Skip to main content

AttrDict for nested dictionary processing and utilities

Project description


This package contains a generic dictionary AttrDict, which implements nested dictionaries that are easy to access, filter, combine, and write to.

Installation: pip install attr-dicts:


Creation: Let's say we want to store the following nested structure of arrays:

- food
    - carrot: [1,2,3]
    - apple: [4,5,6]
    - broccoli: [7,8,9,10]
- utensils
    - fork
        - three_prong: [11,12]
        - four_prong: [13,14,15]
    - spoon: [16,17,18]

AttrDicts use '/' to separate keys, and this is built in to read/write operations. Here's two examples of how to instantiate the above structure.

d = AttrDict()
d['food/carrot'] = [1,2,3] = [4,5,6]
d['food/broccoli'] = [7,8,9,10]
d['utensils/fork/three_prong'] = [11,12]
d['utensils/fork/four_prong'] = [13,14,15]
d['utensils/spoon'] = [16,17,18]

Note that both indexing and dot access work, since AttrDicts inherit from the DotMap class. Here's a slightly less effort way:

d = AttrDict(
    food=AttrDict(carrot=[1,2,3], apple=[4,5,6], broccoli=[7,8,9,10]),
        fork=AttrDict(three_prong=[11,12], four_prong=[13,14,15]), 

Access: There are several ways to access an AttrDict.

  1. d['utensils/fork/three_prong']: standard dictionary access, but using '/' to implicitly sub-index (KeyError if missing)
  2. d.utensils.fork.three_prong: dotmap access (AttributeError if missing)
  3. d.utensils['fork/three_prong']: mixed indexing + dotmap (either AttributeError or KeyError if missing)
  4. d >> 'utensils/fork/three_prong: (DEPRECATED) required key access, will error if not present.
  5. d << 'utensils/fork/three_prong: optional key access, will return None if not present
  6. d > ['utensils/fork/three_prong,'utensils/spoon']: required key filtering, returns sub-dict. errors if a key in the arg list is not present.
  7. d < ['utensils/fork/three_prong,'utensils/spoon']: optional key access, returns sub-dict, ignores keys that aren't present.

Node/Leaf operations: Leaf nodes are any access pattern that returns something that isn't an AttrDict. In the above example, 'food' is a node key, while 'food/carrot' is a leaf key. We can operate on all leaf nodes at once, here are some example methods:

  1. d.leaf_keys(): Generator that yields leaf keys under a depth first traverse.
  2. d.list_leaf_keys(): Outputs a list instead of generator.
  3. d.leaf_values(): Generator that yields leaf values under a depth first traverse.
  4. applied_d = d.leaf_apply(lambda v: <new_v>): Apply a function(value) on all leaf values, and create a new AttrDict.
  5. filtered_d = d.leaf_filter(lambda k,v: <condition>): Only keep leaf keys where condition is true in new AttrDict.

Similarly, there are functions that operate on both nodes and leaves.

Combining: Combining AttrDicts can be done in several ways:

  1. new_d = d1 & d2: Standard join, returns a new AttrDict, which will favor keys from d2 if there are duplicates.
  2. d1.combine(d2): Mutates d1 to join the arrays.
  3. new_d = AttrDict.leaf_combine_and_apply([d1, d2, ...], lambda vs: <return one value>): Given a list of AttrDicts with the same keys, will create one AttrDict where the value for a given key k is some function of vs = [d1[k], d2[k], ...].

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

attr_dicts-1.0.3.tar.gz (7.9 kB view hashes)

Uploaded Source

Built Distribution

attr_dicts-1.0.3-py3-none-any.whl (9.4 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page