Why loops are integral to building thorough statistical models

Loops are quite an important part of learning how to code in Python, and this is particularly true when it comes to implementing calculations across a large array of numbers.

All too often, the temptation for statisticians and data scientists is to skip over the more mundane aspects of coding such as this — we assume that software engineers can simply reformat the code in the proper way.

However, there are many situations where the person writing the code needs to understand both the statistics underlying the model as well as how to iterate the model output through loops — these two processes simply cannot be developed independently.

Here is one example of how the use of for loops in Python can greatly enhance statistical analysis.

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