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I’m confident that many of you, as Python developers, have often resorted to the use of the print() function as a quick and convenient method for debugging your code. In the context of small-scale projects or during initial development stages, this approach typically suffices and may even be considered a preferred debugging technique due to its simplicity and immediacy.
However, as projects grow in size and complexity, relying solely on print() statements for debugging can present significant challenges. In larger-scale projects, the sheer volume of print statements scattered throughout the codebase can quickly clutter the output, creating what feels like an overwhelming sea of information.
This deluge of print output not only makes it arduous to pinpoint the relevant information needed for debugging but also introduces noise that can obscure critical insights into the program’s behavior.
As a result, developers may find themselves spending valuable time sifting through endless lines of print output, struggling to isolate the root cause of issues amidst the chaos.
In such scenarios, the effectiveness of the traditional print() debugging approach diminishes considerably, and alternative debugging techniques become indispensable.
