The Dangerous Workaround: Why You Should Never Use .iloc for Conditional Filtering in Pandas
Imagine spending days building a complex data engineering pipeline. You run your tests, the code executes without a single error, and the final data frame outputs smoothly. You push it to production. A week later, your financial reporting team notices that revenue metrics are completely skewed, customer profiles are cross-contaminated, and the wrong users are targeted for an automated email...
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