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 💎💰

Certainly! Here's a description of each column:

Rank: The numerical ranking of a person or entity in a list or category.

finalWorth: The final worth or net worth of a person or entity, typically in terms of monetary value.

category: The category or classification of a person or entity, such as "entrepreneur", "investor", "celebrity", etc.

personName: The name of a person.

age: The age of a person.

country: The country of residence or origin of a person or entity.

city: The city of residence or origin of a person or entity.

source: The source or origin of wealth or fame for a person or entity.

industries: The industries or sectors in which a person or entity operates or is associated with.

countryOfCitizenship: The country of citizenship of a person.

organization: The organization or company with which a person is associated.

selfMade: Indicates whether a person is self-made or inherited wealth/fame.

**status: **The status or position of a person or entity, such as "CEO", "founder", "chairman", etc.

gender: The gender of a person.

**birthDate: **The date of birth of a person.

lastName: The last name or surname of a person.

**firstName: **The first name of a person.

title: The title or honorific used for a person, such as "Mr.", "Mrs.", "Dr.", etc.

date: The date associated with a particular event or data entry.

**state: **The state or region of residence or origin of a person or entity.

residenceStateRegion: The state or region of residence of a person or entity.

birthYear: The year of birth of a person.

birthMonth: The month of birth of a person.

**birthDay: **The day of birth of a person.

**cpi_country: **Consumer Price Index (CPI) for a specific country.

cpi_change_country: Change in Consumer Price Index (CPI) for a specific country.

**gdp_country: **Gross Domestic Product (GDP) for a specific country.

**gross_tertiary_education_enrollment: **Gross tertiary education enrollment rate for a specific country.

gross_primary_education_enrollment_country: Gross primary education enrollment rate for a specific country.

**life_expectancy_country: **Life expectancy for a specific country.

tax_revenue_country_country: Tax revenue for a specific country.

**total_tax_rate_country: **Total tax rate for a specific country.

population_country: Population of a specific country.

**latitude_country: **Latitude coordinates of a specific country.

**longitude_country: **Longitude coordinates of a specific country.

These columns appear to contain various attributes and metrics related to individuals, countries, and economic indicators.

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.

• 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.

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

Description

This dataset contains statistics on the world's billionaires, including information about their businesses, industries, and personal details. It provides insights into the wealth distribution, business sectors, and demographics of billionaires worldwide.

Key Features

• rank: The ranking of the billionaire in terms of wealth.
• finalWorth: The final net worth of the billionaire in 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.

Potential Use Cases

• Wealth distribution analysis: Explore the distribution of billionaires' wealth across different industries, countries, and regions.
• Demographic analysis: Investigate the age, gender, and birthplace demographics of billionaires.
• Self-made vs. inherited wealth: Analyze the proportion of self-made billionaires and those who inherited their wealth.
• Economic indicators: Study correlations between billionaire wealth and economic indicators such as GDP, CPI, and tax rates.
• Geospatial analysis: Visualize the geographical distribution of billionaires and their wealth on a map.
• Trends over time: Track changes in billionaire demographics and wealth over the years.

If this was helpful, a vote is appreciated 🙂❤️

Total wealth is the sum of the four components of wealth and is therefore net of all liabilities.

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.

The Anatomy of a Jackpot: A Complete Historical Archive of Every Winning Draw

This dataset contains the complete history of all winning numbers from the US Powerball lottery, one of the largest and most popular lottery games in the world. It includes the winning white balls, the red Powerball, and the draw date for every drawing held. This comprehensive archive is perfect for statistical analysis, trend spotting, application development, or simply for satisfying your curiosity about the history of life-changing numbers.

Dive into the data to explore number frequencies, jackpot trends, and the numerical patterns that have created millionaires. Whether you're a data scientist, a developer, a researcher, or a lottery enthusiast, this dataset is your ultimate resource for everything Powerball.

Columns:

date: The date on which the lottery drawing was held. (Format: MM/DD/YYYY)

number1: The first winning white ball number. (Type: Integer)

number2: The second winning white ball number. (Type: Integer)

number3: The third winning white ball number. (Type: Integer)

number4: The fourth winning white ball number. (Type: Integer)

number5: The fifth winning white ball number. (Type: Integer)

powerball: The winning red Powerball number. (Type: Integer)

powerplay: The Power Play multiplier for that draw, where applicable. (Type: Integer)

jackpot: The top prize awarded for matching all the winning numbers. (Type: Integer)

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

The Forbes published "The Definitive Ranking of the Wealthiest Americans In 2022". And here is the top 200 list including additional information on each of the billionaires.

About Dataset

This dataset contains the top 200 richest American based on their net worth.

Columns Attributes

| Column | Meaning | | -- | -- | | rank | their rank | | name | their name | | net worth | their net worth | | age | their age | | title | their title (e.g. CEO, Chairman etc.) | | source of wealth | the source of how they've managed to get this much money | | self made score | shows how far each of these billionaires has climbed to make it to the top. According to Forbes, The score ranges from 1 to 10, with 1 through 5 indicating someone who inherited some or all of his or her fortune; while 6 through 10 are for those who built their company or established a fortune on his or her own. | | philanthropy score | this score shows how much these billionaires donates on nonprofits foundations | | residence | their residence | | marital status | their marital status | | children | their children | | education | their education |

Methodology

This dataset was acquired using a web scraping tool called Beautiful soup and scraped Forbes website

Image by pch.vector on Freepik

About the Ranking

The World's Billionaires is an annual ranking by documented net worth of the wealthiest billionaires in the world, compiled and published in March annually by the American business magazine Forbes. The list was first published in March 1987. The total net worth of each individual on the list is estimated and is cited in United States dollars, based on their documented assets and accounting for debt and other factors. Royalty and dictators whose wealth comes from their positions are excluded from these lists. This ranking is an index of the wealthiest documented individuals, excluding any ranking of those with wealth that is not able to be completely ascertained.

