You can't use += for non-local variable (variable which is not local for function and also not global)

def main(): l = [1, 2, 3] def foo(): l.extend([4]) def boo(): l += [5] foo() print l boo() # this will fail main() 

It's because for extend case compiler will load the variable l using LOAD_DEREF instruction, but for += it will use LOAD_FAST - and you get *UnboundLocalError: local variable 'l' referenced before assignment*

+= only works if the statement would also work with an = sign, i.e. the left-hand side can be assigned to. This is because a += b actually becomes a = a.__iadd__(b) under the hood. So if a is something that can't be assigned to (either by syntax or semantics), such as a function call or an element of an immutable container, the += version will fail.

Example 1: Function calls

You can't assign to a function call (the syntax of Python forbids it), so you also can't += a function call's result directly:

list1 = [5, 6] list2 = [7, 8] def get_list(): return list1 get_list().extend(list2) # works get_list() += list2 # SyntaxError: can't assign to function call 

Example 2: Element of immutable container

A perhaps weirder case is when the list is an element of an immutable container, e.g. a tuple:

my_tuple = ([1, 2], [3, 4], [5, 6]) my_tuple[0].extend([10, 11]) # works my_tuple[0] += [10, 11] # TypeError: 'tuple' object does not support item assignment 

Since you can't do my_tuple[0] = something, you also can't do +=.

To sum up, you can use += if you can use =.

Actually, there are differences among the three options: ADD, INPLACE_ADD and extend. The former is always slower, while the other two are roughly the same.

With this information, I would rather use extend, which is faster than ADD, and seems to me more explicit of what you are doing than INPLACE_ADD.

Try the following code a few times (for Python 3):

import time def test(): x = list(range(10000000)) y = list(range(10000000)) z = list(range(10000000)) # INPLACE_ADD t0 = time.process_time() z += x t_inplace_add = time.process_time() - t0 # ADD t0 = time.process_time() w = x + y t_add = time.process_time() - t0 # Extend t0 = time.process_time() x.extend(y) t_extend = time.process_time() - t0 print('ADD {} s'.format(t_add)) print('INPLACE_ADD {} s'.format(t_inplace_add)) print('extend {} s'.format(t_extend)) print() for i in range(10): test() 

ADD 0.3540440000000018 s INPLACE_ADD 0.10896000000000328 s extend 0.08370399999999734 s ADD 0.2024550000000005 s INPLACE_ADD 0.0972940000000051 s extend 0.09610200000000191 s ADD 0.1680199999999985 s INPLACE_ADD 0.08162199999999586 s extend 0.0815160000000077 s ADD 0.16708400000000267 s INPLACE_ADD 0.0797719999999913 s extend 0.0801490000000058 s ADD 0.1681250000000034 s INPLACE_ADD 0.08324399999999343 s extend 0.08062700000000689 s ADD 0.1707760000000036 s INPLACE_ADD 0.08071900000000198 s extend 0.09226200000000517 s ADD 0.1668420000000026 s INPLACE_ADD 0.08047300000001201 s extend 0.0848089999999928 s ADD 0.16659500000000094 s INPLACE_ADD 0.08019399999999166 s extend 0.07981599999999389 s ADD 0.1710910000000041 s INPLACE_ADD 0.0783479999999912 s extend 0.07987599999999873 s ADD 0.16435900000000458 s INPLACE_ADD 0.08131200000001115 s extend 0.0818660000000051 s 

I would say that there is some difference when it comes with numpy (I just saw that the question ask about concatenating two lists, not numpy array, but since it might be a issue for beginner, such as me, I hope this can help someone who seek the solution to this post), for ex.

import numpy as np a = np.zeros((4,4,4)) b = [] b += a 

it will return with error

ValueError: operands could not be broadcast together with shapes (0,) (4,4,4)

b.extend(a) works perfectly

ary += ext creates a new List object, then copies data from lists "ary" and "ext" into it.

ary.extend(ext) merely adds reference to "ext" list to the end of the "ary" list, resulting in less memory transactions.

