Python list implementation

March 10, 2011

This post describes the CPython implementation of the list object. CPython is the most used Python implementation.

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.

>>> l = []
>>> l.append(1)
>>> l.append(2)
>>> l.append(3)
>>> l
[1, 2, 3]
>>> for e in l:
...   print e
... 
1
2
3

As you can see, lists are iterable.

List object C structure

A list object in CPython is represented by the following C structure. ob_item is a list of pointers to the list elements. allocated is the number of slots allocated in memory.

typedef struct {
    PyObject_VAR_HEAD
    PyObject **ob_item;
    Py_ssize_t allocated;
} PyListObject;

List initialization

Let’s look at what happens when we initialize an empty list. e.g. l = [].

arguments: size of the list = 0
returns: list object = []
PyListNew:
    nbytes = size * size of global Python object = 0
    allocate new list object
    allocate list of pointers (ob_item) of size nbytes = 0
    clear ob_item
    set list's allocated var to 0 = 0 slots
    return list object 

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.

Append

We append an integer to the list: l.append(1). What happens? The internal C function app1() is called:

arguments: list object, new element
returns: 0 if OK, -1 if not
app1:
    n = size of list
    call list_resize() to resize the list to size n+1 = 0 + 1 = 1
    list[n] = list[0] = new element
    return 0

Let’s look at list_resize(). It over-allocates memory to avoid calling list_resize too many time. The growth pattern of the list is: 0, 4, 8, 16, 25, 35, 46, 58, 72, 88, …

arguments: list object, new size
returns: 0 if OK, -1 if not
list_resize:
    new_allocated = (newsize >> 3) + (newsize < 9 ? 3 : 6) = 3
    new_allocated += newsize = 3 + 1 = 4
    resize ob_item (list of pointers) to size new_allocated
    return 0

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 complexity is O(1).

Python lists

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.

Python lists

Insert

Let’s insert a new integer (5) at position 1: l.insert(1,5) and look at what happens internally. ins1() is called:

arguments: list object, where, new element
returns: 0 if OK, -1 if not
ins1:
    resize list to size n+1 = 5 -> 4 more slots will be allocated
    starting at the last element up to the offset where, right shift each element 
    set new element at offset where
    return 0

Python lists

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).

Pop

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.

arguments: list object
returns: element popped
listpop:
    if list empty:
        return null
    resize list with size 5 - 1 = 4. 4 is not less than 8/2 so no shrinkage
    set list object size to 4
    return last element

Pop operation complexity is O(1).

Python lists

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 lists

Remove

Python list object has a method to remove a specific element: l.remove(5). listremove() is called.

arguments: list object, element to remove
returns none if OK, null if not
listremove:
    loop through each list element:
        if correct element:
            slice list between element's slot and element's slot + 1
            return none
    return null

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.

arguments: list object, low offset, high offset
returns: 0 if OK
list_ass_slice:
    copy integer 5 to recycle list to dereference it
    shift elements from slot 2 to slot 1
    resize list to 5 slots
    return 0

Remove operation complexity is O(n).

Python lists

That’s it for now. I hope you enjoyed the article. Please write a comment if you have any feedback. If you need help with a project written in Python or with building a new web service, I am available as a freelancer: LinkedIn profile. Follow me on Twitter @laurentluce.

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posted in Uncategorized by Laurent Luce

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13 Comments to "Python list implementation"

  1. TechnoBits.net wrote:

    Very good article, uncovers the most used feature in python.

  2. klonuo wrote:

    Very interesting article

    I read it in my RSS reader as part of “Planet Python” feeds and was then “forced” to launch Firefox and see your blog :)

    I hope for other interesting post in future

    Cheers

  3. Ivan wrote:

    Hi,

    Could you give us an idea of the running time of these operations?

    O(1) for append ?
    O(n) for insert ?

    And what would be the total running time to append 1000 elements to a [] taking into account operations wasted on growing the list?

  4. Laurent Luce wrote:

    @Ivan: I updated the article with the following information. Append is O(1), Insert is O(n), Pop is O(1), Remove is O(n). Sort is O(n log n). You can see more of those here.

    Appending n elements is O(n). For n = 1000, the list is going to be resized 27 times. The growth pattern is the following: 4, 8, 16, 25, 35, 46, 58, 72, 88, 106, 126, 148, 173, 201, 233, 269, 309, 354, 405, 462, 526, 598, 679, 771, 874, 990, 1120. This means 27 calls to realloc. It doesn’t change the complexity. It is still O(n).

  5. tem wrote:

    L.pop([index]) -> item — remove and return item at index.

    I’m guessing pop is O(n) if you pop the first element.

  6. Ganesh wrote:

    Can you explain how is list implemented so as it can hold different datatypes.

  7. Laurent Luce wrote:

    A list object contains a list of pointers to PyObjects. A PyObject contains a pointer to the corresponding type object. See object.h in the Python source code for more details.

  8. Jan wrote:

    Very interesting reading indeed, thanks a lot :-) I found an article about list comlexity, but these memory pictures are really helpful from the point of view of implementation

  9. dan wrote:

    tem: agreed. I think the python wiki article can be misleading if you don’t read carefully. pop(0) becomes equivalent to remove, which is O(n) for a basic list.

  10. Leo Dirac wrote:

    I don’t see how append can always be O(1). When resize is needed, sometimes it will need to copy the old list of pointers into the newly allocated space, right? The C function realloc can sometimes just expand the block of memory in place, but sometimes needs to copy it. I think append will be O(N) sometimes, with an amortized rate something less than that. I don’t exactly follow the resize formula, but it looks like it’s growing quadratically rather than exponentially which means the amortized cost would be O(sqrt N) rather than O(log N).

  11. Laurent Luce wrote:

    @Leo: From the Python wiki on operations time complexity: “These operations rely on the Amortized part of Amortized Worst Case. Amortized worst case of append is O(1). Individual actions may take surprisingly long, depending on the history of the container. Internally, a list is represented as an array; the largest costs come from growing beyond the current allocation size (because everything must move), or from inserting or deleting somewhere near the beginning (because everything after that must move). If you need to add/remove at both ends, consider using a collections.deque instead.”

  12. Laurent wrote:

    Question just for the sake of understanding the internals: does it make any difference between calling l += [4] and l.append(4) ?

    It looks like the former is a slightly heavier (albeit not significantly) as it’s creating a new list and merging the two lists whereas the latter directly calls INPLACE_ADD (from what dis.dis is saying)

  13. arete wrote:

    hii Laurent Luce’

    Most used feature in python

    Really exciting reading through without a doubt, thank you a great deal I found a write-up in relation to record complexity, however these kind of memory pics are actually beneficial from the point of view of rendering.

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