Python that does real work in a finance team.
String membership lets you check whether text exists inside a larger string using Python's `in` and `not in` operators. Master this technique to validate transactions, filter counterparties, and build robust compliance checks in your finance workflows.
String concatenation is how you build the text your finance code actually produces: alerts, confirmations, reports. Master f strings, the join method, and when to use each one.
Strings in Python cannot be changed after creation. Learn why immutability makes your code safer, and how to transform text using split, join, replace, and slicing.
String slicing extracts substrings from fixed format financial data like ISINs, trade dates, and account codes. Master Python's zero based indexing and exclusive upper bound to parse structured text efficiently.
String indexing lets you pull individual characters from text by position. Master zero based counting and bracket notation to parse trade confirmations, regulatory files, and identifiers with confidence.
Strings are the foundation of text processing in treasury work. Learn how to index, slice, validate, and transform the transaction references, counterparty names, and regulatory tags that flow through your systems every day.
NumPy, SciPy, and Pandas are the foundation of numerical work in treasury and risk. Learn when to reach for each one, how to install them properly, and how they work together on real portfolios.
Python's Decimal module stores decimal numbers exactly, not as binary approximations. This matters in treasury work where rounding errors in interest accruals, position valuations, and regulatory calculations can breach thresholds or break audit trails.
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.
