Flattening a shallow list in Python

Question

Is there a simple way to flatten a list of iterables with a list comprehension  or failing that  what would you all consider to be the best way to flatten a shallow list like this  balancing performance and readability   I tried to flatten such a list with a nested list comprehension  like this    image for image in menuitem for menuitem in list of menuitems    But I get in trouble of the NameError variety there  because the name  menuitem  is not defined   After googling and looking around on Stack Overflow  I got the desired results with a reduce statement   reduce list   add    map lambda x  list x   list of menuitems     But this method is fairly unreadable because I need that list x  call there because x is a Django QuerySet object   Conclusion    Thanks to everyone who contributed to this question   Here is a summary of what I learned   I m also making this a community wiki in case others want to add to or correct these observations   My original reduce statement is redundant and is better written this way    gt  gt  gt  reduce list   add     list mi  for mi in list of menuitems     This is the correct syntax for a nested list comprehension  Brilliant summary dF      gt  gt  gt   image for mi in list of menuitems for image in mi    But neither of these methods are as efficient as using itertools chain    gt  gt  gt  from itertools import chain  gt  gt  gt  list chain  list of menuitems     And as  cdleary notes  it s probably better style to avoid   operator magic by using chain from iterable like so    gt  gt  gt  chain   itertools chain from iterable   1 2   3   5 89      6     gt  gt  gt  print list chain    gt  gt  gt   1  2  3  5  89  6

User · Accepted Answer

If you re just looking to iterate over a flattened version of the data structure and don t need an indexable sequence  consider itertools chain and company    gt  gt  gt  list of menuitems      image00    image01      image10         gt  gt  gt  import itertools  gt  gt  gt  chain   itertools chain  list of menuitems   gt  gt  gt  print list chain     image00    image01    image10     It will work on anything that s iterable  which should include Django s iterable QuerySets  which it appears that you re using in the question   Edit  This is probably as good as a reduce anyway  because reduce will have the same overhead copying the items into the list that s being extended  chain will only incur this  same  overhead if you run list chain  at the end   Meta-Edit  Actually  it s less overhead than the question s proposed solution  because you throw away the temporary lists you create when you extend the original with the temporary   Edit  As J F  Sebastian says itertools chain from iterable avoids the unpacking and you should use that to avoid   magic  but the timeit app shows negligible performance difference

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S Lott  You inspired me to write a timeit app   I figured it would also vary based on the number of partitions  number of iterators within the container list  -- your comment didn t mention how many partitions there were of the thirty items  This plot is flattening a thousand items in every run  with varying number of partitions  The items are evenly distributed among the partitions     Code  Python 2 6       usr bin env python2 6     Usage   prog item count     from   future   import print function  import collections import itertools import operator from timeit import Timer import sys  import matplotlib pyplot as pyplot  def itertools flatten iter lst       return list itertools chain  iter lst    def itertools iterable flatten iter iter       return list itertools chain from iterable iter iter    def reduce flatten iter lst       return reduce operator add  map list  iter lst    def reduce lambda flatten iter lst       return reduce operator add  map lambda x  list x    i for i in iter lst     def comprehension flatten iter lst       return list item for iter  in iter lst for item in iter    METHODS     itertools    itertools iterable    reduce    reduce lambda               comprehension    def  time test assert iter lst          Make sure all methods produce an equivalent value       raise AssertionError  On any non-equivalent value         callables    globals   method     flatten   for method in METHODS      results    callable iter lst  for callable in callables      if not all result    results 0  for result in results 1             raise AssertionError  def time test partition count  item count per partition  test count 10000          Run flatten methods on a list of  param  partition count  iterables      Normalize results over  param  test count  runs       return  Mapping from method to  normalized  microseconds per pass              iter lst     dict      item count per partition    partition count     print  Partition count        partition count      print  Items per partition    item count per partition       time test assert iter lst      test str    flatten  r     iter lst     result by method          for method in METHODS          setup str    from test import  s flatten as flatten    method         t   Timer test str  setup str          per pass   test count   t timeit number test count    test count         print   20s    2f usec pass     method  per pass           result by method method    per pass     return result by method  if   name         main         if len sys argv     2          raise ValueError  Need a number of items to flatten       item count   int sys argv 1       partition counts          pass times by method   collections defaultdict list      for partition count in xrange 1  item count           if item count   partition count    0              continue         items per partition   item count   partition count         result by method   time test partition count  items per partition          partition counts append partition count          for method  result in result by method iteritems                pass times by method method  append result      for method  pass times in pass times by method iteritems            pyplot plot partition counts  pass times  label method      pyplot legend       pyplot title  Flattening Comparison for  d Items    item count      pyplot xlabel  Number of Partitions       pyplot ylabel  Microseconds       pyplot show     Edit  Decided to make it community wiki   Note  METHODS should probably be accumulated with a decorator  but I figure it d be easier for people to read this way

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What about   from operator import add reduce add  map lambda x  list x image set all      mi for mi in list of menuitems      But  Guido is recommending against performing too much in a single line of code since it reduces readability  There is minimal  if any  performance gain by performing what you want in a single line vs  multiple lines

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What about   from operator import add reduce add  map lambda x  list x image set all      mi for mi in list of menuitems      But  Guido is recommending against performing too much in a single line of code since it reduces readability  There is minimal  if any  performance gain by performing what you want in a single line vs  multiple lines

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If each item in the list is a string  and any strings inside those strings use     rather than       you can use regular expressions  re module    gt  gt  gt  flattener   re compile             gt  gt  gt  flattener  lt  sre SRE Pattern object at 0x10d439ca8 gt   gt  gt  gt  stred   str in list   gt  gt  gt  outed   flattener findall stred    The above code converts in list into a string  uses the regex to find all the substrings within quotes  i e  each item of the list  and spits them out as a list

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In Python 2 6  using chain from iterable      gt  gt  gt  from itertools import chain  gt  gt  gt  list chain from iterable mi image set all   for mi in h get image menu       It avoids creating of intermediate list

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pylab provides a flatten   link to numpy flatten

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This solution works for arbitrary nesting depths - not just the  list of lists  depth that some  all   of the other solutions are limited to   def flatten x       result          for el in x          if hasattr el     iter     and not isinstance el  basestring               result extend flatten el           else              result append el      return result   It s the recursion which allows for arbitrary depth nesting - until you hit the maximum recursion depth  of course

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Performance Results   Revised   import itertools def itertools flatten  aList        return list  itertools chain  aList     from operator import add def reduce flatten1  aList        return reduce add  map lambda x  list x    mi for mi in aList     def reduce flatten2  aList        return reduce list   add    map list  aList    def comprehension flatten  aList        return list y for x in aList for y in x    I flattened a 2-level list of 30 items 1000 times  itertools flatten     0 00554 comprehension flatten 0 00815 reduce flatten2       0 01103 reduce flatten1       0 01404   Reduce is always a poor choice

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From my experience  the most efficient way to flatten a list of lists is   flat list      map flat list extend  list of list    Some timeit comparisons with the other proposed methods   list of list    range 10   1000  timeit flat list     map flat list extend  list of list   10000 loops  best of 3  119   s per loop  timeit flat list list itertools chain from iterable list of list    1000 loops  best of 3  210   s per loop  timeit flat list  i for sublist in list of list for i in sublist   1000 loops  best of 3  525   s per loop  timeit flat list reduce list   add   list of list   100 loops  best of 3  18 1 ms per loop   Now  the efficiency gain appears better when processing longer sublists   list of list    range 1000   10  timeit flat list     map flat list extend  list of list   10000 loops  best of 3  60 7   s per loop  timeit flat list list itertools chain from iterable list of list    10000 loops  best of 3  176   s per loop   And this methods also works with any iterative object   class SquaredRange object       def   init   self  n            self range   range n      def   iter   self           for i in self range               yield i  2  list of list    SquaredRange 5   3 flat list      map flat list extend  list of list  print flat list   0  1  4  9  16  0  1  4  9  16  0  1  4  9  16

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pylab provides a flatten   link to numpy flatten

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sum list of lists      would flatten it   l      image00    image01      image10        print sum l       prints   image00    image01    image10

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What about   from operator import add reduce add  map lambda x  list x image set all      mi for mi in list of menuitems      But  Guido is recommending against performing too much in a single line of code since it reduces readability  There is minimal  if any  performance gain by performing what you want in a single line vs  multiple lines

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Off the top of my head  you can eliminate the lambda   reduce list   add    map list   mi image set all   for mi in list of menuitems      Or even eliminate the map  since you ve already got a list-comp   reduce list   add     list mi image set all    for mi in list of menuitems     You can also just express this as a sum of lists   sum  list mi image set all    for mi in list of menuitems

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Here is a version working for multiple levels of list using collectons Iterable   import collections  def flatten o  flatten condition lambda i  isinstance i                 collections Iterable  and not isinstance i  str        result          for i in o          if flatten condition i               result extend flatten i  flatten condition           else              result append i      return result

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def is iterable item      return isinstance item  list  or isinstance item  tuple    def flatten items       for i in items          if is iterable item               for m in flatten i                   yield m         else              yield i   Test   print list flatten2  1 0  2   a    4      6     8        8    9       12    10

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This solution works for arbitrary nesting depths - not just the  list of lists  depth that some  all   of the other solutions are limited to   def flatten x       result          for el in x          if hasattr el     iter     and not isinstance el  basestring               result extend flatten el           else              result append el      return result   It s the recursion which allows for arbitrary depth nesting - until you hit the maximum recursion depth  of course

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Performance Results   Revised   import itertools def itertools flatten  aList        return list  itertools chain  aList     from operator import add def reduce flatten1  aList        return reduce add  map lambda x  list x    mi for mi in aList     def reduce flatten2  aList        return reduce list   add    map list  aList    def comprehension flatten  aList        return list y for x in aList for y in x    I flattened a 2-level list of 30 items 1000 times  itertools flatten     0 00554 comprehension flatten 0 00815 reduce flatten2       0 01103 reduce flatten1       0 01404   Reduce is always a poor choice

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In Python 2 6  using chain from iterable      gt  gt  gt  from itertools import chain  gt  gt  gt  list chain from iterable mi image set all   for mi in h get image menu       It avoids creating of intermediate list

