By telliott99

2010-01-28 22:15:42 8 Comments

Yes, I know this subject has been covered before (here, here, here, here), but as far as I know, all solutions, except for one, fail on a list like this:

L = [[[1, 2, 3], [4, 5]], 6]

Where the desired output is

[1, 2, 3, 4, 5, 6]

Or perhaps even better, an iterator. The only solution I saw that works for an arbitrary nesting is found in this question:

def flatten(x):
    result = []
    for el in x:
        if hasattr(el, "__iter__") and not isinstance(el, basestring):
    return result


Is this the best model? Did I overlook something? Any problems?


@Learning stats by example 2019-12-04 18:42:33

def flatten(item) -> list:
    if not isinstance(item, list): return item
    return reduce(lambda x, y: x + [y] if not isinstance(y, list) else x + [*flatten(y)], item, [])

Two-line reduce function.

@Toothpick Anemone 2019-10-25 18:06:39

The think that following would probably work in python 3:

def get_flat_iter(xparent):
        r = xparent
        if hasattr(xx, '__iter__'):
            iparent = iter(xparent)
            if iparent != xparent:
                r = map(a, xparent)
    return r

irregular_list = [1, [2, [3, 4]]]
flat_list = list(irregular_list)
print(flat_list) # [1, 2, 3, 4]

@Alexey Golyshev 2019-10-18 14:10:28

def nested_list(depth):
    l = [depth]
    for i in range(depth-1, 0, -1):
        l = [i, l]
    return l


[1, [2, [3, [4, [5, [6, [7, [8, [9, [10]]]]]]]]]]

def Flatten(ul):
    fl = []
    for i in ul:
        if type(i) is list:
            fl += Flatten(i)
            fl += [i]
    return fl


[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]


l = nested_list(100)

import collections

def flatten(l):
    for el in l:
        if isinstance(el, collections.Iterable) and not isinstance(el, (str, bytes)):
            yield from flatten(el)
            yield el
%%timeit -n 1000

320 µs ± 14.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%%timeit -n 1000

60 µs ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

list(flatten(l)) == Flatten(l)


@halcyonjuly7 2016-04-04 20:38:15

I am a dumb guy so I'll give a "dumb" solution. All that recursion hurts my brain.

flattened_list = []
nested_list = [[[1, 2, 3], [4, 5]], 6]

def flatten(nested_list, container):
    for item in nested_list:
        if isintance(item, list):
            flatten(item, container)

>>> flatten(nested_list, flattened_list)
>>> flattened_list
[1, 2, 3, 4, 5, 6]

I get that it's using a side effect but well that's to the best of my comprehension of recursion can go

@whackamadoodle3000 2017-08-09 17:42:53

Similar to diligar's answer

weird_list=[[1, 2, 3], [4, 5, 6], [7], [8, 9]]
nice_list = list(map(int, ''.join([e for e in str(weird_list) if e not in '[ ]']).split(',')))

@Matt Farguson 2017-02-27 01:28:19

This will flatten a list or dictionary (or list of lists or dictionaries of dictionaries etc). It assumes that the values are strings and it creates a string that concatenates each item with a separator argument. If you wanted you could use the separator to split the result into a list object afterward. It uses recursion if the next value is a list or a string. Use the key argument to tell whether you want the keys or the values (set key to false) from the dictionary object.

def flatten_obj(n_obj, key=True, my_sep=''):
    my_string = ''
    if type(n_obj) == list:
        for val in n_obj:
            my_sep_setter = my_sep if my_string != '' else ''
            if type(val) == list or type(val) == dict:
                my_string += my_sep_setter + flatten_obj(val, key, my_sep)
                my_string += my_sep_setter + val
    elif type(n_obj) == dict:
        for k, v in n_obj.items():
            my_sep_setter = my_sep if my_string != '' else ''
            d_val = k if key else v
            if type(v) == list or type(v) == dict:
                my_string += my_sep_setter + flatten_obj(v, key, my_sep)
                my_string += my_sep_setter + d_val
    elif type(n_obj) == str:
        my_sep_setter = my_sep if my_string != '' else ''
        my_string += my_sep_setter + n_obj
        return my_string
    return my_string

print(flatten_obj(['just', 'a', ['test', 'to', 'try'], 'right', 'now', ['or', 'later', 'today'],
                [{'dictionary_test': 'test'}, {'dictionary_test_two': 'later_today'}, 'my power is 9000']], my_sep=', ')


just, a, test, to, try, right, now, or, later, today, dictionary_test, dictionary_test_two, my power is 9000