Forbes Methodology

Each year, Forbes employs a team of over 50 reporters from a variety of countries to track the activity of the world's wealthiest individuals and sometimes groups or families – who share wealth. Preliminary surveys are sent to those who may qualify for the list. According to Forbes, they received three types of responses – some people try to inflate their wealth, others cooperate but leave out details, and some refuse to answer any questions. Business deals are then scrutinized and estimates of valuable assets – land, homes, vehicles, artwork, etc. – are made. Interviews are conducted to vet the figures and improve the estimate of an individual's holdings. Finally, positions in a publicly traded stock are priced to market on a date roughly a month before publication. Privately held companies are priced by the prevailing price-to-sales or price-to-earnings ratios. Known debt is subtracted from assets to get a final estimate of an individual's estimated worth in United States dollars. Since stock prices fluctuate rapidly, an individual's true wealth and ranking at the time of publication may vary from their situation when the list was compiled.

Introduction

This dataset contains the top 100 richest people in the world based on their net worth. The dataset includes their rank, name, net worth, birthday, age, and nationality.

Methodology

This dataset was collected using web scraping (Beautiful Soup) on this website and this "https://en.wikipedia.org/wiki/List_of_countries_by_number_of_billionaires">wikipedia

Thumbnail Photo

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

What factors are affecting wealth? and in the answer to that, identify valuable opportunities for underdeveloped or developing countries. By examining the data, we can uncover insights that help nations make informed decisions on how to increase their wealth and well-being.

This dataset aims to provide actionable insights for policymakers, entrepreneurs, and anyone looking to contribute to economic growth and well-being on a global scale.

Based on Bloomberg's Billionaires index...

The Bloomberg Billionaires Index is a daily ranking of the world's richest people. In calculating net worth, Bloomberg News strives to provide the most transparent calculations available, and each individual billionaire profile contains a detailed analysis of how that person's fortune is tallied.

The index is a dynamic measure of personal wealth based on changes in markets, the economy and Bloomberg reporting. Each net worth figure is updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price. Valuations are converted to U.S. dollars at current exchange rates.

Closely held companies are valued in several ways, such as by comparing the enterprise value-to-Ebitda or price-to-earnings ratios of similar public companies or by using comparable transactions. Calculations of closely held company debt -- if net debt cannot be determined -- are based on the net debt-to-Ebitda ratios of comparable peers. The value of closely held companies adjusts daily based on market moves for peer companies or by applying the market movement of a relevant industry index. The criteria used to choose peer companies is based on the closely held asset's industry and size.

When ownership of closely held assets cannot be verified, they aren't included in the calculations. The specific valuation methodology for each closely held company is included in the net worth analysis section of a billionaire's profile. Additional details included in the valuation notes for each asset are available to subscribers of the Bloomberg Professional Service.

A standard liquidity discount of 5 percent is applied to most closely held companies where assets may be hard to sell. When a different percentage is used an explanation is given. No liquidity discounts are applied to the values of public stakes. In some instances, a country risk discount is also applied based on a person's concentration of assets and ease of selling them in a given geography. A country's risk is assessed based on Standard & Poor's sovereign debt ratings.

If a billionaire has pledged as collateral shares he or she holds in a public company, the value of those shares or the value of a loan taken against them is removed from the net worth calculation. If reliable information can be obtained about the ultimate use of those borrowed funds, that value is added back into the calculation.

Hedge fund businesses are valued using the average market capitalization-to-assets under management ratios of the most comparable publicly traded funds. Fee income is not considered because it cannot be uniformly verified. Personal funds invested along with outside capital are not included in the calculation. A "key man" risk discount of 25 percent is applied to funds whose performance is tied to a single individual. Assets under management are updated using ADV forms filed with the federal government and news reports, and returns are factored when sourced to reports from credible news outfits, the HFRI Index and industry analysts.

Net worth calculations include dividend income paid and proceeds from the sale of public and closely held shares. Taxes are deducted based on prevailing income, dividend and capital gains tax rates in a billionaire's country of residence. Taxes are applied at the highest rate unless there is evidence to support a lower percentage, in which case an explanation is given in the net worth summary. For calculations of cash and other investable assets, a hybrid return based on holdings in cash, government bonds, equities and commodities is applied.

No assumptions are made about personal debt. Family members often hold a portion of a billionaire's assets. Such transfers don't change the nature of who ultimately controls the fortune. As a result, Bloomberg News operates under the rule that all billionaire fortunes are inherently family fortunes and credit family fortunes to the founders or ranking family members who are determined to have direct control over the assets. When individual stakes can be verified and adult family members have an active role in a business, the value is credited to each individual.

Each billionaire -- or a representative -- is given an opportunity to respond to questions regarding the net worth calculation, including assets and liabilities.

Bloomberg News editorial policy is to not cover Bloomberg L.P. As a result, Michael Bloomberg, the founder and majority owner of Bloomberg L.P., isn't considered for this ranking.

Because calculating net worth requires a degree of estimation, bull and bear case scenarios that would make a person's fortune higher or lower than the Bloomberg Billionaires Index valuation are included on the Bloomberg Professional Service. A confidence rating also is included on each profile: