How to make a flat list out of list of lists

Question

I wonder whether there is a shortcut to make a simple list out of list of lists in Python   I can do that in a for loop  but maybe there is some cool  one-liner   I tried it with reduce    but I get an error   Code  l     1  2  3    4  5  6    7    8  9   reduce lambda x  y  x extend y   l    Error message  Traceback  most recent call last     File   lt stdin gt    line 1  in  lt module gt    File   lt stdin gt    line 1  in  lt lambda gt  AttributeError   NoneType  object has no attribute  extend

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The reason your function didn t work is because the extend extends an array in-place and doesn t return it  You can still return x from lambda  using something like this   reduce lambda x y  x extend y  or x  l    Note  extend is more efficient than   on lists

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If you want to flatten a data-structure where you don t know how deep it s nested you could use iteration utilities deepflatten1   gt  gt  gt  from iteration utilities import deepflatten   gt  gt  gt  l     1  2  3    4  5  6    7    8  9    gt  gt  gt  list deepflatten l  depth 1    1  2  3  4  5  6  7  8  9    gt  gt  gt  l     1  2  3    4   5  6    7   8  9    gt  gt  gt  list deepflatten l    1  2  3  4  5  6  7  8  9    It s a generator so you need to cast the result to a list or explicitly iterate over it     To flatten only one level and if each of the items is itself iterable you can also use iteration utilities flatten which itself is just a thin wrapper around itertools chain from iterable    gt  gt  gt  from iteration utilities import flatten  gt  gt  gt  l     1  2  3    4  5  6    7    8  9    gt  gt  gt  list flatten l    1  2  3  4  5  6  7  8  9      Just to add some timings  based on Nico Schl  mer answer that didn t include the function presented in this answer      It s a log-log plot to accommodate for the huge range of values spanned  For qualitative reasoning  Lower is better   The results show that if the iterable contains only a few inner iterables then sum will be fastest  however for long iterables only the itertools chain from iterable  iteration utilities deepflatten or the nested comprehension have reasonable performance with itertools chain from iterable being the fastest  as already noticed by Nico Schl  mer    from itertools import chain from functools import reduce from collections import Iterable    or from collections abc import Iterable import operator from iteration utilities import deepflatten  def nested list comprehension lsts       return  item for sublist in lsts for item in sublist   def itertools chain from iterable lsts       return list chain from iterable lsts    def pythons sum lsts       return sum lsts       def reduce add lsts       return reduce lambda x  y  x   y  lsts   def pylangs flatten lsts       return list flatten lsts    def flatten items          Yield items from any nested iterable  see REF         for x in items          if isinstance x  Iterable  and not isinstance x   str  bytes                yield from flatten x          else              yield x  def reduce concat lsts       return reduce operator concat  lsts   def iteration utilities deepflatten lsts       return list deepflatten lsts  depth 1     from simple benchmark import benchmark  b   benchmark       nested list comprehension  itertools chain from iterable  pythons sum  reduce add       pylangs flatten  reduce concat  iteration utilities deepflatten       arguments  2  i    0  5   2  i  for i in range 1  13        argument name  number of inner lists     b plot       1 Disclaimer  I m the author of that library

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There are several answers with the same recursive appending scheme as below  but none makes use of try  which makes the solution more robust and pythonic  Other advantages of this solution are   works with any kind of iterable  even future ones  works with any combination and deepness of nesting no dependencies    def flatten itr       t   tuple       for e in itr          try              t    flatten e          except              t     e       return t  Another variant with same features  def flatten itr       try          t   sum  flatten e  for e in itr   tuple        except          t    itr       return t  If you need to return a list  which is slightly slower  use  list   instead of tuple   and     instead of

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def flatten alist       if alist                return        elif type alist  is not list          return  alist      else          return flatten alist 0     flatten alist 1

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def flatten l  a       for i in l          if isinstance i  list               flatten i  a          else              a append i      return a  print flatten    1   1 1   3   4 5       2  3    4  5  6            1  1  1  3  4  5  2  3  4  5  6

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One posibility is to treat the array as a string   elements     180 0  1  2  3    173 8    164 2    156 5    147 2    138 2   list map float  str elements  replace          replace          split

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If you are willing to give up a tiny amount of speed for a cleaner look  then you could use numpy concatenate   tolist   or numpy concatenate   ravel   tolist     import numpy  l     1  2  3    4  5  6    7    8  9     99   timeit numpy concatenate l  ravel   tolist   1000 loops  best of 3  313   s per loop   timeit numpy concatenate l  tolist   1000 loops  best of 3  312   s per loop   timeit  item for sublist in l for item in sublist  1000 loops  best of 3  31 5   s per loop   You can find out more here in the docs numpy concatenate and numpy ravel

