Extra Dictionary classes and utilities for Python

Some Mapping containers and tools for daily use with Python. This attempts to be a small package with no dependencies, just delivering its data-types as described bellow enough tested for production-usage.

VersionDict

A Python Mutable Mapping Container (dictionary :-) ) that can "remember" previous values. Use it wherever you would use a dict - at each key change or update, it's version attribute is increased by one.

Special and modified methods:

.get method is modified to receive an optional named version parameter that allows one to retrieve for a key the value it contained at that respective version. NB. When using the version parameter, get will raise a KeyError if the key does not exist for that version and no default value is specified.

.copy(version=None): yields a copy of the current dictionary at that version, with history preserved (if version is not given, the current version is used)

.freeze(version=None) yields a snapshot of the versionDict in the form of a plain dictionary for the specified version

Implementation:

It works by internally keeping a list of (named)tuples with (version, value) for each key.

Example:

>>> from extradict import VersionDict
>>> a = VersionDict(b=0)
>>> a["b"] = 1
>>> a["b"]
1
>>> a.get("b", version=0)
0

For extra examples, check the "tests" directory

OrderedVersionDict

Inherits from VersionDict, but preserves and retrieves key insertion order. Unlike a plain "collections.OrderedDict", however, whenever a key's value is updated, it is moved last on the dictionary order.

Example:

>>> from collections import OrderedDict
>>> a = OrderedDict((("a", 1), ("b", 2), ("c", 3)))
>>> list(a.keys())
>>> ['a', 'b', 'c']
>>> a["a"] = 3
>>> list(a.keys())
>>> ['a', 'b', 'c']

>>> from extradict import OrderedVersionDict
>>> a = OrderedVersionDict((("a", 1), ("b", 2), ("c", 3)))
>>> list(a.keys())
['a', 'b', 'c']
>>> a["a"] = 3
>>> list(a.keys())
['b', 'c', 'a']

MapGetter

A Context manager that allows one to pick variables from inside a dictionary, mapping, or any Python object by using the from <myobject> import key1, key2 statement.

>>> from extradict import MapGetter
>>> a = dict(b="test", c="another test")
>>> with MapGetter(a) as a:
...     from a import b, c
...
>>> print (b, c)
test another test

Or:

>>> from collections import namedtuple
>>> a = namedtuple("a", "c d")
>>> b = a(2,3)
>>> with MapGetter(b):
...     from b import c, d
>>> print(c, d)
2, 3

It works with Python 3.4+ "enum"s - which is great as it allow one to use the enums by their own name, without having to prepend the Enum class every time:

>>> from enum import Enum

>>> class Colors(tuple, Enum):
...     red = 255, 0, 0
...     green = 0, 255, 0
...     blue = 0, 0, 255
...

>>> with MapGetter(Colors):
...    from Colors import red, green, blue
...

>>> red
<Colors.red: (255, 0, 0)>
>>> red[0]
255

MapGetter can also have a default value or callable which will generate values for each name that one tries to "import" from it:

>>> with MapGetter(default=lambda x: x) as x:
...    from x import foo, bar, baz
...

>>> foo
'foo'
>>> bar
'bar'
>>> baz
'baz'

If the parameter default is not a callable, it is assigned directly to the imported names. If it is a callable, MapGetter will try to call it passing each name as the first and only positional parameter. If that fails with a type error, it calls it without parameters the way collections.defaultdict works.

The syntax from <mydict> import key1 as var1 works as well.

BijectiveDict

This is a bijective dictionary for which each pair key, value added is also added as value, key.

The explicitly inserted keys can be retrieved as the "assigned_keys" attribute - and a dictionary copy with all such keys is available at the "BijectiveDict.assigned". Conversely, the generated keys are exposed as "BijectiveDict.generated_keys" and can be seen as a dict at "Bijective.generated"

>>> from extradict import BijectiveDict
>>>
>>> a = BijectiveDict(b = 1, c = 2)
>>> a
BijectiveDict({'b': 1, 2: 'c', 'c': 2, 1: 'b'})
>>> a[2]
'c'
>>> a[2] = "d"
>>> a["d"]
2
>>> a["c"]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/gwidion/projetos/extradict/extradict/reciprocal_dict.py", line 31, in __getitem__
    return self._data[item]
KeyError: 'c'
>>>

namedtuple

Alternate, clean room, implementation of 'namedtuple' as in stdlib's collection.namedtuple . This does not make use of "eval" at runtime - and can be up to 10 times faster to create a namedtuple class than the stdlib version.

Instead, it relies on closures to do its magic.

However, these will be slower to instantiate than stdlib version. The "fastnamedtuple" is faster in all respects, although it holds the same API for instantiating as tuples, and performs no length checking.

fastnamedtuple

Like namedtuple but the class returned take an iterable for its values rather than positioned or named parameters. No checks are made towards the iterable length, which should match the number of attributes It is faster for instantiating as compared with stdlib's namedtuple

defaultnamedtuple

Implementation of named-tuple using default parameters - Either pass a sequence of 2-tuples (or an OrderedDict) as the second parameter, or send in kwargs with the default parameters, after the first. (This takes advantage of python3.6 + guaranteed ordering of **kwargs for a function see https://docs.python.org/3.6/whatsnew/3.6.html)

The resulting object can accept positional or named parameters to be instantiated, as a normal namedtuple, however, any omitted parameters are used from the original mapping passed to it.

FallbackNormalizedDict

Dictionary meant for text only keys: will normalize keys in a way that capitalization, whitespace and punctuation will be ignored when retrieving items.