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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  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 simple alternative is to use numpy s concatenate but it converts the contents to float   import numpy as np print np concatenate   1 2   3   5 89      6      array    1     2     3     5    89     6    print list np concatenate   1 2   3   5 89      6          1     2     3     5    89     6

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In Python 3 4 you will be able to do     innerlist for innerlist in outer list

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Here is the correct solution using list comprehensions  they re backward in the question     gt  gt  gt  join   lambda it   y for x in it for y in x   gt  gt  gt  list join   1 2   3 4 5         1  2  3  4  5    In your case it would be   image for menuitem in list of menuitems for image in menuitem image set all      or you could use join and say  join menuitem image set all   for menuitem in list of menuitems    In either case  the gotcha was the nesting of the for loops

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Off the top of my head  you can eliminate the lambda   reduce list   add    map list   mi image set all   for mi in list of menuitems      Or even eliminate the map  since you ve already got a list-comp   reduce list   add     list mi image set all    for mi in list of menuitems     You can also just express this as a sum of lists   sum  list mi image set all    for mi in list of menuitems

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Performance Results   Revised   import itertools def itertools flatten  aList        return list  itertools chain  aList     from operator import add def reduce flatten1  aList        return reduce add  map lambda x  list x    mi for mi in aList     def reduce flatten2  aList        return reduce list   add    map list  aList    def comprehension flatten  aList        return list y for x in aList for y in x    I flattened a 2-level list of 30 items 1000 times  itertools flatten     0 00554 comprehension flatten 0 00815 reduce flatten2       0 01103 reduce flatten1       0 01404   Reduce is always a poor choice

User · Answer

Here is the correct solution using list comprehensions  they re backward in the question     gt  gt  gt  join   lambda it   y for x in it for y in x   gt  gt  gt  list join   1 2   3 4 5         1  2  3  4  5    In your case it would be   image for menuitem in list of menuitems for image in menuitem image set all      or you could use join and say  join menuitem image set all   for menuitem in list of menuitems    In either case  the gotcha was the nesting of the for loops

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S Lott  You inspired me to write a timeit app   I figured it would also vary based on the number of partitions  number of iterators within the container list  -- your comment didn t mention how many partitions there were of the thirty items  This plot is flattening a thousand items in every run  with varying number of partitions  The items are evenly distributed among the partitions     Code  Python 2 6       usr bin env python2 6     Usage   prog item count     from   future   import print function  import collections import itertools import operator from timeit import Timer import sys  import matplotlib pyplot as pyplot  def itertools flatten iter lst       return list itertools chain  iter lst    def itertools iterable flatten iter iter       return list itertools chain from iterable iter iter    def reduce flatten iter lst       return reduce operator add  map list  iter lst    def reduce lambda flatten iter lst       return reduce operator add  map lambda x  list x    i for i in iter lst     def comprehension flatten iter lst       return list item for iter  in iter lst for item in iter    METHODS     itertools    itertools iterable    reduce    reduce lambda               comprehension    def  time test assert iter lst          Make sure all methods produce an equivalent value       raise AssertionError  On any non-equivalent value         callables    globals   method     flatten   for method in METHODS      results    callable iter lst  for callable in callables      if not all result    results 0  for result in results 1             raise AssertionError  def time test partition count  item count per partition  test count 10000          Run flatten methods on a list of  param  partition count  iterables      Normalize results over  param  test count  runs       return  Mapping from method to  normalized  microseconds per pass              iter lst     dict      item count per partition    partition count     print  Partition count        partition count      print  Items per partition    item count per partition       time test assert iter lst      test str    flatten  r     iter lst     result by method          for method in METHODS          setup str    from test import  s flatten as flatten    method         t   Timer test str  setup str          per pass   test count   t timeit number test count    test count         print   20s    2f usec pass     method  per pass           result by method method    per pass     return result by method  if   name         main         if len sys argv     2          raise ValueError  Need a number of items to flatten       item count   int sys argv 1       partition counts          pass times by method   collections defaultdict list      for partition count in xrange 1  item count           if item count   partition count    0              continue         items per partition   item count   partition count         result by method   time test partition count  items per partition          partition counts append partition count          for method  result in result by method iteritems                pass times by method method  append result      for method  pass times in pass times by method iteritems            pyplot plot partition counts  pass times  label method      pyplot legend       pyplot title  Flattening Comparison for  d Items    item count      pyplot xlabel  Number of Partitions       pyplot ylabel  Microseconds       pyplot show     Edit  Decided to make it community wiki   Note  METHODS should probably be accumulated with a decorator  but I figure it d be easier for people to read this way

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Performance Results   Revised   import itertools def itertools flatten  aList        return list  itertools chain  aList     from operator import add def reduce flatten1  aList        return reduce add  map lambda x  list x    mi for mi in aList     def reduce flatten2  aList        return reduce list   add    map list  aList    def comprehension flatten  aList        return list y for x in aList for y in x    I flattened a 2-level list of 30 items 1000 times  itertools flatten     0 00554 comprehension flatten 0 00815 reduce flatten2       0 01103 reduce flatten1       0 01404   Reduce is always a poor choice

