
# %pip install --upgrade plotly


# !pip3 install --upgrade seaborn


import pandas as pd
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt


pd.options.display.float_format = '{:,.2f}'.format


df_data = pd.read_csv('nobel_prize_data.csv')


df_data.shape

(962, 16)


df_data.columns

Index(['year', 'category', 'prize', 'motivation', 'prize_share',
       'laureate_type', 'full_name', 'birth_date', 'birth_city',
       'birth_country', 'birth_country_current', 'sex', 'organization_name',
       'organization_city', 'organization_country', 'ISO'],
      dtype='object')


df_data.head()


df_data.year.min()

1901


df_data.year.max()

2020


df_data.duplicated().values.any()

False


df_data.isna().sum()

year                       0
category                   0
prize                      0
motivation                88
prize_share                0
laureate_type              0
full_name                  0
birth_date                28
birth_city                31
birth_country             28
birth_country_current     28
sex                       28
organization_name        255
organization_city        255
organization_country     254
ISO                       28
dtype: int64


col_subset = ['year','category', 'laureate_type',
              'birth_date','full_name', 'organization_name']
df_data.loc[df_data.birth_date.isna()][col_subset].head()


df_data.loc[df_data.organization_name.isna()][col_subset].head()


df_data.birth_date = pd.to_datetime(df_data.birth_date)
df_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 962 entries, 0 to 961
Data columns (total 16 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   year                   962 non-null    int64         
 1   category               962 non-null    object        
 2   prize                  962 non-null    object        
 3   motivation             874 non-null    object        
 4   prize_share            962 non-null    object        
 5   laureate_type          962 non-null    object        
 6   full_name              962 non-null    object        
 7   birth_date             934 non-null    datetime64[ns]
 8   birth_city             931 non-null    object        
 9   birth_country          934 non-null    object        
 10  birth_country_current  934 non-null    object        
 11  sex                    934 non-null    object        
 12  organization_name      707 non-null    object        
 13  organization_city      707 non-null    object        
 14  organization_country   708 non-null    object        
 15  ISO                    934 non-null    object        
dtypes: datetime64[ns](1), int64(1), object(14)
memory usage: 120.4+ KB


separated_values = df_data.prize_share.str.split('/', expand=True)
numerator = pd.to_numeric(separated_values[0])
denomenator = pd.to_numeric(separated_values[1])
df_data['share_pct'] = numerator / denomenator


df_data.head()


biology = df_data.sex.value_counts()
biology

Male      876
Female     58
Name: sex, dtype: int64


fig = px.pie(labels=biology.index, 
             values=biology.values, 
             names=biology.index, 
             title="Percentage of Male v/s Female Winners", 
             hole=0.5)

fig.update_traces(textposition='inside', textfont_size=15, textinfo='label+percent')

fig.show()


df_data.loc[df_data.sex == "Female"][:3]


is_winner = df_data.duplicated(subset=["full_name"], keep=False)
multiple_winners = df_data[is_winner]
multiple_winners.full_name.nunique()

6


col_subset = ['year', 'category', 'laureate_type', 'full_name']
multiple_winners[col_subset]


df_data.category.nunique()

6


prizes_per_category = df_data.category.value_counts()
prizes_per_category

Medicine      222
Physics       216
Chemistry     186
Peace         135
Literature    117
Economics      86
Name: category, dtype: int64


v_bar = px.bar(x=prizes_per_category.index,
              y=prizes_per_category.values,
              color=prizes_per_category.values,
              color_continuous_scale="Aggrnyl",
              title="Number of Prizes Awarded per Category")

v_bar.update_layout(xaxis_title="Nobel Prize Category",
                   yaxis_title="Number of Awards",
                   coloraxis_showscale=False)

v_bar.show()


df_data[df_data.category == "Economics"].year.min()

