As a seasoned programmer, you know that error handling and exceptions are an essential part of good code design. A program that cannot handle errors gracefully cannot be reliable, especially in critical sectors such as finance or AI, where error tollerance and recovery is key for maintaining strong, fault-tolerant systems.

Errors can come from a variety of sources in a data structure. They might occur when trying to add an invalid element, accessing or removing an item from an empty data structure, or performing a search or sort operation on unsortable data, among other scenarios. It's crucial that our data structure can inform users of these mishaps, ideally in an informative manner than simply crashing or returning undefined results.

In python, we handle errors using the 'try' and 'except' block. Code within the 'try' block is run normally, and if any error occurs while executing this code, the flow of control is passed to the 'except' clause, where we can handle the error or even raise an exception. Raising an exception means stopping normal execution of the program and informing the user that an error has occured. This can be done using the 'raise' keyword in python.

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class MyDataStructure:

    def __init__(self):

        self.data = []

​

    def add_element(self, element):

        try:

            # check if element is valid before adding it

            if not isinstance(element, (int, float)):

                raise ValueError('Element must be an integer or a floating point number!')

            else:

                self.data.append(element)

        except ValueError as ve:

            print(f'Error: {ve}')

​

if __name__ == '__main__':

    ds = MyDataStructure()

    ds.add_element('A')