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A simple alternative is to use numpy s concatenate but it converts the contents to float   import numpy as np print np concatenate   1 2   3   5 89      6      array    1     2     3     5    89     6    print list np concatenate   1 2   3   5 89      6          1     2     3     5    89     6

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You almost have it  The way to do nested list comprehensions is to put the for statements in the same order as they would go in regular nested for statements   Thus  this  for inner list in outer list      for item in inner list                corresponds to       for inner list in outer list for item in inner list    So you want   image for menuitem in list of menuitems for image in menuitem

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This solution works for arbitrary nesting depths - not just the  list of lists  depth that some  all   of the other solutions are limited to   def flatten x       result          for el in x          if hasattr el     iter     and not isinstance el  basestring               result extend flatten el           else              result append el      return result   It s the recursion which allows for arbitrary depth nesting - until you hit the maximum recursion depth  of course

User · Answer

In Python 2 6  using chain from iterable      gt  gt  gt  from itertools import chain  gt  gt  gt  list chain from iterable mi image set all   for mi in h get image menu       It avoids creating of intermediate list

User · Answer

Here is the correct solution using list comprehensions  they re backward in the question     gt  gt  gt  join   lambda it   y for x in it for y in x   gt  gt  gt  list join   1 2   3 4 5         1  2  3  4  5    In your case it would be   image for menuitem in list of menuitems for image in menuitem image set all      or you could use join and say  join menuitem image set all   for menuitem in list of menuitems    In either case  the gotcha was the nesting of the for loops

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If you re looking for a built-in  simple  one-liner you can use   a     1  2  3    4  5  6  b    i x  for i in a for x in range len i    print b   returns   1  2  3  4  5  6

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You almost have it  The way to do nested list comprehensions is to put the for statements in the same order as they would go in regular nested for statements   Thus  this  for inner list in outer list      for item in inner list                corresponds to       for inner list in outer list for item in inner list    So you want   image for menuitem in list of menuitems for image in menuitem

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The easiest way to achieve this in either Python 2 or 3 is to use the morph library using pip install morph   The code is   import morph  list     1 2   3   5 89      6   flattened list   morph flatten list     returns  1  2  3  5  89  6

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The easiest way to achieve this in either Python 2 or 3 is to use the morph library using pip install morph   The code is   import morph  list     1 2   3   5 89      6   flattened list   morph flatten list     returns  1  2  3  5  89  6

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If you have to flat a more complicated list with not iterable elements or with depth more than 2 you can use following function   def flat list list to flat       if not isinstance list to flat  list           yield list to flat     else          for item in list to flat              yield from flat list item    It will return a generator object which you can convert to a list with list   function  Notice that yield from syntax is available starting from python3 3  but you can use explicit iteration instead  Example    gt  gt  gt  a    1   2  3    1   2  3   1   2  3       gt  gt  gt  print list flat list a     1  2  3  1  2  3  1  2  3

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S Lott  You inspired me to write a timeit app   I figured it would also vary based on the number of partitions  number of iterators within the container list  -- your comment didn t mention how many partitions there were of the thirty items  This plot is flattening a thousand items in every run  with varying number of partitions  The items are evenly distributed among the partitions     Code  Python 2 6       usr bin env python2 6     Usage   prog item count     from   future   import print function  import collections import itertools import operator from timeit import Timer import sys  import matplotlib pyplot as pyplot  def itertools flatten iter lst       return list itertools chain  iter lst    def itertools iterable flatten iter iter       return list itertools chain from iterable iter iter    def reduce flatten iter lst       return reduce operator add  map list  iter lst    def reduce lambda flatten iter lst       return reduce operator add  map lambda x  list x    i for i in iter lst     def comprehension flatten iter lst       return list item for iter  in iter lst for item in iter    METHODS     itertools    itertools iterable    reduce    reduce lambda               comprehension    def  time test assert iter lst          Make sure all methods produce an equivalent value       raise AssertionError  On any non-equivalent value         callables    globals   method     flatten   for method in METHODS      results    callable iter lst  for callable in callables      if not all result    results 0  for result in results 1             raise AssertionError  def time test partition count  item count per partition  test count 10000          Run flatten methods on a list of  param  partition count  iterables      Normalize results over  param  test count  runs       return  Mapping from method to  normalized  microseconds per pass              iter lst     dict      item count per partition    partition count     print  Partition count        partition count      print  Items per partition    item count per partition       time test assert iter lst      test str    flatten  r     iter lst     result by method          for method in METHODS          setup str    from test import  s flatten as flatten    method         t   Timer test str  setup str          per pass   test count   t timeit number test count    test count         print   20s    2f usec pass     method  per pass           result by method method    per pass     return result by method  if   name         main         if len sys argv     2          raise ValueError  Need a number of items to flatten       item count   int sys argv 1       partition counts          pass times by method   collections defaultdict list      for partition count in xrange 1  item count           if item count   partition count    0              continue         items per partition   item count   partition count         result by method   time test partition count  items per partition          partition counts append partition count          for method  result in result by method iteritems                pass times by method method  append result      for method  pass times in pass times by method iteritems            pyplot plot partition counts  pass times  label method      pyplot legend       pyplot title  Flattening Comparison for  d Items    item count      pyplot xlabel  Number of Partitions       pyplot ylabel  Microseconds       pyplot show     Edit  Decided to make it community wiki   Note  METHODS should probably be accumulated with a decorator  but I figure it d be easier for people to read this way