1969


df_data.query('category == "Economics" and year == 1969')


cat_men_women = df_data.groupby(["category", "sex"], as_index=False).agg({"prize": pd.Series.count})
cat_men_women.sort_values("prize", inplace=True, ascending=False)
cat_men_women.head()


v_bar_split = px.bar(x=cat_men_women.category,
                    y=cat_men_women.prize,
                    color=cat_men_women.sex,
                    title="Number of Prizes Awarded per Category split by Gender")

v_bar_split.update_layout(xaxis_title="Noble Prize Category",
                         yaxis_title="Number of Prizes")

v_bar_split.show()


prizes_per_year = df_data.groupby("year").count().prize
prizes_per_year

year
1901     6
1902     7
1903     7
1904     6
1905     5
        ..
2016    11
2017    12
2018    13
2019    14
2020    12
Name: prize, Length: 117, dtype: int64


moving_average = prizes_per_year.rolling(window=5).mean()
moving_average

year
1901     NaN
1902     NaN
1903     NaN
1904     NaN
1905    6.20
        ... 
2016   11.60
2017   12.00
2018   12.00
2019   12.20
2020   12.40
Name: prize, Length: 117, dtype: float64


plt.figure(figsize=(8, 4), dpi=200)

plt.scatter(x=prizes_per_year.index,
           y=prizes_per_year.values,
           color="dodgerblue",
           alpha=0.6,
           s=100)

plt.plot(moving_average.index,
        moving_average.values,
        color="crimson",
        linewidth=3,)

plt.show()


plt.figure(figsize=(8, 4), dpi=200)
plt.title("Number of Nobel Prizes per Year", fontsize=10)
plt.yticks(fontsize=8)
plt.xticks(ticks=np.arange(1900, 2021, step=5), fontsize=8, rotation=45)
plt.grid(color="gray", linestyle="--")

ax1 = plt.gca()
ax2 = plt.twinx()
ax1.set_xlim(1900, 2020)

ax1.scatter(x=prizes_per_year.index,
           y=prizes_per_year.values,
           color="dodgerblue",
           alpha=0.6,
           s=60)

ax1.plot(moving_average.index,
        moving_average.values,
        color="crimson",
        linewidth=2,)

ax1.set_xlabel("Year")
ax1.set_ylabel("Number of Nobel Prizes")

plt.show()


yearly_avg_share = df_data.groupby(by='year').agg({'share_pct': pd.Series.mean})
yearly_avg_share.head()


share_moving_average = yearly_avg_share.rolling(window=5).mean()
share_moving_average.head()


plt.figure(figsize=(16,8), dpi=200)
plt.title('Number of Nobel Prizes Awarded per Year', fontsize=18)
plt.yticks(fontsize=14)
plt.xticks(ticks=np.arange(1900, 2021, step=5), 
           fontsize=14, 
           rotation=45)
 
ax1 = plt.gca()
ax2 = ax1.twinx() # create second y-axis
ax1.set_xlim(1900, 2020)
 
ax1.scatter(x=prizes_per_year.index, 
           y=prizes_per_year.values, 
           c='dodgerblue',
           alpha=0.7,
           s=100,)
 
ax1.plot(prizes_per_year.index, 
        moving_average.values, 
        c='crimson', 
        linewidth=3,)
 
# Adding prize share plot on second axis
ax2.plot(prizes_per_year.index, 
        share_moving_average.values, 
        c='grey', 
        linewidth=3,)

ax1.set_xlabel("Year")
ax1.set_ylabel("Number of Nobel Prizes")
ax2.set_ylabel("Moving Average of Prize Share")
 
plt.show()


plt.figure(figsize=(16,8), dpi=200)
plt.title('Number of Nobel Prizes Awarded per Year', fontsize=18)
plt.yticks(fontsize=14)
plt.xticks(ticks=np.arange(1900, 2021, step=5), 
           fontsize=14, 
           rotation=45)
 
ax1 = plt.gca()
ax2 = ax1.twinx()
ax1.set_xlim(1900, 2020)
 
# Can invert axis
ax2.invert_yaxis()
 
ax1.scatter(x=prizes_per_year.index, 
           y=prizes_per_year.values, 
           c='dodgerblue',
           alpha=0.7,
           s=100,)
 
ax1.plot(prizes_per_year.index, 
        moving_average.values, 
        c='crimson', 
        linewidth=3,)
 
ax2.plot(prizes_per_year.index, 
        share_moving_average.values, 
        c='grey', 
        linewidth=3,)
 
plt.show()


top20_countries = df_data.groupby("birth_country_current", as_index=False).agg(
    {"prize":pd.Series.count}).sort_values("prize")[-20:]
top20_countries.head()


