According to the Hurun Global Rich List 2025, the city with the highest number of billionaires in 2025 was New York. In detail, *** billionaires resided in the American city. Furthermore, ** billionaires lived in London, while Shanghai had a billionaire population of ** individuals. New York was the only city in the world with more than 100 billionaires that year. Mega-cities of the world A large number of the world’s billionaires are concentrated in a select number of the world’s megacities. This has as much to do with the location of their wealth, business interests, and further earning potential as it does with the quality of life in those cities. A look at the most significant industries in the global billionaire production line helps to explain the prominence of the traditional capitals of global business, including New York, London, and Hong Kong. The place of many Chinese cities on the list can in part be explained by the strong performance of industrial conglomerates from the country recently. Economic growth in China While New York is the city with the highest number of billionaires, China now boasts the most billionaires of any country in the world. However, ***** of the top ten wealthiest billionaires still came from the United States as of 2025.

The statistic shows the top 20 cities according to the number of millionaires in each in 2012. In 2012, there were around ******* millionaires living in Tokyo in Japan.

In January 2024, approximately ******* hoseholds in Beijing owned assets worth *********** yuan ore more. Beijing, Shanghai, and Hong Kong were home to by far the highest number of millionaires in Greater China.

In 2022, ****** was home to the highest number of millionaires, followed by India’s capital New Delhi, and the IT capital - Bengaluru. This comes as no surprise since all three cities have the largest share of high net worth households along with a booming economic outlook. Overall, India had around *** billionaires as of March 2023, and ranked third globally in terms of its ultra-net-worth individuals. A growing wealth gap Despite this, India also has a very high wealth inequality with millions of people living below the poverty line. In fact, according to the last census, the state of Maharashtra (with Mumbai as its capital city) had the highest number of slums across the country with over *** million households. Furthermore, according to a 2015 study on the geography of the super-rich, Bangalore was ranked first in terms of the inequality between its rich and poor, with the wealth of the city’s billionaires being ******* times that of the average per capita GDP in the city. Mumbai came second in this listing, while Delhi was ranked fifth. It's a rich man's world As of 2018, the richest ** percent of Indians owned **** percent of the country’s wealth. The Indian economy was also seen to be one of the fastest growing economies across the world. This indicates the level of unequal distribution of wealth in the country. This is a matter of grave concern and has several implications in terms of the country’s development and progress.

A dataset listing the 20 richest cities in Montana for 2024, including information on rank, city, county, population, average income, and median income.

Cleaned dataset from the Billionaires Statistic Dataset (2023) that can be found here.

The code I used to clean and re-structure the data is also here.

First things first: a big shout-out to Nidula Elgiriyewithana for providing the original data.

As with it, this dataset contains various information about the world's wealthiest persons in different columns that can be grouped into three different types:

• Business-related information. These columns contain data about the industry in which the billionaires' operate, their source of wealth, total wealth and position they occupy in the ranking.
• Personal information. Such as name, age, nationality, country and city of residence.
• Economic activity information. These columns are related to the country in which the billionaire resides and provide different economic indicators like GDP, education enrollment or Consumer Price Index (CPI).

Column names

• position. Ranking of the billionaire measured by their wealth.
• wealth. The wealth of the billionaire measured in $.
• industry. Industry in which the billionaire's operates their businesses.
• full_name. Complete name of the billionaire.
• age. The age of the billionaire.
• country_of_residence. Country in which the billionaire resides.
• city_of_residence. City in which the billionaire resides.
• source. The source of the billionaire's wealth.
• citizenship. The country of citizenship of the billionaire.
• gender. The gender of the billionaire.
• birth_date. The birth date of the billionaire.
• last_name. The last name of the billionaire.
• first_name. The first name of the billionaire.
• residence_state. State in which the billionaire resides (only for billionaires who reside in the U.S.).
• residence_region. Region in which the billionaire resides (only for billionaires who reside in the U.S.).
• birth_year. The birth year of the billionaire.
• birth_month. The birth month of the billionaire.
• birth_day. The birth data of the billionaire.
• cpi_country. Consumer Price Index (CPI) for the billionaire's country.
• cpi_change_country. CPI change for the billionaire's country.
• gdp_country. Gross Domestic Product (GDP) in $ for the billionaire's country.
• g_tertiary_ed_enroll. Enrollment in tertiary education in the billionaire's country.
• g_primary_ed_enroll. Enrollment in primary education in the billionaire's country.
• life_expectancy. Life expectancy in the billionaire's country.
• tax_revenue. Tax revenue in the billionaire's country.
• tax_rate. Total tax rate in the billionaire's country.
• country_pop. Population of the billionaire's country.
• country_lat. Latitude coordinate of the billionaire's country.
• country_long. Longitude coordinate of the billionaire's country.
• continent. Continent in which the country of the billionaire's residence is located.

