From Shape-Shifting Lists to Bulletproof Tuples
Python provides different data structures, each with unique properties that significantly impact how your code behaves. A key distinction every Python developer should internalize is mutable vs immutable objects, it changes how arguments are passed, how shared state behaves, and how to reason about subtle bugs.
This post walks through the difference, the canonical examples (lists, dicts, sets vs tuples, strings, frozensets), and the gotchas that surface most often in data-science code: mutating a default argument, copying-by-reference inside DataFrames, and the surprising performance implications of immutability.