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Python stores numbers as either exact integers or approximate binary floats, and this distinction matters deeply in finance. Learn how type coercion works, when float precision breaks down, and the defensive patterns that keep your calculations honest.
Python's integers and floats look similar but behave differently, and mixing them carelessly leads to silent errors in financial calculations. Learn how to handle numbers correctly, when to use Decimal for money, and the pitfalls that catch most practitioners.
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.
Constants prevent silent bugs, make regulatory thresholds explicit, and transform magic numbers into readable code. Learn how to use them properly in finance calculations.
Print is your window into what your code is actually doing. In finance work, where a single error cascades through reports, print is not optional—it's your primary tool for validating logic before it goes live.
Good comments signal assumptions and protect your code from breaking when someone else reads it. Learn when to comment, what to say, and how to build the habit so your code is safe to hand over and clear enough to audit.
Alteryx and KNIME are the two platforms that keep coming up in enterprise analytics procurement conversations. This structured comparison cuts through the vendor material so you can make the call based on what your team actually needs.
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.
Every Python script that runs in production starts from the command line, not your IDE. Learn the five commands and the file path thinking you need to move your scripts from your laptop to a server that actually runs them.