Project: Visualizing the History of Nobel Prize Winners

# Loading in required libraries
import pandas as pd
import seaborn as sns
import numpy as np

# Load the Nobel Prize data
nobel_df = pd.read_csv('data/nobel.csv')

# Display the first few rows of the dataframe
nobel_df.head()

# Create a new column 'decade' in the dataframe
nobel_df['decade'] = (nobel_df['year'] // 10) * 10

# Filter for US-born winners
us_winners = nobel_df[nobel_df['birth_country'] == 'United States of America']

# Count the number of winners per decade
us_winners_per_decade = us_winners.groupby('decade').size()

# Identify the decade with the highest ratio of US-born winners
highest_ratio_decade = us_winners_per_decade.idxmax()

highest_ratio_decade

# Group by decade and category, then count the number of female laureates
female_winners = nobel_df[nobel_df['sex'] == 'Female'].groupby(['decade', 'category']).size()

# Group by decade and category, then count the total number of laureates
total_winners = nobel_df.groupby(['decade', 'category']).size()

# Calculate the proportion of female laureates
female_proportion = female_winners / total_winners

# Identify the decade and category with the highest proportion of female laureates
highest_female_proportion = female_proportion.idxmax()

highest_female_proportion

# Find the first woman to win a Nobel Prize
first_female_winner = nobel_df[nobel_df['sex'] == 'Female'].nsmallest(1, 'year')

# Determine repeat winners (those who have won more than once)
repeat_winners = nobel_df['full_name'].value_counts()
repeat_winners = repeat_winners[repeat_winners > 1]

(first_female_winner, repeat_winners)

import pandas as pd


# Extract the top values from 'sex'
top_sex_value = nobel_df['sex'].value_counts().idxmax()

# Extract the top values from 'birth_country'
top_birth_country_value = nobel_df['birth_country'].value_counts().idxmax()

(top_sex_value, top_birth_country_value)

import matplotlib.pyplot as plt

# Create a flag for winners whose birth country is "United States of America"
nobel_df['usa_born_winner'] = nobel_df['birth_country'] == "United States of America"

# Create a decade column
nobel_df['decade'] = (nobel_df['year'] // 10) * 10

# Group by decade and calculate the ratio of USA born winners
usa_winners_ratio = nobel_df.groupby('decade')['usa_born_winner'].mean()

# Plotting
plt.figure(figsize=(10, 6))
usa_winners_ratio.plot(kind='line', marker='o')
plt.title('Ratio of US-born Nobel Prize Winners by Decade')
plt.xlabel('Decade')
plt.ylabel('Ratio of US-born Winners')
plt.grid(True)
plt.show()

# Filtering for female winners
female_winners = nobel_df[nobel_df['sex'] == 'Female']

# Group by two columns: 'decade' and 'category'
grouped_female_winners = female_winners.groupby(['decade', 'category']).size()

# Finding the decade and category with the highest female winners
max_female_winners = grouped_female_winners.idxmax()
max_female_count = grouped_female_winners.max()

# Creating a dictionary
max_female_dict = {'year': max_female_winners[0], 'category': max_female_winners[1], 'count': max_female_count}

# Creating a DataFrame for the proportion of female winners by decade and category
female_winners_proportion = female_winners.groupby(['decade', 'category']).size() / nobel_df.groupby(['decade', 'category']).size()
female_winners_proportion = female_winners_proportion.unstack('category').fillna(0)

# Creating a relational line plot with multiple categories
plt.figure(figsize=(14, 8))
for category in female_winners_proportion.columns:
    plt.plot(female_winners_proportion.index, female_winners_proportion[category], marker='o', label=category)

plt.title('Proportion of Female Nobel Prize Winners by Decade and Category')
plt.xlabel('Decade')
plt.ylabel('Proportion of Female Winners')
plt.legend(title='Category')
plt.grid(True)
plt.show()

# Finding the first woman to win a Nobel Prize and her name
first_female_winner = female_winners[female_winners['year'] == female_winners['year'].min()]

# Displaying the earliest year, corresponding category, and her name
first_female_winner[['year', 'category', 'full_name']]

# Counting the number of times each winner has won
winner_counts = nobel_df['full_name'].value_counts()

# Selecting those with counts of two or more
repeats = winner_counts[winner_counts >= 2].index.tolist()

(repeats,winner_counts)

# Analyzing the most commonly awarded gender and birth country
top_gender = nobel_df['sex'].value_counts().idxmax()
top_country = nobel_df['birth_country'].value_counts().idxmax()

# Finding the decade with the highest ratio of US-born winners to total winners
nobel_df['usa_born_winner'] = nobel_df['birth_country'] == 'United States of America'
nobel_df['decade'] = (nobel_df['year'] // 10) * 10
usa_winners_by_decade = nobel_df.groupby('decade')['usa_born_winner'].sum()
total_winners_by_decade = nobel_df.groupby('decade').size()
ratio_usa_winners = usa_winners_by_decade / total_winners_by_decade
max_decade_usa = ratio_usa_winners.idxmax()

# Identifying the decade and category with the highest proportion of female laureates
female_winners['decade'] = (female_winners['year'] // 10) * 10
female_winners_by_decade_category = female_winners.groupby(['decade', 'category']).size()
total_winners_by_decade_category = nobel_df.groupby(['decade', 'category']).size()
proportion_female_winners = female_winners_by_decade_category / total_winners_by_decade_category
max_female_decade_category = proportion_female_winners.idxmax()
max_female_dict = {max_female_decade_category[0]: max_female_decade_category[1]}

# Finding the first woman to receive a Nobel Prize and her category
first_woman = female_winners[female_winners['year'] == female_winners['year'].min()].iloc[0]
first_woman_name = first_woman['full_name']
first_woman_category = first_woman['category']

# Identifying individuals or organizations that have won more than one Nobel Prize
winner_counts = nobel_df['full_name'].value_counts()
repeat_winners = winner_counts[winner_counts > 1]
repeat_list = repeat_winners.index.tolist()

# Storing the answers in the specified variables
top_gender, top_country, max_decade_usa, max_female_dict, first_woman_name, first_woman_category, repeat_list