Sure enough, adding that one key-value pair to “d” forced the dictionary to allocate the same amount of memory it had before, back when we first created it. If we add a new key-value pair to our dict, then if my theory is right, we should get back to the original size of 240 bytes: > d = 1 Meaning that we now have an “emptier than new” dictionary, taking up a paltry 72 bytes in our system. It would seem that when you run “dict.clear”, it removes not only all of the key-value pairs, but also that initial allocation of memory that is done for new, empty dictionaries. But we’re somehow using less memory than we even did at the start, when we created an empty dict! How can that be? Accessing elements of a list: We can access the elements of a list by their indexes. list1 4, 0.22, Hello, 1, 2, 3, -2.5, 0.22 Lists are mutable, they can be changed after they are created. A list is a sequence of items in an order. Wait a second here: After running “dict.clear”, our dict size is indeed 0. Lists Lists are one of the most commonly used data types in Python. I don’t use it very often, but it might at least provide us with some useful data: > d.clear() After all, why return memory to the system if you’re unlikely to need to do that? But it means that if you do allocate tons of memory to a dict, then you’re unlikely to get it back until the program ends, even if you remove items.īut wait: What if I remove everything from the dict? There’s a method, “ dict.clear“, that does this. Of course, given how rarely I find myself removing items from dicts in actual Python code, I’m not hugely surprised that this happens. In other words: Even though we’ve removed items from our dict, it hasn’t released the memory that it previously allocated. You can also see that after removing these name-value pairs from my dict, it is indeed empty. I thus created a list based on the keys, and iterated over that. Doing so would have led to an error, because changing a dictionary while iterating over it is a problem. Notice that in the above code, I didn’t iterate over “d” or “d.keys”. Add a color item to the dictionary by using the update() method. What happens if we remove items from our dictionary? Will it return memory to the system? Let’s find out: > for key in list(d.keys()): The argument must be a dictionary, or an iterable object with key:value pairs. But it doesn’t grow with each addition each time it needs more space, it allocates more than it needs, so that the allocations can be relative rare. Let’s say that we create a new, empty Python dictionary: > d = ')Īs you can see, the dictionary adds more key-value pairs, it needs more memory. Introduction to machine learning in Python.
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