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You almost have it  The way to do nested list comprehensions is to put the for statements in the same order as they would go in regular nested for statements   Thus  this  for inner list in outer list      for item in inner list                corresponds to       for inner list in outer list for item in inner list    So you want   image for menuitem in list of menuitems for image in menuitem

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have you tried flatten  From matplotlib cbook flatten seq  scalarp      l   1 2 3   4 5 6    7    8 9   33  run  list flatten l              3732 function calls  3303 primitive calls  in 0 007 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 007    0 007  lt string gt  1  lt module gt         429    0 001    0 000    0 001    0 000 cbook py 475 iterable        429    0 002    0 000    0 003    0 000 cbook py 484 is string like        429    0 002    0 000    0 006    0 000 cbook py 565 is scalar or string    727 298    0 001    0 000    0 007    0 000 cbook py 605 flatten        429    0 000    0 000    0 001    0 000 core py 5641 isMaskedArray        858    0 001    0 000    0 001    0 000  isinstance        429    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   66  run  list flatten l              7461 function calls  6603 primitive calls  in 0 007 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 007    0 007  lt string gt  1  lt module gt         858    0 001    0 000    0 001    0 000 cbook py 475 iterable        858    0 002    0 000    0 003    0 000 cbook py 484 is string like        858    0 002    0 000    0 006    0 000 cbook py 565 is scalar or string   1453 595    0 001    0 000    0 007    0 000 cbook py 605 flatten        858    0 000    0 000    0 001    0 000 core py 5641 isMaskedArray       1716    0 001    0 000    0 001    0 000  isinstance        858    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   99  run  list flatten l              11190 function calls  9903 primitive calls  in 0 010 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 010    0 010  lt string gt  1  lt module gt        1287    0 002    0 000    0 002    0 000 cbook py 475 iterable       1287    0 003    0 000    0 004    0 000 cbook py 484 is string like       1287    0 002    0 000    0 009    0 000 cbook py 565 is scalar or string   2179 892    0 001    0 000    0 010    0 000 cbook py 605 flatten       1287    0 001    0 000    0 001    0 000 core py 5641 isMaskedArray       2574    0 001    0 000    0 001    0 000  isinstance       1287    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   132  run  list flatten l              14919 function calls  13203 primitive calls  in 0 013 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 013    0 013  lt string gt  1  lt module gt        1716    0 002    0 000    0 002    0 000 cbook py 475 iterable       1716    0 004    0 000    0 006    0 000 cbook py 484 is string like       1716    0 003    0 000    0 011    0 000 cbook py 565 is scalar or string  2905 1189    0 002    0 000    0 013    0 000 cbook py 605 flatten       1716    0 001    0 000    0 001    0 000 core py 5641 isMaskedArray       3432    0 001    0 000    0 001    0 000  isinstance       1716    0 001    0 000    0 001    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler    UPDATE Which gave me another idea   l   1 2 3   4 5 6    7    8 9   33  run  flattenlist l             564 function calls  432 primitive calls  in 0 000 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      133 1    0 000    0 000    0 000    0 000  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 000    0 000  lt string gt  1  lt module gt         429    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   66  run  flattenlist l             1125 function calls  861 primitive calls  in 0 001 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      265 1    0 001    0 000    0 001    0 001  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 001    0 001  lt string gt  1  lt module gt         858    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   99  run  flattenlist l             1686 function calls  1290 primitive calls  in 0 001 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      397 1    0 001    0 000    0 001    0 001  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 001    0 001  lt string gt  1  lt module gt        1287    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   132  run  flattenlist l             2247 function calls  1719 primitive calls  in 0 002 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      529 1    0 001    0 000    0 002    0 002  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 002    0 002  lt string gt  1  lt module gt        1716    0 001    0 000    0 001    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   1320  run  flattenlist l             22443 function calls  17163 primitive calls  in 0 016 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function     5281 1    0 011    0 000    0 016    0 016  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 016    0 016  lt string gt  1  lt module gt       17160    0 005    0 000    0 005    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects    So to test how effective it is when recursive gets deeper  How much deeper   l   1 2 3   4 5 6    7    8 9   1320  new  l  33  run  flattenlist new             740589 function calls  566316 primitive calls  in 0 418 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   174274 1    0 281    0 000    0 417    0 417  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 001    0 001    0 418    0 418  lt string gt  1  lt module gt      566313    0 136    0 000    0 136    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  66  run  flattenlist new             1481175 function calls  1132629 primitive calls  in 0 809 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   348547 1    0 542    0 000    0 807    0 807  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 002    0 002    0 809    0 809  lt string gt  1  lt module gt     1132626    0 266    0 000    0 266    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  99  run  flattenlist new             2221761 function calls  1698942 primitive calls  in 1 211 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   522820 1    0 815    0 000    1 208    1 208  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 002    0 002    1 211    1 211  lt string gt  1  lt module gt     1698939    0 393    0 000    0 393    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  132  run  flattenlist new             2962347 function calls  2265255 primitive calls  in 1 630 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   697093 1    1 091    0 000    1 627    1 627  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 003    0 003    1 630    1 630  lt string gt  1  lt module gt     2265252    0 536    0 000    0 536    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  1320  run  flattenlist new             29623443 function calls  22652523 primitive calls  in 16 103 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function  6970921 1   10 842    0 000   16 069   16 069  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 034    0 034   16 103   16 103  lt string gt  1  lt module gt    22652520    5 227    0 000    5 227    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects    I will bet  flattenlist  I am going to use this rather than matploblib for a long long time unless I want a yield generator and fast result as  flatten  uses in matploblib cbook  This  is fast     And here is the code      typ  list tuple    def flattenlist d       thelist          for x in d          if not isinstance x typ               thelist     x          else              thelist    flattenlist x      return thelist