Potential analyses

• Analyze which industries contain the biggest groups of billionaires overall and in different countries.
• Explore number of billionaires and total wealth across countries and continents and display the result in a map.
• Focus on personal information columns such as age or gender to explore the distribution of billionaires from this perspective.
• Discover if countries' economic indicators have any impact in the presence of billionaires.
• The U.S. is the country with most billionaires presented in the dataset and also the only one with attributes in the residence_state and residence_region columns. This makes the American billionaires a good focus for a specific analysis.

Bonus

If you want a challenge, you can create a dashboard using tools such as Plotly to dynamically visualize the data using one or different attributes (such as industry, age or country). I did it, leave the link below in case you want to investigate:

Dashboard notebook here

If you find this dataset informative or inspirational, a vote is appreciated for others to easily discover value in it 💎💰

A dataset listing the 20 richest cities in Alabama for 2024, including information on rank, city, county, population, average income, and median income.

A dataset listing the 20 richest cities in Illinois for 2024, including information on rank, city, county, population, average income, and median income.

In the Russian capital and its neighboring Moscow Oblast, 104.5 thousand people owned assets worth one million U.S. dollars or more, based on the millionaire count from 2020. Russia's regions with the second-highest number of U.S. dollar millionaires were Saint Petersburg and the Leningrad Oblast, at 28 thousand. Russia ranked 15th by the number of U.S. dollar millionaires worldwide.

• The billionaires satatistic dataset 2023 is a ranking of billionaires from 1st (with the highest wealth) to the 2640th (the less wealthy) in year 2023. the columns of the dataset are some attributs of the billionaires as follow:

rank: The ranking of the billionaire in terms of wealth. finalWorth: The final net worth of the billionaire in Million U.S. dollars. category: The category or industry in which the billionaire's business operates. personName: The full name of the billionaire. age: The age of the billionaire. country: The country in which the billionaire resides. city: The city in which the billionaire resides. source: The source of the billionaire's wealth. industries: The industries associated with the billionaire's business interests. countryOfCitizenship: The country of citizenship of the billionaire. organization: The name of the organization or company associated with the billionaire. selfMade: Indicates whether the billionaire is self-made (True/False). status: "D" represents self-made billionaires (Founders/Entrepreneurs) and "U" indicates inherited or unearned wealth. gender: The gender of the billionaire. birthDate: The birthdate of the billionaire. lastName: The last name of the billionaire. firstName: The first name of the billionaire. title: The title or honorific of the billionaire. date: The date of data collection. state: The state in which the billionaire resides. residenceStateRegion: The region or state of residence of the billionaire. birthYear: The birth year of the billionaire. birthMonth: The birth month of the billionaire. birthDay: The birth day of the billionaire. cpi_country: Consumer Price Index (CPI) for the billionaire's country. cpi_change_country: CPI change for the billionaire's country. gdp_country: Gross Domestic Product (GDP) for the billionaire's country. gross_tertiary_education_enrollment: Enrollment in tertiary education in the billionaire's country. gross_primary_education_enrollment_country: Enrollment in primary education in the billionaire's country. life_expectancy_country: Life expectancy in the billionaire's country. tax_revenue_country_country: Tax revenue in the billionaire's country. total_tax_rate_country: Total tax rate in the billionaire's country. population_country: Population of the billionaire's country. latitude_country: Latitude coordinate of the billionaire's country. longitude_country: Longitude coordinate of the billionaire's country.

This statistic presents the American states with highest ratio of millionaire households per capita in 2020. In that year, New Jersey had the highest ratio of millionaire households per capita in the country, with 9.76 percent of households holding over one million U.S. dollars in assets.

Introduction: The "Forbes Billionaires Evolution" dataset provides a comprehensive examination of the financial growth and status of global billionaires from 1997 to 2024. It meticulously chronicles the transformations, both subtle and substantial, in the fortunes of these financial titans over nearly three decades.

The cover image used for this dataset is sourced from Forbes, with the illustration credited to Geoff J. Kim for Forbes.

Content: This dataset offers a plethora of valuable information, including: - Yearly rankings. - Net worth details and evolution. - Personal details like names, age, gender, and birth dates. - Residence details spanning country, city, and citizenship. - In-depth business category and industry classifications. - Details about affiliations with various organizations, including positions held. - Indicators like "self-made" and the current wealth status.

Use Cases: The potential applications for this dataset are vast and varied: 1. Financial Analysis: Understand the wealth patterns and trajectories of the world's richest individuals. 2. Sociological Studies: Examine the dynamics of wealth in relation to factors like age, gender, and nationality. 3. Business Strategy: Identify industries and regions with the most billionaire activity and growth. 4. Academic Research: Serve as a foundational resource for theses, dissertations, or coursework related to finance, business, or socio-economics. 5. Journalism: Craft compelling stories around the ascent or decline of billionaires in different epochs.

