AcademyAcademy
Matplotlib, Seaborn, and Plotly each suit a different moment in the treasury and risk workflow. This post shows you which to reach for using worked examples including yield curves, HQLA correlation heatmaps, and cash flow waterfalls.
Variables are how you store and update positions in Python, just as you track them in a treasury system. Learn to assign, name, and update variables so your finance code is clear, maintainable, and reflects real positions.
Jupyter Notebook is where you write Python code, see the output immediately, and document your reasoning all in one place. For treasury and regulatory reporting, it is the tool between learning and production.