📅 2023-02-14 — Session: Enhanced Python scripts for data extraction and graph visualization
🕒 04:25–04:55
🏷️ Labels: Python, Data Extraction, Graph Visualization, Pandas, Networkx
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to enhance Python scripts for data extraction, manipulation, and visualization, focusing on improving file handling, DataFrame operations, and graph visualization using NetworkX.
Key Activities:
- Corrected Python code for extracting filenames and IO information from Jupyter Notebook files, ensuring proper appending to
current_inputs. - Implemented logic to explode lists within Pandas DataFrame columns, transforming them into multiple rows for better data manipulation.
- Modified data processing workflow to explode DataFrame before concatenation, optimizing data handling.
- Enhanced script for detecting file operations, improving pattern recognition for CSV and geospatial files.
- Developed a directed graph using NetworkX to represent relationships between data files and notebooks, including nodes and edges.
- Visualized graphs using NetworkX and Matplotlib, employing flow diagram layouts and addressing module limitations for graph visualization.
Achievements:
- Successfully corrected and optimized Python scripts for file handling and data extraction.
- Improved data manipulation techniques using Pandas, enhancing DataFrame processing.
- Created and visualized directed graphs with NetworkX, effectively representing data relationships.
Pending Tasks:
- Further exploration of advanced graph visualization techniques and optimization strategies for large datasets.
- Review and refine file operation detection logic for broader file type support.