@ADR 2019-03-22 18:00:27

Just use a funcy library: pip install funcy

import funcy

funcy.flatten([[[[1, 1], 1], 2], 3]) # returns generator
funcy.lflatten([[[[1, 1], 1], 2], 3]) # returns list

@Georgy 2019-05-23 13:32:53

FYI: it uses recursive solution: link to source

@unutbu 2010-01-28 22:42:07

This version of flatten avoids python's recursion limit (and thus works with arbitrarily deep, nested iterables). It is a generator which can handle strings and arbitrary iterables (even infinite ones).

import itertools as IT
import collections

def flatten(iterable, ltypes=collections.Iterable):
    remainder = iter(iterable)
    while True:
        first = next(remainder)
        if isinstance(first, ltypes) and not isinstance(first, (str, bytes)):
            remainder = IT.chain(first, remainder)
            yield first

Here are some examples demonstrating its use:

# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

                                       'foo bar'.split(),
# [2, 2, 2, 10, 20, 30, 'foo', 'bar', 1, 1]

# [1, 2, 3, 4]

seq = ([[chr(i),chr(i-32)] for i in range(ord('a'), ord('z')+1)] + list(range(0,9)))
# ['a', 'A', 'b', 'B', 'c', 'C', 'd', 'D', 'e', 'E', 'f', 'F', 'g', 'G', 'h', 'H',
# 'i', 'I', 'j', 'J', 'k', 'K', 'l', 'L', 'm', 'M', 'n', 'N', 'o', 'O', 'p', 'P',
# 'q', 'Q', 'r', 'R', 's', 'S', 't', 'T', 'u', 'U', 'v', 'V', 'w', 'W', 'x', 'X',
# 'y', 'Y', 'z', 'Z', 0, 1, 2, 3, 4, 5, 6, 7, 8]

Although flatten can handle infinite generators, it can not handle infinite nesting:

def infinitely_nested():
    while True:
        yield IT.chain(infinitely_nested(), IT.repeat(1))

print(list(IT.islice(flatten(infinitely_nested()), 10)))
# hangs

@wim 2013-09-15 20:09:53

any consensus on whether to use ABC Iterable or ABC Sequence?

@unutbu 2013-09-15 20:16:48

sets, dicts, deques, listiterators, generators, filehandles, and custom classes with __iter__ defined are all instances of collections.Iterable, but not collections.Sequence. The result of flattening a dict is a bit iffy, but otherwise, I think collections.Iterable is a better default than collections.Sequence. It's definitely the more liberal.

@unutbu 2013-09-17 09:27:56

@wim: One problem with using collections.Iterable is that this includes infinite generators. I've changed my answer handle this case.

@Georgy 2019-05-23 12:43:03

This doesn't seem to work for the 3rd and the 4th examples. It throws StopIteration. Also, looks like while True: first = next(remainder) could be replaced by for first in remainder:.

@Josh Lee 2010-01-28 22:34:45

My solution:

import collections

def flatten(x):
    if isinstance(x, collections.Iterable):
        return [a for i in x for a in flatten(i)]
        return [x]

A little more concise, but pretty much the same.

@abarnert 2013-07-05 08:03:08

You can do this without importing anything if you just try: iter(x) to test whether it's iterable… But I don't think having to import a stdlib module is a downside worth avoiding.

@alfasin 2014-12-07 06:14:15

Worth to note that this solution works only if all the items are of type int

@Zero 2017-07-25 17:13:14

Could make it more concise, def flatten(x): return [a for i in x for a in flatten(i)] if isinstance(x, collections.Iterable) else [x] - but readability might be subjective here.

