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
import matplotlib.pyplot as plt
import seaborn as sns
# Start coding here!nobel_df = pd.read_csv("data/nobel.csv")
nobel_df.head()nobel_df.shapenobel_df.isnull().sum()#nobel_df.dropna()nobel_df.shapeMost commonly awarded gender
gender_count = nobel_df['sex'].value_counts()
top_gender = gender_count.index[0]
top_genderMost awarded country (birth country)
country_count = nobel_df['birth_country'].value_counts()
top_country = country_count.index[0]
top_countryDecade that had the highest ratio of US-born Nobel Prize winners to total winners in all categories
nobel_df['decade'] = nobel_df['year'] - (nobel_df['year']%10)
nobel_df['decade'].head()# Propotion of USA born winners per decade
nobel_df['usa_winners'] = nobel_df['birth_country']=='United States of America'
prop_decade_usa = nobel_df.groupby('decade')['usa_winners'].mean()
prop_decade_usa.head()# Decade with highest ratio of US born winners
max_decade_usa = prop_decade_usa.idxmax()
#max_ratio = prop_decade_usa.max()
#max_decade_usa = prop_decade_usa[prop_decade_usa == max_ratio].index[0]
max_decade_usa