Cleaned and linked voting data for person-level flags

• Day: 2026-04-11
• Time: 10:15 to 10:25
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: HIGH
• Assignee: Matías Nehuen Iglesias
• Tags: Data-Cleaning, Data-Merging, Pandas, Person-Id, Flags

Description

Session Goal

Refine the voting-data cleaning and integration workflow so info_voto.[[csv]] can be standardized, linked to canonical people, and merged into the flags notebook without losing data integrity.

Key Activities

• Designed a cleaning pipeline for info_voto.[[csv]] that preserves raw values while generating auxiliary matching keys for later identity resolution.
• Reviewed parsing issues and corrected normalization logic, including handling of numeric identifiers and inconsistent naming conventions.
• Defined a linking strategy that separates strong-confidence and medium-confidence rows before assigning person_id.
• Planned the integration of voto_clean into the flags notebook, ensuring votes are linked to canonical people before merging with the neighbors dataset.
• Outlined a person-level aggregation approach that summarizes vote information with explicit rules instead of generating dummy columns.
• Emphasized reusable Python/Pandas workflows and data integrity throughout the merge and aggregation steps.

Achievements

• Clarified the end-to-end path from raw vote data to a cleaned staging export (staging/voto_clean.[[csv]]).
• Established a safer matching strategy based on canonical identities and confidence tiers.
• Defined how vote-derived attributes should be aggregated at the person level for downstream flag generation.

Pending Tasks

• Implement and validate the final cleaning/parsing corrections in code.
• Export and inspect voto_clean.[[csv]] for edge cases and matching quality.
• Complete the person_id linkage and integrate the resulting table into the flags notebook.
• Verify the person-level aggregation rules against real records before final merge.

Evidence

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• event_ids: []