https://github.com/nafisalawalidris/analyzing-nobel-prize-dataset-demographics-and-trends

This project analyses a Nobel Prize dataset using Python and data analysis libraries. It explores the distribution of winners by category and country, examines the proportion of female winners over time, investigates the age of winners when they received the prize and identifies the oldest and youngest recipients.
https://github.com/nafisalawalidris/analyzing-nobel-prize-dataset-demographics-and-trends

age-at-award country-distribution data-analysis data-manipulation dataset demographics filtering gender-balance grouping nobel-prize notable-laureates python trends visualisation winners

Last synced: 4 days ago
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This project analyses a Nobel Prize dataset using Python and data analysis libraries. It explores the distribution of winners by category and country, examines the proportion of female winners over time, investigates the age of winners when they received the prize and identifies the oldest and youngest recipients.

• Host: GitHub
• URL: https://github.com/nafisalawalidris/analyzing-nobel-prize-dataset-demographics-and-trends
• Owner: nafisalawalidris
• Created: 2023-06-09T22:55:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-09T23:06:33.000Z (over 1 year ago)
• Last Synced: 2024-11-22T21:16:28.514Z (2 months ago)
• Topics: age-at-award, country-distribution, data-analysis, data-manipulation, dataset, demographics, filtering, gender-balance, grouping, nobel-prize, notable-laureates, python, trends, visualisation, winners
• Language: Jupyter Notebook
• Homepage:
• Size: 476 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


  
Nobel Prize Data Analysis


  
Project Overview

  


    This project analyzes a Nobel Prize dataset using Python and data analysis libraries. It explores the distribution of winners by category and country, examines the proportion of female winners over time, investigates the age of winners when they received the prize, and identifies the oldest and youngest recipients. The code showcases data manipulation, grouping, filtering, and visualization techniques.

  


  
Table of Contents

  


    
Project Overview

    
Installation

    
Usage

    
Dataset

    
Methodology

    
Results

    
Contributing

    
License

  


  
Project Overview

  


    The Nobel Prize is a prestigious international award given annually in several categories, including Physics, Chemistry, Medicine, Literature, Peace, and Economic Sciences. This project aims to analyze a Nobel Prize dataset to gain insights into the demographics and trends of Nobel Prize winners. It involves data exploration, manipulation, and visualization using Python and popular data analysis libraries.

  


  
Installation

  


    
Clone the repository: git clone https://github.com/your-username/nobel-prize-data-analysis.git



    
Navigate to the project directory: cd nobel-prize-data-analysis



    
Install the required dependencies: pip install -r requirements.txt



  


  
Usage

  


    
Open the Jupyter Notebook: jupyter notebook



    
Open the Nobel_Prize_Data_Analysis.ipynb notebook.

    
Follow the instructions in the notebook to run the code and analyze the Nobel Prize dataset.

  


  
Dataset

  


    The dataset used in this project is the Nobel Prize dataset, which contains information about Nobel Prize laureates from 1901 to 2016. The dataset includes details such as the category of the prize, the name of the laureate, their birth date, birth city, country, and more.

  


  
Methodology

  


    The project involves the following steps:

  

  


    
Data loading and preprocessing: The Nobel Prize dataset is loaded into a Pandas DataFrame. Data preprocessing steps may include handling missing values, data type conversions, and feature engineering.

    
Data exploration: The dataset is explored to understand its structure and contents. Summary statistics and visualizations are used to gain insights into the distribution of winners by category, country, gender, and age.

    
Data analysis: Various data analysis techniques are applied