Developed Python Scripts for Jupyter Notebook Processing

• Day: 2025-07-05
• Time: 22:00 to 22:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Jupyter Notebooks, ETL, Data Processing

Description

Session Goal:

The session aimed to develop Python scripts for processing and analyzing Jupyter Notebooks, focusing on reading, checking file existence, and extracting code cells.

Key Activities:

• Imported essential Python libraries for data processing, including JSON handling, file system operations, and data manipulation with pandas.
• Implemented code to check the existence of a Jupyter Notebook file using the os.path.exists method.
• Developed scripts to read Jupyter Notebook files, extract code cells, and enumerate their contents.
• Created workflows to iterate over and print specific cell contents for debugging and data inspection purposes.
• Initiated an ETL scratch-pad for financial data aggregation, processing data to create inflow/outflow tables.
• Designed a modular ETL blueprint for financial data processing, detailing folder structure and pipeline scripts.

Achievements:

• Successfully set up a Python environment for Jupyter Notebook processing.
• Extracted and printed code cells from notebooks, facilitating data inspection and debugging.
• Developed a prototype ETL process for financial data aggregation, setting the stage for further automation.

Pending Tasks:

• Further refine the ETL process for financial data to improve automation and efficiency.
• Implement additional data validation checks within the Python scripts.

Evidence

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• event_ids: []