@aandis 2018-11-29 11:51:04

this doesn't work on strings because strings are iterable too. Replace the condition with if isinstance(x, collections.Iterable) and not isinstance(x, basestring)

@Jorge Orpinel 2019-01-22 10:22:40

No recursion or nested loops. A few lines. Well formatted and easy to read:

def flatten_deep(arr: list):
""" Flattens arbitrarily-nested list `arr` into single-dimensional. """

while arr:
    if isinstance(arr[0], list):  # Checks whether first element is a list
        arr = arr[0] + arr[1:]  # If so, flattens that first element one level
        yield arr.pop(0)  # Otherwise yield as part of the flat array


From my own code at

@Statham 2018-11-28 15:48:09

This is a simple implement of flatten on python2

flatten=lambda l: reduce(lambda x,y:x+y,map(flatten,l),[]) if isinstance(l,list) else [l]

print flatten(test)

#output [1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

@cglacet 2018-08-02 09:02:19

When trying to answer such a question you really need to give the limitations of the code you propose as a solution. If it was only about performances I wouldn't mind too much, but most of the codes proposed as solution (including the accepted answer) fail to flatten any list that has a depth greater than 1000.

When I say most of the codes I mean all codes that use any form of recursion (or call a standard library function that is recursive). All these codes fail because for every of the recursive call made, the (call) stack grow by one unit, and the (default) python call stack has a size of 1000.

If you're not too familiar with the call stack, then maybe the following will help (otherwise you can just scroll to the Implementation).

Call stack size and recursive programming (dungeon analogy)

Finding the treasure and exit

Imagine you enter a huge dungeon with numbered rooms, looking for a treasure. You don't know the place but you have some indications on how to find the treasure. Each indication is a riddle (difficulty varies, but you can't predict how hard they will be). You decide to think a little bit about a strategy to save time, you make two observations:

  1. It's hard (long) to find the treasure as you'll have to solve (potentially hard) riddles to get there.
  2. Once the treasure found, returning to the entrance may be easy, you just have to use the same path in the other direction (though this needs a bit of memory to recall your path).

When entering the dungeon, you notice a small notebook here. You decide to use it to write down every room you exit after solving a riddle (when entering a new room), this way you'll be able to return back to the entrance. That's a genius idea, you won't even spend a cent implementing your strategy.

You enter the dungeon, solving with great success the first 1001 riddles, but here comes something you hadn't planed, you have no space left in the notebook you borrowed. You decide to abandon your quest as you prefer not having the treasure than being lost forever inside the dungeon (that looks smart indeed).

Executing a recursive program

Basically, it's the exact same thing as finding the treasure. The dungeon is the computer's memory, your goal now is not to find a treasure but to compute some function (find f(x) for a given x). The indications simply are sub-routines that will help you solving f(x). Your strategy is the same as the call stack strategy, the notebook is the stack, the rooms are the functions' return addresses:

x = ["over here", "am", "I"]
y = sorted(x) # You're about to enter a room named `sorted`, note down the current room address here so you can return back: 0x4004f4 (that room address looks weird)
# Seems like you went back from your quest using the return address 0x4004f4
# Let's see what you've collected 
print(' '.join(y))

The problem you encountered in the dungeon will be the same here, the call stack has a finite size (here 1000) and therefore, if you enter too many functions without returning back then you'll fill the call stack and have an error that look like "Dear adventurer, I'm very sorry but your notebook is full": RecursionError: maximum recursion depth exceeded. Note that you don't need recursion to fill the call stack, but it's very unlikely that a non-recursive program call 1000 functions without ever returning. It's important to also understand that once you returned from a function, the call stack is freed from the address used (hence the name "stack", return address are pushed in before entering a function and pulled out when returning). In the special case of a simple recursion (a function f that call itself once -- over and over --) you will enter f over and over until the computation is finished (until the treasure is found) and return from f until you go back to the place where you called f in the first place. The call stack will never be freed from anything until the end where it will be freed from all return addresses one after the other.

