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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Source

The data file is from https://simplemaps.com/data/world-cities.

🌍 Global Capitals Daily Weather (Open-Meteo)

License: CC BY 4.0

🧭 Overview

This dataset provides daily weather summaries for national capital cities worldwide, automatically updated each day from the free Open-Meteo API.

Each record contains temperature extremes, precipitation totals, wind data, and daylight information for one capital on one date.

⚙️ Update Process

• The data is collected by a Kaggle Notebook scheduled to run daily (~06:00 UTC).
• Each run fetches yesterday’s data from Open-Meteo, appends it to the cumulative history, and republishes a new dataset version.
• No raw or intermediate files are kept — only the three clean output files below.

📁 Files

🧾 Schema (for history.parquet and history_latest.csv)

📡 Data Sources

• Weather: Open-Meteo — free weather API (CC BY 4.0)
• Capital coordinates: Wikidata — open geographic data (CC0 1.0)

⚖️ License & Attribution

This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0).

Please credit:

Weather data © Open-Meteo (CC BY 4.0)
Capital metadata © Wikidata contributors (CC0 1.0)
Compiled and processed by [wafaaelhusseini]

🧩 Citation

[Your Name or Kaggle handle] (2025).
Global Capitals Daily Weather (Open-Meteo). Kaggle Datasets.

https://www.kaggle.com/datasets/wafaaelhusseini/daily-global-capitals-weather-data/

🔖 Tags

weather climate time-series global capitals daily open-data

The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau Disclaimer: The designations employed and the presentation of material at this site do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Derived projected datasets for the eight Australian capital cities in 2016-2045 and 2036-2065, centred around 2030 and 2050, respectively. Projects used eight general circulation models (GCMs) under Representative Concentration Pathway [RCP]2.6, RCP4.5, RCP6.0 and RCP8.5. The scenarios were under Coupled Model Intercomparison Project [CMIP]5. The eight GCM models are ACCESS1-0, CESM1-CAM5, CNRM-CM5, CanESM2, GFDL-ESM2M, HadGEM2-CC, MIROC5 and NorESM1-M, and are described online: https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/. Only data from five GCMs are available for RCP2.6 and four for RCP6.0.

For each city, seven*seven 5 km grids were extracted at grid centroids correlating to the centre of its central business district. These coordinates are in the file "City coordinate." The corresponding datasets for each city, RCP, GCM, time period, and meteorological variable are located in their respective city folder in the folder "future." The meteorological variables are relative humidity ("hurs"), solar radiation ("rsds"), average air temperature ("tas"), maximum air temperature "(tasmax") and minimum air temperature ("tasmin"). These were used to create derived .csv files also stored in the "future" folder, which in turn were used to create derived R datasets ("ccia_future.rda" and "ccia_future2.rda") combining all the datasets into one and creating additional meteorological indices using the available data. The R code used to create these datasets is included "CCiA data manipulation.R". It uses functions stored in the R code file "Climate functions.R". The additional meteorological indices include alternate humidity variables, apparent temperature variables and the Excess Heat Factor (EHF). The heatwave thresholds values used to calculate EHF (the 95th percentile of daily mean temperature from a reference period) per city are included in "barra_ehfr.R" and were calculated from a separate dataset (not included) derived from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis (BARRA).

The original projected climate datasets were sourced from Climate Change in Australia (CCiA), published by the Commonwealth Science Industrial Research Organisation (CSIRO). The original datasets are available online: https://data-cbr.csiro.au/thredds/catalog/catch_all/oa-aus5km/Climate_Change_in_Australia_User_Data/Application_Ready_Data_Gridded_Daily/catalog.html. The license under which the data were used is available online: https://www.climatechangeinaustralia.gov.au/en/overview/about-site/licences-and-acknowledgements/.

I acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modelling groups (listed at https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Further information regarding these datasets and meteorological variables is listed in the author's PhD thesis, available online: https://digital.library.adelaide.edu.au/dspace/handle/2440/137773. For any queries, please do not hesitate to contact the author: matthew.borg@adelaide.edu.au.

🌍 Global Capitals Real-time Weather Data (2025)

This dataset contains real-time and near real-time weather observations for 195 world capitals, collected throughout September and October 2025.

Each row corresponds to one weather observation in both UTC and local time. The dataset will be updated monthly with new data from subsequent months.

📊 Dataset Overview

🗺️ Coverage

• Geographic: 195 capital cities across all continents
• Temporal: September and October 2025
• Update frequency: Monthly (new data added every month)

💡 Applications

• Weather monitoring dashboards 🌦️
• Climate visualization and analytics 🌍
• Machine learning model training for forecasting 🤖
• Educational purposes in meteorology and data science 📘

⚙️ Technical Notes

• Units: Metric (°C, m/s, hPa)
• Missing values: Empty cells (not NaN strings)
• Encoding: UTF-8
• Format: CSV

📈 Update Schedule

The dataset is updated every month, adding new CSV data for the latest period.
For example: - v1 → September–October 2025
- v2 → Add November 2025 data
- v3 → Add December 2025 data

All updates will appear in the Versions tab.

⚖️ License

CC BY 4.0 — Attribution required
You are free to share and adapt this dataset, provided that proper credit is given.

✍️ Author

Created and maintained by Lê Duy Hải (@lduyhi)
Automatically collected and processed via PythonAnywhere scripts.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

The United States recorded a capital and financial account surplus of 190139 USD Million in September of 2025. This dataset provides the latest reported value for - United States Net Treasury International Capital Flows - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Success.ai’s Company Financial Data for Banking & Capital Markets Professionals in the Middle East offers a reliable and comprehensive dataset designed to connect businesses with key stakeholders in the financial sector. Covering banking executives, capital markets professionals, and financial advisors, this dataset provides verified contact details, decision-maker profiles, and firmographic insights tailored for the Middle Eastern market.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers your organization to build meaningful connections in the region’s thriving financial industry.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Financial Professionals

   • Access verified email addresses, direct phone numbers, and LinkedIn profiles of banking executives, capital markets advisors, and financial consultants.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and minimizing data inefficiencies.

2. Targeted Insights for the Middle East Financial Sector

   • Includes profiles from major Middle Eastern financial hubs such as Dubai, Riyadh, Abu Dhabi, and Doha, covering diverse institutions like banks, investment firms, and regulatory bodies.
   • Gain insights into region-specific financial trends, regulatory frameworks, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market activities, and organizational structures.
   • Stay ahead of emerging opportunities and align your strategies with evolving market dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and professionals in banking, investment management, and capital markets across the Middle East.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Leadership Contact Information: Connect directly with CEOs, CFOs, risk managers, and regulatory professionals driving financial strategies.
• Decision-Maker Insights: Understand key decision-makers’ roles and responsibilities to tailor your outreach effectively.

Key Features of the Dataset:

1. Decision-Maker Profiles in Banking & Capital Markets

   • Identify and connect with executives, portfolio managers, and analysts shaping investment strategies and financial operations.
   • Target professionals responsible for compliance, risk management, and operational efficiency.

2. Advanced Filters for Precision Targeting

   • Filter institutions by segment (retail banking, investment banking, private equity), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to align with specific financial needs, such as digital transformation, customer retention, or risk mitigation.

3. Firmographic and Leadership Insights

   • Access detailed firmographic data, including company hierarchies, financial health indicators, and service specializations.
   • Gain a deeper understanding of organizational structures and market positioning.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer financial technology solutions, consulting services, or compliance tools to banking institutions and investment firms.
   • Build relationships with decision-makers responsible for vendor selection and financial strategy implementation.

2. Market Research and Competitive Analysis

   • Analyze trends in Middle Eastern banking and capital markets to guide product development and market entry strategies.
   • Benchmark against competitors to identify market gaps, emerging niches, and growth opportunities.

