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, Finance
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to enhance data processing and visualization techniques for financial data, focusing on streamlining processes and improving clarity in visual outputs.

Key Activities:

• Developed a function to streamline data processing for financial tickers, encapsulating multiple steps into a single callable function for efficiency and maintainability.
• Created a process_stock_data function to fetch and enhance stock data, adding residuals and return columns for comprehensive analysis.
• Explored combining line plots and candlestick charts in Matplotlib and mplfinance, detailing the setup for separate y-scales and ensuring clarity in visualization.
• Addressed the SettingWithCopyWarning in Pandas, providing solutions to avoid it using the .copy() method and .loc[] for assignments.
• Improved DataFrame modification practices by emphasizing the creation of copies and proper use of .loc[] for stable behavior.
• Resolved Unix timestamp issues in date handling for line plots, ensuring dates are parsed correctly using pandas.
• Enhanced date display on Matplotlib x-axis by customizing date formats and rotating labels for better readability.

Achievements:

• Successfully streamlined data processing functions for financial data.
• Improved visualization techniques, combining different chart types for better insights.
• Clarified and resolved common data manipulation warnings and issues in Pandas.

Pending Tasks:

• Further testing and validation of the combined chart techniques in different scenarios.
• Exploration of additional visualization libraries for potential enhancements.