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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
import matplotlib.pyplot as plt

# Start coding here!
nobel = pd.read_csv('data/nobel.csv')
nobel.head()
nobel.shape
nobel.info()

What is the most commonly awarded gender and birth country?

#top gender
top_gender_count = nobel['sex'].value_counts()
top_gender = top_gender_count.index[0]
print(top_gender_count)
print('')
print('The top gender for winner of a Nobel Prize is ' + top_gender)
#top country
top_country_count = nobel['birth_country'].value_counts()
top_country = top_country_count.index[0]
print(top_country_count)
print('')
print('The top country for winner of a Nobel Prize is ' + top_country)

Which decade had the highest ratio of US-born Nobel Prize winners to total winners in all categories?

#Find all usa born winners
nobel['usa_born_winner'] = nobel['birth_country'] == "United States of America"

#Create a decade colum
nobel['decade'] = (np.floor(nobel['year'] / 10) * 10).astype(int)

#calulate the ration of USA winners to all winners 
prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()
max_decade_usa = prop_usa_winners[prop_usa_winners['usa_born_winner'] == prop_usa_winners['usa_born_winner'].max()]['decade'].values[0]

ax1 = sns.relplot(
    x='decade',
    y='usa_born_winner',
    data=prop_usa_winners,
    kind='line'
)

Which decade and Nobel Prize category combination had the highest proportion of female laureates?

nobel['female_winner'] = nobel['sex'] == 'Female'
prop_female_winners = nobel.groupby(['decade', 'category'], as_index=False)['female_winner'].mean()

max_female_decade_category = prop_female_winners[prop_female_winners['female_winner'] == prop_female_winners['female_winner'].max()][['decade', 'category']]
print(max_female_decade_category)

max_female_dict = {
    max_female_decade_category['decade'].values[0]: max_female_decade_category['category'].values[0]
}
print(max_female_dict)

ax2 = sns.relplot(
    x='decade',
    y='female_winner',
    hue='category',
    data=prop_female_winners,
    kind='line'
)

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

nobel_woman_winners = nobel[nobel['female_winner']]
first_woman = nobel_woman_winners[nobel_woman_winners['year'] == nobel_woman_winners['year'].min()]
first_woman_name = first_woman['full_name'].values[0]
first_woman_category = first_woman['category'].values[0]

print(f"\n The firest woman to win a Nobel Prize was {first_woman_name}, in the category of {first_woman_category}.")

Which individuals or organizations have won more than one Nobel Prize throughout the years?

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