Athlete Biographical Data Analysis

This project involves cleaning, transforming, and analyzing a dataset of international athletes using Python and Pandas.

Dataset

• File: bios.csv
• Type: CSV
• Fields include athlete name, height, birthplace, birthdate, and more

Tools Used

• Python 3.10.11
• Pandas
• Jupyter Notebook

Project Steps

1. Data Loading & Initial Exploration

   • Loaded CSV into a DataFrame using pd.read_csv()
   • Checked null values, structure, and sample rows

2. Filtering

   • Filtered by height > 215 cm
   • Filtered by birthplace (country, city)
   • Regex filtering for names (e.g., "Keith", "Patrick")

3. Feature Engineering

   • Extracted first_name from full names
   • Converted born_date to datetime format
   • Created born_year column

4. Output

   • Saved cleaned dataset as bios_new.csv

Key Insights

• Tallest athletes identified across countries
• Seattle and specific U.S. states were major contributor regions
• Extracted clean year-based insights from raw date data

Files in This Repo

• README.md
• bios.csv: Original dataset
• bios_analysis.ipynb: Jupyter Notebook with analysis
• bios_new.csv: Cleaned and enriched dataset

Author Contributed by: Divya Bariya (Hands-on implementation based on publicly available learning content)