As a result, .extend works orders of magnitude faster and doesn't use any additional memory outside of the list being extended and the list it's being extended with.

╰─➤ time ./list_plus.py ./list_plus.py 36.03s user 6.39s system 99% cpu 42.558 total ╰─➤ time ./list_extend.py ./list_extend.py 0.03s user 0.01s system 92% cpu 0.040 total 

The first script also uses over 200MB of memory, while the second one doesn't use any more memory than a 'naked' python3 process.

Having said that, the in-place addition does seem to do the same thing as .extend.

The .extend() method on lists works with any iterable*, += works with some but can get funky.

import numpy as np l = [2, 3, 4] t = (5, 6, 7) l += t l [2, 3, 4, 5, 6, 7] l = [2, 3, 4] t = np.array((5, 6, 7)) l += t l array([ 7, 9, 11]) l = [2, 3, 4] t = np.array((5, 6, 7)) l.extend(t) l [2, 3, 4, 5, 6, 7] 

Python 3.6
*pretty sure .extend() works with any iterable but please comment if I am incorrect

Edit: "extend()" changed to "The .extend() method on lists" Note: David M. Helmuth's comment below is nice and clear.

From the CPython 3.5.2 source code: No big difference.

static PyObject * list_inplace_concat(PyListObject *self, PyObject *other) { PyObject *result; result = listextend(self, other); if (result == NULL) return result; Py_DECREF(result); Py_INCREF(self); return (PyObject *)self; } 

I've looked up the official Python tutorial but couldn't find anything anything about this topic

This information happens to be buried in the Programming FAQ:

... for lists, __iadd__ [i.e. +=] is equivalent to calling extend on the list and returning the list. That's why we say that for lists, += is a "shorthand" for list.extend

You can also see this for yourself in the CPython source code: https://github.com/python/cpython/blob/v3.8.2/Objects/listobject.c#L1000-L1011

The += operator is negligibly if at all faster than list.extend() which is confirmed by dalonsoa's answer. You're literally exchanging a method call for two other operations.

>>> dis.dis("_list.extend([1])") 1 0 LOAD_NAME 0 (_list) 2 LOAD_METHOD 1 (extend) 4 LOAD_CONST 0 (4) 6 BUILD_LIST 1 8 CALL_METHOD 1 10 RETURN_VALUE >>> dis.dis("_list += [1]") 1 0 LOAD_NAME 0 (_list) 2 LOAD_CONST 0 (4) 4 BUILD_LIST 1 6 INPLACE_ADD 8 STORE_NAME 0 (_list) 10 LOAD_CONST 1 (None) 12 RETURN_VALUE 

Note, that this does not apply to numpy arrays, since numpy arrays are not at all Python lists and should not be treated as such (Lance Ruo Zhang's answer).

The += will not work for list in tuples most likely because of the STORE_SUBSCR operation (Jann Poppinga's answer). Note, however, that in this case list.__iadd__() (being a method call) works perfectly fine.

The += does not create a new list (ding's answer).

I apologise for posting all this as an answer, I do not have enough rep to comment.

Only .extend() can be used when the list is in a tuple

This will work

t = ([],[]) t[0].extend([1,2]) 

while this won't

t = ([],[]) t[0] += [1,2] 

The reason is that += generates a new object. If you look at the long version:

t[0] = t[0] + [1,2] 

you can see how that would change which object is in the tuple, which is not possible. Using .extend() modifies an object in the tuple, which is allowed.

According to the Python for Data Analysis.

“Note that list concatenation by addition is a comparatively expensive operation since a new list must be created and the objects copied over. Using extend to append elements to an existing list, especially if you are building up a large list, is usually preferable. ” Thus,

everything = [] for chunk in list_of_lists: everything.extend(chunk) 

is faster than the concatenative alternative:

everything = [] for chunk in list_of_lists: everything = everything + chunk