As a senior engineer, you truly appreciate the unique characteristics of each programming language. When it comes to building a basic data structure from scratch, the choice of language truly matters.

Why is this so? Think about when we choose a financial strategy for instance; we consider risk tolerance, investment horizon, and financial goals equally as we would consider memory management, execution speed, and syntactical simplicity when choosing a programming language.

Python is often chosen for its simplicity and ease of understanding, much like a conservative investment strategy. It's ideal for scholars in the fields of AI and finance. This is partly due to rich library support such as NumPy, pandas for finance, and TensorFlow, PyTorch for AI. The simplicity of its syntax also helps to keep your data structures code readable and maintainable.

Our code example below demonstrates FizzBuzz using Python; it may seem simple, but it's a starting point for creating more complex data structures.

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if __name__ == "__main__":

  # Python logic here

  for i in range(1, 101):

    if i % 3 == 0 and i % 5 == 0:

        print("FizzBuzz")

    elif i % 3 == 0:

        print("Fizz")

    elif i % 5 == 0:

        print("Buzz")

    else:

        print(i)

​

  print("Now we are ready to build our data structure.")