The Nobel Prize has been among the most prestigious international awards since 1901. Each year, awards are bestowed in chemistry, literature, physics, physiology or medicine, economics, and peace. In addition to the honor, prestige, and substantial prize money, the recipient also gets a gold medal with an image of Alfred Nobel (1833 - 1896), who established the prize.
The Nobel Foundation has made a dataset available of all prize winners from the outset of the awards from 1901 to 2023. The dataset used in this project is from the Nobel Prize API and is available in the nobel.csv file in the data folder.
In this project, you'll get a chance to explore and answer several questions related to this prizewinning data. And we encourage you then to explore further questions that you're interested in!
# Loading in required libraries
import pandas as pd
import seaborn as sns
import numpy as np
# Start coding here!
# The most commonly awarded gender and birth country
nobel = pd.read_csv('data/nobel.csv')
top_gender = nobel['sex'].value_counts().index[0]
top_country = nobel['birth_country'].value_counts().index[0]
print('The gender with the most Nobel laureates is:', top_gender)
print('The most common birth country of Nobel laureates is:', top_country)
# The highest ratio of US-born Nobel Prize winners to total winners in all categories
nobel['usa_born_winner'] = nobel['birth_country']=='United States of America'
nobel['decade'] = (np.floor(nobel['year']/10)*10).astype(int)
prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()
max_decade_usa = prop_usa_winners[prop_usa_winners['usa_born_winner'] == prop_usa_winners['usa_born_winner'].max()]['decade'].values[0]
# The decade and category that had the highest proportion of female laureates
nobel['female_winner'] = nobel['sex'] == 'Female'
prop_female_winners = nobel.groupby(['decade', 'category'], as_index=False)['female_winner'].mean()
max_female_decade_category = prop_female_winners[prop_female_winners['female_winner'] == prop_female_winners['female_winner'].max()][['decade', 'category']]
max_female_dict = {max_female_decade_category['decade'].values[0] : max_female_decade_category['category'].values[0]}
# The first woman to receive a Nobel Prize and the category
nobel_women = nobel[nobel['female_winner']]
first_woman = nobel_women[nobel_women['year'] == nobel_women['year'].min()]
first_woman_name = first_woman['full_name'].values[0]
first_woman_category = first_woman['category'].values[0]
print('The first woman to win a Nobel Prize was', first_woman_name, 'in the category of', first_woman_category)
# Individuals or organizations that have won more than one Nobel Prize throughout the years
counts = nobel['full_name'].value_counts()
repeats = counts[counts > 1].index
repeat_list = list(repeats)
print('The repeat winners are: ', repeat_list)