For Python developers who work primarily with data, it’s hard not to find yourself constantly knee-deep in SQL and Python’s open source data library, pandas. Despite how easy these tools have made it to manipulate and transform data—sometimes as concisely as one line of code—analysts still must always understand their data and what their code means. Even calculating something as simple as summary statistics can be prone to serious mistakes.
