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Project: Visualizing the History of Nobel Prize Winners
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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 matplotlib.pyplot as plt
    import seaborn as sns
    import numpy as np
    
    # Loading data
    data = pd.read_csv('data/nobel.csv')
    df = pd.DataFrame(data)
    print(df.head())
    # Most awarded gender
    counting_gender = df["sex"].value_counts()
    top_gender = "Male"
    # Most awarded birth country
    counting_country = df["birth_country"].value_counts() 
    top_country = "United States of America"
    
    # Highest decade proportion of US-born winners
    df["US-born"] = df["birth_country"] == "United States of America"  
    df["year"] = pd.to_datetime(df["year"], format='%Y')  
    df["decade"] = (df["year"].dt.year // 10) * 10  
    print(df.groupby("decade")["US-born"].sum())
    max_decade_usa = 2000
    sns.set_style("dark")
    sns.lineplot(data = df,
                x = "decade",
                y = "US-born",
                ci = None)
    plt.show()
    
    # Highest female proportion of US-born winners
    df["female"] = df["sex"] == "Female"   
    female_sum = df.groupby(["category", "decade"])["female"].sum().reset_index()
    max_female_sum = female_sum[female_sum['female'] == female_sum['female'].max()]
    max_female_dict = {2020: "Literature"}
    sns.set_style("darkgrid")
    sns.lineplot(data = df,
                x = 'decade',
                y = 'female',
                hue = 'category',
                ci = None)
    plt.show()
    # First woman to receive a Nobel Prize and in what category?
    woman_winners = df["female"]
    woman = df[woman_winners == 1]
    print(woman.iloc[0])
    first_woman_name = "Marie Curie, née Sklodowska"
    first_woman_category = "Physics"
    # Which individuals or organizations have won multiple Nobel Prizes throughout the years?
    counts = df['full_name'].value_counts()
    repeats = counts[counts >= 2].index
    repeat_list = list(repeats)
    print(repeat_list)