How to avoid this issue?

That's actually pretty simple: "don't use recursion if you don't know how deep it can go". That's not always true as in some cases, Tail Call recursion can be Optimized (TCO). But in python, this is not the case, and even "well written" recursive function will not optimize stack use. There is an interesting post from Guido about this question: Tail Recursion Elimination.

There is a technique that you can use to make any recursive function iterative, this technique we could call bring your own notebook. For example, in our particular case we simply are exploring a list, entering a room is equivalent to entering a sublist, the question you should ask yourself is how can I get back from a list to its parent list? The answer is not that complex, repeat the following until the stack is empty:

  1. push the current list address and index in a stack when entering a new sublist (note that a list address+index is also an address, therefore we just use the exact same technique used by the call stack);
  2. every time an item is found, yield it (or add them in a list);
  3. once a list is fully explored, go back to the parent list using the stack return address (and index).

Also note that this is equivalent to a DFS in a tree where some nodes are sublists A = [1, 2] and some are simple items: 0, 1, 2, 3, 4 (for L = [0, [1,2], 3, 4]). The tree looks like this:

           |     |     |     |
           0   --A--   3     4
               |   |
               1   2

The DFS traversal pre-order is: L, 0, A, 1, 2, 3, 4. Remember, in order to implement an iterative DFS you also "need" a stack. The implementation I proposed before result in having the following states (for the stack and the flat_list):

init.:  stack=[(L, 0)]
**0**:  stack=[(L, 0)],         flat_list=[0]
**A**:  stack=[(L, 1), (A, 0)], flat_list=[0]
**1**:  stack=[(L, 1), (A, 0)], flat_list=[0, 1]
**2**:  stack=[(L, 1), (A, 1)], flat_list=[0, 1, 2]
**3**:  stack=[(L, 2)],         flat_list=[0, 1, 2, 3]
**3**:  stack=[(L, 3)],         flat_list=[0, 1, 2, 3, 4]
return: stack=[],               flat_list=[0, 1, 2, 3, 4]

In this example, the stack maximum size is 2, because the input list (and therefore the tree) have depth 2.


For the implementation, in python you can simplify a little bit by using iterators instead of simple lists. References to the (sub)iterators will be used to store sublists return addresses (instead of having both the list address and the index). This is not a big difference but I feel this is more readable (and also a bit faster):

def flatten(iterable):
    return list(items_from(iterable))

def items_from(iterable):
    cursor_stack = [iter(iterable)]
    while cursor_stack:
        sub_iterable = cursor_stack[-1]
            item = next(sub_iterable)
        except StopIteration:   # post-order
        if is_list_like(item):  # pre-order
        elif item is not None:
            yield item          # in-order

def is_list_like(item):
    return isinstance(item, list)

Also, notice that in is_list_like I have isinstance(item, list), which could be changed to handle more input types, here I just wanted to have the simplest version where (iterable) is just a list. But you could also do that:

def is_list_like(item):
        return not isinstance(item, str)  # strings are not lists (hmm...) 
    except TypeError:
        return False

This considers strings as "simple items" and therefore flatten_iter([["test", "a"], "b]) will return ["test", "a", "b"] and not ["t", "e", "s", "t", "a", "b"]. Remark that in that case, iter(item) is called twice on each item, let's pretend it's an exercise for the reader to make this cleaner.

Testing and remarks on other implementations

In the end, remember that you can't print a infinitely nested list L using print(L) because internally it will use recursive calls to __repr__ (RecursionError: maximum recursion depth exceeded while getting the repr of an object). For the same reason, solutions to flatten involving str will fail with the same error message.

If you need to test your solution, you can use this function to generate a simple nested list:

def build_deep_list(depth):
    """Returns a list of the form $l_{depth} = [depth-1, l_{depth-1}]$
    with $depth > 1$ and $l_0 = [0]$.
    sub_list = [0]
    for d in range(1, depth):
        sub_list = [d, sub_list]
    return sub_list

Which gives: build_deep_list(5) >>> [4, [3, [2, [1, [0]]]]].