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There seems to be a confusion with operator add  When you add two lists together  the correct term for that is concat  not add  operator concat is what you need to use   If you re thinking functional  it is as easy as this     gt  gt  gt  from functools import reduce  gt  gt  gt  list2d     1  2  3    4  5  6    7     8  9    gt  gt  gt  reduce operator concat  list2d   1  2  3  4  5  6  7  8  9    You see reduce respects the sequence type  so when you supply a tuple  you get back a tuple  Let s try with a list     gt  gt  gt  list2d     1  2  3   4  5  6    7    8  9    gt  gt  gt  reduce operator concat  list2d   1  2  3  4  5  6  7  8  9    Aha  you get back a list   How about performance     gt  gt  gt  list2d     1  2  3   4  5  6    7    8  9    gt  gt  gt   timeit list itertools chain from iterable list2d   1000000 loops  best of 3  1 36   s per loop   from iterable is pretty fast  But it s no comparison to reduce with concat    gt  gt  gt  list2d     1  2  3   4  5  6    7     8  9    gt  gt  gt   timeit reduce operator concat  list2d  1000000 loops  best of 3  492 ns per loop

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A bad feature of Anil s function above is that it requires the user to always manually specify the second argument to be an empty list     This should instead be a default  Due to the way Python objects work  these should be set inside the function  not in the arguments   Here s a working function   def list flatten l  a None        check a     if a is None           initialize with empty list         a           for i in l          if isinstance i  list               list flatten i  a          else              a append i      return a   Testing   In  2   lst    1  2   3     4    5  6     In  3   lst Out 3    1  2   3     4     5   6     In  11   list flatten lst  Out 11    1  2  3  4  5  6

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This can be done using toolz concat or cytoolz concat  cythonized version  that could be faster in some cases    from cytoolz import concat l     1  2  3    4  5  6    7    8  9   list concat l     or just  concat l   if one only wants to iterate over the items   On my computer  in python 3 6  this seems to time almost as fast as  item for sublist in l for item in sublist   not counting the import time    In  611    timeit L    item for sublist in l for item in sublist  695 ns    2 75 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   In  612    timeit L    item for sublist in l for item in sublist  701 ns    5 5 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   In  613    timeit L   list concat l   719 ns    12 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   In  614    timeit L   list concat l   719 ns    22 9 ns per loop  mean    std  dev  of 7 runs  1000000 loops each    The toolz version is indeed slower   In  618   from toolz import concat  In  619    timeit L   list concat l   845 ns    29 ns per loop  mean    std  dev  of 7 runs  1000000 loops each   In  620    timeit L   list concat l   833 ns    8 73 ns per loop  mean    std  dev  of 7 runs  1000000 loops each

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Why do you use extend   reduce lambda x  y  x y  l    This should work fine

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This may not be the most efficient way but I thought to put a one-liner  actually a two-liner   Both versions will work on arbitrary hierarchy nested lists  and exploits language features  Python3 5  and recursion   def make list flat  l       flist          flist extend   l   if  type  l  is not list  else  flist extend  make list flat  e   for e in l      return flist  a     1  2       3  4  5   6     7   8   9   10  11   12   13  14   15    16  17   18       flist   make list flat a  print  flist    The output is   1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18    This works in a depth first manner  The recursion goes down until it finds a non-list element  then extends the local variable flist and then rolls back it to the parent  Whenever flist is returned  it is extended to the parent s flist in the list comprehension  Therefore  at the root  a flat list is returned   The above one creates several local lists and returns them which are used to extend the parent s list  I think the way around for this may be creating a gloabl flist  like below   a     1  2       3  4  5   6     7   8   9   10  11   12   13  14   15    16  17   18       flist      def make list flat  l       flist extend   l   if  type  l  is not list  else  make list flat  e  for e in l   make list flat a  print  flist    The output is again   1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18    Although I am not sure at this time about the efficiency

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This is a play on the original poster s code   He wasn t far off  f      list map f extend  l

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np hstack listoflist  tolist

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Throwing my hat in the ring     B                         A      for i in B    A extend i

