Is there a shortcut to make a simple list out of a list of lists in Python?

I can do it in a `for`

loop, but is there some cool “one-liner”?

I tried it with `functools.reduce()`

:

```
from functools import reduce
l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
reduce(lambda x, y: x.extend(y), l)
```

But I get this error:

```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 1, in <lambda>
AttributeError: 'NoneType' object has no attribute 'extend'
```

Given a list of lists `t`

,

```
flat_list = [item for sublist in t for item in sublist]
```

which means:

```
flat_list = []
for sublist in t:
for item in sublist:
flat_list.append(item)
```

is faster than the shortcuts posted so far. (`t`

is the list to flatten.)

Here is the corresponding function:

```
def flatten(t):
return [item for sublist in t for item in sublist]
```

As evidence, you can use the `timeit`

module in the standard library:

```
$ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' '[item for sublist in t for item in sublist]'
10000 loops, best of 3: 143 usec per loop
$ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'sum(t, [])'
1000 loops, best of 3: 969 usec per loop
$ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'reduce(lambda x,y: x+y,t)'
1000 loops, best of 3: 1.1 msec per loop
```

Explanation: the shortcuts based on `+`

(including the implied use in `sum`

) are, of necessity, `O(T**2)`

when there are T sublists — as the intermediate result list keeps getting longer, at each step a new intermediate result list object gets allocated, and all the items in the previous intermediate result must be copied over (as well as a few new ones added at the end). So, for simplicity and without actual loss of generality, say you have T sublists of k items each: the first k items are copied back and forth T-1 times, the second k items T-2 times, and so on; total number of copies is k times the sum of x for x from 1 to T excluded, i.e., `k * (T**2)/2`

.

The list comprehension just generates one list, once, and copies each item over (from its original place of residence to the result list) also exactly once.

You can use `itertools.chain()`

:

```
import itertools
list2d = [[1,2,3], [4,5,6], [7], [8,9]]
merged = list(itertools.chain(*list2d))
```

Or you can use `itertools.chain.from_iterable()`

which doesn’t require unpacking the list with the `*`

operator:

```
merged = list(itertools.chain.from_iterable(list2d))
```

**Note from the author**: This is inefficient. But fun, because monoids are awesome. It’s not appropriate for production Python code.

```
>>> l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
>>> sum(l, [])
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

This just sums the elements of iterable passed in the first argument, treating second argument as the initial value of the sum (if not given, `0`

is used instead and this case will give you an error).

Because you are summing nested lists, you actually get `[1,3]+[2,4]`

as a result of `sum([[1,3],[2,4]],[])`

, which is equal to `[1,3,2,4]`

.

Note that only works on lists of lists. For lists of lists of lists, you’ll need another solution.

I tested most suggested solutions with perfplot (a pet project of mine, essentially a wrapper around `timeit`

), and found

```
import functools
import operator
functools.reduce(operator.iconcat, a, [])
```

to be the fastest solution, both when many small lists and few long lists are concatenated. (`operator.iadd`

is equally fast.)

Code to reproduce the plot:

```
import functools
import itertools
import numpy
import operator
import perfplot
def forfor(a):
return [item for sublist in a for item in sublist]
def sum_brackets(a):
return sum(a, [])
def functools_reduce(a):
return functools.reduce(operator.concat, a)
def functools_reduce_iconcat(a):
return functools.reduce(operator.iconcat, a, [])
def itertools_chain(a):
return list(itertools.chain.from_iterable(a))
def numpy_flat(a):
return list(numpy.array(a).flat)
def numpy_concatenate(a):
return list(numpy.concatenate(a))
perfplot.show(
setup=lambda n: [list(range(10))] * n,
# setup=lambda n: [list(range(n))] * 10,
kernels=[
forfor,
sum_brackets,
functools_reduce,
functools_reduce_iconcat,
itertools_chain,
numpy_flat,
numpy_concatenate,
],
n_range=[2 ** k for k in range(16)],
xlabel="num lists (of length 10)",
# xlabel="len lists (10 lists total)"
)
```

# Don’t reinvent the wheel

## If you use –

…**Django**:

