Python 3.6为我们提供了f字符串，这是一个令人高兴的东西:

var1 = "foo"
var2 = "bar"
var3 = f"{var1}{var2}"
print(var3)                       # prints foobar

你可以在花括号内做任何事情

print(f"1 + 1 == {1 + 1}")        # prints 1 + 1 == 2

如果你只对一个字符串有一个引用，并且你将另一个字符串连接到末尾，CPython现在会对这种情况进行特殊处理，并尝试在适当的地方扩展字符串。

最终结果是这个操作平摊了O(n)。

e.g.

s = ""
for i in range(n):
    s += str(i)

以前是O(n²)现在是O(n)

更多的信息

从源代码(bytesobject.c):

void
PyBytes_ConcatAndDel(register PyObject **pv, register PyObject *w)
{
    PyBytes_Concat(pv, w);
    Py_XDECREF(w);
}


/* The following function breaks the notion that strings are immutable:
   it changes the size of a string.  We get away with this only if there
   is only one module referencing the object.  You can also think of it
   as creating a new string object and destroying the old one, only
   more efficiently.  In any case, don't use this if the string may
   already be known to some other part of the code...
   Note that if there's not enough memory to resize the string, the original
   string object at *pv is deallocated, *pv is set to NULL, an "out of
   memory" exception is set, and -1 is returned.  Else (on success) 0 is
   returned, and the value in *pv may or may not be the same as on input.
   As always, an extra byte is allocated for a trailing \0 byte (newsize
   does *not* include that), and a trailing \0 byte is stored.
*/

int
_PyBytes_Resize(PyObject **pv, Py_ssize_t newsize)
{
    register PyObject *v;
    register PyBytesObject *sv;
    v = *pv;
    if (!PyBytes_Check(v) || Py_REFCNT(v) != 1 || newsize < 0) {
        *pv = 0;
        Py_DECREF(v);
        PyErr_BadInternalCall();
        return -1;
    }
    /* XXX UNREF/NEWREF interface should be more symmetrical */
    _Py_DEC_REFTOTAL;
    _Py_ForgetReference(v);
    *pv = (PyObject *)
        PyObject_REALLOC((char *)v, PyBytesObject_SIZE + newsize);
    if (*pv == NULL) {
        PyObject_Del(v);
        PyErr_NoMemory();
        return -1;
    }
    _Py_NewReference(*pv);
    sv = (PyBytesObject *) *pv;
    Py_SIZE(sv) = newsize;
    sv->ob_sval[newsize] = '\0';
    sv->ob_shash = -1;          /* invalidate cached hash value */
    return 0;
}

这很容易用经验来验证。

$ python -m timeit -s"s=''" "for i in xrange(10):s+='a'"
1000000 loops, best of 3: 1.85 usec per loop
$ python -m timeit -s"s=''" "for i in xrange(100):s+='a'"
10000 loops, best of 3: 16.8 usec per loop
$ python -m timeit -s"s=''" "for i in xrange(1000):s+='a'"
10000 loops, best of 3: 158 usec per loop
$ python -m timeit -s"s=''" "for i in xrange(10000):s+='a'"
1000 loops, best of 3: 1.71 msec per loop
$ python -m timeit -s"s=''" "for i in xrange(100000):s+='a'"
10 loops, best of 3: 14.6 msec per loop
$ python -m timeit -s"s=''" "for i in xrange(1000000):s+='a'"
10 loops, best of 3: 173 msec per loop

然而，重要的是要注意，这种优化不是Python规范的一部分。据我所知，它只在cPython实现中。例如，对pypy或jython进行相同的经验测试可能会显示旧的O(n**2)性能。

$ pypy -m timeit -s"s=''" "for i in xrange(10):s+='a'"
10000 loops, best of 3: 90.8 usec per loop
$ pypy -m timeit -s"s=''" "for i in xrange(100):s+='a'"
1000 loops, best of 3: 896 usec per loop
$ pypy -m timeit -s"s=''" "for i in xrange(1000):s+='a'"
100 loops, best of 3: 9.03 msec per loop
$ pypy -m timeit -s"s=''" "for i in xrange(10000):s+='a'"
10 loops, best of 3: 89.5 msec per loop

到目前为止还不错，但是，

$ pypy -m timeit -s"s=''" "for i in xrange(100000):s+='a'"
10 loops, best of 3: 12.8 sec per loop

哎哟，比二次方程还糟糕。所以，pypy在处理短字符串时工作得很好，但在处理大字符串时表现很差。

这取决于你的应用。如果要遍历数百个单词，并希望将它们全部追加到列表中，那么.join()更好。但是如果你要组成一个长句子，你最好使用+=。

另一种选择是使用.format，如下所示:

print("{}{}".format(var1, var2))

a='foo'
b='baaz'

a.__add__(b)

out: 'foobaaz'

基本上没有区别。唯一一致的趋势是Python似乎在每个版本中都变得越来越慢……：(

List

%%timeit
x = []
for i in range(100000000):  # xrange on Python 2.7
    x.append('a')
x = ''.join(x)

Python 2.7

1圈，最好的3:7.34秒每圈

Python 3.4

1个循环，3个最好:7.99 s每循环

Python 3.5

1个循环，最好的3个循环:8.48秒

Python 3.6

1个循环，最好的3:9.93秒每循环

字符串

%%timeit
x = ''
for i in range(100000000):  # xrange on Python 2.7
    x += 'a'

Python 2.7:

1个循环，最好的3:7.41秒每循环

Python 3.4

1个循环，最好的3:9.08秒每循环

Python 3.5

1个循环，最好的3:8.82秒每循环

Python 3.6

1个循环，最好的3:9.24秒每循环