Billionaires Analysis with Python

The number of billionaires in a country says a lot about the business environment, startup success rate, and many other economic features of a Country. So if you want to learn more about how we can find relationships among billionaires around the world, this article is for you. In this article, I will walk you through the task of billionaires analysis with Python.

Billionaires Analysis with Python

The dataset that I am using to analyze the data about billionaires around the world was curated by Forbes and is downloaded from Kaggle. The dataset contains information about global billionaires in 2021, including their:

  1. Names
  2. Net Worth 
  3. Country 
  4. Source 
  5. Rank
  6. Age
  7. Industry

So let’s get started with the task of billionaires analysis by importing the necessary Python libraries and the dataset:

                       Name NetWorth        Country         Source  Rank   Age          Industry
0                Jeff Bezos   $177 B  United States         Amazon     1  57.0        Technology
1                 Elon Musk   $151 B  United States  Tesla, SpaceX     2  49.0        Automotive
2  Bernard Arnault & family   $150 B         France           LVMH     3  72.0  Fashion & Retail
3                Bill Gates   $124 B  United States      Microsoft     4  65.0        Technology
4           Mark Zuckerberg    $97 B  United States       Facebook     5  36.0        Technology

Before we go ahead, let’s see whether or not this dataset contains missing values:

print(data.isnull().sum())
Name         0
NetWorth     0
Country      0
Source       0
Rank         0
Age         79
Industry     0
dtype: int64

So this dataset has 79 missing values in the Age column, let’s remove these rows:

data = data.dropna()

The NetWorth column in this dataset has a $ sign at the beginning of Billionaires’ Net worth and B at the end. So we need to remove these signs and convert the NetWorth column to float:

data["NetWorth"] = data["NetWorth"].str.strip("$")
data["NetWorth"] = data["NetWorth"].str.strip("B")
data["NetWorth"] = data["NetWorth"].astype(float)

Now let’s have a look at the top 10 billionaires according to their NetWorth:

Top 10 Billionaires of the world

Now let’s have a look at the top 5 domains with the most number of billionaires:

top 5 domains with the most number of billionaires

Now let’s have a look at the top 5 industries with the most number of billionaires:

top 5 industries

Now let’s have a look at the top 5 countries with the most number of billionaires:

countries with most billionaires

The visualization above shows that the United States and China are the countries from which most people become billionaires. So that means the business environment and the startup success rate is really good in the US and China compared to the rest of the world.

Summary

So this is how you can find patterns among billionaires around the world to analyze the business environment of countries. The success of a business or startup depends a lot on the business environment of a country. At the end of the analysis of global billionaires, I found that China and the United States are the countries with the most billionaires which concludes that the business environment and the success rate of a startup is much better in the US and China than in the rest of the world. Hope you liked this article on Billionaires Analysis with Python. Please feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1054

2 Comments

  1. Thanks fo such a amazing explanation

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