3. Partnership Development and Vendor Evaluation

   • Connect with financial institutions seeking strategic partnerships or evaluating service providers for operational improvements.
   • Foster alliances that drive mutual growth and innovation.

4. Recruitment and Talent Solutions

   • Engage HR professionals and hiring managers seeking top talent in finance, compliance, or risk management.
   • Provide staffing solutions, training programs, or workforce optimization tools tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your outreach, marketing, and partners...

Welcome to the City Data Strategy for London. This document is built around the following vision: We want London to have the most dynamic and productive City Data Market in the world. In our City Data Market, the capabilities, talents and capacity of all our city data partners will impact on our huge social, economic and service-based challenges. To make this happen, friction in the sharing and value-driven exploitation of city data will be reduced to a minimum. City data will be recognised as part of the capital’s infrastructure. We will use it to save money, incubate innovation and drive economic growth. And London will achieve global renown for data impact. We will of course use this strategy in support of City Hall initiatives like the Smart London Plan, but we acknowledge at the outset that the Greater London Authority simply cannot deliver this strategy on its own. Indeed, data knows no boundaries and is hard to contain, so we should not try. Our simple aim is to make sure that London, its economy and its communities are able to derive maximum benefit from the undoubted potential London’s Data Market can deliver. It therefore signals the start of a plan which will actively integrate and mobilise all the ‘working parts’ of the city data economy. We want the various audiences that the Strategy is aimed at to engage with it. We will listen and evolve the strategy as this rapidly developing part of the cityscape develops.

Success.ai presents an exclusive opportunity to connect directly with top-tier decision-makers in the finance sector through our CEO Contact Data, specifically designed for venture capital and private equity investors based in the USA. This tailored database is part of our expansive collection that draws from over 700 million global profiles, meticulously verified to ensure the highest quality and reliability.

Why Choose Success.ai’s CEO Contact Data?

Specialized Investor Profiles: Access detailed profiles of CEOs and senior executives from leading venture capital and private equity firms across the United States. Investment Insights: Gain valuable insights into investment trends, fund sizes, and sectors of interest directly from the decision-makers. Verified Contact Details: We provide up-to-date email addresses and phone numbers, ensuring that you reach the right people without the hassle of outdated information. Data Features:

Targeted Financial Sector Data: Directly target influential figures in the financial sector who have the authority to make investment decisions. Comprehensive Executive Information: Profiles include not just contact information but also professional backgrounds, areas of investment focus, and operational histories. Geographic Precision: Focus your outreach efforts on US-based investors with our geographically segmented data. Flexible Delivery and Integration: Choose from various delivery options including API access for real-time integration or static files for periodic campaign use, allowing for seamless incorporation into your CRM or marketing automation tools.

Competitive Pricing with Best Price Guarantee: Success.ai is committed to providing competitive pricing without compromising on quality, backed by our Best Price Guarantee.

Effective Use Cases for CEO Contact Data:

Fundraising Initiatives: Connect with venture capital and private equity firms for fundraising activities or financial endorsements. Partnership Development: Forge strategic partnerships and collaborations with leading investors in the industry. Event Invitations: Send personalized invites to investment summits, roundtables, and networking events catered to top financial executives. Market Analysis: Utilize executive insights to better understand the investment landscape and refine your market strategies. Quality Assurance and Compliance:

Rigorous Data Verification: Our data undergoes continuous verification processes to maintain accuracy and completeness. Compliance with Regulations: All data handling practices adhere to GDPR and other relevant data protection laws, ensuring ethical and lawful use. Support and Custom Solutions:

Client Support: Our team is available to assist with any queries or specific data needs you may have. Tailored Data Solutions: Customize data sets according to specific criteria such as investment size, sector focus, or geographic location. Start Connecting with Venture Leaders: Empower your business strategy and network building by accessing Success.ai’s CEO Contact Data for venture capital and private equity investors. Whether you're looking to initiate funding rounds, explore investment opportunities, or engage with top financial leaders, our reliable data will pave the way for meaningful connections and successful outcomes.

