🌍 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.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1800. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1939. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

In 2023, the Faroe Islands was the European country estimated to have the highest fertility rate. The small Atlantic island state had a fertility rate of 2.71 children per woman. Other small countries such as Monaco and Gibraltar also came towards the top of the list for 2023, while the large country with the highest fertility rate was France, with 1.79 children per woman. On the other hand, Andorra, San Marino, and Malta had the lowest fertility rates in Europe, with Ukraine, Spain, and Italy being the largest countries with low fertility rates in that year, averaging around 1.3 children per woman.

By 1800, London had grown to be the largest city in Western Europe with just under one million inhabitants. Paris was now the second largest city, with over half a million people, and Naples was the third largest city with 450 thousand people. The only other cities with over two hundred thousand inhabitants at this time were Vienna, Amsterdam and Dublin. Another noticeable development is the inclusion of many more northern cities from a wider variety of countries. The dominance of cities from France and Mediterranean countries was no longer the case, and the dispersal of European populations in 1800 was much closer to how it is today, more than two centuries later.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_Middle_Eastern_countries_by_population. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Context

As of 1 September 2021, 5.34 billion COVID-19 vaccine doses had been administered worldwide, with 39.6 per cent of the global population having received at least one dose. While 40.5 million vaccines were then being administered daily, only 1.8 per cent of people in low-income countries had received at least a first vaccine by September 2021, according to official reports from national health agencies, which is collated by Our World in Data.

Content

The dataset contains the list of countries, the Number of people who have received at least one dose of a COVID-19 vaccine (unless noted otherwise), and Percentage of population that has received at least one dose of a COVID-19 vaccine.

Acknowledgements

Wikipedai: https://en.wikipedia.org/wiki/Deployment_of_COVID-19_vaccines#cite_note-14

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

Context

This dataset contains the locations found in the Kiva datasets included in an administrative or geographical region. You can also find poverty data about this region. This facilitates answering some of the tough questions about a region's poverty.

Content

In the interest of preserving the original names and spelling for the locations/countries/regions all the data is in Excel format and has no preview (I think only the Kaggle recommended file types have preview - if anyone can show me how to do this for an xlsx file, it will be greatly appreciated)

The Tables datasets contain the most recent analysis of the MPI on countries and regions. These datasets are updated regularly. In unique regions_names_from_google_api you will find 3 levels of inclusion for every geocode provided in Kiva datasets. (village/town, administrative region, sub-national region - which can be administrative or geographical). These are the results from the Google API Geocoding process.

Files:

• all_kiva_loans.csv

Dropped multiple columns, kept all the rows from loans.csv with names, tags, descriptions and got a csv file of 390MB instead of 2.13 GB. Basically is a simplified version of loans.csv (originally included in the analysis by beluga)

• country_stats.csv

1. population source: https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)
2. population_below_poverty_line: Percentage
3. hdi: Human Development Index
4. life_expectancy: Life expectancy at birth
5. expected_years_of_schooling: Expected years of schooling
6. mean_years_of_schooling: Mean years of schooling
7. gni: Gross national income (GNI) per capita This dataset was originally created by beluga.

• all_loan_theme_merged_with_geo_mpi_regions.xlsx

This is the loan_themes_by_region left joined with Tables_5.3_Contribution_of_Deprivations. (all the original entries from loan_themes and only the entries that match from Tables_5; for the regions that lack MPI data, you will find Nan)

These are the columns in the database:

1. Partner ID
2. Field Partner
3. Name
4. sector
5. Loan Theme ID
6. Loan Theme Type
7. Country
8. forkiva
9. number
10. amount
11. geo
12. rural_pct
13. City
14. Administrative region
15. Sub-national region
16. ISO
17. World region
18. Population Share of the Region (%)
19. region MPI
20. Education (%)
21. Health (%)
22. Living standards (%)
23. Schooling (%)
24. Child school attendance (%)
25. Child Mortality (%)
26. Nutrition (%)
27. Electricity (%)
28. Improved sanitation (%)
29. Drinking water (%)
30. Floor (%)
31. Cooking fuel (%)
32. Asset ownership (%)

• mpi_on_regions.xlsx

Matched the loans in loan_themes_by_region with the regions that have info regarding MPI. This dataset brings together the amount invested in a region and the biggest problems the said region has to deal with. It is a join between the loan_themes_by_region provided by Kiva and Tables 5.3 Contribution_of_Deprivations.

It is a subset of the all_loan_theme_merged_with_geo_mpi_regions.xlsx, which contains only the entries that I could match with poverty decomposition data. It has the same columns.

• Tables_5_SubNational_Decomposition_MPI_2017-18.xlsx

Multidimensional poverty index decomposition for over 1000 regions part of 79 countries.

