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!

In [26]:

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

# Start coding here!
nobel = pd.read_csv("../datasets/nobel.csv")
nobel

Out[26]:

	year	category	prize	motivation	prize_share	laureate_id	laureate_type	full_name	birth_date	birth_city	birth_country	sex	organization_name	organization_city	organization_country	death_date	death_city	death_country
0	1901	Chemistry	The Nobel Prize in Chemistry 1901	"in recognition of the extraordinary services ...	1/1	160	Individual	Jacobus Henricus van 't Hoff	1852-08-30	Rotterdam	Netherlands	Male	Berlin University	Berlin	Germany	1911-03-01	Berlin	Germany
1	1901	Literature	The Nobel Prize in Literature 1901	"in special recognition of his poetic composit...	1/1	569	Individual	Sully Prudhomme	1839-03-16	Paris	France	Male	NaN	NaN	NaN	1907-09-07	Châtenay	France
2	1901	Medicine	The Nobel Prize in Physiology or Medicine 1901	"for his work on serum therapy, especially its...	1/1	293	Individual	Emil Adolf von Behring	1854-03-15	Hansdorf (Lawice)	Prussia (Poland)	Male	Marburg University	Marburg	Germany	1917-03-31	Marburg	Germany
3	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	462	Individual	Jean Henry Dunant	1828-05-08	Geneva	Switzerland	Male	NaN	NaN	NaN	1910-10-30	Heiden	Switzerland
4	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	463	Individual	Frédéric Passy	1822-05-20	Paris	France	Male	NaN	NaN	NaN	1912-06-12	Paris	France
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	2023	Chemistry	The Nobel Prize in Chemistry 2023	"for the discovery and synthesis of quantum dots"	1/3	1030	Individual	Louis Brus	1943-00-00	Cleveland, OH	United States of America	Male	Columbia University	New York, NY	United States of America	NaN	NaN	NaN
996	2023	Chemistry	The Nobel Prize in Chemistry 2023	"for the discovery and synthesis of quantum dots"	1/3	1031	Individual	Aleksey Yekimov	1945-00-00	NaN	USSR (now Russia)	Male	Nanocrystals Technology Inc.	New York, NY	United States of America	NaN	NaN	NaN
997	2023	Literature	The Nobel Prize in Literature 2023	"for his innovative plays and prose which give...	1/1	1032	Individual	Jon Fosse	1959-09-29	Haugesund	Norway	Male	NaN	NaN	NaN	NaN	NaN	NaN
998	2023	Peace	The Nobel Peace Prize 2023	"for her fight against the oppression of women...	1/1	1033	Individual	Narges Mohammadi	1972-04-21	Zanjan	Iran	Female	NaN	NaN	NaN	NaN	NaN	NaN
999	2023	Economics	The Sveriges Riksbank Prize in Economic Scienc...	"for having advanced our understanding of wome...	1/1	1034	Individual	Claudia Goldin	1946-00-00	New York, NY	United States of America	Female	Harvard University	Cambridge, MA	United States of America	NaN	NaN	NaN

1000 rows × 18 columns

Question 1: What is the most commonly awarded gender and birth country?

Solution with Pandas

In [27]:

genders = nobel.value_counts("sex").sort_values(ascending=False)
genders

Out[27]:

sex
Male      905
Female     65
Name: count, dtype: int64

In [28]:

countries = nobel.value_counts("birth_country").sort_values(ascending=False)
countries.head(5)

Out[28]:

birth_country
United States of America    291
United Kingdom               91
Germany                      67
France                       58
Sweden                       30
Name: count, dtype: int64

Solution with Seaborn

In [29]:

sns.countplot(x='sex',data=nobel)

Out[29]:

<Axes: xlabel='sex', ylabel='count'>

In [30]:

top_gender = 'Male'
top_country = 'United States of America'

Question 2: What decade had te highest propotion of US-born winners?

In [31]:

nobel["decade"] = nobel["year"].apply(lambda x: x // 10 * 10)
nobel["is_usa"] = nobel["birth_country"].apply(lambda x: 1 if x == 'United States of America' else 0)
nobels_proportion_usa = nobel.groupby("decade", as_index=False)["is_usa"].mean()
nobels_proportion_usa

Out[31]:

	decade	is_usa
0	1900	0.017544
1	1910	0.075000
2	1920	0.074074
3	1930	0.250000
4	1940	0.302326
5	1950	0.291667
6	1960	0.265823
7	1970	0.317308
8	1980	0.319588
9	1990	0.403846
10	2000	0.422764
11	2010	0.314050
12	2020	0.360000

In [32]:

ax = sns.barplot(data=nobels_proportion_usa,x='decade',y='is_usa')
ax.set_title("Proportion of USA nobels per decade")
ax.set_ylabel("Proportion of Nobel prizes earned")
ax.set_xlabel("Decade");

In [33]:

max_decade_usa = 2000

Question 3: What decade and category pair had the highest proportion of female laureates?

In [34]:

nobel["is_female"] = nobel["sex"].apply(lambda x: 1 if x == 'Female' else 0)
nobels_proportion_female = nobel.groupby(["decade","category"],as_index=False)["is_female"].mean()
sns.catplot(kind='bar', 
            x='decade', 
            hue='category',
            y='is_female',
            data=nobels_proportion_female);

In [35]:

max_female_dict = {2020:"Literature"}

Question 4: Who was the first woman to receive a Nobel Prize, and in what category?

In [36]:

nobel_first_woman = nobel[nobel["sex"]=="Female"]\
                    .sort_values(["year"], ascending=True)\
                    .iloc[0]
nobel_first_woman

Out[36]:

year                                                                 1903
category                                                          Physics
prize                                     The Nobel Prize in Physics 1903
motivation              "in recognition of the extraordinary services ...
prize_share                                                           1/4
laureate_id                                                             6
laureate_type                                                  Individual
full_name                                     Marie Curie, née Sklodowska
birth_date                                                     1867-11-07
birth_city                                                         Warsaw
birth_country                                     Russian Empire (Poland)
sex                                                                Female
organization_name                                                     NaN
organization_city                                                     NaN
organization_country                                                  NaN
death_date                                                     1934-07-04
death_city                                                     Sallanches
death_country                                                      France
decade                                                               1900
is_usa                                                                  0
is_female                                                               1
Name: 19, dtype: object

In [37]:

first_woman_name = "Marie Curie, née Sklodowska"
first_woman_category = "Physics"

Quesiton 5 Which individuals or organizations have won multiple Nobel Prizes throughout the years?

In [38]:

amount_nobels = nobel.value_counts("full_name")
multiple_nobels = amount_nobels[amount_nobels > 1]
repeat_list = multiple_nobels.index.tolist()