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问题：为什么（’x’，）中的’x’比’x’==’x’快？
回答 0
TL; DR：
TL;DR:
回答 1

问题：为什么（’x’，）中的’x’比’x’==’x’快？

>>> timeit.timeit("'x' in ('x',)")
0.04869917374131205
>>> timeit.timeit("'x' == 'x'")
0.06144205736110564

也适用于具有多个元素的元组，两个版本似乎线性增长：

>>> timeit.timeit("'x' in ('x', 'y')")
0.04866674801541748
>>> timeit.timeit("'x' == 'x' or 'x' == 'y'")
0.06565782838087131
>>> timeit.timeit("'x' in ('y', 'x')")
0.08975995576448526
>>> timeit.timeit("'x' == 'y' or 'x' == 'y'")
0.12992391047427532

基于此，我认为我应该完全开始in在任何地方而不是在所有地方使用==！

点击查看英文原文 
>>> timeit.timeit("'x' in ('x',)")
0.04869917374131205
>>> timeit.timeit("'x' == 'x'")
0.06144205736110564

Also works for tuples with multiple elements, both versions seem to grow linearly:

>>> timeit.timeit("'x' in ('x', 'y')")
0.04866674801541748
>>> timeit.timeit("'x' == 'x' or 'x' == 'y'")
0.06565782838087131
>>> timeit.timeit("'x' in ('y', 'x')")
0.08975995576448526
>>> timeit.timeit("'x' == 'y' or 'x' == 'y'")
0.12992391047427532

Based on this, I think I should totally start using in everywhere instead of ==!

回答 0

正如我对大卫·沃尔沃（David Wolever）所提到的那样，这不仅仅是眼神。两种方法都发送到is; 你可以通过做证明

min(Timer("x == x", setup="x = 'a' * 1000000").repeat(10, 10000))
#>>> 0.00045456900261342525

min(Timer("x == y", setup="x = 'a' * 1000000; y = 'a' * 1000000").repeat(10, 10000))
#>>> 0.5256857610074803

第一个只能如此之快，因为它通过身份检查。

为了找出为什么一个比另一个要花更长的时间，让我们追溯执行。

它们都以开头ceval.c，COMPARE_OP因为这是所涉及的字节码

TARGET(COMPARE_OP) {
    PyObject *right = POP();
    PyObject *left = TOP();
    PyObject *res = cmp_outcome(oparg, left, right);
    Py_DECREF(left);
    Py_DECREF(right);
    SET_TOP(res);
    if (res == NULL)
        goto error;
    PREDICT(POP_JUMP_IF_FALSE);
    PREDICT(POP_JUMP_IF_TRUE);
    DISPATCH();
}

这会从堆栈中弹出值（从技术上讲，它只会弹出一个）

PyObject *right = POP();
PyObject *left = TOP();

并运行比较：

PyObject *res = cmp_outcome(oparg, left, right);

cmp_outcome 这是：

static PyObject *
cmp_outcome(int op, PyObject *v, PyObject *w)
{
    int res = 0;
    switch (op) {
    case PyCmp_IS: ...
    case PyCmp_IS_NOT: ...
    case PyCmp_IN:
        res = PySequence_Contains(w, v);
        if (res < 0)
            return NULL;
        break;
    case PyCmp_NOT_IN: ...
    case PyCmp_EXC_MATCH: ...
    default:
        return PyObject_RichCompare(v, w, op);
    }
    v = res ? Py_True : Py_False;
    Py_INCREF(v);
    return v;
}

这是路径分开的地方。该PyCmp_IN分支不

int
PySequence_Contains(PyObject *seq, PyObject *ob)
{
    Py_ssize_t result;
    PySequenceMethods *sqm = seq->ob_type->tp_as_sequence;
    if (sqm != NULL && sqm->sq_contains != NULL)
        return (*sqm->sq_contains)(seq, ob);
    result = _PySequence_IterSearch(seq, ob, PY_ITERSEARCH_CONTAINS);
    return Py_SAFE_DOWNCAST(result, Py_ssize_t, int);
}

请注意，元组定义为

static PySequenceMethods tuple_as_sequence = {
    ...
    (objobjproc)tuplecontains,                  /* sq_contains */
};

PyTypeObject PyTuple_Type = {
    ...
    &tuple_as_sequence,                         /* tp_as_sequence */
    ...
};

所以分公司

if (sqm != NULL && sqm->sq_contains != NULL)

