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How to choose data structures

As we've seen, Python provides you with several built-in data types and sometimes, if you're not that experienced, choosing the one that serves you best can be tricky, especially when it comes to collections. For example, say you have many dictionaries to store, each of which represents a customer. Within each customer dictionary, there's an 'id': 'code' unique identification code. In what kind of collection would you place them? Well, unless I know more about these customers, it's very hard to answer. What kind of access will I need? What sort of operations will I have to perform on each of them, and how many times? Will the collection change over time? Will I need to modify the customer dictionaries in any way? What is going to be the most frequent operation I will have to perform on the collection?

If you can answer the preceding questions, then you will know what to choose. If the collection never shrinks or grows (in other words, it won't need to add/delete any customer object after creation) or shuffles, then tuples are a possible choice. Otherwise, lists are a good candidate. Every customer dictionary has a unique identifier though, so even a dictionary could work. Let me draft these options for you:

# example customer objects 
customer1 = {'id': 'abc123', 'full_name': 'Master Yoda'} 
customer2 = {'id': 'def456', 'full_name': 'Obi-Wan Kenobi'} 
customer3 = {'id': 'ghi789', 'full_name': 'Anakin Skywalker'} 
# collect them in a tuple 
customers = (customer1, customer2, customer3) 
# or collect them in a list 
customers = [customer1, customer2, customer3] 
# or maybe within a dictionary, they have a unique id after all 
customers = { 
    'abc123': customer1, 
    'def456': customer2, 
    'ghi789': customer3, 
}

Some customers we have there, right? I probably wouldn't go with the tuple option, unless I wanted to highlight that the collection is not going to change. I'd say usually a list is better, as it allows for more flexibility.

Another factor to keep in mind is that tuples and lists are ordered collections. If you use a dictionary (prior to Python 3.6) or a set, you lose the ordering, so you need to know if ordering is important in your application.

What about performances? For example, in a list, operations such as insertion and membership can take O(n), while they are O(1) for a dictionary. It's not always possible to use dictionaries though, if we don't have the guarantee that we can uniquely identify each item of the collection by means of one of its properties, and that the property in question is hashable (so it can be a key in dict).

If you're wondering what O(n) and O(1) mean, please Google big O notation. In this context, let's just say that if performing an operation Op on a data structure takes O(f(n)), it would mean that Op takes at most a time t ≤ c * f(n) to complete, where c is some positive constant, n is the size of the input, and f is some function. So, think of O(...) as an upper bound for the running time of an operation (it can be used also to size other measurable quantities, of course).

Another way of understanding if you have chosen the right data structure is by looking at the code you have to write in order to manipulate it. If everything comes easily and flows naturally, then you probably have chosen correctly, but if you find yourself thinking your code is getting unnecessarily complicated, then you probably should try and decide whether you need to reconsider your choices. It's quite hard to give advice without a practical case though, so when you choose a data structure for your data, try to keep ease of use and performance in mind and give precedence to what matters most in the context you are in.