@wihlke 2018-01-15 15:25:30

From my previous answer, this function flattens most cases I can think of. I believe this works down to python 2.3.

def flatten(item, keepcls=(), keepobj=()):
    if not hasattr(item, '__iter__') or isinstance(item, keepcls) or item in keepobj:
        yield item
        for i in item:
            for j in flatten(i, keepcls, keepobj + (item,)):
                yield j

Circular lists

>>> list(flatten([1, 2, [...], 3]))
[1, 2, [1, 2, [...], 3], 3]

Depth first lists

>>> list(flatten([[[1, 2, 3], [4, 5]], 6]))
[1, 2, 3, 4, 5, 6]

Nested repeated lists:

>>> list(flatten([[1,2],[1,[1,2]],[1,2]]))
[1, 2, 1, 1, 2, 1, 2]

Lists with dicts (or other objects to not flatten)

>>> list(flatten([1,2, {'a':1, 'b':2}, 'text'], keepcls=(dict, str)))
[1, 2, {'a': 1, 'b': 2}, 'text']

Any iterables

>>> list(flatten((x for x in [1,2, set([3,(4,5),6])])))
[1, 2, 4, 5, 3, 6]

You may want to keep some default classes in keepcls to make calling the function more terse.

@Shivpe_R 2017-11-22 12:26:03

Simple Function Without using instances

L = [[[1, 2, 3], [4, 5]], 6]
l1 = []
def FlattenList(List1):
    for i in range(len(List1)):
        if type(List1[i]) == type([]):
    return l1

[1, 2, 3, 4, 5, 6]

@Fuji Komalan 2017-02-27 16:01:25


from collections import Iterable

L = [[[1, 2, 3], [4, 5]], 6,[7,[8,9,[10]]]]

def flatten(thing):
    result = []

    if isinstance(thing, Iterable):
        for item in thing:

    return result

flat = flatten(L)

@MSeifert 2017-07-29 14:01:42

You could use deepflatten from the 3rd party package iteration_utilities:

>>> from iteration_utilities import deepflatten
>>> L = [[[1, 2, 3], [4, 5]], 6]
>>> list(deepflatten(L))
[1, 2, 3, 4, 5, 6]

>>> list(deepflatten(L, types=list))  # only flatten "inner" lists
[1, 2, 3, 4, 5, 6]

It's an iterator so you need to iterate it (for example by wrapping it with list or using it in a loop). Internally it uses an iterative approach instead of an recursive approach and it's written as C extension so it can be faster than pure python approaches:

>>> %timeit list(deepflatten(L))
12.6 µs ± 298 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
>>> %timeit list(deepflatten(L, types=list))
8.7 µs ± 139 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

>>> %timeit list(flatten(L))   # Cristian - Python 3.x approach from
86.4 µs ± 4.42 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

>>> %timeit list(flatten(L))   # Josh Lee -
107 µs ± 2.99 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

>>> %timeit list(genflat(L, list))  # Alex Martelli -
23.1 µs ± 710 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

I'm the author of the iteration_utilities library.

@MarkS 2019-08-02 21:55:11

Just used your iteration_utilities to flatten a mixed list that I then used to flatten a multi-index dataframe. Worked like a charm. :-)

@DeaD_EyE 2017-07-20 11:42:27

Iterative solution with Python 3

This solution may work with all objects except str and bytes.

from collections import Iterable
from collections import Iterator

def flat_iter(obj):
    stack = [obj]
    while stack:
        element = stack.pop()
        if element and isinstance(element, Iterator):
            except StopIteration:
        elif isinstance(element, Iterable) and not isinstance(element, (str, bytes)):
            yield element

tree_list = [[(1,2,3),(4,5,6, (7,8, 'next element is 5')), (5,6), [[[3,4,5],'foo1'],'foo2'],'foo3']]

not_iterable = 10

it1 = flat_iter(tree_list)
it2 = flat_iter(not_iterable)