```
>>> from django.contrib.admin.utils import flatten
>>> l = [[1,2,3], [4,5], [6]]
>>> flatten(l)
>>> [1, 2, 3, 4, 5, 6]
```

…**Pandas**:

```
>>> from pandas.core.common import flatten
>>> list(flatten(l))
```

…**Itertools**:

```
>>> import itertools
>>> flatten = itertools.chain.from_iterable
>>> list(flatten(l))
```

…**Matplotlib**

```
>>> from matplotlib.cbook import flatten
>>> list(flatten(l))
```

…**Unipath**:

```
>>> from unipath.path import flatten
>>> list(flatten(l))
```

…**Setuptools**:

```
>>> from setuptools.namespaces import flatten
>>> list(flatten(l))
```

```
>>> from functools import reduce
>>> l = [[1,2,3], [4,5,6], [7], [8,9]]
>>> reduce(lambda x, y: x+y, l)
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

The `extend()`

method in your example modifies `x`

instead of returning a useful value (which `functools.reduce()`

expects).

A faster way to do the `reduce`

version would be

```
>>> import operator
>>> l = [[1,2,3], [4,5,6], [7], [8,9]]
>>> reduce(operator.concat, l)
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

Here is a general approach that applies to **numbers**, **strings**, **nested** lists and **mixed** containers. This can flatten both simple and complicated containers (see also *Demo*).

**Code**

```
from typing import Iterable
#from collections import Iterable # < py38
def flatten(items):
"""Yield items from any nested iterable; see Reference."""
for x in items:
if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
for sub_x in flatten(x):
yield sub_x
else:
yield x
```

*Notes*:

- In Python 3,
`yield from flatten(x)`

can replace`for sub_x in flatten(x): yield sub_x`

- In Python 3.8, abstract base classes are moved from
`collection.abc`

to the`typing`

module.

**Demo**

```
simple = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
list(flatten(simple))
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
complicated = [[1, [2]], (3, 4, {5, 6}, 7), 8, "9"] # numbers, strs, nested & mixed
list(flatten(complicated))
# [1, 2, 3, 4, 5, 6, 7, 8, '9']
```

**Reference**

- This solution is modified from a recipe in
*Beazley, D. and B. Jones. Recipe 4.14, Python Cookbook 3rd Ed., O’Reilly Media Inc. Sebastopol, CA: 2013.* - Found an earlier SO post, possibly the original demonstration.

If you want to flatten a data-structure where you don’t know how deep it’s nested you could use `iteration_utilities.deepflatten`

^{1}

```
>>> from iteration_utilities import deepflatten
>>> l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
>>> list(deepflatten(l, depth=1))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> l = [[1, 2, 3], [4, [5, 6]], 7, [8, 9]]
>>> list(deepflatten(l))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

It’s a generator so you need to cast the result to a `list`

or explicitly iterate over it.

To flatten only one level and if each of the items is itself iterable you can also use `iteration_utilities.flatten`

which itself is just a thin wrapper around `itertools.chain.from_iterable`

:

```
>>> from iteration_utilities import flatten
>>> l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
>>> list(flatten(l))
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

Just to add some timings (based on Nico Schlömer’s answer that didn’t include the function presented in this answer):

It’s a log-log plot to accommodate for the huge range of values spanned. For qualitative reasoning: Lower is better.

The results show that if the iterable contains only a few inner iterables then `sum`

will be fastest, however for long iterables only the `itertools.chain.from_iterable`

, `iteration_utilities.deepflatten`

or the nested comprehension have reasonable performance with `itertools.chain.from_iterable`

being the fastest (as already noticed by Nico Schlömer).