Contact Success.ai today to discover how our precise and comprehensive data can transform your business approach and help you achieve your strategic goals.

The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.

India recorded a capital and financial account deficit of 157 USD Million in the third quarter of 2025. This dataset provides - India Capital Flows- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The services of ecological systems and the natural capital stocks that produce them are critical to the functioning of the Earth's life-support system. They contribute to human welfare, both directly and indirectly, and therefore represent part of the total economic value of the planet. We have estimated the current economic value of 17 ecosystem services for 16 biomes, based on published studies and a few original calculations. For the entire biosphere, the value (most of which is outside the market) is estimated to be in the range of US$16-54 trillion per year, with an average of US$33 trillion per year. Because of the nature of the uncertainties, this must be considered a minimum estimate. Global gross national product total is around US$18 trillion per year. Because ecosystem services are not fully 'captured' in commercial markets or adequately quantified in terms comparable with economic services and manufactured capital, they are often given too little weight in policy decisions. This neglect may ultimately compromise the sustainability of humans in the biosphere. The economies of the Earth would grind to a halt without the services of ecological life- support systems, so in one sense their total value to the economy is infinite. However, it can be instructive to estimate the 'incremental' or 'marginal' value of ecosystem services (the estimated rate of change of value compared with changes in ecosystem services from their current levels). There have been many studies in the past few decades aimed at estimating the value of a wide variety of ecosystem services. We have gathered together this large (but scattered) amount of information and present it here in a form useful for ecologists, economists, policy makers and the general public. From this synthesis, we have estimated values for ecosystem services per unit area by biome, and then multiplied by the total area of each biome and summed over all services and biomes.

This table provides an overview of the non-financial transactions of the institutional sectors of the Dutch economy, distinguishing between uses and resources. Non-financial transactions consist of current transactions and transactions from the capital account. Furthermore, this table provides the main balancing items of the (sub)sectors. Non-financial transactions are estimated for the main institutional sectors of the economy and the rest of the world. Sectors are presented both consolidated and non-consolidated.

Data available from: Annual figures from 1995. Quarterly figures from first quarter 1999.

Status of the figures: Annual figures from 1995 up to and including 2023 are final. Quarterly data from 2023 are provisional.

Changes as of September 23rd, 2025: Data of the second quarter 2025 have been added.

Adjustment as of April 10th 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. We refer to the Government Finance Statistics for the current figures. Links to the Government Finance Statistics could be found in paragraph 3. Until the publication end of June the Sector accounts therefore diverge from the Government Finance Statistics.

Adjustment as of July 12th 2024: Total consolidated resources and uses are adjusted for most sectors, due to a calculation error. For the sector rest of the world, the non-consolidated total resources and uses have also been adjusted. Imports and exports of goods and services were wrongly not included in the total resources and uses. For the sectors non-financial corporations and financial corporations, capital taxes (uses) were wrongly shown as empty cell (figure not applicable).

When will new figures be published? Annual figures: The first annual data are published 85 day after the end of the reporting year as the sum of the four quarters of the year. Subsequently provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Furthermore the sector accounts are annually revised for all reporting periods. These data are published each year in June. Quarterly figures: The first quarterly estimate is available 85 days after the end of each reporting quarter. The first quarter may be revised in September, the second quarter in December. Should further quarterly information become available thereafter, the estimates for the first three quarters may be revised in March. If (new) annual figures become available in June, the quarterly figures will be revised again to bring them in line with the annual figures. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest figures. Revised yearly figures are published in June each year.

🌍 Global Major Cities Daily Weather (1995 – 2024) [CURRENTLY UNDER CONSTRUCTION]

This dataset provides daily historical weather data for major cities worldwide, combining reliable climate reanalysis from the Open-Meteo Historical API with geographic and population data from Wikidata.

It includes all national capitals and the largest population centers in every country, giving a detailed global view of urban climate from 1995 to 2024.