[1, 2, 3, 4, 5, 6, 7, 8, 'next element is 5', 5, 6, 3, 4, 5, 'foo1', 'foo2', 'foo3']


@diligar 2017-05-13 02:32:08

I'm not sure if this is necessarily quicker or more effective, but this is what I do:

def flatten(lst):
    return eval('[' + str(lst).replace('[', '').replace(']', '') + ']')

L = [[[1, 2, 3], [4, 5]], 6]

The flatten function here turns the list into a string, takes out all of the square brackets, attaches square brackets back onto the ends, and turns it back into a list.

Although, if you knew you would have square brackets in your list in strings, like [[1, 2], "[3, 4] and [5]"], you would have to do something else.

@cglacet 2018-08-02 06:47:05

This has no advantage over the simple solution as this fails to process deep lists, ie "RecursionError: maximum recursion depth exceeded while getting the repr of an object".

@Leo wahyd 2016-10-03 15:46:27

I am aware that there are already many awesome answers but i wanted to add an answer that uses the functional programming method of solving the question. In this answer i make use of double recursion :

def flatten_list(seq):
    if not seq:
        return []
    elif isinstance(seq[0],list):
        return (flatten_list(seq[0])+flatten_list(seq[1:]))
        return [seq[0]]+flatten_list(seq[1:])



[1, 2, 3, 4, 5, 6, 7]

@Cristian 2010-01-28 22:35:51

Using generator functions can make your example a little easier to read and probably boost the performance.

Python 2

def flatten(l):
    for el in l:
        if isinstance(el, collections.Iterable) and not isinstance(el, basestring):
            for sub in flatten(el):
                yield sub
            yield el

I used the Iterable ABC added in 2.6.

Python 3

In Python 3, the basestring is no more, but you can use a tuple of str and bytes to get the same effect there.

The yield from operator returns an item from a generator one at a time. This syntax for delegating to a subgenerator was added in 3.3

def flatten(l):
    for el in l:
        if isinstance(el, collections.Iterable) and not isinstance(el, (str, bytes)):
            yield from flatten(el)
            yield el

@nemesisfixx 2012-06-07 15:04:35

Of all the suggestions on this page, this is the only one that flattened this list l = ([[chr(i),chr(i-32)] for i in xrange(ord('a'), ord('z')+1)] + range(0,9)) in a snap when i did this list(flatten(l)). All the others, would start working and take forever!

@josch 2015-06-21 12:37:40

This also flattens dictionaries. Maybe you want to use collections.Sequence instead of collections.Iteratable?

@RolKau 2015-08-18 11:17:41

This doesn't work with things that aren't lists initially, e.g. for i in flatten(42): print (i). This could be fixed by moving the isinstance-test and the else-clause outside of the for el-loop. (Then you could throw anything at it, and it would make a flattened list out of it)

@yelsayed 2016-05-09 04:41:13

Why do you need the compound check if isinstance(el, collections.Iterable) and not isinstance(el, basestring)? Why not just if not isinstance(el, list)?

@dawg 2018-09-30 15:53:47

For Python 3.7, using collections.Iterable is deprecated. Use instead.

@jorijnsmit 2018-12-30 09:26:39

@yelsayed because you want to prevent separating strings into their individual letters.

@jorijnsmit 2018-12-30 10:21:45

Put a concise version of this in a Gist for further reference:

@Jorge Orpinel 2019-01-22 10:01:11

No recursion needed.

@cglacet 2019-03-11 09:13:22

Indeed, recursion is never needed. In this specific case using recursion is not the best solution as it will crash on deeply nested lists (depth > 1000). But if you don't aim at having something safe, then yes recursive function are better as they are way easier to read/write.

