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!
nobel_df = pd.read_csv('data/nobel.csv')
pd.set_option('display.max_columns', 4)
# Find the most commonly awarded gender & country
top_gender = nobel_df['sex'].value_counts()
top_gender = top_gender.index[0]
top_country = nobel_df['birth_country'].value_counts()
top_country = top_country.index[0]
print("Most commonly awarded gender is", top_gender, "and birth country is", top_country, "\n")
# Decade with highest ratio of US-born Nobel Prize winners to total winners for ALL categories
nobel_df['US_born_winners'] = nobel_df['birth_country'] == 'United States of America'
nobel_df['decade'] = (np.floor(nobel_df['year']/10) * 10).astype('int')
max_decade_usa_df = nobel_df.groupby('decade', as_index=False)['US_born_winners'].mean()
max_decade_usa_df = max_decade_usa_df.sort_values(by='US_born_winners', ascending=False)
max_decade_usa = max_decade_usa_df['decade'].values[0]
print(f'The decade that had the highest ratio of US-born Nobel Prize is, {max_decade_usa}\n')
# Decade & Nobel Prize category combination with highest proportion of female laureates
nobel_df['female_laureates'] = nobel_df['sex'] == 'Female'
max_female = nobel_df.groupby(['decade', 'category'], as_index=False)['female_laureates'].mean()
max_female = max_female.sort_values(by='female_laureates', ascending=False)
max_female_dict = {max_female['decade'].values[0]:max_female['category'].values[0]}
print(f'The decade & Nobel Prize category combo with highest proportion of female laureates is, {max_female_dict}\n')
# Name of first woman & the category to receive a Nobel Prize in
female_df = nobel_df[nobel_df['sex'] == 'Female']
first_woman_df = female_df.groupby(['category', 'full_name'], as_index=False)['year'].min()
first_woman_df = first_woman_df.sort_values(by='year', ascending=True)
first_woman_name = first_woman_df.values[0][1]
first_woman_category = first_woman_df.values[0][0]
print(f'The first woman to win a Nobel Prize is, {first_woman_name} in {first_woman_category}\n')
# Individuals who have won more than one Nobel Prize throughout the years
repeat_individuals_df = nobel_df['full_name'].value_counts()
repeat_individuals_df = repeat_individuals_df[repeat_individuals_df > 1]
repeat_list = repeat_individuals_df.index.tolist()
print(f'This is the list of repeat list of individuals/orgs that have won more than one Nobel Prize through out the years: {repeat_list}')