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A Visual History of Nobel Prize Winners
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  • 1. The most Nobel of Prizes

    The Nobel Prize is perhaps the world's most well known scientific award. Except for the honor, prestige and substantial prize money the recipient also gets a gold medal showing Alfred Nobel (1833 - 1896) who established the prize. Every year it's given to scientists and scholars in the categories chemistry, literature, physics, physiology or medicine, economics, and peace. The first Nobel Prize was handed out in 1901, and at that time the Prize was very Eurocentric and male-focused, but nowadays it's not biased in any way whatsoever. Surely. Right?

    Well, we're going to find out! The Nobel Foundation has made a dataset available of all prize winners from the start of the prize, in 1901, to 2016. Let's load it in and take a look.

    # Loading in required libraries
    import pandas as pd
    import seaborn as sns
    import numpy as np
    # Reading in the Nobel Prize data
    nobel = pd.read_csv('datasets/nobel.csv')
    # Taking a look at the first several winners

    2. So, who gets the Nobel Prize?

    Just looking at the first couple of prize winners, or Nobel laureates as they are also called, we already see a celebrity: Wilhelm Conrad Röntgen, the guy who discovered X-rays. And actually, we see that all of the winners in 1901 were guys that came from Europe. But that was back in 1901, looking at all winners in the dataset, from 1901 to 2016, which sex and which country is the most commonly represented?

    (For country, we will use the birth_country of the winner, as the organization_country is NaN for all shared Nobel Prizes.)

    # Display the number of (possibly shared) Nobel Prizes handed
    # out between 1901 and 2016
    # Display the number of prizes won by male and female recipients.
    # Display the number of prizes won by the top 10 nationalities.

    3. USA dominance

    Not so surprising perhaps: the most common Nobel laureate between 1901 and 2016 was a man born in the United States of America. But in 1901 all the winners were European. When did the USA start to dominate the Nobel Prize charts?

    # Calculating the proportion of USA born winners per decade
    nobel['usa_born_winner'] = nobel['birth_country']=='United States of America'
    nobel['decade'] = (np.floor(nobel['year']/10)*10).astype(int)
    prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()
    # Display the proportions of USA born winners per decade

    4. USA dominance, visualized

    A table is OK, but to see when the USA started to dominate the Nobel charts we need a plot!

    # Setting the plotting theme
    # and setting the size of all plots.
    import matplotlib.pyplot as plt
    plt.rcParams['figure.figsize'] = [11, 7]
    # Plotting USA born winners 
    ax = sns.lineplot(data=nobel,x='decade',y='usa_born_winner')
    # Adding %-formatting to the y-axis
    from matplotlib.ticker import PercentFormatter

    5. What is the gender of a typical Nobel Prize winner?

    So the USA became the dominating winner of the Nobel Prize first in the 1930s and had kept the leading position ever since. But one group that was in the lead from the start, and never seems to let go, are men. Maybe it shouldn't come as a shock that there is some imbalance between how many male and female prize winners there are, but how significant is this imbalance? And is it better or worse within specific prize categories like physics, medicine, literature, etc.?

    # Calculating the 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()
    # Plotting USA born winners with % winners on the y-axis
    # Setting the plotting theme
    # and setting the size of all plots.
    import matplotlib.pyplot as plt
    plt.rcParams['figure.figsize'] = [11, 7]
    # Plotting USA born winners 
    ax = sns.lineplot(data=nobel,x='decade',y='female_winner', hue='category')
    # Adding %-formatting to the y-axis
    from matplotlib.ticker import PercentFormatter

    6. The first woman to win the Nobel Prize

    The plot above is a bit messy as the lines are overplotting. But it does show some interesting trends and patterns. Overall the imbalance is pretty large with physics, economics, and chemistry having the largest imbalance. Medicine has a somewhat positive trend, and since the 1990s the literature prize is also now more balanced. The big outlier is the peace prize during the 2010s, but keep in mind that this just covers the years 2010 to 2016.

    Given this imbalance, who was the first woman to receive a Nobel Prize? And in what category?

    # Picking out the first woman to win a Nobel Prize
    first_woman= (nobel[nobel['sex']=='Female']).nsmallest(1, 'year')

    7. Repeat laureates

    For most scientists/writers/activists a Nobel Prize would be the crowning achievement of a long career. But for some people, one is just not enough, and few have gotten it more than once. Who are these lucky few? (Having won no Nobel Prize myself, I'll assume it's just about luck.)

    # Selecting the laureates that have received 2 or more prizes.
    more_than_one= nobel.groupby('full_name').filter(lambda x: len(x) >1)

    8. How old are you when you get the prize?

    The list of repeat winners contains some illustrious names! We again meet Marie Curie, who got the prize in physics for discovering radiation and in chemistry for isolating radium and polonium. John Bardeen got it twice in physics for transistors and superconductivity, Frederick Sanger got it twice in chemistry, and Linus Carl Pauling got it first in chemistry and later in peace for his work in promoting nuclear disarmament. We also learn that organizations also get the prize as both the Red Cross and the UNHCR have gotten it twice.

    But how old are you generally when you get the prize?