@Max Ghenis 2019-11-19 06:04:49

I've recently started getting this warning with this: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from '' is deprecated, and in 3.8 it will stop working

@Shreyas 2016-04-08 13:24:02

I didn't go through all the already available answers here, but here is a one liner I came up with, borrowing from lisp's way of first and rest list processing

def flatten(l): return flatten(l[0]) + (flatten(l[1:]) if len(l) > 1 else []) if type(l) is list else [l]

here is one simple and one not-so-simple case -

>>> flatten([1,[2,3],4])
[1, 2, 3, 4]

>>> flatten([1, [2, 3], 4, [5, [6, {'name': 'some_name', 'age':30}, 7]], [8, 9, [10, [11, [12, [13, {'some', 'set'}, 14, [15, 'some_string'], 16], 17, 18], 19], 20], 21, 22, [23, 24], 25], 26, 27, 28, 29, 30])
[1, 2, 3, 4, 5, 6, {'age': 30, 'name': 'some_name'}, 7, 8, 9, 10, 11, 12, 13, set(['set', 'some']), 14, 15, 'some_string', 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]

@cs95 2018-01-09 22:44:57

It's not a one liner. No matter how much you attempt to fit it into one, the def foo(): is a separate line. Also, this is very unreadable.

@Emilio M Bumachar 2018-02-05 17:20:40

I've de-one-line-ified the code, and did some further refactoring. (edit is pending peer review as I write this) This particular method seemed very readable to me, though the original code did need some refactoring.

@noobcoder 2016-03-22 13:55:38

We can also use the 'type' function of python. When iterating the list we check if the item is a list or not. If not we 'append' it else we 'extend' it. Here is a sample code -

for i in l:
    if type(i) is list:
print x


[1, 2, 3, 4, 5, 6, 7, 8]

For more info on append() and extend() check this website :

@Jorge Orpinel 2019-01-22 10:16:57

Only works for single level nested list.

@YPCrumble 2015-12-15 20:03:50

The easiest way is to use the morph library using pip install morph.

The code is:

import morph

list = [[[1, 2, 3], [4, 5]], 6]
flattened_list = morph.flatten(list)  # returns [1, 2, 3, 4, 5, 6]

@Zion 2015-11-14 05:59:47

I'm surprised no one has thought of this. Damn recursion I don't get the recursive answers that the advanced people here made. anyway here is my attempt on this. caveat is it's very specific to the OP's use case

import re

L = [[[1, 2, 3], [4, 5]], 6]
flattened_list = re.sub("[\[\]]", "", str(L)).replace(" ", "").split(",")
new_list = list(map(int, flattened_list))


[1, 2, 3, 4, 5, 6]

@Cong Ma 2015-10-13 22:41:14

Shamelessly taken from my own answer to another question.

This function

  • Does not use isinstance, because it's evil and breaks duck typing.
  • Uses reduce recursively. There has to be an answer using reduce.
  • Works with arbitrary nested-lists whose elements are either nested-lists, or non-nested lists of atoms, or atoms (subjected to recursion limit).
  • Does not LBYL.
  • But not with nested-lists that contain strings as atoms.

Code below:

def flattener(left, right):
        res = reduce(flattener, right, left)
    except TypeError:
        res = left
    return res

def flatten(seq):
    return reduce(flattener, seq, [])

>>> nested_list = [0, [1], [[[[2]]]],
                   [3, [], [4, 5]],
                   [6, [7, 8],
                    9, [[[]], 10,
                   11, [], [],
>>> flatten(nested_list)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

@dansalmo 2013-01-23 23:04:43

Generator using recursion and duck typing (updated for Python 3):

def flatten(L):
    for item in L:
            yield from flatten(item)
        except TypeError:
            yield item

list(flatten([[[1, 2, 3], [4, 5]], 6]))
>>>[1, 2, 3, 4, 5, 6]

@dansalmo 2015-09-08 14:54:45

Thanks, that works nice for Python 3. For 2.x the previous is needed: for i in flatten(item): yield i

@sten 2018-03-18 23:06:29

list(flatten([['X'], 'Y'])) fails on 2.X variant

@dansalmo 2018-03-20 00:14:03

@user1019129 see my comment above yours

@sten 2018-03-20 20:16:40

yes it fails with the cycle.. i think because a string is also an "array"-of-chars