h_bar = px.bar(x=top20_countries.prize,
               y=top20_countries.birth_country_current,
               orientation='h',
               color=top20_countries.prize,
               color_continuous_scale='Viridis',
               title='Top 20 Countries by Number of Prizes')
 
h_bar.update_layout(xaxis_title='Number of Prizes', 
                    yaxis_title='Country',
                    coloraxis_showscale=False)
h_bar.show()


df_countries = df_data.groupby(["birth_country_current", "ISO"], as_index=False).agg({"prize":pd.Series.count})
df_countries


world_map = px.choropleth(df_countries,
                          locations='ISO',
                          color='prize', 
                          hover_name='birth_country_current', 
                          color_continuous_scale=px.colors.sequential.matter)
 
world_map.update_layout(coloraxis_showscale=True,)
 
world_map.show()


cat_country = df_data.groupby(['birth_country_current', 'category'], 
                               as_index=False).agg({'prize': pd.Series.count})

cat_country.sort_values(by='prize', ascending=False, inplace=True)

cat_country[cat_country.birth_country_current == "India"]


merged_df = pd.merge(cat_country, top20_countries, on='birth_country_current')
# change column names
merged_df.columns = ['birth_country_current', 'category', 'cat_prize', 'total_prize'] 
merged_df.sort_values(by='total_prize', inplace=True)

merged_df.head()


cat_cntry_bar = px.bar(x=merged_df.cat_prize,
                       y=merged_df.birth_country_current,
                       color=merged_df.category,
                       orientation='h',
                       title='Top 20 Countries by Number of Prizes and Category')
 
cat_cntry_bar.update_layout(xaxis_title='Number of Prizes', 
                            yaxis_title='Country')
cat_cntry_bar.show()


prize_by_year = df_data.groupby(by=['birth_country_current', 'year'], as_index=False).count()
prize_by_year = prize_by_year.sort_values('year')[['year', 'birth_country_current', 'prize']]
prize_by_year


cumulative_prizes = prize_by_year.groupby(by=['birth_country_current',
                                              'year']).sum().groupby(level=[0]).cumsum()
cumulative_prizes.reset_index(inplace=True) 
cumulative_prizes


l_chart = px.line(cumulative_prizes,
                  x='year', 
                  y='prize',
                  color='birth_country_current',
                  hover_name='birth_country_current')
 
l_chart.update_layout(xaxis_title='Year',
                      yaxis_title='Number of Prizes')
 
l_chart.show()


df_data.head()


df_organisations = df_data.groupby("organization_name", as_index=False).prize.count()
df_organisations


top20_organisations = df_organisations.sort_values("prize")[-20:]
top20_organisations.tail()


org_h_bar = px.bar(x=top20_organisations.prize,
                  y=top20_organisations.organization_name,
                  color=top20_organisations.prize,
                  color_continuous_scale=px.colors.sequential.haline,
                  title="Top 20 Research Institutions by Number of Prizes",
                  orientation='h')

org_h_bar.update_layout(xaxis_title="Number of Prizes",
                       yaxis_title="Organisation",
                       coloraxis_showscale=False)

org_h_bar.show()


df_city = df_data.groupby("organization_city", as_index=False).prize.count()
top20_city = df_city.sort_values("prize")[-20:]
top20_city.tail()


city_h_bar = px.bar(x=top20_city.prize,
                  y=top20_city.organization_city,
                  color=top20_city.prize,
                  color_continuous_scale=px.colors.sequential.solar,
                  title="Top 20 Research Cities by Number of Prizes",
                  orientation='h')

city_h_bar.update_layout(xaxis_title="Number of Prizes",
                       yaxis_title="City",
                       coloraxis_showscale=False)

city_h_bar.show()


df_birth = df_data.groupby("birth_city", as_index=False).prize.count()
top20_birth = df_birth.sort_values("prize")[-20:]
top20_birth.tail()


birth_h_bar = px.bar(x=top20_birth.prize,
                  y=top20_birth.birth_city,
                  color=top20_birth.prize,
                  color_continuous_scale=px.colors.sequential.Plasma,
                  title="Top 20 Birth Cities by Number of Prizes",
                  orientation='h')

