Python: Equivalent to flatMap for Flattening an Array of Arrays

I found myself wanting to flatten an array of arrays while writing some Python code earlier this afternoon and being lazy my first attempt involved building the flattened array manually:

episodes = [
    {"id": 1, "topics": [1,2,3]},
    {"id": 2, "topics": [4,5,6]}
]

flattened_episodes = []
for episode in episodes:
    for topic in episode["topics"]:
        flattened_episodes.append({"id": episode["id"], "topic": topic})

for episode in flattened_episodes:
    print episode

If we run that we’ll see this output:

$ python flatten.py

{'topic': 1, 'id': 1}
{'topic': 2, 'id': 1}
{'topic': 3, 'id': 1}
{'topic': 4, 'id': 2}
{'topic': 5, 'id': 2}
{'topic': 6, 'id': 2}

What I was really looking for was the Python equivalent to the flatmap function which I learnt can be achieved in Python with a list comprehension like so: 

flattened_episodes = [{"id": episode["id"], "topic": topic}
                      for episode in episodes
                      for topic in episode["topics"]]

for episode in flattened_episodes:
    print episode

We could also choose to use itertools in which case we’d have the following code: 

from itertools import chain, imap
flattened_episodes = chain.from_iterable(
                        imap(lambda episode: [{"id": episode["id"], "topic": topic}
                                             for topic in episode["topics"]],
                             episodes))
for episode in flattened_episodes:
    print episode

We can then simplify this approach a little by wrapping it up in a ‘flatmap’ function: 

def flatmap(f, items):
        return chain.from_iterable(imap(f, items))

flattened_episodes = flatmap(
    lambda episode: [{"id": episode["id"], "topic": topic} for topic in episode["topics"]], episodes)

for episode in flattened_episodes:
    print episode

I think the list comprehensions approach still works but I need to look into itertools more – it looks like it could work well for other list operations.

 

 

 

 

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