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
# Load dataset
nobel = pd.read_csv('data/nobel.csv')
# 1. Most common gender and birth country
top_gender = nobel['sex'].mode()[0]
top_country = nobel['birth_country'].mode()[0]
# 2. Decade with highest ratio of US-born winners
nobel['decade'] = (nobel['year'] // 10) * 10
nobel['usa_born'] = nobel['birth_country'] == 'United States of America'
usa_ratio = nobel.groupby('decade')['usa_born'].mean()
max_decade_usa = int(usa_ratio.idxmax())
# 3. Decade-category combination with highest proportion of female laureates
# Group by decade and category
female_ratio = nobel.groupby(['decade', 'category'])['sex'].apply(lambda x: (x == 'Female').mean())
# Find highest proportion
max_female = female_ratio.idxmax()
max_female_dict = {max_female[0]: max_female[1]}
# 4. First woman to receive a Nobel Prize and category
first_woman = nobel[nobel['sex'] == 'Female'].sort_values('year').iloc[0]
first_woman_name = first_woman['full_name']
first_woman_category = first_woman['category']
# 5. Individuals/Organizations with more than one Nobel Prize
repeat_winners = nobel['full_name'].value_counts()
repeat_list = repeat_winners[repeat_winners > 1].index.tolist()
# --- OPTIONAL: Print results ---
print("Top Gender:", top_gender)
print("Top Country:", top_country)
print("Decade with Highest US-born Ratio:", max_decade_usa)
print("Max Female Laureates Decade/Category:", max_female_dict)
print("First Woman Winner:", first_woman_name)
print("First Woman Category:", first_woman_category)
print("Repeat Winners:", repeat_list)