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Consider installing the more itertools package   gt  pip install more itertools  It ships with an implementation for flatten  source  from the itertools recipes   import more itertools   lst     1  2  3    4  5  6    7    8  9   list more itertools flatten lst      1  2  3  4  5  6  7  8  9   Note  as mentioned in the docs  flatten requires a list of lists   See below on flattening more irregular inputs   As of version 2 4  you can flatten more complicated  nested iterables with more itertools collapse  source  contributed by  abarnet   lst     1  2  3    4  5  6    7    8  9   list more itertools collapse lst       1  2  3  4  5  6  7  8  9   lst     1  2  3     4  5  6       7     8  9                 complex nesting list more itertools collapse lst      1  2  3  4  5  6  7  8  9

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I take my statement back  sum is not the winner  Although it is faster when the list is small  But the performance degrades significantly with larger lists     gt  gt  gt  timeit Timer            item for sublist in l for item in sublist             l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7     10000        timeit 100  2 0440959930419922   The sum version is still running for more than a minute and it hasn t done processing yet   For medium lists    gt  gt  gt  timeit Timer            item for sublist in l for item in sublist             l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7     10        timeit   20 126545906066895  gt  gt  gt  timeit Timer           reduce lambda x y  x y l             l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7     10        timeit   22 242258071899414  gt  gt  gt  timeit Timer           sum l                 l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7     10        timeit   16 449732065200806   Using small lists and timeit  number 1000000   gt  gt  gt  timeit Timer            item for sublist in l for item in sublist             l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7          timeit   2 4598159790039062  gt  gt  gt  timeit Timer           reduce lambda x y  x y l             l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7          timeit   1 5289170742034912  gt  gt  gt  timeit Timer           sum l                 l   1  2  3    4  5  6  7  8    1  2  3  4  5  6  7          timeit   1 0598428249359131

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Note  Below applies to Python 3 3  because it uses yield from   six is also a third-party package  though it is stable   Alternately  you could use sys version     In the case of obj     1  2     3  4    5  6    all of the solutions here are good  including list comprehension and itertools chain from iterable   However  consider this slightly more complex case    gt  gt  gt  obj     1  2  3    4  5   6   abc    7    8   9  10      There are several problems here    One element  6  is just a scalar  it s not iterable  so the above routes will fail here  One element   abc   is technically iterable  all strs are    However  reading between the lines a bit  you don t want to treat it as such--you want to treat it as a single element  The final element   8   9  10   is itself a nested iterable   Basic list comprehension and chain from iterable only extract  1 level down     You can remedy this as follows    gt  gt  gt  from collections import Iterable  gt  gt  gt  from six import string types   gt  gt  gt  def flatten obj           for i in obj              if isinstance i  Iterable  and not isinstance i  string types                   yield from flatten i              else                  yield i    gt  gt  gt  list flatten obj    1  2  3  4  5  6   abc   7  8  9  10    Here  you check that the sub-element  1  is iterable with Iterable  an ABC from itertools  but also want to ensure that  2  the element is not  string-like

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Here is a general approach that applies to numbers  strings  nested lists and mixed containers   Code   from typing import Iterable  from collections import Iterable                               lt  py38   def flatten items          Yield items from any nested iterable  see Reference         for x in items          if isinstance x  Iterable  and not isinstance x   str  bytes                for sub x in flatten x                   yield sub x         else              yield x   Notes     In Python 3  yield from flatten x  can replace for sub x in flatten x   yield sub x In Python 3 8  abstract base classes are moved from collection abc to the typing module    Demo  lst     1  2  3    4  5  6    7    8  9   list flatten lst                                             nested lists    1  2  3  4  5  6  7  8  9   mixed     1   2     3  4   5  6   7   8   9                  numbers  strs  nested  amp  mixed list flatten mixed      1  2  3  4  5  6  7  8   9     Reference   This solution is modified from a recipe in Beazley  D  and B  Jones   Recipe 4 14  Python Cookbook 3rd Ed   O Reilly Media Inc  Sebastopol  CA  2013  Found an earlier SO post  possibly the original demonstration

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from functools import reduce  python 3   gt  gt  gt  l     1 2 3   4 5 6    7    8 9    gt  gt  gt  reduce lambda x y  x y l   1  2  3  4  5  6  7  8  9    The extend   method in your example modifies x instead of returning a useful value  which reduce   expects    A faster way to do the reduce version would be   gt  gt  gt  import operator  gt  gt  gt  l     1 2 3   4 5 6    7    8 9    gt  gt  gt  reduce operator concat  l   1  2  3  4  5  6  7  8  9