```
from itertools import chain
from functools import reduce
from collections import Iterable # or from collections.abc import Iterable
import operator
from iteration_utilities import deepflatten
def nested_list_comprehension(lsts):
return [item for sublist in lsts for item in sublist]
def itertools_chain_from_iterable(lsts):
return list(chain.from_iterable(lsts))
def pythons_sum(lsts):
return sum(lsts, [])
def reduce_add(lsts):
return reduce(lambda x, y: x + y, lsts)
def pylangs_flatten(lsts):
return list(flatten(lsts))
def flatten(items):
"""Yield items from any nested iterable; see REF."""
for x in items:
if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
yield from flatten(x)
else:
yield x
def reduce_concat(lsts):
return reduce(operator.concat, lsts)
def iteration_utilities_deepflatten(lsts):
return list(deepflatten(lsts, depth=1))
from simple_benchmark import benchmark
b = benchmark(
[nested_list_comprehension, itertools_chain_from_iterable, pythons_sum, reduce_add,
pylangs_flatten, reduce_concat, iteration_utilities_deepflatten],
arguments={2**i: [[0]*5]*(2**i) for i in range(1, 13)},
argument_name="number of inner lists"
)
b.plot()
```

^{1 Disclaimer: I’m the author of that library}

**I take my statement back. sum is not the winner. Although it is faster when the list is small. But the performance degrades significantly with larger lists.**

```
>>> timeit.Timer(
'[item for sublist in l for item in sublist]',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]] * 10000'
).timeit(100)
2.0440959930419922
```

The sum version is still running for more than a minute and it hasn’t done processing yet!

For medium lists:

```
>>> timeit.Timer(
'[item for sublist in l for item in sublist]',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]] * 10'
).timeit()
20.126545906066895
>>> timeit.Timer(
'reduce(lambda x,y: x+y,l)',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]] * 10'
).timeit()
22.242258071899414
>>> timeit.Timer(
'sum(l, [])',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]] * 10'
).timeit()
16.449732065200806
```

Using small lists and timeit: number=1000000

```
>>> timeit.Timer(
'[item for sublist in l for item in sublist]',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]]'
).timeit()
2.4598159790039062
>>> timeit.Timer(
'reduce(lambda x,y: x+y,l)',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]]'
).timeit()
1.5289170742034912
>>> timeit.Timer(
'sum(l, [])',
'l=[[1, 2, 3], [4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7]]'
).timeit()
1.0598428249359131
```

There seems to be 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:

```
>>> from functools import reduce
>>> list2d = ((1, 2, 3), (4, 5, 6), (7,), (8, 9))
>>> 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:

```
>>> list2d = [[1, 2, 3],[4, 5, 6], [7], [8, 9]]
>>> reduce(operator.concat, list2d)
[1, 2, 3, 4, 5, 6, 7, 8, 9]
```

Aha, you get back a list.

How about performance:

```
>>> list2d = [[1, 2, 3],[4, 5, 6], [7], [8, 9]]
>>> %timeit list(itertools.chain.from_iterable(list2d))
1000000 loops, best of 3: 1.36 µs per loop
```

`from_iterable`

is pretty fast! But it’s no comparison to reduce with `concat`

.

```
>>> list2d = ((1, 2, 3),(4, 5, 6), (7,), (8, 9))
>>> %timeit reduce(operator.concat, list2d)
1000000 loops, best of 3: 492 ns per loop
```

You don’t need *extend*. This should work fine:

```
reduce(lambda x, y: x+y, l)
```

Consider installing the `more_itertools`

package.

```
> pip install more_itertools
```

It ships with an implementation for `flatten`

(source, from the itertools recipes):

```
import more_itertools
lst = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
list(more_itertools.flatten(lst))
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
```

*Note: as mentioned in the docs, flatten requires a list of lists. See below on flattening more irregular inputs.*

As of version 2.4, you can flatten more complicated, nested iterables with `more_itertools.collapse`

(source, contributed by abarnet).

```
lst = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
list(more_itertools.collapse(lst))
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
lst = [[1, 2, 3], [[4, 5, 6]], [[[7]]], 8, 9] # complex nesting
list(more_itertools.collapse(lst))
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
```

The reason your function didn’t work is because the *extend* extends an array in-place and doesn’t return it. You can still return x from lambda, using something like this:

```
reduce(lambda x,y: x.extend(y) or x, l)
```

Note: extend is more efficient than + on lists.

```
def flatten(l, a):
for i in l:
if isinstance(i, list):
flatten(i, a)
else:
a.append(i)
return a
print(flatten([[[1, [1,1, [3, [4,5,]]]], 2, 3], [4, 5],6], []))
# [1, 1, 1, 3, 4, 5, 2, 3, 4, 5, 6]
```