Table 5.3: Contribution of deprivations to the MPI, by sub-national regions
This table shows which dimensions and indicators contribute most to a region's MPI, which is useful for understanding the major source(s) of deprivation in a sub-national region.

Source: http://ophi.org.uk/multidimensional-poverty-index/global-mpi-2016/

• Tables_7_MPI_estimations_country_levels.xlsx

MPI decomposition for 120 countries.

Table 7 All Published MPI Results since 2010
The table presents an archive of all MPI estimations published over the past 5 years, together with MPI, H, A and censored headcount ratios. For comparisons over time please use Table 6, which is strictly harmonised. The full set of data tables for each year published (Column A), is found on the 'data tables' page under 'Archive'.

The data in this file is shown in interactive plots on Oxford Poverty and Human Development Initiative website. http://www.dataforall.org/dashboard/ophi/index.php/

• unique_regions_from_kiva_loan_themes.xlsx

These are all the regions corresponding to the geocodes found in Kiva's loan_themes_by_region. There are 718 unique entries, that you can join with any database from Kiva that has either a coordinates or region column.
Columns:

• geo: pair of Lat, Lon (from loan_themes_by_region)

• City: name of the city (has the most NaN's)

• Administrative region: first level of administrative inclusion for the city/location; (the equivalent of county for US)

• Sub-national region: second level of administrative inclusion for the geo pair. (like state for US)

• Country: name of the country

Acknowledgements

Thanks to Shane Lynn for the batch geocoding and to Joseph Deferio for reverse geocoding:

https://www.shanelynn.ie/batch-geocoding-in-python-with-google-geocoding-api/

https://github.com/jdeferio/Reverse_Geocode

The MPI datasets you can find on the Oxford website (http://ophi.org.uk/) under Research.

"Citation: Alkire, S. and Kanagaratnam, U. (2018)

“Multidimensional Poverty Index Winter 2017-18: Brief methodological note and results.” Oxford Poverty and Human Development Initiative, University of Oxford, OPHI Methodological Notes 45."

As of April 2024, there were almost 90 million Pinterest users in the United States, making it the country with the most Pinterest users by far. Ranking second was Brazil, with 38.925 million users, followed by Mexico, Germany, and France, respectively. Pinterest’s audience In the first quarter of 2024, Pinterest had a total of 518 million monthly active users (MAUs) worldwide, an increase of two million users from the previous quarter. Throughout 2021, the social media site saw a steady decline in MAUs, after seeing a constant increase throughout 2020. The increase in usage of many social media platforms coincides with the COVID-19 pandemic and consequent lockdowns. As of April 2024, significantly more women than men used Pinterest, with women making up almost three-quarters of its user base. The platform’s largest audience, in terms of gender and age, was women between the ages of 25 to 34, who accounted for more than a fifth of all users, followed by women aged 18 to 24 years. User satisfaction According to the American Customer Satisfaction Index (ACSI), as of June 2021, Pinterest scored the highest level of customer satisfaction for selected social media sites, ranking ahead of platforms such as YouTube, Wikipedia, and TikTok. Overall, Pinterest received a total score of 78 out of 100 index points. Pinterest’s financials For the financial year 2023, Pinterest generated over three billion U.S. dollars in global annual revenue, a rise from the previous year’s result of 2.8 billion U.S. dollars. The majority of this revenue was generated in the United States. Additionally, in the last quarter of 2023, the social media platform reported a net loss of over 200 million U.S. dollars. As of June 2023, Pinterest was one of the biggest consumer internet and online service companies worldwide in terms of market capitalization. With a market cap of 19 billion U.S. dollars, Pinterest ranked in 16th place, ahead of Chewy, Delivery Hero, and Etsy.

Over 1.2 million refugees from Ukraine due to the Russian invasion fled to Germany as of April 2025. Furthermore, the second-highest number was recorded in Russia. In total, around 6.4 million Ukrainian refugees were registered across Europe and 6.9 million worldwide as of April 2025. Most of them fled the country by crossing the border with Poland. Ukrainian refugees in Germany As of January 2025, over 1.2 million refugees from Ukraine were recorded in Germany. The first increases in the number of Ukrainian refugees were registered in March and April 2022. At the end of January 2023, over one million refugees were officially counted by the authorities. Germany had the highest monthly financial allowance for Ukrainians who fled the war compared to other European countries as of June 2022. Temporary protection for Ukrainian refugees in the EU European Union (EU) members implemented the Temporary Protection Directive (TPD), which guaranteed access to accommodation, welfare, and healthcare to refugees from Ukraine. People fleeing the war had a right to a residence permit in the EU, enter the labor market, and enroll children in educational institutions. The protection is granted until March 4, 2026, but it can be extended in the future depending on the situation in the country.