Implemented DataFrame boolean count with Pandas

• Day: 2023-04-25
• Time: 14:00 to 14:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Pandas, Data Analysis, Boolean Columns, Python, Dataframe

Description

Session Goal

The goal of this session was to implement a method using Pandas to group data by boolean columns and count the number of projects with scores above a certain threshold.

Key Activities

• Utilized the groupby method in Pandas to group data by boolean columns.
• Created a new DataFrame to count occurrences of projects with scores above 0.5 for specified boolean columns.
• Updated the code to replace the deprecated .append() method with pd.concat() for aggregating data.
• Implemented logic to count projects with scores above and below 0.5, and calculated corresponding percentages.

Achievements

• Successfully implemented a method to count and aggregate project scores using boolean columns in a DataFrame.
• Replaced deprecated methods with current best practices, ensuring code maintainability and performance.

Pending Tasks

• Review and test the implemented code in a larger dataset to ensure scalability and accuracy.

Evidence

• source_file=2023-04-25.sessions.jsonl, line_number=0, event_count=0, session_id=88eae61ce9210c56047760e1cb27428deae33596d957494f981e1fb3d4d371de
• event_ids: []