📅 2023-08-17 — Session: Implemented Data Merging and Database Transition Strategies
🕒 21:10–22:40
🏷️ Labels: Data Merging, Python, Pandas, Database, API
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to implement effective data merging strategies using Python’s pandas library and transition data processing from CSV files to a database-driven approach.
Key Activities:
- Developed structured workflows for merging multiple datasets in Python using pandas, providing specific code snippets for implementation.
- Adapted existing data loading procedures to accommodate new CSV file paths and merge multiple DataFrames.
- Transitioned data processing scripts from reading CSV files to querying a relational database, including establishing a database connection and understanding the database structure.
- Generated a tree view of Google Drive using the Google Drive API, demonstrating the integration of external APIs with Python scripting.
Achievements:
- Successfully outlined and implemented data merging strategies, enhancing data manipulation capabilities.
- Transitioned data processing workflows from CSV to database, improving data management efficiency.
- Developed a Python script for generating a tree view of Google Drive, showcasing API usage.
Pending Tasks:
- Further refine the database querying process to optimize data retrieval and processing.
- Continue testing and validation of the new data processing workflows to ensure accuracy and performance.