views:

126

answers:

7

I have a tuple of tuples - for example:

tupleOfTuples = ((1, 2), (3, 4), (5,))

I want to convert this into a flat, one-dimensional list of all the elements in order:

[1, 2, 3, 4, 5]

I've been trying to accomplish this with list comprehension. But I can't seem to figure it out. I was able to accomplish it with a for-each loop:

myList = []
for tuple in tupleOfTuples:
   myList = myList + list(tuple)

But I feel like there must be a way to do this with a list comprehension.

A simple [list(tuple) for tuple in tupleOfTuples] just gives you a list of lists, instead of individual elements. I thought I could perhaps build on this by using the unpacking operator to then unpack the list, like so:

[*list(tuple) for tuple in tupleOfTuples]

or

[*(list(tuple)) for tuple in tupleOfTuples]

... but that didn't work. Any ideas? Or should I just stick to the loop?

+7  A: 

it's typically referred to as flattening a nested structure.

>>> tupleOfTuples = ((1, 2), (3, 4), (5,))
>>> [element for tupl in tupleOfTuples for element in tupl]
[1, 2, 3, 4, 5]

Just to demonstrate efficiency:

>>> import timeit
>>> it = lambda: list(chain(*tupleOfTuples))
>>> timeit.timeit(it)
2.1475738355700913
>>> lc = lambda: [element for tupl in tupleOfTuples for element in tupl]
>>> timeit.timeit(lc)
1.5745135182887857

ETA: Please don't use tuple as a variable name, it shadows built-in.

SilentGhost
thanks... do you mind giving i and j more meaningful names so I can more easily follow this logic?
froadie
@froadie: corrected
SilentGhost
@SilentGhost - thanks! is this widely accepted? I find it harder to understand at first glance than the longer loop... but if programmers recognize the pattern then I would use it
froadie
@froadie: it's an idiomatic way of flattening a shallow list.
SilentGhost
I wondered how it looks for longer lists, ie `tupleOfTuples=tuple(zip(range(0,100,2), range(1,100,2)))` -- in that case, `chain` is slightly faster than the LC (and Fabian's `chain.from_iterable` is the fastest)
THC4k
@THC4k: Not on my machine, here fastest one is `list(chain.from_iterable(tupleOfTuples))`. Fabians, solution is as slow as mine or even slower.
SilentGhost
@SilentGhost yeah you're right, i actually tested that same code, not Fabians LC.
THC4k
+1  A: 

I like using 'reduce' in this situation (this is what reduce made for!)

lot = ((1, 2), (3, 4), (5,))
print list(reduce(lambda t1, t2: t1 + t2, lot))

 > [1,2,3,4,5]
orangeoctopus
In before Alex votes you down with his hatred-for-map/reduce-wrath
Dominic Bou-Samra
+5  A: 

You're chaining the tuples together:

from itertools import chain
print list(chain(*listOfTuples))

Should be pretty readable if you're familiar with itertools, and without the explicit list you even have your result in generator form.

THC4k
+1  A: 

Another solution using itertools.chain

>>> tupleOfTuples = ((1, 2), (3, 4), (5,))
>>> from itertools import chain
>>> [x for x in chain.from_iterable(tupleOfTuples)]
[1, 2, 3, 4, 5]
Fabian
+1  A: 

Most of these answers will only work for a single level of flattening. For a more comprehensive solution, try this (from http://rightfootin.blogspot.com/2006/09/more-on-python-flatten.html):

def flatten(l, ltypes=(list, tuple)):
    ltype = type(l)
    l = list(l)
    i = 0
    while i < len(l):
        while isinstance(l[i], ltypes):
            if not l[i]:
                l.pop(i)
                i -= 1
                break
            else:
                l[i:i + 1] = l[i]
        i += 1
    return ltype(l)
Craig Trader
A: 

For multilevel, and readable code:

def flatten(bla):
    output = []
    for item in bla:
        output += flatten(item) if hasattr (item, "__iter__") or hasattr (item, "__len__") else [item]
    return output

I could not get this to fit in one line (and remain readable, even by far)

jsbueno
Some code just wasn't meant to be in one line.
Paul McGuire
+2  A: 

Just use sum.

