Are dictionaries ordered in Python 3.6+?
They are insertion ordered[1]. As of Python 3.6, for the CPython implementation of Python, dictionaries remember the order of items inserted. This is considered an implementation detail in Python 3.6; you need to use OrderedDict
if you want insertion ordering that's guaranteed across other implementations of Python (and other ordered behavior[1]).
As of Python 3.7, this is no longer an implementation detail and instead becomes a language feature. From a python-dev message by GvR:
Make it so. "Dict keeps insertion order" is the ruling. Thanks!
This simply means that you can depend on it. Other implementations of Python must also offer an insertion ordered dictionary if they wish to be a conforming implementation of Python 3.7.
How does the Python 3.6
dictionary implementation perform better[2] than the older one while preserving element order?
Essentially, by keeping two arrays.
The first array, dk_entries
, holds the entries (of type PyDictKeyEntry
) for the dictionary in the order that they were inserted. Preserving order is achieved by this being an append only array where new items are always inserted at the end (insertion order).
The second, dk_indices
, holds the indices for the dk_entries
array (that is, values that indicate the position of the corresponding entry in dk_entries
). This array acts as the hash table. When a key is hashed it leads to one of the indices stored in dk_indices
and the corresponding entry is fetched by indexing dk_entries
. Since only indices are kept, the type of this array depends on the overall size of the dictionary (ranging from type int8_t
(1
byte) to int32_t
/int64_t
(4
/8
bytes) on 32
/64
bit builds)
In the previous implementation, a sparse array of type PyDictKeyEntry
and size dk_size
had to be allocated; unfortunately, it also resulted in a lot of empty space since that array was not allowed to be more than 2/3 * dk_size
full for performance reasons. (and the empty space still had PyDictKeyEntry
size!).
This is not the case now since only the required entries are stored (those that have been inserted) and a sparse array of type intX_t
(X
depending on dict size) 2/3 * dk_size
s full is kept. The empty space changed from type PyDictKeyEntry
to intX_t
.
So, obviously, creating a sparse array of type PyDictKeyEntry
is much more memory demanding than a sparse array for storing int
s.
You can see the full conversation on Python-Dev regarding this feature if interested, it is a good read.
In the original proposal made by Raymond Hettinger, a visualization of the data structures used can be seen which captures the gist of the idea.
For example, the dictionary:
d = {'timmy': 'red', 'barry': 'green', 'guido': 'blue'}
is currently stored as [keyhash, key, value]:
entries = [['--', '--', '--'],
[-8522787127447073495, 'barry', 'green'],
['--', '--', '--'],
['--', '--', '--'],
['--', '--', '--'],
[-9092791511155847987, 'timmy', 'red'],
['--', '--', '--'],
[-6480567542315338377, 'guido', 'blue']]
Instead, the data should be organized as follows:
indices = [None, 1, None, None, None, 0, None, 2]
entries = [[-9092791511155847987, 'timmy', 'red'],
[-8522787127447073495, 'barry', 'green'],
[-6480567542315338377, 'guido', 'blue']]
As you can visually now see, in the original proposal, a lot of space is essentially empty to reduce collisions and make look-ups faster. With the new approach, you reduce the memory required by moving the sparseness where it's really required, in the indices.
[1]: I say "insertion ordered" and not "ordered" since, with the existence of OrderedDict, "ordered" suggests further behavior that the `dict` object *doesn't provide*. OrderedDicts are reversible, provide order sensitive methods and, mainly, provide an order-sensive equality tests (`==`, `!=`). `dict`s currently don't offer any of those behaviors/methods.
[2]: The new dictionary implementations performs better **memory wise** by being designed more compactly; that's the main benefit here. Speed wise, the difference isn't so drastic, there's places where the new dict might introduce slight regressions ([key-lookups, for example][10]) while in others (iteration and resizing come to mind) a performance boost should be present.
Overall, the performance of the dictionary, especially in real-life situations, improves due to the compactness introduced.
**kwargs
and as such the wording used is diplomatic:**kwargs
in a function signature is now guaranteed to be an insertion-order-preserving mapping. They've used the term mapping in order to not force any other implementations to make the dict ordered (and use anOrderedDict
internally) and as a way to signal that this isn't supposed to depend on the fact that thedict
is not ordered. – Dimitris Fasarakis Hilliard