Resolved DataFrame issues and optimized data pipeline

📅 2025-06-22 — Session: Resolved DataFrame issues and optimized data pipeline

🕒 20:25–21:00
🏷️ Labels: Data Cleaning, Pandas, Data Enrichment, Python, Data Pipeline
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session focused on diagnosing, fixing, and optimizing issues related to DataFrame operations in Python, particularly involving JSONL file loading, data enrichment, and merging processes.

Key Activities

• Diagnosed and fixed empty column issues in the DataFrame df_scraped after loading a JSONL file.
• Enriched articles data using master_ref.csv by constructing keys and merging data.
• Debugged key mismatches during DataFrame merges, ensuring consistent data types and key existence.
• Resolved a KeyError in DataFrame processing by correctly constructing necessary columns.
• Finalized data merging steps in the pipeline using index_id.
• Addressed NaN values in DataFrame columns when using regex, ensuring safe handling of nulls.
• Optimized DataFrame merge operations in Pandas to prevent column duplication and maintain relevant values.

Achievements

• Successfully resolved technical issues related to DataFrame operations, ensuring data integrity and process efficiency.
• Enhanced the data pipeline by implementing robust data enrichment and merging strategies.

Pending Tasks

• Further validation of the data pipeline to ensure all edge cases are handled effectively.
• Continuous monitoring of data integrity post-merge operations.