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From my experience  the most efficient way to flatten a list of lists is   flat list      map flat list extend  list of list    Some timeit comparisons with the other proposed methods   list of list    range 10   1000  timeit flat list     map flat list extend  list of list   10000 loops  best of 3  119   s per loop  timeit flat list list itertools chain from iterable list of list    1000 loops  best of 3  210   s per loop  timeit flat list  i for sublist in list of list for i in sublist   1000 loops  best of 3  525   s per loop  timeit flat list reduce list   add   list of list   100 loops  best of 3  18 1 ms per loop   Now  the efficiency gain appears better when processing longer sublists   list of list    range 1000   10  timeit flat list     map flat list extend  list of list   10000 loops  best of 3  60 7   s per loop  timeit flat list list itertools chain from iterable list of list    10000 loops  best of 3  176   s per loop   And this methods also works with any iterative object   class SquaredRange object       def   init   self  n            self range   range n      def   iter   self           for i in self range               yield i  2  list of list    SquaredRange 5   3 flat list      map flat list extend  list of list  print flat list   0  1  4  9  16  0  1  4  9  16  0  1  4  9  16

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Off the top of my head  you can eliminate the lambda   reduce list   add    map list   mi image set all   for mi in list of menuitems      Or even eliminate the map  since you ve already got a list-comp   reduce list   add     list mi image set all    for mi in list of menuitems     You can also just express this as a sum of lists   sum  list mi image set all    for mi in list of menuitems

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Here is the correct solution using list comprehensions  they re backward in the question     gt  gt  gt  join   lambda it   y for x in it for y in x   gt  gt  gt  list join   1 2   3 4 5         1  2  3  4  5    In your case it would be   image for menuitem in list of menuitems for image in menuitem image set all      or you could use join and say  join menuitem image set all   for menuitem in list of menuitems    In either case  the gotcha was the nesting of the for loops

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sum list of lists      would flatten it   l      image00    image01      image10        print sum l       prints   image00    image01    image10

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In Python 2 6  using chain from iterable      gt  gt  gt  from itertools import chain  gt  gt  gt  list chain from iterable mi image set all   for mi in h get image menu       It avoids creating of intermediate list

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def is iterable item      return isinstance item  list  or isinstance item  tuple    def flatten items       for i in items          if is iterable item               for m in flatten i                   yield m         else              yield i   Test   print list flatten2  1 0  2   a    4      6     8        8    9       12    10

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If each item in the list is a string  and any strings inside those strings use     rather than       you can use regular expressions  re module    gt  gt  gt  flattener   re compile             gt  gt  gt  flattener  lt  sre SRE Pattern object at 0x10d439ca8 gt   gt  gt  gt  stred   str in list   gt  gt  gt  outed   flattener findall stred    The above code converts in list into a string  uses the regex to find all the substrings within quotes  i e  each item of the list  and spits them out as a list

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In Python 3 4 you will be able to do     innerlist for innerlist in outer list

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Here is a version working for multiple levels of list using collectons Iterable   import collections  def flatten o  flatten condition lambda i  isinstance i                 collections Iterable  and not isinstance i  str        result          for i in o          if flatten condition i               result extend flatten i  flatten condition           else              result append i      return result

