September 18, 2018
This is fun project on data analysis I did a few years ago on data on Nobel Laureates I found in Kaggle.com.
The data had 969 entries giving information on the year of award, category, motivation, prize share, full name, birth date, birth city, birth country, organization country, death date and death country, among others. Some questions:
I obtained the following plots using seaborn and matplotlib.
Update: This plot excludes numbers from Literature Nobel laureates since the data does not include their organization countries. By comparison, here is a plot of Nobel laureates by country of birth (top 25 only):
Update: The categories above does not include “Literature”, which is weird. After looking at the pandas DataFrame I created, I found that there are missing values under “Organization Country” for “Literature” as category. I have no idea why, but maybe this is because of some political reasons. It is probably better to look at “Birth Country” instead of “Organization Country”.
In conclusion, one has a high chance of getting a Nobel Prize if one is a male, works in medicine, around 60 years old, and resides in the United States.