📅 2023-03-29 — Session: Optimized Data Storage and Transformation Techniques
🕒 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 and transformation techniques using Python’s pandas library.
Key Activities
- Discussed efficient methods for storing data with low unique values, including sparse matrices, pivot tables, and databases.
- Explored optimization of data storage by converting columns to categorical data types in Python using pandas.
- Provided code snippets for converting DataFrame columns, concatenating DataFrames, and ensuring data type retention.
- Demonstrated saving a DataFrame as a JSON file and implementing JSON data storage using Python.
Achievements
- Clarified the methods for optimizing data storage using categorical types and Apache Parquet.
- Successfully demonstrated the conversion of DataFrame columns to categorical types and data type retention.
- Implemented JSON data storage techniques for efficient data management.
Pending Tasks
- Further exploration of Apache Parquet for large datasets.
- Investigate additional data storage techniques for mixed data types.