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
# Loading the data file
nobel = pd.read_csv("data/nobel.csv")
print(nobel.head)
print(nobel.columns)What is the most commonly awarded gender and birth country?
top_gender = nobel["sex"].value_counts().idxmax()
top_country = nobel["birth_country"].value_counts().idxmax()
print("The most commonly awarded gender is",top_gender,"and most awarded laureate are from",top_country)What decade had the highest proportion of US-born winners?
nobel["decade"] = ((nobel["year"] // 10) * 10).astype(int)
max_decade_usa = (100 * (nobel[nobel["birth_country"] == "United States of America"].groupby("decade")["laureate_id"].count()) / (nobel.groupby("decade")["laureate_id"].count())).idxmax()
print(max_decade_usa)What decade and category pair had the highest proportion of female laureates?
max_female_decade = (100 * ((nobel[nobel["sex"]=="Female"].groupby("decade")["laureate_id"].count()) / nobel.groupby("decade")["laureate_id"].count())).idxmax()
max_female_category = (100 * ((nobel[nobel["sex"]=="Female"].groupby("category")["laureate_id"].count()) / nobel.groupby("category")["laureate_id"].count())).idxmax()
max_female_dict = {max_female_decade:max_female_category}
print(max_female_dict)Who was the first woman to receive a Nobel Prize, and in what category? Save your string answers as first_woman_name and first_woman_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"]
print(first_woman_name,"in category",first_woman_category)Which individuals or organizations have won multiple Nobel Prizes throughout the years?
prizes = nobel.groupby("full_name").size()
repeated = prizes[prizes > 1]
repeat_list = repeated.index
#Another Way:
#counts = nobel["full_name"].value_counts()
#repeat_list = counts[counts >1].index
print(repeat_list)