birth_h_bar.update_layout(xaxis_title="Number of Prizes",
                       yaxis_title="City",
                       coloraxis_showscale=False)

birth_h_bar.show()


country_city_org = df_data.groupby(by=['organization_country', 
                                       'organization_city', 
                                       'organization_name'], as_index=False).agg({'prize': pd.Series.count})
 
country_city_org = country_city_org.sort_values('prize', ascending=False)

country_city_org.head()


burst = px.sunburst(country_city_org, 
                    path=['organization_country', 'organization_city', 'organization_name'], 
                    values='prize',
                    title='Where do Discoveries Take Place?',
                   )
 
burst.update_layout(xaxis_title='Number of Prizes', 
                    yaxis_title='City',
                    coloraxis_showscale=False)
 
burst.show()


birth_years = df_data.birth_date.dt.year


df_data['winning_age'] = df_data.year - birth_years


df_data.head()


display(df_data.nlargest(n=1, columns='winning_age'))
display(df_data.nsmallest(n=1, columns='winning_age'))


df_data.winning_age.describe()

count   934.00
mean     59.95
std      12.62
min      17.00
25%      51.00
50%      60.00
75%      69.00
max      97.00
Name: winning_age, dtype: float64


plt.figure(figsize=(8, 4), dpi=200)
sns.histplot(data=df_data,
             x=df_data.winning_age,
             bins=30)
plt.xlabel('Age')
plt.title('Distribution of Age on Receipt of Prize')
plt.show()


plt.figure(figsize=(8,4), dpi=200)
with sns.axes_style("whitegrid"):
    sns.regplot(data=df_data,
                x='year',
                y='winning_age',
                scatter_kws = {'alpha': 0.4},
                line_kws={'color': 'black'})
 
plt.show()


plt.figure(figsize=(8,4), dpi=200)
with sns.axes_style("whitegrid"):
    sns.boxplot(data=df_data,
                x='category',
                y='winning_age')
 
plt.show()


with sns.axes_style('whitegrid'):
    sns.lmplot(data=df_data,
               x='year', 
               y='winning_age',
               row = 'category',
               aspect=2,
               scatter_kws = {'alpha': 0.6},
               line_kws = {'color': 'black'},)
 
plt.show()


with sns.axes_style("whitegrid"):
    sns.lmplot(data=df_data,
               x='year',
               y='winning_age',
               hue='category',
               aspect=2,
               scatter_kws={'alpha': 0.5},
               line_kws={'linewidth': 5})
 
plt.show()

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO
0	1901	Chemistry	The Nobel Prize in Chemistry 1901	"in recognition of the extraordinary services ...	1/1	Individual	Jacobus Henricus van 't Hoff	1852-08-30	Rotterdam	Netherlands	Netherlands	Male	Berlin University	Berlin	Germany	NLD
1	1901	Literature	The Nobel Prize in Literature 1901	"in special recognition of his poetic composit...	1/1	Individual	Sully Prudhomme	1839-03-16	Paris	France	France	Male	NaN	NaN	NaN	FRA
2	1901	Medicine	The Nobel Prize in Physiology or Medicine 1901	"for his work on serum therapy, especially its...	1/1	Individual	Emil Adolf von Behring	1854-03-15	Hansdorf (Lawice)	Prussia (Poland)	Poland	Male	Marburg University	Marburg	Germany	POL
3	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Frédéric Passy	1822-05-20	Paris	France	France	Male	NaN	NaN	NaN	FRA
4	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Jean Henry Dunant	1828-05-08	Geneva	Switzerland	Switzerland	Male	NaN	NaN	NaN	CHE

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO	share_pct
0	1901	Chemistry	The Nobel Prize in Chemistry 1901	"in recognition of the extraordinary services ...	1/1	Individual	Jacobus Henricus van 't Hoff	1852-08-30	Rotterdam	Netherlands	Netherlands	Male	Berlin University	Berlin	Germany	NLD	1.00
1	1901	Literature	The Nobel Prize in Literature 1901	"in special recognition of his poetic composit...	1/1	Individual	Sully Prudhomme	1839-03-16	Paris	France	France	Male	NaN	NaN	NaN	FRA	1.00
2	1901	Medicine	The Nobel Prize in Physiology or Medicine 1901	"for his work on serum therapy, especially its...	1/1	Individual	Emil Adolf von Behring	1854-03-15	Hansdorf (Lawice)	Prussia (Poland)	Poland	Male	Marburg University	Marburg	Germany	POL	1.00
3	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Frédéric Passy	1822-05-20	Paris	France	France	Male	NaN	NaN	NaN	FRA	0.50
4	1901	Peace	The Nobel Peace Prize 1901	NaN	1/2	Individual	Jean Henry Dunant	1828-05-08	Geneva	Switzerland	Switzerland	Male	NaN	NaN	NaN	CHE	0.50

