On 06Sep2019 22:48, Ralf M. <Ralf_M at t-online.de> wrote:
>Recently I wrote a quick and dirty script to do some counting and
>statistics. When I re-read it a bit later I noticed that I had been
>using two different ways to create two-dimensional (default-)dicts.
>Now I'm wondering whether one of them is "better" or more pythonic
>than the other.
>What I did:
>ddd_a = collections.defaultdict(set)
>ddd_b = collections.defaultdict(lambda: collections.defaultdict(set))
>Both work as expected.
>Trying to think about differences I only noticed that ddd_a more
>easily generalises to more dimensions, and ddd_b has the benefit that
>ddd_b[key1] is a dict, which might help if one "row" needs to be fed
>to a function that expects a dict.
>More general ddd_a looks more symmetric (key1 and key2 are
>exchangeable, if done consistently) and ddd_b looks more hierarchic
>(like a tree traversed from root to leaves where key1, key2 etc.
>determine which way to go at each level). ddd_b also is more simmilar
>to how two-dimensional lists are done in python.
>Any recommendations / comments as to which to prefer?
As you'd imagine, it depends on what yuou're doing.
If (key1,key2) are a Cartesian-like "space" of value, for example the
domain of key2 values it the same regardless of key1, I lean toward
If (key1,key2) are a tree like structure such as the clause names and
field values form a .ini config file:
field1 = 1
field2 = 3
field2 = 9
I lean towards the ddd_b[key1][key2] approach.
So: are they a "flat" space or a tree structure? The is my normal rule
of thumb for deciding how to key things.
In particular, if you need to ask "what are the key2 values for
key1==x?" then you might want a tree structure.
The ddd_a (key1,key2) approach is easier to manage in terms of creating
new nodes. OTOH, using a nested defaultdict can handle that work for
ddd_d = defaultdict(lambda: defaultdict(int))
(Pick a suitable type in place of "int" maybe.)
Cameron Simpson <cs at cskk.id.au>