Enhanced Data Processing and Visualization Techniques

📅 2024-01-15 — Session: Enhanced Data Processing and Visualization Techniques

🕒 18:10–18:30
🏷️ Labels: Data Processing, Visualization, Python, Pandas, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to enhance data processing functions for financial tickers and improve data visualization techniques using Python libraries.

Key Activities:

• Developed a streamlined function for processing stock data, which includes fetching and enhancing data with additional columns for residuals and returns.
• Explored combining line plots and candlestick charts using Matplotlib and mplfinance, providing insights on setting up twin axes for better visualization.
• Addressed the SettingWithCopyWarning in Pandas, offering solutions to avoid it by using .copy() and .loc[] methods.
• Improved practices for DataFrame modifications in Pandas to ensure stable behavior and prevent warnings.
• Resolved issues with Unix timestamp handling in date visualizations, ensuring proper parsing to datetime objects.
• Enhanced the readability of date labels on Matplotlib x-axis by customizing date formats and label rotations.

Achievements:

• Successfully designed a comprehensive data processing function for stock data.
• Developed clear guidelines for combining different chart types in data visualization.
• Implemented best practices for DataFrame manipulation to avoid common warnings.
• Improved date handling and display in visualizations, enhancing clarity and accuracy.

Pending Tasks:

• Further testing and validation of the new data processing function in different scenarios.
• Exploration of additional visualization libraries for more complex data representations.