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S Lott  You inspired me to write a timeit app   I figured it would also vary based on the number of partitions  number of iterators within the container list  -- your comment didn t mention how many partitions there were of the thirty items  This plot is flattening a thousand items in every run  with varying number of partitions  The items are evenly distributed among the partitions     Code  Python 2 6       usr bin env python2 6     Usage   prog item count     from   future   import print function  import collections import itertools import operator from timeit import Timer import sys  import matplotlib pyplot as pyplot  def itertools flatten iter lst       return list itertools chain  iter lst    def itertools iterable flatten iter iter       return list itertools chain from iterable iter iter    def reduce flatten iter lst       return reduce operator add  map list  iter lst    def reduce lambda flatten iter lst       return reduce operator add  map lambda x  list x    i for i in iter lst     def comprehension flatten iter lst       return list item for iter  in iter lst for item in iter    METHODS     itertools    itertools iterable    reduce    reduce lambda               comprehension    def  time test assert iter lst          Make sure all methods produce an equivalent value       raise AssertionError  On any non-equivalent value         callables    globals   method     flatten   for method in METHODS      results    callable iter lst  for callable in callables      if not all result    results 0  for result in results 1             raise AssertionError  def time test partition count  item count per partition  test count 10000          Run flatten methods on a list of  param  partition count  iterables      Normalize results over  param  test count  runs       return  Mapping from method to  normalized  microseconds per pass              iter lst     dict      item count per partition    partition count     print  Partition count        partition count      print  Items per partition    item count per partition       time test assert iter lst      test str    flatten  r     iter lst     result by method          for method in METHODS          setup str    from test import  s flatten as flatten    method         t   Timer test str  setup str          per pass   test count   t timeit number test count    test count         print   20s    2f usec pass     method  per pass           result by method method    per pass     return result by method  if   name         main         if len sys argv     2          raise ValueError  Need a number of items to flatten       item count   int sys argv 1       partition counts          pass times by method   collections defaultdict list      for partition count in xrange 1  item count           if item count   partition count    0              continue         items per partition   item count   partition count         result by method   time test partition count  items per partition          partition counts append partition count          for method  result in result by method iteritems                pass times by method method  append result      for method  pass times in pass times by method iteritems            pyplot plot partition counts  pass times  label method      pyplot legend       pyplot title  Flattening Comparison for  d Items    item count      pyplot xlabel  Number of Partitions       pyplot ylabel  Microseconds       pyplot show     Edit  Decided to make it community wiki   Note  METHODS should probably be accumulated with a decorator  but I figure it d be easier for people to read this way

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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  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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If you re looking for a built-in  simple  one-liner you can use   a     1  2  3    4  5  6  b    i x  for i in a for x in range len i    print b   returns   1  2  3  4  5  6

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have you tried flatten  From matplotlib cbook flatten seq  scalarp      l   1 2 3   4 5 6    7    8 9   33  run  list flatten l              3732 function calls  3303 primitive calls  in 0 007 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 007    0 007  lt string gt  1  lt module gt         429    0 001    0 000    0 001    0 000 cbook py 475 iterable        429    0 002    0 000    0 003    0 000 cbook py 484 is string like        429    0 002    0 000    0 006    0 000 cbook py 565 is scalar or string    727 298    0 001    0 000    0 007    0 000 cbook py 605 flatten        429    0 000    0 000    0 001    0 000 core py 5641 isMaskedArray        858    0 001    0 000    0 001    0 000  isinstance        429    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   66  run  list flatten l              7461 function calls  6603 primitive calls  in 0 007 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 007    0 007  lt string gt  1  lt module gt         858    0 001    0 000    0 001    0 000 cbook py 475 iterable        858    0 002    0 000    0 003    0 000 cbook py 484 is string like        858    0 002    0 000    0 006    0 000 cbook py 565 is scalar or string   1453 595    0 001    0 000    0 007    0 000 cbook py 605 flatten        858    0 000    0 000    0 001    0 000 core py 5641 isMaskedArray       1716    0 001    0 000    0 001    0 000  isinstance        858    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   99  run  list flatten l              11190 function calls  9903 primitive calls  in 0 010 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 010    0 010  lt string gt  1  lt module gt        1287    0 002    0 000    0 002    0 000 cbook py 475 iterable       1287    0 003    0 000    0 004    0 000 cbook py 484 is string like       1287    0 002    0 000    0 009    0 000 cbook py 565 is scalar or string   2179 892    0 001    0 000    0 010    0 000 cbook py 605 flatten       1287    0 001    0 000    0 001    0 000 core py 5641 isMaskedArray       2574    0 001    0 000    0 001    0 000  isinstance       1287    0 000    0 000    0 000    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   132  run  list flatten l              14919 function calls  13203 primitive calls  in 0 013 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function          1    0 000    0 000    0 013    0 013  lt string gt  1  lt module gt        1716    0 002    0 000    0 002    0 000 cbook py 475 iterable       1716    0 004    0 000    0 006    0 000 cbook py 484 is string like       1716    0 003    0 000    0 011    0 000 cbook py 565 is scalar or string  2905 1189    0 002    0 000    0 013    0 000 cbook py 605 flatten       1716    0 001    0 000    0 001    0 000 core py 5641 isMaskedArray       3432    0 001    0 000    0 001    0 000  isinstance       1716    0 001    0 000    0 001    0 000  iter          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler    UPDATE Which gave me another idea   l   1 2 3   4 5 6    7    8 9   33  run  flattenlist l             564 function calls  432 primitive calls  in 0 000 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      133 1    0 000    0 000    0 000    0 000  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 000    0 000  lt string gt  1  lt module gt         429    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   66  run  flattenlist l             1125 function calls  861 primitive calls  in 0 001 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      265 1    0 001    0 000    0 001    0 001  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 001    0 001  lt string gt  1  lt module gt         858    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   99  run  flattenlist l             1686 function calls  1290 primitive calls  in 0 001 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      397 1    0 001    0 000    0 001    0 001  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 001    0 001  lt string gt  1  lt module gt        1287    0 000    0 000    0 000    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   132  run  flattenlist l             2247 function calls  1719 primitive calls  in 0 002 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function      529 1    0 001    0 000    0 002    0 002  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 002    0 002  lt string gt  1  lt module gt        1716    0 001    0 000    0 001    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     l   1 2 3   4 5 6    7    8 9   1320  run  flattenlist l             22443 function calls  17163 primitive calls  in 0 016 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function     5281 1    0 011    0 000    0 016    0 016  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 000    0 000    0 016    0 016  lt string gt  1  lt module gt       17160    0 005    0 000    0 005    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects    So to test how effective it is when recursive gets deeper  How much deeper   l   1 2 3   4 5 6    7    8 9   1320  new  l  33  run  flattenlist new             740589 function calls  566316 primitive calls  in 0 418 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   174274 1    0 281    0 000    0 417    0 417  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 001    0 001    0 418    0 418  lt string gt  1  lt module gt      566313    0 136    0 000    0 136    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  66  run  flattenlist new             1481175 function calls  1132629 primitive calls  in 0 809 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   348547 1    0 542    0 000    0 807    0 807  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 002    0 002    0 809    0 809  lt string gt  1  lt module gt     1132626    0 266    0 000    0 266    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  99  run  flattenlist new             2221761 function calls  1698942 primitive calls  in 1 211 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   522820 1    0 815    0 000    1 208    1 208  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 002    0 002    1 211    1 211  lt string gt  1  lt module gt     1698939    0 393    0 000    0 393    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  132  run  flattenlist new             2962347 function calls  2265255 primitive calls  in 1 630 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function   697093 1    1 091    0 000    1 627    1 627  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 003    0 003    1 630    1 630  lt string gt  1  lt module gt     2265252    0 536    0 000    0 536    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects     new  l  1320  run  flattenlist new             29623443 function calls  22652523 primitive calls  in 16 103 seconds     Ordered by  standard name     ncalls  tottime  percall  cumtime  percall filename lineno function  6970921 1   10 842    0 000   16 069   16 069  lt ipython-input-55-39b139bad497 gt  4 flattenlist          1    0 034    0 034   16 103   16 103  lt string gt  1  lt module gt    22652520    5 227    0 000    5 227    0 000  isinstance          1    0 000    0 000    0 000    0 000  method  disable  of   lsprof Profiler  objects    I will bet  flattenlist  I am going to use this rather than matploblib for a long long time unless I want a yield generator and fast result as  flatten  uses in matploblib cbook  This  is fast     And here is the code      typ  list tuple    def flattenlist d       thelist          for x in d          if not isinstance x typ               thelist     x          else              thelist    flattenlist x      return thelist