将采用和*sqm->sq_contains，即功能(objobjproc)tuplecontains。

这确实

static int
tuplecontains(PyTupleObject *a, PyObject *el)
{
    Py_ssize_t i;
    int cmp;

    for (i = 0, cmp = 0 ; cmp == 0 && i < Py_SIZE(a); ++i)
        cmp = PyObject_RichCompareBool(el, PyTuple_GET_ITEM(a, i),
                                           Py_EQ);
    return cmp;
}

…等等，那不是PyObject_RichCompareBool其他分支所采取的吗？不，那是PyObject_RichCompare。

该代码路径很短，因此很可能取决于这两者的速度。让我们比较一下。

int
PyObject_RichCompareBool(PyObject *v, PyObject *w, int op)
{
    PyObject *res;
    int ok;

    /* Quick result when objects are the same.
       Guarantees that identity implies equality. */
    if (v == w) {
        if (op == Py_EQ)
            return 1;
        else if (op == Py_NE)
            return 0;
    }

    ...
}

代码路径PyObject_RichCompareBool几乎立即终止。对于PyObject_RichCompare，它确实

PyObject *
PyObject_RichCompare(PyObject *v, PyObject *w, int op)
{
    PyObject *res;

    assert(Py_LT <= op && op <= Py_GE);
    if (v == NULL || w == NULL) { ... }
    if (Py_EnterRecursiveCall(" in comparison"))
        return NULL;
    res = do_richcompare(v, w, op);
    Py_LeaveRecursiveCall();
    return res;
}

将Py_EnterRecursiveCall/ Py_LeaveRecursiveCall组合不采取在前面的路径，但这些都是比较快的宏将递增和递减一些全局后短路。

do_richcompare 确实：

static PyObject *
do_richcompare(PyObject *v, PyObject *w, int op)
{
    richcmpfunc f;
    PyObject *res;
    int checked_reverse_op = 0;

    if (v->ob_type != w->ob_type && ...) { ... }
    if ((f = v->ob_type->tp_richcompare) != NULL) {
        res = (*f)(v, w, op);
        if (res != Py_NotImplemented)
            return res;
        ...
    }
    ...
}

这做一些快速的检查，以电话v->ob_type->tp_richcompare是

PyTypeObject PyUnicode_Type = {
    ...
    PyUnicode_RichCompare,      /* tp_richcompare */
    ...
};

哪个

PyObject *
PyUnicode_RichCompare(PyObject *left, PyObject *right, int op)
{
    int result;
    PyObject *v;

    if (!PyUnicode_Check(left) || !PyUnicode_Check(right))
        Py_RETURN_NOTIMPLEMENTED;

    if (PyUnicode_READY(left) == -1 ||
        PyUnicode_READY(right) == -1)
        return NULL;

    if (left == right) {
        switch (op) {
        case Py_EQ:
        case Py_LE:
        case Py_GE:
            /* a string is equal to itself */
            v = Py_True;
            break;
        case Py_NE:
        case Py_LT:
        case Py_GT:
            v = Py_False;
            break;
        default:
            ...
        }
    }
    else if (...) { ... }
    else { ...}
    Py_INCREF(v);
    return v;
}

即，此快捷方式left == right仅在…之后

    if (!PyUnicode_Check(left) || !PyUnicode_Check(right))

    if (PyUnicode_READY(left) == -1 ||
        PyUnicode_READY(right) == -1)

所有路径都看起来像这样（手动递归内联，展开和修剪已知分支）

POP()                           # Stack stuff
TOP()                           #
                                #
case PyCmp_IN:                  # Dispatch on operation
                                #
sqm != NULL                     # Dispatch to builtin op
sqm->sq_contains != NULL        #
*sqm->sq_contains               #
                                #
cmp == 0                        # Do comparison in loop
i < Py_SIZE(a)                  #
v == w                          #
op == Py_EQ                     #
++i                             # 
cmp == 0                        #
                                #
res < 0                         # Convert to Python-space
res ? Py_True : Py_False        #
Py_INCREF(v)                    #
                                #
Py_DECREF(left)                 # Stack stuff
Py_DECREF(right)                #
SET_TOP(res)                    #
res == NULL                     #
DISPATCH()                      #

