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The History of Nobel Prize Winners

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, we will answer several questions related to this prizewinning data.

# Loading in required libraries
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# Start coding here!

Taking a Look at How the Data is Structured

#Looking at the structure of the data
nobel = pd.read_csv("data/nobel.csv")
nobel.head()

Which Countries Have had the Most Nobel Laureates

Let's explore the data and see which countries have had the most amount of Nobel laureates.

#Let's create a subset of our data and group them by the birth country of past winners
birth_country_count = nobel[["year","birth_country"]].groupby("birth_country").count().sort_values("year",ascending=False).head()
birth_country_count
Hidden code

Gender Distribution of Past Winners

Let's explore the distribution of genders for past nobel prize awardees

#Let's filter our data to show only the year of the awards and the sex column and group them by sex so we can count the number of awards for each gender
gender_count = nobel[["year","sex"]].groupby("sex").count().sort_values("year",ascending=False).head()
gender_count
Hidden code
Hidden code

First Inisghts

From these visualizations we can conclude that the most commonly awarded gender is Male, and the country with the most amount of Nobel Laureates is The United States of America.

nobel["usa_born"] = nobel["birth_country"] == "United States of America"
nobel["decade"] = ((nobel["year"] // 10) * 10).astype(int)
nobel.head()                
decade_prop_usa = nobel.groupby("decade", as_index = False)['usa_born'].mean()
max_decade_usa = decade_prop_usa[decade_prop_usa["usa_born"] == decade_prop_usa["usa_born"].max()]["decade"].values[0]
max_decade_usa


decade_prop_usa_relplot = sns.relplot(x = decade_prop_usa['decade'], y = decade_prop_usa['usa_born'], kind = 'line', data = decade_prop_usa)
plt.xlabel('Decade')
plt.ylabel("USA Born Winners vs. Total Winners")
plt.show(decade_prop_usa_relplot)
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