📅 2025-10-26 — Session: Enhancements to Data Processing and Normalization Scripts
🕒 18:05–19:40
🏷️ Labels: Data_Processing, Normalization, Python, Pandas, CSV
📂 Project: Dev
Session Goal
The session aimed to address key errors and improve the robustness of data processing and normalization scripts used in handling election data.
Key Activities
- Fixing KeyError in Data Deduplication: Resolved a
KeyError: Noneby excludingNonevalues and provenance columns from hashing operations in a pandas DataFrame to enhance performance and prevent errors. - Data Quality Assessment: Conducted a detailed assessment of a CSV file containing election results, identifying potential issues with data formatting and recommending normalization processes.
- Fixing OSError in File Management: Provided a solution for the
OSErrorencountered during file renaming across different filesystems, along with suggestions for improving logging and configuration warnings. - Mapping and Operational Guidelines: Detailed the functionality and operational guidelines for the script
20_normalize_core.py, including its role in the data pipeline and common failure modes. - Schema Creation and Data Validation: Outlined steps for creating a missing JSON schema file for votos types and performing data validation to ensure normalization integrity.
- Enhancements to 20_normalize_core.py: Improved the script’s resilience against missing auxiliary tables and schema drift, introducing optional fallbacks and stricter ID handling.
- Creating a CSV for Election Data: Demonstrated how to create and export a DataFrame containing election data as a CSV file.
Achievements
- Successfully resolved key errors and improved the robustness of data processing scripts.
- Enhanced data quality and normalization processes for election data.
Pending Tasks
- Further testing of the enhanced scripts to ensure robustness across different datasets.
- Implementation of additional audit artifacts for failure tracking.