Recursive version

```
x = [1,2,[3,4],[5,[6,[7]]],8,9,[10]]
def flatten_list(k):
result = list()
for i in k:
if isinstance(i,list):
#The isinstance() function checks if the object (first argument) is an
#instance or subclass of classinfo class (second argument)
result.extend(flatten_list(i)) #Recursive call
else:
result.append(i)
return result
flatten_list(x)
#result = [1,2,3,4,5,6,7,8,9,10]
```

The accepted answer did not work for me when dealing with text-based lists of variable lengths. Here is an alternate approach that did work for me.

```
l = ['aaa', 'bb', 'cccccc', ['xx', 'yyyyyyy']]
```

### Accepted answer that did *not* work:

```
flat_list = [item for sublist in l for item in sublist]
print(flat_list)
['a', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'c', 'c', 'c', 'xx', 'yyyyyyy']
```

### New proposed solution that *did* work for me:

```
flat_list = []
_ = [flat_list.extend(item) if isinstance(item, list) else flat_list.append(item) for item in l if item]
print(flat_list)
['aaa', 'bb', 'cccccc', 'xx', 'yyyyyyy']
```

A bad feature of Anil’s function above is that it requires the user to always manually specify the second argument to be an empty list `[]`

. This should instead be a default. Due to the way Python objects work, these should be set inside the function, not in the arguments.

Here’s a working function:

```
def list_flatten(l, a=None):
#check a
if a is None:
#initialize with empty list
a = []
for i in l:
if isinstance(i, list):
list_flatten(i, a)
else:
a.append(i)
return a
```

Testing:

```
In [2]: lst = [1, 2, [3], [[4]],[5,[6]]]
In [3]: lst
Out[3]: [1, 2, [3], [[4]], [5, [6]]]
In [11]: list_flatten(lst)
Out[11]: [1, 2, 3, 4, 5, 6]
```

`matplotlib.cbook.flatten()`

will work for nested lists even if they nest more deeply than the example.

```
import matplotlib
l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
print(list(matplotlib.cbook.flatten(l)))
l2 = [[1, 2, 3], [4, 5, 6], [7], [8, [9, 10, [11, 12, [13]]]]]
print list(matplotlib.cbook.flatten(l2))
```

Result:

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

This is 18x faster than underscore._.flatten:

```
Average time over 1000 trials of matplotlib.cbook.flatten: 2.55e-05 sec
Average time over 1000 trials of underscore._.flatten: 4.63e-04 sec
(time for underscore._)/(time for matplotlib.cbook) = 18.1233394636
```

The following seems simplest to me:

```
>>> import numpy as np
>>> l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
>>> print (np.concatenate(l))
[1 2 3 4 5 6 7 8 9]
```

One can also use NumPy’s flat:

```
import numpy as np
list(np.array(l).flat)
```

It only works when sublists have identical dimensions.

I personally find it hard to remember all the modules that needed to be imported. Thus I tend to use a simple method, even though I don’t know how its performance is compared to other answers.

If you just want to flatten nested lists, then the following will do the job:

```
def flatten(lst):
for item in lst:
if isinstance(item, list):
yield from flatten(item)
else:
yield item
# test case:
a =[0, [], "fun", [1, 2, 3], [4, [5], 6], 3, [7], [8, 9]]
list(flatten(a))
# output
# [0, 'fun', 1, 2, 3, 4, 5, 6, 3, 7, 8, 9]
```

