And also, you should also control the memory and CPU usage, as it can point you towards new portions of code that could be improved. Therefore, in this post I’ll comment on 7 different Python tools that give you some insight about the execution time of your functions and the Memory and CPU usage. Use a decorator to time your functions. Python Memory Management and Tips Transcripts Chapter: Memory and classes Lecture: Slots are faster, not just smaller Learn more about this course Login or purchase this course to watch this video and the rest of the course contents. 0:00 The final example when we're talking about. Python Slots it. For instance, you may get a $25 no deposit bonus with a 30x wagering requirement. This means you will have to wager a total of Python Slots $750 – 30 times $25 – to cashout the maximum cap winning amount. You can see that the memory layout is vastly different than the C layout from before. Instead of x owning the block of memory where the value 2337 resides, the newly created Python object owns the memory where 2337 lives. The Python name x doesn’t directly own any memory address in the way the C variable x owned a static slot in memory.
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Decorator to add __slots__ in dataclasses
Python3.7 provides dataclasses module for faster class creation (PEP 557).Unfortunately there's no support for __slots__. If you want to create more memory efficient instances, you need todo it by yourself or use dataslots.dataslots decorator.
As described in docs, in derived class __dict__ is created, because base class does not have __slots__.Slots are created from all defined properties (returned by dataclasses.fields() function).
With __slots__ it's possible to define read-only class variables. When using dataclasses you cannot provide typefor attribute or use typing.ClassVar to declare one.
Because of an issue 36424 you need custom __setstate__ method. In dataslots there isimplemented default version and it is used if decorated class has no __getstate__ and __setstate__ function declared.
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This post describes the CPython implementation of the list object.
Lists in Python are powerful and it is interesting to see how they are implemented internally.
Following is a simple Python script appending some integers to a list and printing them.
As you can see, lists are iterable.
A list object in CPython is represented by the following C structure. “ob_item” is an array of pointers to the list elements. “allocated” is the number of slots allocated in memory.
Let’s look at what happens when we initialize an empty list. e.g. l = [].
It is important to notice the difference between allocated slots and the size of the list. The size of a list is the same as len(l). The number of allocated slots is what has been allocated in memory. Often, you will see that allocated can be greater than size. This is to avoid needing calling realloc each time a new elements is appended to the list. We will see more about that later.
We append an integer to the list: l.append(1). What happens? The internal C function app1() is called:
Let’s look at list_resize(). It over-allocates memory to avoid calling list_resize too many times. The growth pattern of the list is: 0, 4, 8, 16, 25, 35, 46, 58, 72, 88, …
4 slots are now allocated to contain elements and the first one is the integer 1. You can see on the following diagram that l[0] points to the integer object that we just appended. The dashed squares represent the slots allocated but not used yet.
Append operation amortized complexity is O(1).
We continue by adding one more element: l.append(2). list_resize is called with n+1 = 2 but because the allocated size is 4, there is no need to allocate more memory. Same thing happens when we add 2 more integers: l.append(3), l.append(4). The following diagram shows what we have so far.
Let’s insert a new integer (5) at position 1: l.insert(1,5) and look at what happens internally. ins1() is called:
The dashed squares represent the slots allocated but not used yet. Here, 8 slots are allocated but the size or length of the list is only 5.
Insert operation complexity is O(n).
When you pop the last element: l.pop(), listpop() is called. list_resize is called inside listpop() and if the new size is less than half of the allocated size then the list is shrunk.
Pop operation complexity is O(1).
You can observe that slot 4 still points to the integer but the important thing is the size of the list which is now 4.
Let’s pop one more element. In list_resize(), size – 1 = 4 – 1 = 3 is less than half of the allocated slots so the list is shrunk to 6 slots and the new size of the list is now 3.
You can observe that slot 3 and 4 still point to some integers but the important thing is the size of the list which is now 3.
Python list object has a method to remove a specific element: l.remove(5). listremove() is called.
To slice the list and remove the element, list_ass_slice() is called and it is interesting to see how it works. Here, low offset is 1 and high offset is 2 as we are removing the element 5 at position 1.
Remove operation complexity is O(n).
That’s it for now. I hope you enjoyed the article.