Implemented stock data processing and visualization workflow

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Implemented stock data processing and visualization workflow

  • Day: 2024-01-15
  • Time: 18:30 to 19:00
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Matplotlib, Dataframe, Python, Visualization, Stock Analysis

Description

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:

Pending Tasks:

  • Further refine the plotting functions to handle different data structures and analysis objectives.
  • Explore additional visualization techniques for enhanced data insights.

Evidence

  • source_file=2024-01-15.sessions.jsonl, line_number=4, event_count=0, session_id=2dd1a0bba0e8f19939e4150ff6814f289d2cb72ec09a9f8a1e7889ceabcb0e85
  • event_ids: []

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