📅 2023-03-29 — Session: Optimized data storage using Python and Pandas
🕒 21:00–21:20
🏷️ Labels: Data Storage, Pandas, Categorical Data, JSON, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement efficient data storage techniques using Python and Pandas, focusing on optimizing memory usage and data processing efficiency.
Key Activities
- Discussed three methods for efficient data storage: sparse matrix, pivot table, and database usage for large datasets.
- Explored converting ‘variable’ and ‘year’ columns to categorical data types in Pandas to improve memory efficiency.
- Provided code snippets for converting DataFrame columns to categorical types and ensuring data type retention post-processing.
- Demonstrated saving a Pandas DataFrame to a JSON file, considering dataset size and file type efficiency.
Achievements
- Successfully implemented categorical conversion for specific DataFrame columns, enhancing data processing efficiency.
- Developed a solution for retaining category data types after DataFrame processing.
- Achieved efficient data storage by saving DataFrame as JSON, organized by variable and year.
Pending Tasks
- Further exploration of Apache Parquet for large dataset storage to enhance performance.