In this project, I conducted a comprehensive data analysis of football player attributes using Python and the Pandas library. The dataset, sourced from a Google Sheets link, contains detailed information about various football players, including their nationalities, ages, preferred positions, and specific skill ratings.
- Data Loading and Inspection: o Loaded the dataset from a Google Sheets link using Pandas. o Inspected the dataset to understand its structure and identify key columns for analysis.
- Numpy and Pandas Proficiency: o Demonstrated proficiency in using Numpy arrays and Pandas DataFrames for data manipulation and analysis. o Answered theoretical questions related to Numpy arrays and Pandas DataFrames.
- Data Analysis and Insights: o Identified the country with the oldest player. o Determined the attribute with the lowest average value for goalkeepers. o Analyzed the dataset to find the preferred position type with the most entries. o Identified the highest-rated player from Portugal under the age of 25.
• Python: Utilized Python for data loading, manipulation, and analysis. • Pandas: Leveraged Pandas for DataFrame operations, filtering, and aggregation. • SQL: Formulated and executed SQL queries for data retrieval and manipulation. • Data Visualization: (Optional) Created visualizations using Matplotlib or Seaborn to present key findings.
This project showcases my ability to work with real-world datasets, perform detailed data analysis, and derive meaningful insights. It highlights my proficiency in Python, Pandas, and SQL, as well as my capability to communicate findings effectively.