📅 2025-07-05 — Session: Enhanced Financial Data Analysis with Pandas
🕒 21:55–22:15
🏷️ Labels: Data_Analysis, Financial_Data, Python, Pandas, Visualization
📂 Project: Accounting
⭐ Priority: MEDIUM
Session Goal
The session aimed to refine the processing and analysis of financial data files using Python and Pandas, focusing on data cleaning, aggregation, and visualization.
Key Activities
- Conducted a diagnostic overview of financial data files to assess their status and outline next steps for aggregation.
- Developed a Python script to analyze monthly inflow and outflow from ledger files, employing Pandas for data loading, cleaning, and summarization.
- Addressed issues with non-datetime columns in DataFrames, providing solutions for date coercion and error handling.
- Resolved problems with timezone-aware datetimes by converting them to naive datetimes for consistent processing.
- Implemented robust date handling in Pandas to manage mixed-type date columns and prevent silent failures.
- Solved mixed timezone offsets in
posted_datecolumns by converting timestamps to a uniform UTC format. - Created bar chart visualizations for financial flows using Matplotlib, including data pivoting and looping through files.
Achievements
- Successfully cleaned and processed financial data, resolving datetime and timezone issues.
- Enhanced data visualization capabilities with clear financial flow representations.
Pending Tasks
- Further refinement of data aggregation methods to improve efficiency.
- Exploration of additional visualization techniques for more insightful analysis.