Enhanced DataFrame filtering for financial analysis

• Day: 2025-02-06
• Time: 16:20 to 17:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Pandas, Dataframe, Finance, Function

Description

Session Goal

The goal of this session was to enhance the modularity and readability of Python functions used for filtering financial ledger data within a Pandas DataFrame.

Key Activities

• Developed a Python function to extract rent-related records from a financial ledger DataFrame.
• Implemented the get_from_ledger function to filter DataFrames based on specified criteria using predefined masks.
• Updated the get_from_ledger function to improve logic flexibility and maintainability.
• Enhanced the compute_transaction_series function to compute time-series data with flexible filtering and aggregation options.
• Addressed the SettingWithCopyWarning in Pandas by ensuring proper DataFrame copy handling.

Achievements

• Successfully modularized and enhanced the readability of functions for financial data filtering.
• Improved the flexibility and maintainability of the get_from_ledger function.
• Resolved common warnings in Pandas to ensure robust data processing.

Pending Tasks

• Further testing and validation of the updated functions in different financial scenarios.

Evidence

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• event_ids: []