	year	category	laureate_type	full_name
18	1903	Physics	Individual	Marie Curie, née Sklodowska
62	1911	Chemistry	Individual	Marie Curie, née Sklodowska
89	1917	Peace	Organization	Comité international de la Croix Rouge (Intern...
215	1944	Peace	Organization	Comité international de la Croix Rouge (Intern...
278	1954	Chemistry	Individual	Linus Carl Pauling
283	1954	Peace	Organization	Office of the United Nations High Commissioner...
297	1956	Physics	Individual	John Bardeen
306	1958	Chemistry	Individual	Frederick Sanger
340	1962	Peace	Individual	Linus Carl Pauling
348	1963	Peace	Organization	Comité international de la Croix Rouge (Intern...
424	1972	Physics	Individual	John Bardeen
505	1980	Chemistry	Individual	Frederick Sanger
523	1981	Peace	Organization	Office of the United Nations High Commissioner...

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO	share_pct
393	1969	Economics	The Sveriges Riksbank Prize in Economic Scienc...	"for having developed and applied dynamic mode...	1/2	Individual	Jan Tinbergen	1903-04-12	the Hague	Netherlands	Netherlands	Male	The Netherlands School of Economics	Rotterdam	Netherlands	NLD	0.50
394	1969	Economics	The Sveriges Riksbank Prize in Economic Scienc...	"for having developed and applied dynamic mode...	1/2	Individual	Ragnar Frisch	1895-03-03	Oslo	Norway	Norway	Male	University of Oslo	Oslo	Norway	NOR	0.50

	birth_country_current	ISO	prize
0	Algeria	DZA	2
1	Argentina	ARG	4
2	Australia	AUS	10
3	Austria	AUT	18
4	Azerbaijan	AZE	1
...	...	...	...
74	United States of America	USA	281
75	Venezuela	VEN	1
76	Vietnam	VNM	1
77	Yemen	YEM	1
78	Zimbabwe	ZWE	1

Nobel Prize Analysis: Setup and Context¶

Introduction¶

Upgrade plotly (only Google Colab Notebook)¶

Import Statements¶

Notebook Presentation¶

Read the Data¶

Data Exploration & Cleaning¶

Check for Duplicates¶

Check for NaN Values¶

Type Conversions¶

Convert Year and Birth Date to Datetime¶

Plotly Donut Chart: Percentage of Male vs. Female Laureates¶

Who were the first 3 Women to Win the Nobel Prize?¶

Find the Repeat Winners¶

Number of Prizes per Category¶

Male and Female Winners by Category¶

Number of Prizes Awarded Over Time¶

Are More Prizes Shared Than Before?¶

The Countries with the Most Nobel Prizes¶

Use a Choropleth Map to Show the Number of Prizes Won by Country¶

In Which Categories are the Different Countries Winning Prizes?¶

Number of Prizes Won by Each Country Over Time¶

What are the Top Research Organisations?¶

Which Cities Make the Most Discoveries?¶

Where are Nobel Laureates Born? Chart the Laureate Birth Cities¶

Plotly Sunburst Chart: Combine Country, City, and Organisation¶

Patterns in the Laureate Age at the Time of the Award¶

Who were the oldest and youngest winners?¶

Descriptive Statistics for the Laureate Age at Time of Award¶

Age at Time of Award throughout History¶

Winning Age Across the Nobel Prize Categories¶

	year	category	laureate_type	birth_date	full_name	organization_name
24	1904	Peace	Organization	NaN	Institut de droit international (Institute of ...	NaN
60	1910	Peace	Organization	NaN	Bureau international permanent de la Paix (Per...	NaN
89	1917	Peace	Organization	NaN	Comité international de la Croix Rouge (Intern...	NaN
200	1938	Peace	Organization	NaN	Office international Nansen pour les Réfugiés ...	NaN
215	1944	Peace	Organization	NaN	Comité international de la Croix Rouge (Intern...	NaN

