Few days back, an interesting post caught my eye at ycombinator news, a data sheet containing the list of 1919 Forbes Billionaires for the year 2016.
I used the data available in the sheet for analysis. Apparently, Kimbal Musk was not included in the list. (source: comments in the same post)
There is so much insight in this data. It is difficult to explore all the insights in a single post. If you are interested, I shared my entire Jupyter Notebook with all the code here at github. (Note: Github is not rendering bokeh visualizations, please download or clone the source and run all the cells to see the visualizations)
United States dominates the rest of the countries with 555 billionaires followed by China with 269 billionaries and Germany with 120 billionaires followed by India and Russia.
I created a feature called "change" based on the column worthChange. The "change" column contains either "gain" or "loss". The reasons for negative change in total worth is unknown. Since we don't have detailed information about total worth, we keep this discussion aside and explore the visualization.
The ratio of positive vs. negative change in the total worth of Billionaires in United States looks pretty bad. 140 billionaires are on the positive side(green color) while 415 are on the negative side(red color). The same applies in the case of Germany, 28 billionaires had positive change in their worth while 92 billionaires had negative change in their net worth.
United States again tops the list with highest number of female billionaires with 66 billionaires, followed by Germany and China. While India ranked 4th in top 10 countries, it ranks 8th in the case of female billionaires. Russia and United Kingdom which are part of top 10 countries dont fall in this category.
Overall 780 billionaires had positive change in their total worth while 1139 billionaires had negative change in their total worth.
Finance & Investments is the top industry with 277 billionaires followed by Fashion & Retail with 232 billionaires.
How about Ages?
Visual below is multi modal. There are three peaks at 53, 65 and 75.
There is a billionaire whose age is above 100. Let's see who this is.
data[data['age'] > 100]
David Rockefeller Sr's age is 101
I used folium to visualize how the billionaires are spread out across the world. The below image of the map show the high level overview. The map is shared in the same Github repository. Zoom out to see the high level overview of the map.
If we click on the circles, the map will zoom in and show the exact locations as a pin. If we further click on a pin, it will show the country and the count of billionaires.
For example, the below image shows the count for USA.
How about the positive vs negative change in net worth of billionaires across industries?
- Python 3.
- Pandas, Numpy - for Data Analysis.
- Bokeh - for Data Visualization.
- Folium - for Maps.
- Geopy - for getting coordinates for Countries.
- Jupyter Notebook - for Interactive coding.