Visualizing Nobel Prizes

Project Overview

• Explored Nobel Prizes awarded over a period of a hundred years through detailed visualizations.
• Explored different data visualization packages such as Seaborn and Plotly to effectively display data insights.
• Detailed visual analysis of factors contributing to being a Nobel Prize awardee.

Initial Overview of the Dataset

This image provides a high level overview of the dataset. The dataset has 969 observations with both numerical and categorical features. The pie charts show the distribution of a single feature and the stacked bar graph displays the prize share in all categories.

Data Exploration

Data exploration through visualization often provides interesting insights into a dataset. This visual exploration project focuses on Nobel prizes awarded over a period of more than a hundred years. These data explorations through bar graph, scatter plots and histograms provide a detailed and clear picture of the background of the awardees. Several columns contain missing values and these too can be vizualized through Python modules. Further study of the dataset provides key insights into the gender disparity between male and female laureates. Also, a comprehensive view about the dataset can be obtainbed through age, gender and category related visualizations.

1. Missing Values Visualized

The graphs below illustrate the number of missing values in each column.

a. Bar Graph of Missing Values

This graph shows the number of missing values in each column (feature).

b. Matrix of Missing Values

The empty lines inside the matix correspond to the missing values.

c. Correlation Heatmap

The heatmap vizualizes the correlation between the missing values in each column.

2. Number of Nobel Prizes Awarded over the Years in all Categories

It can be observed that the number of Nobel Prizes awarded over the years have increased. This increase in number is due to the introduction of the new Economics Prize in the 1960s and also due to increasing number of laureates who share a prize each year.

3. Number of Nobel Prizes Awarded by Gender (1996-2016)

This graph displays the number of prizes awarded in a 20 year period.

4. Number of Individuals who Received Awards in each Category (2006-2016)

This graph displays the number of prizes awarded in a ten year period, by category. Chemistry and Medicine prizes have the most prize shares.

5. Age Distribution of Nobel Prize Awardees

This graph represents a kernel density estimate (kde), which is used to visualize the distribution of observations related to a particular feature. The solid line represents the expected distribution and the histogram represents the distribution of the ‘Age’ feature. This graph essentially represents the underlying probability density function of the distribution.

The above graph can be further elaborated to represent the age distribution of each category.

6. Relplot of Age vs Years, indexed by hue (Category) and circle size (Prize Share)

Scatterplot with varying point sizes and hues. Each data point represents an awardee filtered by age and year. The size of the point indicates the fraction of the prize received by the awardee.

7. Relplot of Age vs Years, indexed by hue (Category) and circle size (Gender)

Scatterplot with varying point sizes and hues.

Note the disparity between the number of men and women awarded over the years

8. Conditional Kernel Density Estimate for Age for each Category

This graph is an extension of plot 5 above. It displays the probability distribution underlying the ‘Age’ feature for all the categories. The shape of the density curve highlighted in white is essentially the same shape as the histogram, which displays the number of laureates in each age bin.

9. Stacked Histogram on a Log Scale

Number of awards in each category displayed on a logarithmic scale.

10. Relplot of Age vs Years plotted for both Genders

This plot is an extension of Figure 6, with the axes reversed. Each point on the both the graphs represents a Nobel Prize awardee categorized by age and year awarded.

11. Line plots of Ages of Awardees in all Categories

These plots display the age at which individuals received the Nobel Prizes over the years.

12. Country, City and Organization based Graphs

These charts display the top 20 (by count) countries, cities and organizations that have received Nobel Prizes.

a. Country of Birth

b. City of Birth

c. Organization Affiliated With

General Observations

a. By Gender

• Men are more likely to be awarded a Nobel Prize in all categories.
• Women are more likely to receive a Nobel Prize in Peace or Literature.
• Women awarded too few Physics and Economics awards to provide a meaningful analysis.

b. By Category

• Median age is lower for women in Chemistry, Peace and Physics categories.
• Medicine has the highest number of awardees among all categories.

c. By Age

• The highest median ages to receive a Nobel Prize are in Economics and Literature.
• Physics laureates are the youngest among all prize categories.
• The average age of Nobel Prize laureates has increased over the years.

d. By Country and City

• Individuals born in the United States have been awarded the most number of prizes in Science categories.
• Individuals from the University of California system have been awarded the highest number of prizes.
• Individuals born in New York City have received the highest number of Nobel Prizes.

Final Note

Based on the visual analyses performed above, the most favorable path to become a Nobel Prize laureate is to be a male from the United States, born in New York City and to have studied or worked in the University of California system or Harvard University.