22 datasets found
  1. List_of_countries_by_population_in_1900

    • kaggle.com
    zip
    Updated Jul 17, 2020
    + more versions
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    Mathurin Aché (2020). List_of_countries_by_population_in_1900 [Dataset]. https://www.kaggle.com/mathurinache/list-of-countries-by-population-in-1900
    Explore at:
    zip(355 bytes)Available download formats
    Dataset updated
    Jul 17, 2020
    Authors
    Mathurin Aché
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1900. 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?

  2. List of Countries and their Population

    • kaggle.com
    Updated Apr 12, 2025
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    Anah Chukwujekwu (2025). List of Countries and their Population [Dataset]. https://www.kaggle.com/datasets/anahchukwujekwu/list-of-countries-and-their-population
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Anah Chukwujekwu
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    🌍 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.
  3. A

    ‘Population by Country - 2020’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Population by Country - 2020’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-population-by-country-2020-c8b7/latest
    Explore at:
    Dataset updated
    Feb 13, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

    Content

    Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

    Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

    https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

    You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

    Below is the code that I used to scrape the code from the website

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

    Acknowledgements

    Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

    Inspiration

    As far as I know, I don't have any questions to ask. 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

    --- Original source retains full ownership of the source dataset ---

  4. s

    Geonames - All Cities with a population > 1000

    • data.smartidf.services
    • public.opendatasoft.com
    • +2more
    csv, excel, geojson +1
    Updated Mar 10, 2024
    + more versions
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    (2024). Geonames - All Cities with a population > 1000 [Dataset]. https://data.smartidf.services/explore/dataset/geonames-all-cities-with-a-population-1000/
    Explore at:
    csv, geojson, json, excelAvailable download formats
    Dataset updated
    Mar 10, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  5. World Population Dataset

    • kaggle.com
    Updated Jan 6, 2024
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    CHANDAN CHOUDHURY (2024). World Population Dataset [Dataset]. https://www.kaggle.com/datasets/chandanchoudhury/world-population-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 6, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    CHANDAN CHOUDHURY
    Area covered
    World
    Description

    World Population Datasets Overview:

    Explore a comprehensive collection of datasets offering profound insights into global demographics and country-specific characteristics. These datasets, sourced from reputable platforms including worldometers.info and Wikipedia, cover a wide array of key indicators, providing a rich resource for in-depth analysis and exploration.

    Dataset 1: World Country Stats:

    Delve into detailed statistics for countries worldwide, encompassing essential factors such as regions, land area, fertility rates, and median ages. This dataset, provides a holistic view of demographic and geographical attributes.

    Dataset 2: World Population Details (2023):

    Gain nuanced insights into the demographic landscape of countries for the year 2023. This dataset, covers a multitude of population-related details, including yearly changes, density, net migrants, urban populations, and more.

    Dataset 3: World Population by Year (1950-2023):

    Uncover the evolution of world populations from 1950 to 2023, with yearly granularity for each country. This dataset allows you to analyze and understand population trends over more than seven decades.

    These datasets collectively form a robust foundation for researchers, analysts, and enthusiasts seeking to explore and understand the intricate dynamics of global populations and country-specific characteristics. Whether studying historical trends or focusing on the latest demographic profiles, these datasets offer a wealth of information for diverse analytical perspectives.

    Note This Dataset is created from worldometers and wikipedia.org. If you want to learn more, you can visit the Websites.

    Upvote this dataset if found helpful. :blush:

  6. n

    ගොනුව:World Population.svg

    • wiki-data.si-lk.nina.az
    Updated Jun 18, 2024
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    (2024). ගොනුව:World Population.svg [Dataset]. https://www.wiki-data.si-lk.nina.az/%E0%B6%9C%E0%B7%9C%E0%B6%B1%E0%B7%94%E0%B7%80:World_Population.svg.html
    Explore at:
    Dataset updated
    Jun 18, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    ලෝකය
    Description

    ග න ව ග න ඉත හ සය ග න භ ව තය ග ල ය ග න භ ව තය ප රදත තSize of this PNG preview of this SVG file 800 406 ප ක සල අන ක ත ව භ

  7. List_of_countries_by_population_in_1939

    • kaggle.com
    zip
    Updated Jul 17, 2020
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    Mathurin Aché (2020). List_of_countries_by_population_in_1939 [Dataset]. https://www.kaggle.com/mathurinache/list-of-countries-by-population-in-1939
    Explore at:
    zip(351 bytes)Available download formats
    Dataset updated
    Jul 17, 2020
    Authors
    Mathurin Aché
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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?