Leverage this dataset to extract trends, narratives, and crucial data-driven insights into the financial landscape of our world's most affluent individuals.

Content

This dataset consists of top most billionaires in the world and respective their names, whether it is a finance company or any software company, how much money they have ,these all the details which are in the dataset

Researchers have compiled a multi-decade database of the super-rich. Building off the Forbes World’s Billionaires lists from 1996-2014, scholars at Peterson Institute for International Economics have added a couple dozen more variables about each billionaire - including whether they were self-made or inherited their wealth. (Roughly half of European billionaires and one-third of U.S. billionaires got a significant financial boost from family, the authors estimate.)

Acknowledgements

Reference : https://corgis-edu.github.io/corgis/csv/billionaires/

Inspiration

Some of the datasets which I have seen in the kaggle or somewhere but it is limited to less number of columns . Kagglers are not able to get an insights from very low amount of data. so that I decided that to be more helpful to them or we can able to get an more insights from this dataset

This comprehensive dataset encapsulates a detailed snapshot of the wealthiest individuals globally, as listed by Forbes in 2024. Compiled through meticulous web scraping and data aggregation, the dataset includes a wide range of attributes for each billionaire. Fields encompass basic personal information such as name, age, and gender, alongside financial details including net worth and sources of wealth. The dataset further delves into aspects like industry involvement, organizational affiliations, philanthropic endeavors, and educational backgrounds.

Key attributes in this dataset include:

Name: Full legal name of the billionaire. Age: Age of the individual. 2024 Net Worth: Estimated net worth in USD for the year 2024. Industry: Primary industry or sector of operation. Source of Wealth: Origin of the billionaire’s wealth. Title: Professional title or position. Organization: Name of the associated organization. Self-Made: Indicator if the wealth is self-made. Self-Made Score: A quantitative score assessing how self-made their wealth is. Philanthropy Score: A score reflecting the extent of their philanthropic activities. Residence: Main residence of the individual. Citizenship: Legal citizenship. Gender: Gender identity. Marital Status: Current marital status. Children: Number of children. Education: Highest level of education attained.

This dataset is ideal for analysis, offering insights into the distribution of wealth, the influence of education on wealth accumulation, and trends across different industries. It also provides a foundation for exploring the impact of socioeconomic factors on personal wealth. The data were collected and formatted with careful consideration to ensure accuracy, making it a valuable resource for researchers, economists, and anyone interested in the dynamics of wealth and success.

Please note that some data is missing in this dataset, primarily due to the unavailability of information from Forbes. This issue becomes more prevalent beyond the top 400 entries. Many individuals lack a self-made score, a philanthropy score, or specific details regarding their title or organization as per Forbes' listings. I am currently working to update the dataset with this missing information. However, this update process is quite tedious and time-consuming since it is mostly manual. I appreciate your patience and understanding as I work through these details.

This dataset provides a synthetic overview of the 1,000 wealthiest individuals in the world, offering insights into the distribution of wealth across industries and regions. It is designed to help analysts, researchers, and data enthusiasts explore global wealth trends, industry dominance, and regional wealth concentration.

Whether you're conducting market research, financial analysis, or data modeling, this dataset serves as a valuable resource for understanding the characteristics of the world's top billionaires.

📊 Key Features: Name 👤: The name of the billionaire. Country 🌍: Country of residence or primary business operation. Industry 🏭: Industry in which the individual has built their wealth. Net Worth (in billions) 💵: Estimated net worth in billions of USD. Company 🏢: The primary company or business associated with the billionaire. ⚠️ Important Note: This dataset is 100% synthetic and does not contain real financial or personal data. It is artificially generated for educational, analytical, and research purposes.

This statistic shows the top 5 African cities in 2014 by number of residing billionaires. In 2014, ** billionaires were living in Lagos, Nigeria.

This statistic illustrates the number of billionaires in leading cities in the EMEA (Europe, the Middle East and Asia) region as of 2016. It can be seen that the number of billionaires in Moscow stood at a total of **, making Moscow the city in the EMEA region with the most billionaires at that time. Although this is the case London was not far behind, ranking second with a total of ** billionaires as of 2016.

Dataset Card for 100 Richest People In World

  Dataset Summary

This dataset contains the list of Top 100 Richest People in the World Column Information:-

Name - Person Name NetWorth - His/Her Networth Age - Person Age Country - The country person belongs to Source - Information Source Industry - Expertise Domain

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  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/100-richest-people-in-world.

As of March 2024, California was the U.S. state with most billionaires, with *** billionaires calling the state home. New York was second, with *** resident billionaires.

Dataset

This dataset was created by Mohamed Fawzy Hassan

Released under Other (specified in description)

Contents