A parallel dictionary is maintained with the original keys, so that strings that would clash on normalization can still be used as separated key/value pairs if original punctuation is passed in the key.

Primary use case if for keeping translation strings when the source for the original strings is loose in terms of whitespace/punctuation (for example, in an http snippet)

NormalizedDict

Dictionary meant for text only keys: will normalize keys in a way that capitalization, whitespace and punctuation will be ignored when retrieving items.

Unlike FallbackNormalizedDict this does not keep the original version of the keys.

TreeDict

A Python mapping with an underlying auto-balancing binary tree data structure. As such, it allows seeking ranges of keys - so, that `mytreedict["aa":"bz"] will return a list with all values in the dictionary whose keys are strings starting from "aa" up to those starting with "by".

It also features a .get_closest_keys method that will retrieve the closest existing keys for the required element.

>>> from extradict import TreeDict
>>> a = TreeDict()
>>> a[1] = "one word"
>>> a[3] = "another word"
>>> a[:]
['one word', 'another word']
>>> a.get_closest_keys(2)
(1, 3)

Another feature of these dicts is that as they do not rely on an object hash, any Python object can be used as a key. Of course key objects should be comparable with <=, ==, >=. If they are not, errors will be raised. HOWEVER, there is an extra feature - when creating the TreeDict a named argument key parameter can be passed that works the same as Python's sorted "key" parameter: a callable that will receive the key/value pair as its sole argument and should return a comparable object. The returned object is the one used to keep the Binary Tree organized.

If the output of the given key_func ties, that is it: the new pair simply overwrites whatever other key/value had the same key_func output. To avoid that, craft the key_funcs so that they return a tuple with the original key as the second item:

>>> from extradict import TreeDict
>>> b = TreeDict(key=len)
>>> b["red"] = 1
>>> b["blue"] = 2
>>> b
TreeDict('red'=1, 'blue'=2, key_func= <built-in function len>)

>>> b["1234"] = 5
>>> b
TreeDict('red'=1, '1234'=5, key_func= <built-in function len>)

>>> TreeDict(key=lambda k: (len(k), k))
>>> b["red"] = 1
>>> b["blue"] = 2
>>> b["1234"] = 5
>>> b
>>> TreeDict('red'=1, '1234'=5, 'blue'=2, key_func= <function <lambda> at 0x7fbc7f462320>)

PlainNode and AVLNode

To support the TreeDict mapping interface, the standalone PlainNode and AVLNode classes are available at the extradict.binary_tree_dict module - and can be used to create a lower level tree data structure, which can have more capabilities. For one, the "raw" use allows repeated values in the Nodes, all Nodes are root to their own subtrees and know nothing of their parents, and if one wishes, no need to work with "key: value" pairs: if a "pair" argument is not supplied to a Node, it reflects the given Key as its own value.

PlainNode will build non-autobalancing trees, while those built with AVLNode will be self-balancing. Trying to manually mix node types in the same tree, or changing the key_func in different notes, will obviously wreck everything.

Grouper

Think of it as an itertools.groupby which returns a mapping Or as an itertools.tee that splits the stream into filtered substreams according to the passed key-callable.

Given an iterable and a key callable, each element in the iterable is run through the key callable and made available in an iterator under a bucket using the resulting key-value.

The source iterable need not be ordered (unlike itertools.groupby). If no key function is given, the identity function is used.

The items will be made available under the iterable-values as requested, in a lazy way when possible. Note that several different method calls may precipatate an eager processing of all items in the source iterator: .keys() or len(), for example.

Whenever a new key is found during input consumption, a "Queue" iterator, which is a thin wrapper over collections.deque is created under that key and can be further iterated to retrieve more elements that map to the same key.

In short, this is very similar to itertools.tee, but with a filter so that each element goes to a mapped bucket.

Once created, the resulting object may obtionally be called. Doing this will consume all data in the source iterator at once, and return a a plain dictionary with all data fetched into lists.

For example, to divide a sequence of numbers from 0 to 10 in 5 buckets, all one need to do is: Grouper(myseq, lambda x: x // 2)

Or:

>>> from extradict import Grouper
>>> even_odd = Grouper(range(10), lambda x: "even" if not x % 2 else "odd")
>>> print(list(even_odd["even"]))
[0, 2, 4, 6, 8]
>>> print(list(even_odd["odd"]))
[1, 3, 5, 7, 9]

NestedData

Nestable mappings and sequences data structure to facilitate field access

The idea is a single data structure that can hold "JSON" data, adding some helper methods and functionalities.

Primarily, one can use a dotted string path to access a deply nested key, value pair, instead of concatenating several dictionary ".get" calls.

Examples: person["address.city"] instead of person["address"]["city"]

  or

  `persons["10.contacts.emails.0"]`

The first tool available is the ability to merge mappings with extra keys into existing nested mappings, without deleting non colidng keys: a "person.address" key that would contain "city" but no "street" or "zip-code" can be updated with: record["person"].merge({"address": {"street": "5th ave", "zip-code": "000000"}}) preserving the "person.address.city" value in the process.

The ".data" attribute stores the object contents as a tree of dicionary and lists as needed - these are lazily wrapped as NestedData instances if retrieved through the class, but can be freely manipulated directly.

>>> import json
>>> from extradict import NestedData
>>> a = NestedData(json.load(open("myfile.json")))
>>> assert a["persons.0.address"] == a["persons"][0]["address"]
True
>>> a.merge({"city": None}, "persons.*.address")  # creates a new "city" key in all addresses