Skip to main content

Python dict with automatic and arbitrary levels of nesting along with additional utility methods.

Project description

FlexDict Logo


Travis (.org) Codacy coverage Codacy grade GitHub repo size PyPI PyPI - Python Version security: bandit

Elegantly nested Python dictionaries.

Easily work with deeply nested dictionaries and write clean code using FlexDict; a small subclass of dict. FlexDict provides automatic and arbitrary levels of nesting along with additional utility functions.

Getting Started

  1. Install

    pip install flexdict
  2. Import

    from flexdict import FlexDict
  3. Create

    f = FlexDict()

User's Guide

The main purpose of FlexDict is to allow you to work with deeply nested dictionaries with minimal amount of code. It achieves this purpose by providing an automatic nesting algorithm. It can be a dangerous feature if not used with caution. That's why, FlexDict provides some helper methods to prevent any unintentional side-effects.

Setting Items

When it comes to setting dictionary items, FlexDict provides many options. Let's start with the most slick way:

f = FlexDict()

f['easily', 'create', 'deeply', 'nested', 'structures'] = 1

The resulting dictionary would be:

{'easily':{'create':{'deeply':{'nested':{'structures': 1}}}}

You can directly pass instances of list, tuple or set instead:

f[['easily', 'create', 'deeply', 'nested', 'structures']]
f[('easily', 'create', 'deeply', 'nested', 'structures')]
f[{'easily', 'create', 'deeply', 'nested', 'structures'}]

You also have other options:

f['easily']['create']['deeply']['nested']['structures'] = 1

f.set(['easily', 'create', 'deeply', 'nested', 'structures'], 1)
f.set(('easily', 'create', 'deeply', 'nested', 'structures'), 1)
f.set({'easily', 'create', 'deeply', 'nested', 'structures'}, 1)

The resulting dictionary would be the same for all these examples. However, the set method provides many other features. For example, you may only want to set the dictionary items if they do not already exist:

f = FlexDict({'a': {'b':1}})

f.set(['a', 'b'], 2, overwrite=False)
f.set(['a', 'c'], 2, overwrite=False)

This prevents you to overwrite existing values:

{'a': {'b': 1, 'c': 2}}

Or, if you need a counter, you can use the increment argument to do exactly that:

f = FlexDict()

for i in range(20):
    if i % 2 == 0:
        f.set('Even', 1, increment=True)
        f.set('Odd', 1, increment=True)



{'Even': 10, 'Odd': 10}

(Note that overwrite argument has no effect when increment is enabled.)

Getting Items

Again, FlexDict provides many alternative ways to access your dictionary items:

f = FlexDict({'key1': {'key2': {'key3': 1}}})

# 1
f['key1', 'key2', 'key3']

# 2

# 3
f.get(['key1', 'key2', 'key3'])
f.get(('key1', 'key2', 'key3'))
f.get({'key1', 'key2', 'key3'})

They will all return the same result:


There is a crucial distinction between these alternatives. Whenever you use squared brackets to access an item, FlexDict will automatically create the keys and fill the value with an empty FlexDict if there is no such item:

f = FlexDict()

f['a', 'b']



{'a': {'b': {}}}

To prevent this side-effect, FlexDict provides two options. First one, is the get method:

f = FlexDict()

f.get(['a', 'b']), f.get(['a', 'b'], default=0), f

The get method returns the value provided with the default argument if the target item does not exist:

(None, 0, {})

The other option to avoid the aformentioned side-effect is to use the recursive locking mechnasim via the lock method. We will cover it later in this guide. However, just to give you a taste of it, the following example is added:

f = FlexDict()


f['a', 'b']


KeyError: 'a'

Getting the top level keys and values works just like a regular dict:

f = FlexDict({'a': 1, 'b': 2})

f.keys(), f.values()

The only difference you would notice is f.values() returns a list instead of dict_values. This is an intentional behavior since we are working with nested dictionaries:

