How to merge multiple dictionaries from separate lists if they share any key-value pairs?

Question

How to combine dictionaries from multiple lists if they share a common key-value pair?

For example, here are three lists of dictionaries:

l1 = [{'fruit':'banana','category':'B'},{'fruit':'apple','category':'A'}]
l2 = [{'type':'new','category':'A'},{'type':'old','category':'B'}]
l3 = [{'order':'2','type':'old'},{'order':'1','type':'new'}]

Desired Result:

l = [{'fruit':'apple','category':'A','order':'1','type':'new'},{'fruit':'banana','category':'B','order':'2','type':'old'}]

The tricky part is that I want this function to only take in the lists as arguments and not the keys, because I want to only have to plug in any number of list of dictionaries and not be concerned by which key-names are the overlapping ones (in this case they key-names that bring all three together are 'category' and 'type').

I should note index shouldn't matter, as it only should be based on common elements.

Here's my attempt:

def combine_lists(*args):
    base_list = args[0]
    L = []
    for sublist in args[1:]:
        L.extend(sublist)
    for D in base_list:
        for Dict in L:
            if any([tup in Dict.items() for tup in D.items()]): 
                D.update(Dict)
    return base_list

Answer 1

For this problem it is convenient to regard the dicts as lists of tuples:

In [4]: {'fruit':'apple','category':'A'}.items()
Out[4]: [('category', 'A'), ('fruit', 'apple')]

Since we wish to connect dicts which share a key-value pair, we can regard each tuple as a node in a graph and pairs of tuples as edges. Once you have a graph the problem is reduced to finding the graph's connected components.

Using networkx,

import itertools as IT
import networkx as nx

l1 = [{'fruit':'apple','category':'A'},{'fruit':'banana','category':'B'}]
l2 = [{'type':'new','category':'A'},{'type':'old','category':'B'}]
l3 = [{'order':'1','type':'new'},{'order':'2','type':'old'}]

data = [l1, l2, l3]
G = nx.Graph()
for dct in IT.chain.from_iterable(data):
    items = list(dct.items())
    node1 = node1[0]
    for node2 in items:
        G.add_edge(node1, node22)

for cc in nx.connected_component_subgraphs(G):
    print(dict(IT.chain.from_iterable(cc.edges())))

yields

{'category': 'A', 'fruit': 'apple', 'type': 'new', 'order': '1'}
{'category': 'B', 'fruit': 'banana', 'type': 'old', 'order': '2'}

---

If you wish to remove the networkx dependency, you could use, for example, pillmuncher's implementation:

import itertools as IT

def connected_components(neighbors):
    """
    https://stackoverflow.com/a/13837045/190597 (pillmuncher)
    """
    seen = set()
    def component(node):
        nodes = set([node])
        while nodes:
            node = nodes.pop()
            seen.add(node)
            nodes |= neighbors[node] - seen
            yield node
    for node in neighbors:
        if node not in seen:
            yield component(node)

l1 = [{'fruit':'apple','category':'A'},{'fruit':'banana','category':'B'}]
l2 = [{'type':'new','category':'A'},{'type':'old','category':'B'}]
l3 = [{'order':'1','type':'new'},{'order':'2','type':'old'}]

data = [l1, l2, l3]
G = {}
for dct in IT.chain.from_iterable(data):
    items = dct.items()
    node1 = items[0]
    for node2 in items[1:]:
        G.setdefault(node1, set()).add(node2)
        G.setdefault(node2, set()).add(node1)

for cc in connected_components(G):
    print(dict(cc))

which prints the same result as above.