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This version is a generator Tweak it if you want a list   def list or tuple l       return isinstance l  list tuple      predicate will select the container  to be flattened    write your own as required    this one flattens every list tuple   def flatten seq predicate list or tuple                  recursive generator      for i in seq          if predicate seq               for j in flatten i                   yield j         else              yield i   You can add a predicate  if want to flatten those which satisfy a condition  Taken from python cookbook

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Off the top of my head  you can eliminate the lambda   reduce list   add    map list   mi image set all   for mi in list of menuitems      Or even eliminate the map  since you ve already got a list-comp   reduce list   add     list mi image set all    for mi in list of menuitems     You can also just express this as a sum of lists   sum  list mi image set all    for mi in list of menuitems

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You almost have it  The way to do nested list comprehensions is to put the for statements in the same order as they would go in regular nested for statements   Thus  this  for inner list in outer list      for item in inner list                corresponds to       for inner list in outer list for item in inner list    So you want   image for menuitem in list of menuitems for image in menuitem

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This version is a generator Tweak it if you want a list   def list or tuple l       return isinstance l  list tuple      predicate will select the container  to be flattened    write your own as required    this one flattens every list tuple   def flatten seq predicate list or tuple                  recursive generator      for i in seq          if predicate seq               for j in flatten i                   yield j         else              yield i   You can add a predicate  if want to flatten those which satisfy a condition  Taken from python cookbook

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This solution works for arbitrary nesting depths - not just the  list of lists  depth that some  all   of the other solutions are limited to   def flatten x       result          for el in x          if hasattr el     iter     and not isinstance el  basestring               result extend flatten el           else              result append el      return result   It s the recursion which allows for arbitrary depth nesting - until you hit the maximum recursion depth  of course

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What about   from operator import add reduce add  map lambda x  list x image set all      mi for mi in list of menuitems      But  Guido is recommending against performing too much in a single line of code since it reduces readability  There is minimal  if any  performance gain by performing what you want in a single line vs  multiple lines

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If you have to flat a more complicated list with not iterable elements or with depth more than 2 you can use following function   def flat list list to flat       if not isinstance list to flat  list           yield list to flat     else          for item in list to flat              yield from flat list item    It will return a generator object which you can convert to a list with list   function  Notice that yield from syntax is available starting from python3 3  but you can use explicit iteration instead  Example    gt  gt  gt  a    1   2  3    1   2  3   1   2  3       gt  gt  gt  print list flat list a     1  2  3  1  2  3  1  2  3

[python] Flattening a shallow list in Python

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