Automated Data Processing and Scripting Enhancements

• Day: 2023-10-14
• Time: 00:40 to 03:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Processing, Automation, Jupyter, Descriptive Statistics

Description

Session Goal

The session aimed to enhance and automate data processing workflows using Python scripting, focusing on file management, data generation, and statistical analysis.

Key Activities

• Developed a workflow to check file existence and execute a sampling script if files are missing.
• Integrated a command to execute the samplear.py script, including logging for data processing.
• Refactored code to generate quarterly dates and loop through years, utilizing the subprocess module for external script execution.
• Ensured restoration of the original working directory post script execution.
• Provided an overview of code structure for data processing in Jupyter Notebooks, covering configuration and auxiliary data loading.
• Outlined a framework for descriptive statistics Jupyter notebooks, detailing data exploration and synthesis.
• Implemented a new convention for yearly and quarterly data processing using Python.

Achievements

• Successfully automated the execution of data processing scripts with integrated logging and file management.
• Refactored and organized code for better maintainability and scalability.
• Established a structured approach for descriptive statistics analysis in Jupyter Notebooks.

Pending Tasks

• Further testing of the automated workflows to ensure robustness across different datasets.
• Expansion of the descriptive statistics framework to include more complex analyses.

Evidence

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• event_ids: []