	year	category	prize	motivation	prize_share	laureate_type	full_name	birth_date	birth_city	birth_country	birth_country_current	sex	organization_name	organization_city	organization_country	ISO	share_pct
18	1903	Physics	The Nobel Prize in Physics 1903	"in recognition of the extraordinary services ...	1/4	Individual	Marie Curie, née Sklodowska	1867-11-07	Warsaw	Russian Empire (Poland)	Poland	Female	NaN	NaN	NaN	POL	0.25
29	1905	Peace	The Nobel Peace Prize 1905	NaN	1/1	Individual	Baroness Bertha Sophie Felicita von Suttner, n...	1843-06-09	Prague	Austrian Empire (Czech Republic)	Czech Republic	Female	NaN	NaN	NaN	CZE	1.00
51	1909	Literature	The Nobel Prize in Literature 1909	"in appreciation of the lofty idealism, vivid ...	1/1	Individual	Selma Ottilia Lovisa Lagerlöf	1858-11-20	Mårbacka	Sweden	Sweden	Female	NaN	NaN	NaN	SWE	1.00

	birth_country_current	category	prize
89	India	Economics	2
90	India	Literature	2
91	India	Medicine	2
88	India	Chemistry	1
92	India	Peace	1
93	India	Physics	1

	birth_country_current	year	prize
0	Algeria	1957	1
1	Algeria	1997	2
2	Argentina	1936	1
3	Argentina	1947	2
4	Argentina	1980	3
...	...	...	...
622	United States of America	2020	281
623	Venezuela	1980	1
624	Vietnam	1973	1
625	Yemen	2011	1
626	Zimbabwe	1960	1

	organization_name	prize
0	A.F. Ioffe Physico-Technical Institute	1
1	Aarhus University	1
2	Academy of Sciences	3
3	Amsterdam University	2
4	Argonne National Laboratory	1
...	...	...
259	École Nationale Supérieur des Mines de Paris	1
260	École Normale Supérieure	1
261	École Polytechnique	2
262	École Supérieure de Physique et Chimie	1
263	École municipale de physique et de chimie indu...	1

	organization_name	prize
198	University of Chicago	20
117	Massachusetts Institute of Technology (MIT)	21
167	Stanford University	23
68	Harvard University	29
196	University of California	40

	organization_city	prize
128	Paris	25
92	London	27
33	Cambridge	31
121	New York, NY	45
34	Cambridge, MA	50

	birth_city	prize
112	Chicago, IL	12
572	Vienna	14
313	London	19
418	Paris	26
382	New York, NY	53

Nobel Prize Analysis: Setup and Context¶

Introduction¶

Upgrade plotly (only Google Colab Notebook)¶

Import Statements¶

Notebook Presentation¶

Read the Data¶

Data Exploration & Cleaning¶

Check for Duplicates¶

Check for NaN Values¶

Type Conversions¶

Convert Year and Birth Date to Datetime¶

Add a Column with the Prize Share as a Percentage¶

Plotly Donut Chart: Percentage of Male vs. Female Laureates¶

Who were the first 3 Women to Win the Nobel Prize?¶

Find the Repeat Winners¶

Number of Prizes per Category¶

Male and Female Winners by Category¶

Number of Prizes Awarded Over Time¶

Are More Prizes Shared Than Before?¶

The Countries with the Most Nobel Prizes¶

Use a Choropleth Map to Show the Number of Prizes Won by Country¶

In Which Categories are the Different Countries Winning Prizes?¶

Number of Prizes Won by Each Country Over Time¶

What are the Top Research Organisations?¶

Which Cities Make the Most Discoveries?¶

Where are Nobel Laureates Born? Chart the Laureate Birth Cities¶

Plotly Sunburst Chart: Combine Country, City, and Organisation¶

Patterns in the Laureate Age at the Time of the Award¶

Who were the oldest and youngest winners?¶

Descriptive Statistics for the Laureate Age at Time of Award¶

Age at Time of Award throughout History¶

Winning Age Across the Nobel Prize Categories¶