📅 2023-08-17 — Session: Implemented data merging and database transition strategies
🕒 21:10–22:45
🏷️ Labels: Data Merging, Python, Pandas, Database, API
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement strategies for merging datasets in Python using pandas, transitioning data processing from CSV files to a database, and generating a tree view of Google Drive using Python.
Key Activities
- Developed a structured approach to merge multiple datasets in Python using pandas, including specific merge operations and code snippets.
- Provided Python code for merging two DataFrames and adding specific columns from external datasets.
- Outlined steps to modify a script to read data from a database instead of a CSV, including establishing a database connection and understanding the database structure.
- Offered guidance on connecting to a relational database, querying specific data columns, and processing results using Python and pandas.
- Described a process for loading CSV files into DataFrames and joining them according to DBML relationships.
- Provided a code snippet for adapting data loading procedures to new CSV file paths, including merging DataFrames and filtering columns.
- Guided the generation of a tree view of Google Drive using the Google Drive API and Python.
Achievements
- Successfully implemented data merging strategies in Python using pandas.
- Transitioned data processing from CSV to database, establishing a connection and querying data.
- Generated a tree view of Google Drive using Python and the Google Drive API.
Pending Tasks
- Further refine database querying and data processing scripts for specific use cases.
- Explore additional automation opportunities in data loading and merging processes.