📅 2024-01-15 — Session: Implemented stock data processing and visualization workflow
🕒 18:30–19:00
🏷️ Labels: Matplotlib, Dataframe, Python, Visualization, Stock Analysis
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The session aimed to address date formatting issues in Matplotlib plots and develop a comprehensive workflow for processing and analyzing stock data using Python.
Key Activities:
- Manual Plotting with Matplotlib: Addressed x-axis date formatting issues by manually plotting DataFrame columns, ensuring better control over data visualization.
- Workflow Planning: Outlined a plan to create a workflow for processing and analyzing stock data, including defining functions for data processing and plotting.
- Function Implementation: Implemented functions for processing stock data and plotting analysis using Matplotlib, including date parsing and setting dates as an index.
- Atlas Creation: Developed a method for creating an ‘atlas’ of business cases from stock data, involving sampling, sorting, and visualizing data in Markdown format.
- Data Conversion: Provided a solution for converting Excel-style date integers to Pandas Timestamps using
pandas.to_datetime().
Achievements:
- Successfully implemented a workflow for stock data processing and visualization.
- Resolved date formatting issues in Matplotlib plots.
- Developed reusable functions for data processing and plotting.
Pending Tasks:
- Further refine the plotting functions to handle different data structures and analysis objectives.
- Explore additional visualization techniques for enhanced data insights.