与

POP()                           # Stack stuff
TOP()                           #
                                #
default:                        # Dispatch on operation
                                #
Py_LT <= op                     # Checking operation
op <= Py_GE                     #
v == NULL                       #
w == NULL                       #
Py_EnterRecursiveCall(...)      # Recursive check
                                #
v->ob_type != w->ob_type        # More operation checks
f = v->ob_type->tp_richcompare  # Dispatch to builtin op
f != NULL                       #
                                #
!PyUnicode_Check(left)          # ...More checks
!PyUnicode_Check(right))        #
PyUnicode_READY(left) == -1     #
PyUnicode_READY(right) == -1    #
left == right                   # Finally, doing comparison
case Py_EQ:                     # Immediately short circuit
Py_INCREF(v);                   #
                                #
res != Py_NotImplemented        #
                                #
Py_LeaveRecursiveCall()         # Recursive check
                                #
Py_DECREF(left)                 # Stack stuff
Py_DECREF(right)                #
SET_TOP(res)                    #
res == NULL                     #
DISPATCH()                      #

现在，PyUnicode_Check和PyUnicode_READY相当便宜，因为他们只检查了几个领域，但它应该是显而易见的是，上面一个是较小的代码路径，它具有较少的函数调用，只有一个开关语句是只是有点薄。

TL; DR：

都派往if (left_pointer == right_pointer); 不同之处在于他们为达到目标需要做多少工作。in只是做得更少。

点击查看英文原文

As I mentioned to David Wolever, there’s more to this than meets the eye; both methods dispatch to is; you can prove this by doing

min(Timer("x == x", setup="x = 'a' * 1000000").repeat(10, 10000))
#>>> 0.00045456900261342525

min(Timer("x == y", setup="x = 'a' * 1000000; y = 'a' * 1000000").repeat(10, 10000))
#>>> 0.5256857610074803

The first can only be so fast because it checks by identity.

To find out why one would take longer than the other, let’s trace through execution.

They both start in ceval.c, from COMPARE_OP since that is the bytecode involved

TARGET(COMPARE_OP) {
    PyObject *right = POP();
    PyObject *left = TOP();
    PyObject *res = cmp_outcome(oparg, left, right);
    Py_DECREF(left);
    Py_DECREF(right);
    SET_TOP(res);
    if (res == NULL)
        goto error;
    PREDICT(POP_JUMP_IF_FALSE);
    PREDICT(POP_JUMP_IF_TRUE);
    DISPATCH();
}

This pops the values from the stack (technically it only pops one)

PyObject *right = POP();
PyObject *left = TOP();

and runs the compare:

PyObject *res = cmp_outcome(oparg, left, right);

cmp_outcome is this:

static PyObject *
cmp_outcome(int op, PyObject *v, PyObject *w)
{
    int res = 0;
    switch (op) {
    case PyCmp_IS: ...
    case PyCmp_IS_NOT: ...
    case PyCmp_IN:
        res = PySequence_Contains(w, v);
        if (res < 0)
            return NULL;
        break;
    case PyCmp_NOT_IN: ...
    case PyCmp_EXC_MATCH: ...
    default:
        return PyObject_RichCompare(v, w, op);
    }
    v = res ? Py_True : Py_False;
    Py_INCREF(v);
    return v;
}

This is where the paths split. The PyCmp_IN branch does

int
PySequence_Contains(PyObject *seq, PyObject *ob)
{
    Py_ssize_t result;
    PySequenceMethods *sqm = seq->ob_type->tp_as_sequence;
    if (sqm != NULL && sqm->sq_contains != NULL)
        return (*sqm->sq_contains)(seq, ob);
    result = _PySequence_IterSearch(seq, ob, PY_ITERSEARCH_CONTAINS);
    return Py_SAFE_DOWNCAST(result, Py_ssize_t, int);
}

Note that a tuple is defined as

static PySequenceMethods tuple_as_sequence = {
    ...
    (objobjproc)tuplecontains,                  /* sq_contains */
};

PyTypeObject PyTuple_Type = {
    ...
    &tuple_as_sequence,                         /* tp_as_sequence */
    ...
};

So the branch

if (sqm != NULL && sqm->sq_contains != NULL)

will be taken and *sqm->sq_contains, which is the function (objobjproc)tuplecontains, will be taken.

This does

static int
tuplecontains(PyTupleObject *a, PyObject *el)
{
    Py_ssize_t i;
    int cmp;

    for (i = 0, cmp = 0 ; cmp == 0 && i < Py_SIZE(a); ++i)
        cmp = PyObject_RichCompareBool(el, PyTuple_GET_ITEM(a, i),
                                           Py_EQ);
    return cmp;
}

…Wait, wasn’t that PyObject_RichCompareBool what the other branch took? Nope, that was PyObject_RichCompare.