@Oldyoung 2014-09-30 22:02:25

I used recursive to solve nested list with any depth

def combine_nlist(nlist,init=0,combiner=lambda x,y: x+y):
    apply function: combiner to a nested list element by element(treated as flatten list)
    for each_item in nlist:
        if isinstance(each_item,list):
            current_value =combine_nlist(each_item,current_value,combiner)
            current_value = combiner(current_value,each_item)
    return current_value

So after i define function combine_nlist, it is easy to use this function do flatting. Or you can combine it into one function. I like my solution because it can be applied to any nested list.

def flatten_nlist(nlist):
    return combine_nlist(nlist,[],lambda x,y:x+[y])


In [379]: flatten_nlist([1,2,3,[4,5],[6],[[[7],8],9],10])
Out[379]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

@cglacet 2018-08-01 16:48:46

"nested list with any depth" not true. Just try you'll see: current_value = combiner(current_value,each_item) RecursionError: maximum recursion depth exceeded

@Oldyoung 2018-08-01 20:19:55

hmmm I are you trying to flaten list with more than 1000 layers?

@cglacet 2018-08-02 06:44:11

Of course, that's the whole point of the discussion about recursive vs. iterative solutions. If you know in advance that the number of layers is < than 1000 then the most simple solution will work. When you say "any depth" this includes list with depth > 1000.

@Saksham Varma 2015-02-27 02:38:33

Using itertools.chain:

import itertools
from collections import Iterable

def list_flatten(lst):
    flat_lst = []
    for item in itertools.chain(lst):
        if isinstance(item, Iterable):
            item = list_flatten(item)
    return flat_lst

Or without chaining:

def flatten(q, final):
    if not q:
    if isinstance(q, list):
        if not isinstance(q[0], list):
            flatten(q[0], final)
        flatten(q[1:], final)

@alfasin 2014-12-07 06:06:19

Without using any library:

def flat(l):
    def _flat(l, r):    
        if type(l) is not list:
            for i in l:
                r = r + flat(i)
        return r
    return _flat(l, [])

# example
test = [[1], [[2]], [3], [['a','b','c'] , [['z','x','y']], ['d','f','g']], 4]    
print flat(test) # prints [1, 2, 3, 'a', 'b', 'c', 'z', 'x', 'y', 'd', 'f', 'g', 4]

@Noctis Skytower 2013-07-25 20:46:26

It was fun trying to create a function that could flatten irregular list in Python, but of course that is what Python is for (to make programming fun). The following generator works fairly well with some caveats:

def flatten(iterable):
        for item in iterable:
            yield from flatten(item)
    except TypeError:
        yield iterable

It will flatten datatypes that you might want left alone (like bytearray, bytes, and str objects). Also, the code relies on the fact that requesting an iterator from a non-iterable raises a TypeError.

>>> L = [[[1, 2, 3], [4, 5]], 6]
>>> def flatten(iterable):
        for item in iterable:
            yield from flatten(item)
    except TypeError:
        yield iterable

>>> list(flatten(L))
[1, 2, 3, 4, 5, 6]


I disagree with the previous implementation. The problem is that you should not be able to flatten something that is not an iterable. It is confusing and gives the wrong impression of the argument.

>>> list(flatten(123))

The following generator is almost the same as the first but does not have the problem of trying to flatten a non-iterable object. It fails as one would expect when an inappropriate argument is given to it.

def flatten(iterable):
    for item in iterable:
            yield from flatten(item)
        except TypeError:
            yield item

Testing the generator works fine with the list that was provided. However, the new code will raise a TypeError when a non-iterable object is given to it. Example are shown below of the new behavior.

>>> L = [[[1, 2, 3], [4, 5]], 6]
>>> list(flatten(L))
[1, 2, 3, 4, 5, 6]
>>> list(flatten(123))
Traceback (most recent call last):
  File "<pyshell#32>", line 1, in <module>
  File "<pyshell#27>", line 2, in flatten
    for item in iterable:
TypeError: 'int' object is not iterable

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