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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 seaborn as sns
    import numpy as np
    
    nobel=pd.read_csv('data/nobel.csv')
    
    
    # Loading in required libraries
    import pandas as pd
    import seaborn as sns
    import numpy as np
    
    # Reading in the Nobel Prize data
    nobel = pd.read_csv('data/nobel.csv')
    
    # Store and display the most commonly awarded gender and birth country in requested variables
    top_gender = nobel['sex'].value_counts().index[0]
    top_country = nobel['birth_country'].value_counts().index[0]
    
    print("\n The gender with the most Nobel laureates is :", top_gender)
    print(" The most common birth country of Nobel laureates is :", top_country)
    
    # Calculating the proportion of USA born winners per decade
    nobel['usa_born_winner'] = nobel['birth_country'] == 'United States of America'
    nobel['decade'] = (np.floor(nobel['year'] / 10) * 10).astype(int)
    prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()
    
    # Display the proportions of USA born winners per decade
    print("\n The proportions of USA born winners per decade :\n", prop_usa_winners)
    
    
    # Plotting USA born winners
    ax1 = sns.relplot(x='decade', y='usa_born_winner', data=prop_usa_winners, kind="line")
    
    
    # Calculating the proportion of female laureates per decade
    nobel['female_winner'] = nobel['sex'] == 'Female'
    prop_female_winners = nobel.groupby(['decade', 'category'], as_index=False)['female_winner'].mean()
    
    # Plotting female winners with % winners on the y-axis
    ax2 = sns.relplot(x='decade', y='female_winner', hue='category', data=prop_female_winners, kind="line")
    
    
    # Finding the first woman to win a Nobel Prize
    nobel_women = nobel[nobel['female_winner']]
    min_row = nobel_women[nobel_women['year'] == nobel_women['year'].min()]
    first_woman_name = min_row['full_name'].values[0]
    first_woman_category = min_row['category'].values[0]
    print(f"\n The first woman to win a Nobel Prize was {first_woman_name}, in the category of {first_woman_category}.")
    
    
    # Selecting the laureates that have received 2 or more prizes
    counts = nobel['full_name'].value_counts()
    repeats = counts[counts >= 2].index
    repeat_list = list(repeats)
    
    print("\n The repeat winners are :", repeat_list)