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I personally find it hard to remember all the modules that needed to be imported  Thus I tend to use a simple method  even though I don t know how its performance is compared to other answers  If you just want to flatten nested lists  then the following will do the job  def flatten lst       for item in lst          if isinstance item  list               yield from flatten item          else              yield item    test case  a   0       quot fun quot    1  2  3    4   5   6   3   7    8  9   list flatten a     output     0   fun   1  2  3  4  5  6  3  7  8  9    However  if you want to flatten a list of iterables  list and or tuples   it can also do the job with a slight modification  from collections abc import Iterable def flatten lst       for item in lst          if isinstance item Iterable  and not isinstance item str               yield from flatten item          else              yield item    test case  a   0       quot fun quot    1  2  3    4   5    6    3   7    8  9   list flatten a     output      0   fun   1  2  3  4  5  6  3  7  8  9

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from nltk import flatten  l     1  2  3    4  5  6    7    8  9   flatten l    The advantage of this solution over most others here is that if you have a list like   l    1   2  3    4  5  6    7    8  9     while most other solutions throw an error this solution handles them

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Fastest solution I have found  for large list anyway    import numpy as np  turn list into an array and flatten   np array l  flatten     Done  You can of course turn it back into a list by executing list l

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you can use list extend method  it shows to be the fastest   flat list      for sublist in l      flat list extend sublist    performance   import functools import itertools import numpy import operator import perfplot    def functools reduce iconcat a       return functools reduce operator iconcat  a        def itertools chain a       return list itertools chain from iterable a     def numpy flat a       return list numpy array a  flat    def extend a       n           list map n extend  a        return n    perfplot show      setup lambda n   list range 10      n      kernels           functools reduce iconcat  extend itertools chain  numpy flat                n range  2  k for k in range 16        xlabel  num lists           output

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Following seem simplest to me    gt  gt  gt  import numpy as np  gt  gt  gt  l     1  2  3    4  5  6    7    8  9    gt  gt  gt  print  np concatenate l    1 2 3 4 5 6 7 8 9

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I tested most suggested solutions with perfplot  a pet project of mine  essentially a wrapper around timeit   and found import functools import operator functools reduce operator iconcat  a       to be the fastest solution  both when many small lists and few long lists are concatenated   operator iadd is equally fast      Code to reproduce the plot  import functools import itertools import numpy import operator import perfplot   def forfor a       return  item for sublist in a for item in sublist    def sum brackets a       return sum a        def functools reduce a       return functools reduce operator concat  a    def functools reduce iconcat a       return functools reduce operator iconcat  a        def itertools chain a       return list itertools chain from iterable a     def numpy flat a       return list numpy array a  flat    def numpy concatenate a       return list numpy concatenate a     perfplot show      setup lambda n   list range 10      n        setup lambda n   list range n      10      kernels           forfor          sum brackets          functools reduce          functools reduce iconcat          itertools chain          numpy flat          numpy concatenate             n range  2    k for k in range 16        xlabel  quot num lists  of length 10  quot         xlabel  quot len lists  10 lists total  quot

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One can also use NumPy s flat   import numpy as np list np array l  flat    Edit 11 02 2016  Only works when sublists have identical dimensions

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This works with abitrarily nested lists  It can be easily extended to work with other kinds of iterables   def flatten seq          list - gt  list                                                                                                                                                                                return a flattend list from an abitrarily nested list                                                                                                                                                  if not seq          return        if not isinstance seq 0   list           return  seq 0     flatten seq 1        return flatten seq 0     flatten seq 1      Sample run   gt  gt  gt  flatten  1   2  3      4  5  6   7     8     9    1  2  3  4  5  6  7  8  9

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Don t reinvent the wheel if you use Django    gt  gt  gt  from django contrib admin utils import flatten  gt  gt  gt  l     1 2 3    4 5    6    gt  gt  gt  flatten l   gt  gt  gt   1  2  3  4  5  6       Pandas    gt  gt  gt  from pandas core common import flatten  gt  gt  gt  list flatten l        Itertools    gt  gt  gt  import itertools  gt  gt  gt  flatten   itertools chain from iterable  gt  gt  gt  list flatten l        Matplotlib   gt  gt  gt  from matplotlib cbook import flatten  gt  gt  gt  list flatten l        Unipath    gt  gt  gt  from unipath path import flatten  gt  gt  gt  list flatten l        Setuptools    gt  gt  gt  from setuptools namespaces import flatten  gt  gt  gt  list flatten l