  8. M

    Russia Population (1950-2025)

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). Russia Population (1950-2025) [Dataset]. https://www.macrotrends.net/global-metrics/countries/rus/russia/population
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1950 - Dec 31, 2025
    Area covered
    Russia
    Description

    Historical chart and dataset showing total population for Russia by year from 1950 to 2025.

  9. World Bank: GHNP Data

    • kaggle.com
    zip
    Updated Mar 20, 2019
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    World Bank (2019). World Bank: GHNP Data [Dataset]. https://www.kaggle.com/theworldbank/world-bank-health-population
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    zip(0 bytes)Available download formats
    Dataset updated
    Mar 20, 2019
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

    Content

    This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

    Update Frequency: Biannual

    For more information, see the World Bank website.

    Fork this kernel to get started with this dataset.

    Acknowledgements

    https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

    https://cloud.google.com/bigquery/public-data/world-bank-hnp

    Dataset Source: World Bank. 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.

    Citation: The World Bank: Health Nutrition and Population Statistics

    Banner Photo by @till_indeman from Unplash.

    Inspiration

    What’s the average age of first marriages for females around the world?

  10. World Population 2023 (UN Data)

    • kaggle.com
    Updated Apr 4, 2025
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    Siddartha Khetan (2025). World Population 2023 (UN Data) [Dataset]. https://www.kaggle.com/datasets/siddarthakhetan/world-population-2023-un-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 4, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Siddartha Khetan
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United Nations, World
    Description

    This dataset contains the population data for countries as of 1 July 2023, sourced from the United Nations via Wikipedia. Includes country names, population figures, percentage change, and continental regions. Ideal for demographic analysis, research, and visualization.

  11. COVID-19 cases and deaths per million in 210 countries as of July 13, 2022

    • statista.com
    • ai-chatbox.pro
    Updated Nov 25, 2024
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    Statista (2024). COVID-19 cases and deaths per million in 210 countries as of July 13, 2022 [Dataset]. https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/
    Explore at:
    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  12. List_of_continents_by_population

    • kaggle.com
    Updated Jul 17, 2020
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    Mathurin Aché (2020). List_of_continents_by_population [Dataset]. https://www.kaggle.com/mathurinache/list-of-continents-by-population/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 17, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Mathurin Aché
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset is extracted from https://en.wikipedia.org/wiki/List_of_continents_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?

  13. S

    Digital Marketing Statistics And Facts (2025)

    • sci-tech-today.com
    Updated May 22, 2025
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    Sci-Tech Today (2025). Digital Marketing Statistics And Facts (2025) [Dataset]. https://www.sci-tech-today.com/stats/digital-marketing-statistics-updated/
    Explore at:
    Dataset updated
    May 22, 2025
    Dataset authored and provided by
    Sci-Tech Today
    License

    https://www.sci-tech-today.com/privacy-policyhttps://www.sci-tech-today.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Digital Marketing Statistics: In 2024, the digital marketing landscape experienced significant growth, reflecting the increasing reliance on online platforms for business engagement. Global internet users reached 5.35 billion, marking a 1.8% increase from the previous year. Social media usage also expanded, with over 5 billion active user identities, accounting for 62.3% of the global population.

    Digital advertising budgets saw a 10% increase between 2023 and 2024, indicating a strategic shift towards online marketing channels. Mobile advertising spending reached a record USD 327 billion worldwide in 2023, while global mobile internet advertising spending surpassed USD 400 billion.

    The influencer marketing industry grew substantially, with the global market estimated at USD 24 billion in 2024. Additionally, as of September 2024, 2.71 billion people were shopping online, representing about 34% of the global population.

    These statistics underscore the pivotal role of digital marketing in today's business environment, highlighting the necessity for companies to adapt and invest in online strategies to remain competitive. So, let's buckle up because we're diving into the fascinating world of digital marketing statistics to show you why it's more important than ever.

  14. Largest cities in Europe in 2025

    • statista.com
    Updated May 28, 2025
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    Statista (2025). Largest cities in Europe in 2025 [Dataset]. https://www.statista.com/statistics/1101883/largest-european-cities/
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    Dataset updated
    May 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Europe
    Description

    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.

