I Reduced My Pandas Runtime by 95% — Here’s What I Was Doing Wrong

Pandas code that runs without errors can still be highly inefficient, especially on large datasets. The article highlights the importance of profiling to identify bottlenecks, such as using `%timeit` to measure execution time. A major performance killer is performing row-wise operations with `.iterrows()` or `.apply(axis=1)`, which should be replaced with vectorized column-based operations for massive speed improvements. Using functions like `np.where` for conditional logic is also shown to be thousands of times faster than row-wise application.