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Another unusual approach that works for hetero- and homogeneous lists of integers   from typing import List   def flatten l  list  - gt  List int          Flatten an arbitrary deep nested list of lists of integers       Examples           gt  gt  gt  flatten  1  2   1   10              1  2  1  10       Args          l  Union l  Union int  List int        Returns          Flatted list of integer             return  int i strip         for i in str l  split

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Given a list of lists t  flat list    item for sublist in t for item in sublist   which means  flat list      for sublist in t      for item in sublist          flat list append item   is faster than the shortcuts posted so far   t is the list to flatten   Here is the corresponding function  flatten   lambda t   item for sublist in t for item in sublist   As evidence  you can use the timeit module in the standard library    python -mtimeit -s t   1 2 3   4 5 6    7    8 9   99    item for sublist in t for item in sublist   10000 loops  best of 3  143 usec per loop   python -mtimeit -s t   1 2 3   4 5 6    7    8 9   99   sum t       1000 loops  best of 3  969 usec per loop   python -mtimeit -s t   1 2 3   4 5 6    7    8 9   99   reduce lambda x y  x y t   1000 loops  best of 3  1 1 msec per loop  Explanation  the shortcuts based on    including the implied use in sum  are  of necessity  O T  2  when there are T sublists -- as the intermediate result list keeps getting longer  at each step a new intermediate result list object gets allocated  and all the items in the previous intermediate result must be copied over  as well as a few new ones added at the end   So  for simplicity and without actual loss of generality  say you have T sublists of k items each  the first k items are copied back and forth T-1 times  the second k items T-2 times  and so on  total number of copies is k times the sum of x for x from 1 to T excluded  i e   k    T  2  2  The list comprehension just generates one list  once  and copies each item over  from its original place of residence to the result list  also exactly once

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You can avoid recursive calls to the stack using an actual stack data structure pretty simply   alist    1  1 2   1 2  4 5 6  3   33    newlist       while len alist   gt  0     templist   alist pop     if type templist     type list          while len templist   gt  0         temp   templist pop         if type temp     type list              for x in temp             templist append x        else           newlist append temp    else       newlist append templist  print list reversed newlist

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You can use numpy   flat list   list np concatenate list of list

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Here is a function using recursion which will work on any arbitrary nested list   def flatten nested lst           Return a list after transforming the inner lists         so that it s a 1-D list        gt  gt  gt  flatten        a    a      ab       abc           a    a    ab    abc               if not isinstance nested lst  list           return nested lst       res          for l in nested lst          if not isinstance l  list               res     l          else              res    flatten l        return res      gt  gt  gt  flatten        a    a      ab       abc       a    a    ab    abc

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A simple recursive method using reduce from functools and the add operator on lists    gt  gt  gt  from functools import reduce  gt  gt  gt  from operator import add  gt  gt  gt  flatten   lambda lst   lst  if type lst  is int else reduce add   flatten ele  for ele in lst    gt  gt  gt  flatten l   1  2  3  4  5  6  7  8  9    The function flatten takes in lst as parameter  It loops all the elements of lst until reaching integers  can also change int to float  str  etc  for other data types   which are added to the return value of the outermost recursion   Recursion  unlike methods like for loops and monads  is that it is a general solution not limited by the list depth  For example  a list with depth of 5 can be flattened the same way as l    gt  gt  gt  l2     3   1  2      6   5   4  0   7    8     9  10     gt  gt  gt  flatten l2   3  1  2  6  5  4  0  7  8  9  10

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another fun way to do this   from functools import reduce from operator import add  li   1 2   3 4   x  reduce add  li

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Recursive version  x    1 2  3 4   5  6  7    8 9  10    def flatten list k       result   list       for i in k          if isinstance i list                 The isinstance   function checks if the object  first argument  is an               instance or subclass of classinfo class  second argument               result extend flatten list i    Recursive call         else              result append i      return result  flatten list x   result    1 2 3 4 5 6 7 8 9 10