  15. n

    ගොනුව:World Muslim Population (Pew Forum).svg

    • wiki-data.si-lk.nina.az
    Updated Jun 20, 2024
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    (2024). ගොනුව:World Muslim Population (Pew Forum).svg [Dataset]. https://www.wiki-data.si-lk.nina.az/%E0%B6%9C%E0%B7%9C%E0%B6%B1%E0%B7%94%E0%B7%80:World_Muslim_Population_(Pew_Forum).svg.html
    Explore at:
    Dataset updated
    Jun 20, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ග න ව ග න ඉත හ සය ග න භ ව තය ග ල ය ග න භ ව තයSize of this PNG preview of this SVG file 800 411 ප ක සල අන ක ත ව භ දනයන 32

  16. E

    Gmail Statistics By Users, Usage and Facts

    • electroiq.com
    Updated Feb 27, 2025
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    Electro IQ (2025). Gmail Statistics By Users, Usage and Facts [Dataset]. https://electroiq.com/stats/gmail-statistics/
    Explore at:
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Electro IQ
    License

    https://electroiq.com/privacy-policyhttps://electroiq.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Gmail Statistics: Gmail, the popular email service by Google, has become an essential tool for communication in today's digital age. But how much do you know about how Gmail works and how people use it globally? This article includes a range of effective analyses on current trends of Gmail, such as market share, users, country-wise usage, etc. All the statistics described below will be valuable.

    So, let’s get ready to explore some fascinating statistics about this email giant.

  17. Additional resources for Kiva Crowdfunding

    • kaggle.com
    zip
    Updated Apr 12, 2018
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    Luke (2018). Additional resources for Kiva Crowdfunding [Dataset]. https://www.kaggle.com/forums/f/26443/additional-resources-for-kiva-crowdfunding/t/54374/dataset-suggestion
    Explore at:
    zip(104671314 bytes)Available download formats
    Dataset updated
    Apr 12, 2018
    Authors
    Luke
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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."

  18. E

    Google Chrome Statistics By Market Share, Demographics and Usage

    • electroiq.com
    Updated Jan 20, 2025
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    Electro IQ (2025). Google Chrome Statistics By Market Share, Demographics and Usage [Dataset]. https://electroiq.com/stats/google-chrome-statistics/
    Explore at:
    Dataset updated
    Jan 20, 2025
    Dataset authored and provided by
    Electro IQ
    License

    https://electroiq.com/privacy-policyhttps://electroiq.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Google Chrome Statistics: According to the web-tracking firm Stat Counter, Chrome is the world’s number one internet browser. Between the period of July and August 2023, Chrome was used by almost 63.6% of the internet population all over the globe. Chrome is mainly famous in South America, where it has an internet browser share of almost 78.9% of the market.

    In North American and European countries, the share of Google Chrome is low compared to 53.1% and 58.6%, respectively. Google Chrome was launched in 2008, but it became the most popular web browser across the world in 2012. In this article, we will shed more light on Google Chrome statistics.

  19. S

    Agriculture Statistics By Revenue, Consumers and Facts

    • sci-tech-today.com
    Updated Jun 27, 2025
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    Sci-Tech Today (2025). Agriculture Statistics By Revenue, Consumers and Facts [Dataset]. https://www.sci-tech-today.com/stats/agriculture-statistics/
    Explore at:
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Sci-Tech Today
    License

    https://www.sci-tech-today.com/privacy-policyhttps://www.sci-tech-today.com/privacy-policy

    Time period covered
    2022 - 2032
    Area covered
    Global
    Description

    Introduction

    Agriculture Statistics: By 2050, the demand for food is projected to increase by almost 70%, in line with the rapidly growing global population. A study conducted in the United States revealed that approximately 9.9% of the world's population still suffers from hunger, highlighting the significant challenge of feeding around 20 million people. Given the uncertain environmental changes, technological innovation in agriculture has become crucial.

    The agricultural industry has undergone remarkable transformation in recent years, largely attributed to technological advancements. From drones to automated tractors, technology has played a pivotal role in enhancing the effectiveness, sustainability, and precision of agriculture. This article aims to delve deeper into agricultural statistics.

  20. Largest cities in western Europe 1800

    • statista.com
    Updated Mar 1, 1992
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    Statista (1992). Largest cities in western Europe 1800 [Dataset]. https://www.statista.com/statistics/1022001/thirty-largest-cities-western-europe-1800/
    Explore at:
    Dataset updated
    Mar 1, 1992
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1800
    Area covered
    Europe
    Description

    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.

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Mathurin Aché (2020). List_of_countries_by_population_in_1900 [Dataset]. https://www.kaggle.com/mathurinache/list-of-countries-by-population-in-1900
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List_of_countries_by_population_in_1900

List_of_countries_by_population_in_1900.csv

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3 scholarly articles cite this dataset (View in Google Scholar)
zip(355 bytes)Available download formats
Dataset updated
Jul 17, 2020
Authors
Mathurin Aché
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1900. 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?

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