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!
import pandas as pd
import seaborn as sns
import numpy as np
# read-in and preview the data
nobel = pd.read_csv('data/nobel.csv')
nobel.head()
nobel.columnsnobel.shape## What is the most commonly awarded gender and birth country?
# get the counts for each unique gender and country, grab the first/max observation
top_gender = nobel['sex'].value_counts().index[0]
top_country = nobel['birth_country'].value_counts().index[0]
print(f"The gender with the most Nobel laureates is: {top_gender}")
print(f"The most common birth country of Nobel laureates is: {top_country}")## Which decade had the highest ratio of US-born Nobel Prize winners to total winners in all categories?
# calculate the proportion of USA born winners per decade
nobel['usa_born_winner'] = nobel['birth_country'] == 'United States of America'
# create a decade column
nobel['decade'] = (np.floor(nobel['year'] / 10) * 10).astype(int)
# group by decade and calculate mean for each winner, as_index so it stores as a df instead of a series
prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()
# find the decade with the highest proportion of US-born winners
max_decade_usa = prop_usa_winners[prop_usa_winners['usa_born_winner'] == prop_usa_winners['usa_born_winner'].max()]['decade'].values[0]
# plot USA born winners
ax1 = sns.relplot(x='decade', y='usa_born_winner', data=prop_usa_winners, kind="line")## Which decade and Nobel Prize category combination had the highest proportion of female laureates?
# calculate proportion of female laureates per decade
nobel['female_winner'] = nobel['sex'] == 'Female'
prop_female_winners = nobel.groupby(['decade', 'category'], as_index=False)['female_winner'].mean()
# get the decade and category with the highest proportion of female laureates
max_female_decade_category = prop_female_winners[prop_female_winners['female_winner'] == prop_female_winners['female_winner'].max()][['decade', 'category']]
# create a dictionary with the decade and category pair
max_female_dict = {max_female_decade_category['decade'].values[0]: max_female_decade_category['category'].values[0]}
# plot female winners with % winners on the y-axis
ax2 = sns.relplot(x='decade', y='female_winner', hue='category', data=prop_female_winners, kind="line")## Who was the first woman to receive a Nobel Prize, and in what category?
# find female winner
nobel_women = nobel[nobel['female_winner']]
# get the minimum year value
min_row = nobel_women[nobel_women['year'] == nobel_women['year'].min()]
# get the name and category values
first_woman_name = min_row['full_name'].values[0]
first_woman_category = min_row['category'].values[0]
print(f"\n The first woman to win a Nobel Prize was {first_woman_name}, in the category of {first_woman_category}.")## Which individuals or organizations have won more than one Nobel Prize throughout the years?
# get the win counts for the laureates
counts = nobel['full_name'].value_counts()
# create a list of those with 2+ wins
repeats = counts[counts >= 2].index
repeat_list = list(repeats)
print("Repeat winners include:")
for name in repeat_list:
print(f"\t-{name}")