However, if you want to flatten a list of iterables (list and/or tuples), it can also do the job with a slight modification:

```
from collections.abc import Iterable
def flatten(lst):
for item in lst:
if isinstance(item,Iterable) and not isinstance(item,str):
yield from flatten(item)
else:
yield item
# test case:
a =[0, [], "fun", (1, 2, 3), [4, [5], (6)], 3, [7], [8, 9]]
list(flatten(a))
# output:
# [0, 'fun', 1, 2, 3, 4, 5, 6, 3, 7, 8, 9]
```

you can use `list`

`extend`

method, it shows to be the fastest:

```
flat_list = []
for sublist in l:
flat_list.extend(sublist)
```

performance:

```
import functools
import itertools
import numpy
import operator
import perfplot
def functools_reduce_iconcat(a):
return functools.reduce(operator.iconcat, a, [])
def itertools_chain(a):
return list(itertools.chain.from_iterable(a))
def numpy_flat(a):
return list(numpy.array(a).flat)
def extend(a):
n = []
list(map(n.extend, a))
return n
perfplot.show(
setup=lambda n: [list(range(10))] * n,
kernels=[
functools_reduce_iconcat, extend,itertools_chain, numpy_flat
],
n_range=[2**k for k in range(16)],
xlabel="num lists",
)
```

```
from nltk import flatten
l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
flatten(l)
```

The advantage of this solution over most others here is that if you have a list like:

```
l = [1, [2, 3], [4, 5, 6], [7], [8, 9]]
```

while most other solutions throw an error this solution handles them.

This is a play on the original poster’s code. (He wasn’t far off)

```
f = []
list(map(f.extend, l))
```

**Note**: Below applies to Python 3.3+ because it uses `yield_from`

. `six`

is also a third-party package, though it is stable. Alternately, you could use `sys.version`

.

In the case of `obj = [[1, 2,], [3, 4], [5, 6]]`

, all of the solutions here are good, including list comprehension and `itertools.chain.from_iterable`

.

However, consider this slightly more complex case:

```
>>> obj = [[1, 2, 3], [4, 5], 6, 'abc', [7], [8, [9, 10]]]
```

There are several problems here:

- One element,
`6`

, is just a scalar; it’s not iterable, so the above routes will fail here. - One element,
`'abc'`

,*is*technically iterable (all`str`

s are). However, reading between the lines a bit, you don’t want to treat it as such–you want to treat it as a single element. - The final element,
`[8, [9, 10]]`

is itself a nested iterable. Basic list comprehension and`chain.from_iterable`

only extract “1 level down.”

You can remedy this as follows:

```
>>> from collections import Iterable
>>> from six import string_types
>>> def flatten(obj):
... for i in obj:
... if isinstance(i, Iterable) and not isinstance(i, string_types):
... yield from flatten(i)
... else:
... yield i
>>> list(flatten(obj))
[1, 2, 3, 4, 5, 6, 'abc', 7, 8, 9, 10]
```

Here, you check that the sub-element (1) is iterable with `Iterable`

, an ABC from `itertools`

, but also want to ensure that (2) the element is *not* “string-like.”

You can use numpy :

`flat_list = list(np.concatenate(list_of_list))`

Simple code for `underscore.py`

package fan

```
from underscore import _
_.flatten([[1, 2, 3], [4, 5, 6], [7], [8, 9]])
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
```

It solves all flatten problems (none list item or complex nesting)

```
from underscore import _
# 1 is none list item
# [2, [3]] is complex nesting
_.flatten([1, [2, [3]], [4, 5, 6], [7], [8, 9]])
# [1, 2, 3, 4, 5, 6, 7, 8, 9]
```

You can install `underscore.py`

with pip

```
pip install underscore.py
```

```
def flatten(alist):
if alist == []:
return []
elif type(alist) is not list:
return [alist]
else:
return flatten(alist[0]) + flatten(alist[1:])
```

```
flat_list = []
for i in list_of_list:
flat_list+=i
```

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

If you are willing to give up a tiny amount of speed for a cleaner look, then you could use `numpy.concatenate().tolist()`

or `numpy.concatenate().ravel().tolist()`

:

```
import numpy
l = [[1, 2, 3], [4, 5, 6], [7], [8, 9]] * 99
%timeit numpy.concatenate(l).ravel().tolist()
1000 loops, best of 3: 313 µs per loop
%timeit numpy.concatenate(l).tolist()
1000 loops, best of 3: 312 µs per loop
%timeit [item for sublist in l for item in sublist]
1000 loops, best of 3: 31.5 µs per loop
```

You can find out more here in the documentation, numpy.concatenate and numpy.ravel.