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Although it wasn t asked in the original question  there is also an interest in flattenning several times if there are lists of lists of lists    Also doing at most depth times of un-nesting  Or total flattening  when depth is None   i e  converting any nesting depth to one global flat list  same as infinite depth  And supporting varying depth of list-nesting  Everything that is achieved in my next recursive flatten l  depth  function and example below  doesn t depend on any module import and works lazily  emits iterator   Try it online  def flatten l  depth   1       done  ndepth   False  None     if depth is not None          done  ndepth   depth  lt   0  depth - 1     if not isinstance l  list           l  done    l   True     return iter l  if done else  e1 for e0 in l for e1 in flatten e0  ndepth    l        1   2   3    4  5        6  7    8   9  10         ab    c    11  12        for depth in  0  1  2  3  4  None       print  depth   str depth  rjust 5        list flatten l  depth   depth     Outputs  depth     0      1   2   3    4  5      6  7    8   9  10       ab    c    11  12  depth     1     1   2   3    4  5    6  7    8   9  10     ab    c   11  12  depth     2    1   2   3  4  5  6  7  8   9  10    ab    c   11  12  depth     3    1  2  3  4  5  6  7  8  9  10   ab    c   11  12  depth     4    1  2  3  4  5  6  7  8  9  10   ab    c   11  12  depth  None    1  2  3  4  5  6  7  8  9  10   ab    c   11  12

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Note from the author  This is inefficient  But fun  because monoids are awesome  It s not appropriate for production Python code    gt  gt  gt  sum l       1  2  3  4  5  6  7  8  9    This just sums the elements of iterable passed in the first argument  treating second argument as the initial value of the sum  if not given  0 is used instead and this case will give you an error    Because you are summing nested lists  you actually get  1 3   2 4  as a result of sum   1 3   2 4        which is equal to  1 3 2 4    Note that only works on lists of lists  For lists of lists of lists  you ll need another solution

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I would like to use the concatenate and  ravel of the numpy as below import numpy as np l     1  2  3    4  5  6    7    8  9    out   np concatenate np array l   ravel

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You can use itertools chain     import itertools list2d     1 2 3    4 5 6    7    8 9   merged   list itertools chain  list2d     Or you can use itertools chain from iterable   which doesn t require unpacking the list with the   operator   import itertools list2d     1 2 3    4 5 6    7    8 9   merged   list itertools chain from iterable list2d

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matplotlib cbook flatten   will work for nested lists even if they nest more deeply than the example   import matplotlib l     1  2  3    4  5  6    7    8  9   print list matplotlib cbook flatten l    l2     1  2  3    4  5  6    7    8   9  10   11  12   13      print list matplotlib cbook flatten l2     Result    1  2  3  4  5  6  7  8  9   1  2  3  4  5  6  7  8  9  10  11  12  13    This is 18x faster than underscore   flatten   Average time over 1000 trials of matplotlib cbook flatten  2 55e-05 sec Average time over 1000 trials of underscore   flatten  4 63e-04 sec  time for underscore     time for matplotlib cbook    18 1233394636

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A funny solution  import pandas as pd list pd DataFrame   lists  l    lists   explode       1  2  3  4  5  6  7  8  9

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Simple code for underscore py package fan  from underscore import     flatten   1  2  3    4  5  6    7    8  9       1  2  3  4  5  6  7  8  9    It solves all flatten problems  none list item or complex nesting   from underscore import     1 is none list item    2   3   is complex nesting   flatten  1   2   3     4  5  6    7    8  9       1  2  3  4  5  6  7  8  9    You can install underscore py with pip  pip install underscore py

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The accepted answer did not work for me when dealing with text-based lists of variable lengths  Here is an alternate approach that did work for me   l     aaa    bb    cccccc     xx    yyyyyyy      Accepted answer that did not work   flat list    item for sublist in l for item in sublist  print flat list    a    a    a    b    b    c    c    c    c    c    c    xx    yyyyyyy     New proposed solution that did work for me   flat list           flat list extend item  if isinstance item  list  else flat list append item  for item in l if item  print flat list    aaa    bb    cccccc    xx    yyyyyyy

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flat list      for i in list of list      flat list  i   This Code also works fine as it just extend the list all the way  Although it is much similar but only have one for loop  So It have less complexity than adding 2 for loops

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I recently came across a situation where I had a mix of strings and numeric data in sublists such as  test     591212948     special    assoc    of    Chicago    Jon    Doe      Jon      Doe      fl    92001  555555555   hello     hello2    a     b     hello33     z    w     b      where methods like flat list    item for sublist in test for item in sublist  have not worked  So  I came up with the following solution for 1  level of sublists  def concatList data       results          for rec in data          if type rec     list              results    rec             results   concatList results          else              results append rec      return results   And the result  In  38   concatList test  Out 38    Out 60     591212948    special    assoc    of    Chicago    Jon    Doe    Jon    Doe    fl   92001  555555555   hello    hello2    a    b    hello33    z    w    b

[python] How to make a flat list out of list of lists?

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