One-line Tree in Python

Using Python's built-in defaultdict we can easily define a tree data structure:

def tree(): return defaultdict(tree)

That's it!

It simply says that a tree is a dict whose default values are trees.

(If you're following along at home, make sure to from collections import defaultdict)

(Also: Hacker News reader @zbuc points out that this is called autovivification. Cool!)

Examples

JSON-esque

Now we can create JSON-esque nested dictionaries without explicitly creating sub-dictionaries—they magically come into existence as we reference them:

users = tree()
users['harold']['username'] = 'hrldcpr'
users['handler']['username'] = 'matthandlersux'

We can print this as json with print(json.dumps(users)) and we get the expected:

{"harold": {"username": "hrldcpr"}, "handler": {"username": "matthandlersux"}}

Without assignment

We can even create structure with no assignment at all:

taxonomy = tree()
taxonomy['Animalia']['Chordata']['Mammalia']['Carnivora']['Felidae']['Felis']['cat']
taxonomy['Animalia']['Chordata']['Mammalia']['Carnivora']['Felidae']['Panthera']['lion']
taxonomy['Animalia']['Chordata']['Mammalia']['Carnivora']['Canidae']['Canis']['dog']
taxonomy['Animalia']['Chordata']['Mammalia']['Carnivora']['Canidae']['Canis']['coyote']
taxonomy['Plantae']['Solanales']['Solanaceae']['Solanum']['tomato']
taxonomy['Plantae']['Solanales']['Solanaceae']['Solanum']['potato']
taxonomy['Plantae']['Solanales']['Convolvulaceae']['Ipomoea']['sweet potato']

We'll prettyprint this time, which requires us to convert to standard dicts first:

def dicts(t): return {k: dicts(t[k]) for k in t}

Now we can prettyprint the structure with pprint(dicts(taxonomy)):

{'Animalia': {'Chordata': {'Mammalia': {'Carnivora': {'Canidae': {'Canis': {'coyote': {},
                                                                            'dog': {}}},
                                                      'Felidae': {'Felis': {'cat': {}},
                                                                  'Panthera': {'lion': {}}}}}}},
 'Plantae': {'Solanales': {'Convolvulaceae': {'Ipomoea': {'sweet potato': {}}},
                           'Solanaceae': {'Solanum': {'potato': {},
                                                      'tomato': {}}}}}}

So the substructures we referenced now exist as dicts, with empty dicts at the leaves.

Iteration

This tree can be fun to iteratively walk through, again because structure comes into being simply by referring to it.

For example, suppose we are parsing a list of new animals to add to our taxonomy above, so we want to call a function like:

add(taxonomy,
    'Animalia,Chordata,Mammalia,Cetacea,Balaenopteridae,Balaenoptera,blue whale'.split(','))

We can implement this simply as:

def add(t, keys):
  for key in keys:
    t = t[key]

Again we are never assigning to the dictionary, but just by referencing the keys we have created our new structure:

{'Animalia': {'Chordata': {'Mammalia': {'Carnivora': {'Canidae': {'Canis': {'coyote': {},
                                                                            'dog': {}}},
                                                      'Felidae': {'Felis': {'cat': {}},
                                                                  'Panthera': {'lion': {}}}},
                                        'Cetacea': {'Balaenopteridae': {'Balaenoptera': {'blue whale': {}}}}}}},
 'Plantae': {'Solanales': {'Convolvulaceae': {'Ipomoea': {'sweet potato': {}}},
                           'Solanaceae': {'Solanum': {'potato': {},
                                                      'tomato': {}}}}}}

Conclusion

This probably isn't very useful, and it makes for some perplexing code (see add() above).

But if you like Python then I hope this was fun to think about or worthwhile to understand.