>>> tupleOfTuples = ((1, 2), (3, 4), (5,))
>>> sum(tupleOfTuples, ())
(1, 2, 3, 4, 5)
>>> list(sum(tupleOfTuples, ())) # if you really need a list
[1, 2, 3, 4, 5]

Micro-benchmarks:

  • Python 2.6

    • Long tuple of short tuples

      $ python2.6 -m timeit -s 'tot = ((1, 2), )*500' '[element for tupl in tot for element in tupl]'
      10000 loops, best of 3: 134 usec per loop
      $ python2.6 -m timeit -s 'tot = ((1, 2), )*500' 'list(sum(tot, ()))'
      1000 loops, best of 3: 1.1 msec per loop
      $ python2.6 -m timeit -s 'tot = ((1, 2), )*500; from itertools import chain; ci = chain.from_iterable' 'list(ci(tot))'
      10000 loops, best of 3: 60.1 usec per loop
      $ python2.6 -m timeit -s 'tot = ((1, 2), )*500; from itertools import chain' 'list(chain(*tot))'
      10000 loops, best of 3: 64.8 usec per loop
      
    • Short tuple of long tuples

      $ python2.6 -m timeit -s 'tot = ((1, )*500, (2, )*500)' '[element for tupl in tot for element in tupl]'
      10000 loops, best of 3: 65.6 usec per loop
      $ python2.6 -m timeit -s 'tot = ((1, )*500, (2, )*500)' 'list(sum(tot, ()))'
      100000 loops, best of 3: 16.9 usec per loop
      $ python2.6 -m timeit -s 'tot = ((1, )*500, (2, )*500); from itertools import chain; ci = chain.from_iterable' 'list(ci(tot))'
      10000 loops, best of 3: 25.8 usec per loop
      $ python2.6 -m timeit -s 'tot = ((1, )*500, (2, )*500); from itertools import chain' 'list(chain(*tot))'
      10000 loops, best of 3: 26.5 usec per loop
      
  • Python 3.1

    • Long tuple of short tuples

      $ python3.1 -m timeit -s 'tot = ((1, 2), )*500' '[element for tupl in tot for element in tupl]'
      10000 loops, best of 3: 121 usec per loop
      $ python3.1 -m timeit -s 'tot = ((1, 2), )*500' 'list(sum(tot, ()))'
      1000 loops, best of 3: 1.09 msec per loop
      $ python3.1 -m timeit -s 'tot = ((1, 2), )*500; from itertools import chain; ci = chain.from_iterable' 'list(ci(tot))'
      10000 loops, best of 3: 59.5 usec per loop
      $ python3.1 -m timeit -s 'tot = ((1, 2), )*500; from itertools import chain' 'list(chain(*tot))'
      10000 loops, best of 3: 63.2 usec per loop
      
    • Short tuple of long tuples

      $ python3.1 -m timeit -s 'tot = ((1, )*500, (2, )*500)' '[element for tupl in tot for element in tupl]'
      10000 loops, best of 3: 66.1 usec per loop
      $ python3.1 -m timeit -s 'tot = ((1, )*500, (2, )*500)' 'list(sum(tot, ()))'
      100000 loops, best of 3: 16.3 usec per loop
      $ python3.1 -m timeit -s 'tot = ((1, )*500, (2, )*500); from itertools import chain; ci = chain.from_iterable' 'list(ci(tot))'
      10000 loops, best of 3: 25.4 usec per loop
      $ python3.1 -m timeit -s 'tot = ((1, )*500, (2, )*500); from itertools import chain' 'list(chain(*tot))'
      10000 loops, best of 3: 25.6 usec per loop
      

Observation:

  • sum is faster if the outer tuple is short.
  • list(chain.from_iterable(x)) is faster if the outer tuple is long.
KennyTM