Population Growth product is based on sensitivity and adaptation analysis. Please refer to the documentation for the methodology of preparing the dataset. "The resilience dataset is an important reference for analysing and comparing the current resilience of population growth in countries along the Belt and Road.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

Context

This dataset contains information about Anime scraped from Anime Planet on 28/06/2023. It contains information about anime (episodes, aired date, rating, genre, etc.), and favorite anime based on the countries and top countries that watch the most anime.

• The scraped program of this dataset is in Anime.Quest GitHub repository.
• Tableau visualization of this dataset can also be found in Anime Quest: Visualization.

Content

The dataset contains 3 files:

📁 anime_data.csv: 1. Name: Full name of the anime 2. Media Type: TV, Web, Movie, etc. 3. Episodes: Total episodes of the anime 4. Studio: Name of the studios of the anime, from most recent to oldest. 5. Start Year: Release Year of the anime 6. End Year: Last year of the anime airing 7. Ongoing: Is the anime currently airing or not? True or False. 8. Release Season: Spring, Fall, Winter, and Summer 9. Rating: The global rating ranges from 0 to 5. 10. Rank: Global ranking of the anime 11. Members: Total members of the anime 12. Genre: The category of the anime 13. Creator: Creator of the anime

📁 anime_top_by_country_data.csv: 1. Country: Individual country name 2. Most Popular: The most popular anime in the country 3. 2nd Place: Second-most popular anime in the country 4. 3rd Place: Third-most popular anime in the country 5. 4th Place: Fourth-most popular anime in the country 6. 5th Place: The fifth-most popular anime in the country

📁 anime_watching_data.csv: 1. Rank: Ranking of countries based on the number of anime viewers 2. Country: Individual country name 3. Population: Total population of the country 4. Percentage of People Watching: Percentage of people watching anime in the country 5. Number of People Watching: Total number of people watching anime in the country

Acknowledgements

The website Anime Planet was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

This dataset can be used to find the factors determining an anime's rating and ranking. Additionally, it can be used to make anime recommendations. The pattern can be observed in anime.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

LAC is the most water-rich region in the world by most metrics; however, water resource distribution throughout the region does not correspond demand. To understand water risk throughout the region, this dataset provides population and land area estimates for factors related to water risk, allowing users to explore vulnerability throughout the region to multiple dimensions of water risk. This dataset contains estimates of populations living in areas of water stress and risk in 27 countries in Latin America and the Caribbean (LAC) at the municipal level. The dataset contains categories of 18 factors related to water risk and 39 indices of water risk and population estimates within each with aggregations possible at the basin, state, country, and regional level. The population data used to generate this dataset were obtained from the WorldPop project 2020 UN-adjusted population projections, while estimates of water stress and risk come from WRI’s Aqueduct 3.0 Water Risk Framework. Municipal administrative boundaries are from the Database of Global Administrative Areas (GADM). For more information on the methodology users are invited to read IADB Technical Note IDB-TN-2411: “Scarcity in the Land of Plenty”, and WRIs “Aqueduct 3.0: Updated Decision-relevant Global Water Risk Indicators”.

This dataset provides a detailed exploration of global warming and climate change trends across 195 countries from 1900 to 2023. It includes 1,00,000 rows and 26 columns, capturing environmental, economic, and societal factors impacting global warming. Key indicators such as temperature anomalies, CO2 emissions, deforestation rates, sea-level rise, and renewable energy usage are included, making this dataset suitable for climate change prediction and analysis.

Whether you're a beginner exploring trends or an advanced data scientist building models, this dataset is an excellent resource for learning, experimentation, and insights into one of the most pressing challenges of our time.

Insights to Explore:

For Beginners:

Trend Analysis:

Track how global temperature anomalies have changed over the decades. Identify countries with the highest and lowest CO2 emissions. Explore population growth trends and their correlation with CO2 emissions.

Visualization Practice:

Create line charts showing changes in renewable energy usage over time. Develop bar charts comparing extreme weather events between countries.

For Intermediate Users:

Correlation Analysis:

Analyze relationships between deforestation rates and temperature anomalies. Explore how GDP and fossil fuel usage correlate with CO2 emissions. Feature Engineering:

Create new features like Per Capita CO2 Emissions or Energy Efficiency Score to enhance predictive modeling. Clustering:

Group countries based on their environmental policies and renewable energy usage.

For Advanced Users:

Predictive Modeling:

Build time-series models to forecast future temperature anomalies or sea-level rise. Develop machine learning models to predict CO2 emissions based on socioeconomic factors. Anomaly Detection:

Detect outliers in extreme weather events or CO2 emissions.

Deep Learning Applications:

Train deep learning models to predict Arctic ice extent using multi-year trends.