📦 Files

🌆 City Coverage

“Major cities” are defined using a reproducible Wikidata query that returns all entities of type city (Q515) with coordinates.
Cities are included if they meet at least one of the following:

• Are a national capital, or
• Have a population ≥ 300 000, or
• Rank among the 10 most populous cities in their country.

If a live query to Wikidata fails, the generator automatically falls back to a previously saved cities_clean.parquet file from a prior run or from the dataset input, ensuring continuity between updates.

☀️ Weather Variables

Daily statistics from Open-Meteo’s ERA5-based reanalysis include:

All values are daily aggregates in UTC.

⚙️ Data Collection & Processing

• Coordinates and population metadata are fetched via Wikidata’s public SPARQL endpoint.
• Historical weather is pulled year-by-year from the Open-Meteo archive.
• The script checks for existing yearly records per city to avoid redundant API calls.
• Robust retry logic handles rate limits and network errors.
• After each batch of merges, a new version of history.parquet is saved for incremental backup.
• If five consecutive 429 (“Too Many Requests”) responses occur, the run pauses gracefully and resumes automatically in the next scheduled execution.

⚖️ License & Attribution

Data is released under CC BY 4.0. Please credit:

The dataset contains general information about world countries as well as information about their flags, economy, and geographical location.

File description:

world_flags_2024.csv - dataset data_description.txt - full description of each column.

Source:

The dataset contains 41 columns: 8 of them are numeric-valued, others are either Boolean or nominal-valued. In the CSV file fields are separated by commas.

Note: Possible errors or inaccuracies in the interpretation of blazon images or other symbols on flags are not intentional, but arise from a lack of awareness on the part of the author.

Data fields:

Country - Names of all sovereign states as of 2024.

FlagUrl - Link to country's flag on Flagpedia.net.

AspectRatio - Aspect ration of the flag. Format: Height:Width.

LatestAdoption - Year of the last changes in the flag design.

White - 1 if white color present in the flag, 0 otherwise.

Red - 1 if red color present in the flag, 0 otherwise.

Blue - 1 if blue color present in the flag, 0 otherwise.

Black - 1 if black color present in the flag, 0 otherwise.

Yellow - 1 if yellow color present in the flag, 0 otherwise.

Green - 1 if green color present in the flag, 0 otherwise.

Orange - 1 if orange color present in the flag, 0 otherwise.

OtherColor - 1 if any other color present in the flag, 0 otherwise.

StripesEqual - 1 if all the stripes that make up the flag have equal width, 0 otherwise.

StripesVertical - 1 if stripes are arranged vertically, 0 otherwise.

StripesHorizontal - 1 if stripes are arranged horizontally, 0 otherwise.

StripesDiagonal - 1 if stripes are arranged diagonally, 0 otherwise.

StripesOther - 1 if the direction of stripes is mixed, 0 otherwise.

SingleColor - 1 if the flag is single color, i.e. there is no stripes, 0 otherwise.

LeftTriangle - 1 if there is a triangle on the left hand side of the flag, 0 otherwise.

Canton - 1 if there is an insert with an image in the top-left corner of the flag, 0 otherwise.

Cross - 1 if the flag contains a cross, 0 otherwise.

Crescent - 1 if the flag contains a crescent, 0 otherwise.

Sun - 1 if the flag contains the sun, 0 otherwise.

Bird - 1 if the flag contains a bird, 0 otherwise.

Stars - Number of stars on the flag.

Circle - 1 if the flag contains a circle, 0 otherwise.

BlazonOrOther - 1 if the flag contains a blazon or any other symbol, 0 otherwise.

Continent - Continent where the country is located. Note: Some countries have their parts located on multiple continents. For those countries the continent where the majority of its territory is located is chosen. Example: Russian Federation and Turkey.

Landlocked - 1 if the country has no direct access to an ocean, 0 otherwise.

TotalArea - Area of the country in km^2.

Population - Population of the country as of 2024.

Capital - Name of the capital of the country.

CapitalPopulation - Population of the capital.

HighestPoint - The highest point of the country.

LowestPoint - The lowest point of the country.