(dict_keys(['a', 'b']), [1, 2])

You may also want to get every key and/or value inside your FlexDict instance, even the nested ones. FlexDict can do this with recursion:

f = FlexDict({
    'a': {
        'b': 1,
        'c': {
            'd': 1,
            'e': {
                'a': 3
    'g': 4

f.keys(nested=True), f.values(nested=True)

This allows you to check exactly what is inside your FlexDict instance:

(['a', 'b', 'c', 'd', 'e', 'a', 'g'], [1, 1, 3, 4])

You can even get rid of the duplicates:

f.keys(nested=True, unique=True), f.values(nested=True, unique=True)

Note that unique items gets returned inside of a set:

({'a', 'b', 'c', 'd', 'e', 'g'}, {1, 3, 4})

If you wish, you can flatten the entire FlexDict instance. The flatten method returns a list of values and their respective key-paths:



[(['a', 'b'], 1), (['a', 'c', 'd'], 1), (['a', 'c', 'e', 'a'], 3), (['g'], 4)]

Last but not least, if you wish to get the last item and remove it from the FlexDict instance, you can use the pop method:

f = FlexDict({'a': 1, 'b': 2})

f.pop(), f


({'b': 2}, {'a': 1})

Locking & Unlocking Automatic Nesting

Like we discussed above, automatic nesting can be very dangerous in some cases. Thats why, aside from the previously mentioned workarounds, FlexDict provides a recursive algorithm to lock and unlock this feature:

f = FlexDict()


f['a'] = 1  # Normal `dict` behavior works as expected

    f['b', 'c'] = 1 # Will throw a KeyError
except KeyError:
    f['b', 'c'] = 1



{'a': 1, 'b': {'c': 1}}

Each FlexDict instance has an attribute called locked which tells if it is locked. Each nested dictionary inside a FlexDict instance is also a seperate FlexDict instance! This means, each of them has seperate locked attributes. The lock method sets the locked attribute of the specified FlexDict instance and of all the other nested dictionaries inside of it to True. unlock method on the other hand, does the exact opposite. This means that you can create any hybrid lock structure you want (Do that with caution!):

f = FlexDict({'secure': {}, 'not_secure': {}})


f.locked, f['secure'].locked, f['not_secure'].locked


(False, True, False)

Both lock and unlock methods provide an argument called inplace which allows you to create locked/unlocked copies of your FlexDict instances:

f = FlexDict()

f_locked = f.lock(inplace=False)

f.locked, f_locked.locked


(False, True)

Other Utility Methods

You can check if your FlexDict instance contains (is a superset of) or inside of (is a subset of) another dict instance.

f = FlexDict({'a': {'b': 1}})

f.contains({'b': 1}), f.inside({'c': {'a': {'b': 1}}})


(True, True)

FlexDict also allows you to easily get the length (number of keys) and size (number of keys and values) inside your dictionaries via length and size methods. They both utilize the previously mentioned keys and values methods. Hence, they can work recursively and get rid of duplicates if you wish:

f = FlexDict({
    'a': {
        'b': 1,
        'c': {
            'd': 1,
            'e': {
                'a': 3
    'g': 4

# Can be used as a replacement for len()
print(f'Number of keys:', f.length())
print(f'Number of keys (Recursive):', f.length(nested=True))
print(f'Number of keys (Recursive, Unique):', f.length(nested=True, unique=True))

# Saves some of your time
print(f'\nNumber of items (Recursive):', f.size())
print(f'Number of items (Recursive, Unique):', f.size(unique=True))


Number of keys: 2
Number of keys (Recursive): 7
Number of keys (Recursive, Unique): 6

Number of items (Recursive): 11
Number of items (Recursive, Unique): 9


See contributing for the details.

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

flexdict-0.0.1a1.tar.gz (8.5 kB view hashes)

Uploaded source

Built Distribution

flexdict-0.0.1a1-py2.py3-none-any.whl (7.8 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page