Enhancements to Data Processing and Normalization Scripts

2 min read

Enhancements to Data Processing and Normalization Scripts

  • Day: 2025-10-26
  • Time: 18:05 to 19:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Data_Processing, Normalization, Python, Pandas, CSV

Description

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: None by excluding None values 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 OSError encountered 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.

Evidence

  • source_file=2025-10-26.sessions.jsonl, line_number=0, event_count=0, session_id=8baef380ec4e64ca620e183fd51fe9f1ae8ae13cc1da2a1faddd30009bc539c2
  • event_ids: []

Graph View

Backlinks