Python’s simplicity makes it a favorite for rapid development, but performance often becomes a bottleneck once projects scale.

Large datasets, complex loops, or real-time applications can quickly expose limitations.

Some data professionals rely on vectorization with NumPy and Pandas, others parallelize tasks with multiprocessing or libraries like Dask, and in some cases, performance-critical parts are rewritten in Cython or even integrated with Rust.

The real challenge is balancing raw speed with code readability, maintainability, and deployment complexity.