DataFrame Filtering and Data Integrity Checks

• Day: 2026-01-09
• Time: 15:55 to 16:05
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Pandas, Dataframe, Data Integrity, Python, Data Manipulation

Description

Session Goal: The session aimed to perform data manipulation and integrity checks using pandas DataFrames.

Key Activities:

1. Filtered a DataFrame to extract rows where the ‘relpath’ column contains ‘stage_D_materialize’, retrieving ‘structure’ and ‘bytes’ columns.
2. Identified missing ‘structure’ entries in a DataFrame by filtering for missing values and selecting specific columns.
3. Conducted a file manifest comparison to ensure data integrity by identifying missing and extra files.
4. Filtered a DataFrame for rows where the ‘stage’ column equals ‘E.reports’, retrieving and resetting the index for specific columns.
5. Extracted ledger-related columns from a DataFrame based on conditions in ‘name’ and ‘stage’.
6. Explored narrative business dynamics queries related to accounting storypacks.

Achievements:

• Successfully filtered and extracted relevant data from DataFrames for various conditions.
• Ensured data integrity through comprehensive file manifest comparisons.

Pending Tasks:

• Further exploration of narrative business dynamics queries for deeper insights into accounting storypacks.

Evidence

• source_file=2026-01-09.sessions.jsonl, line_number=19, event_count=0, session_id=44b27717e5cf5d0106d4d6621fe97f19d53fc027016ced176e7d14fd842092b0
• event_ids: []