Religion - Dominant religion. If multiple, the most popular is chosen.

Currency - Name of the currency of the country.

CallingCode - Calling code of the country.

GDPPerCapita - GDP per capita in USD as of 2022. Zero if unknown.

HDI - Human Development Index as of 2022.

Gini - Income inequality: Gini coefficient as of 2023.

References:

1. https://www.un.org/dgacm/sites/www.un.org.dgacm/files/Documents_Protocol/officialnamesofcountries.pdf
2. https://flagpedia.net
3. https://www.worldometers.info
4. https://www.britannica.com
5. https://ourworldindata.org
6. https://www.jetpunk.com/info/capital-cities-by-population
7. https://wisevoter.com/country-rankings/religion-by-country
8. https://assets.pewresearch.org/wp-content/uploads/sites/11/2012/12/globalReligion-tables.pdf
9. Header image generated by leonardo.ai

Inspired by:

https://www.kaggle.com/datasets/edoardoba/world-flags https://www.kaggle.com/code/mscgeorges/country-flags-analysis

CIA World Factbook 2024–2025: Global Multi-Domain Metrics

💡 Dataset Overview: A Comprehensive Global Reference

This dataset is a comprehensive, multi-domain compilation derived from the CIA World Factbook 2024–2025.
It provides a unified and structured source for comparative analysis across seven critical dimensions covering 259 global entities including sovereign nations and dependent territories.

It serves as a foundational resource for projects in: - 🌍 Economics
- 🏛️ Political Science
- 🌱 Environmental Studies
- 📊 Data Visualization
- 🤖 Machine Learning

The dataset enables cross-domain insights into the factors driving global stability and development.

📚 Data Source and Provenance

• Official Source: CIA World Factbook (2024–2025)
• Authority: U.S. Central Intelligence Agency
• Coverage: Global (Sovereign nations + dependent territories)

The Factbook is a high-authority governmental reference providing detailed geographic, political, demographic, and socio-economic data.

🗃️ Dataset Structure: 7 Linked CSV Files

Each file is linked by a common key column - Country - enabling easy joins across domains.

⚙️ Data Integrity & Preprocessing Requirements

Raw values from the Factbook are human-readable and contain: - Unit suffixes (e.g., "sq km", "km", "m") - Percent signs ("%") - Thousands separators (e.g., "2,381,740")

Before quantitative analysis, perform data cleaning.

🔧 Required Cleaning Steps:

1. Remove unit labels, commas, and percentage signs.
2. Convert the cleaned columns to a numeric type (e.g., float).

🧹 Example Preprocessing Workflow (pandas)

# Cleaning a column with comma separators and unit suffix
df['Area_Total'] = (
  df['Area_Total']
   .str.replace(',', '', regex=True)
   .str.replace(' sq km', '', regex=True)
   .astype(float)
)

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

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

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

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

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

• Capital: The capital city of the country.

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

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

• Population: Total population of the country.

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

• Population Density: Number of people per square kilometer.

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

• GDP per Capita: GDP divided by the population.

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

• Currency: Name of the currency used.

• Timezones: Timezones in which the country falls.

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

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

• Latitude / Longitude: Coordinates for geographical plotting.

Since South Sudan became an independent state on 9 July 2011, there are now 195 independent sovereign nations in the world (not including the disputed but de facto independent Taiwan, plus some 60 dependent areas and several disputed territories, such as Kosovo.

193 sovereign states are United Nations members and equally represented in the UN General Assembly. Two non-member countries have permanent observer states: the Holy See and the State of Palestine.

Below is a list of countries and areas of the world in alphabetical order, with official names and alternative designations. The list contains English and French country names as well as the local names of the countries.

Links will lead to the respective One World - Nations Online country profiles.

Each country profile contains links to and information about important official websites of a country/territory, geographic information, maps, the national flag, history, culture, and tourist destinations. The country profiles include information on a country's population and languages, its capital(s) and largest cities, tourist attractions, and world heritage sites, as well as education, economy, newspapers and news sources, and other country information