That code path was short so it likely just comes down to the speed of these two. Let’s compare.

int
PyObject_RichCompareBool(PyObject *v, PyObject *w, int op)
{
    PyObject *res;
    int ok;

    /* Quick result when objects are the same.
       Guarantees that identity implies equality. */
    if (v == w) {
        if (op == Py_EQ)
            return 1;
        else if (op == Py_NE)
            return 0;
    }

    ...
}

The code path in PyObject_RichCompareBool pretty much immediately terminates. For PyObject_RichCompare, it does

PyObject *
PyObject_RichCompare(PyObject *v, PyObject *w, int op)
{
    PyObject *res;

    assert(Py_LT <= op && op <= Py_GE);
    if (v == NULL || w == NULL) { ... }
    if (Py_EnterRecursiveCall(" in comparison"))
        return NULL;
    res = do_richcompare(v, w, op);
    Py_LeaveRecursiveCall();
    return res;
}

The Py_EnterRecursiveCall/Py_LeaveRecursiveCall combo are not taken in the previous path, but these are relatively quick macros that’ll short-circuit after incrementing and decrementing some globals.

do_richcompare does:

static PyObject *
do_richcompare(PyObject *v, PyObject *w, int op)
{
    richcmpfunc f;
    PyObject *res;
    int checked_reverse_op = 0;

    if (v->ob_type != w->ob_type && ...) { ... }
    if ((f = v->ob_type->tp_richcompare) != NULL) {
        res = (*f)(v, w, op);
        if (res != Py_NotImplemented)
            return res;
        ...
    }
    ...
}

This does some quick checks to call v->ob_type->tp_richcompare which is

PyTypeObject PyUnicode_Type = {
    ...
    PyUnicode_RichCompare,      /* tp_richcompare */
    ...
};

which does

PyObject *
PyUnicode_RichCompare(PyObject *left, PyObject *right, int op)
{
    int result;
    PyObject *v;

    if (!PyUnicode_Check(left) || !PyUnicode_Check(right))
        Py_RETURN_NOTIMPLEMENTED;

    if (PyUnicode_READY(left) == -1 ||
        PyUnicode_READY(right) == -1)
        return NULL;

    if (left == right) {
        switch (op) {
        case Py_EQ:
        case Py_LE:
        case Py_GE:
            /* a string is equal to itself */
            v = Py_True;
            break;
        case Py_NE:
        case Py_LT:
        case Py_GT:
            v = Py_False;
            break;
        default:
            ...
        }
    }
    else if (...) { ... }
    else { ...}
    Py_INCREF(v);
    return v;
}

Namely, this shortcuts on left == right… but only after doing

    if (!PyUnicode_Check(left) || !PyUnicode_Check(right))

    if (PyUnicode_READY(left) == -1 ||
        PyUnicode_READY(right) == -1)

All in all the paths then look something like this (manually recursively inlining, unrolling and pruning known branches)

POP()                           # Stack stuff
TOP()                           #
                                #
case PyCmp_IN:                  # Dispatch on operation
                                #
sqm != NULL                     # Dispatch to builtin op
sqm->sq_contains != NULL        #
*sqm->sq_contains               #
                                #
cmp == 0                        # Do comparison in loop
i < Py_SIZE(a)                  #
v == w                          #
op == Py_EQ                     #
++i                             # 
cmp == 0                        #
                                #
res < 0                         # Convert to Python-space
res ? Py_True : Py_False        #
Py_INCREF(v)                    #
                                #
Py_DECREF(left)                 # Stack stuff
Py_DECREF(right)                #
SET_TOP(res)                    #
res == NULL                     #
DISPATCH()                      #

vs

POP()                           # Stack stuff
TOP()                           #
                                #
default:                        # Dispatch on operation
                                #
Py_LT <= op                     # Checking operation
op <= Py_GE                     #
v == NULL                       #
w == NULL                       #
Py_EnterRecursiveCall(...)      # Recursive check
                                #
v->ob_type != w->ob_type        # More operation checks
f = v->ob_type->tp_richcompare  # Dispatch to builtin op
f != NULL                       #
                                #
!PyUnicode_Check(left)          # ...More checks
!PyUnicode_Check(right))        #
PyUnicode_READY(left) == -1     #
PyUnicode_READY(right) == -1    #
left == right                   # Finally, doing comparison
case Py_EQ:                     # Immediately short circuit
Py_INCREF(v);                   #
                                #
res != Py_NotImplemented        #
                                #
Py_LeaveRecursiveCall()         # Recursive check
                                #
Py_DECREF(left)                 # Stack stuff
Py_DECREF(right)                #
SET_TOP(res)                    #
res == NULL                     #
DISPATCH()                      #

Now, PyUnicode_Check and PyUnicode_READY are pretty cheap since they only check a couple of fields, but it should be obvious that the top one is a smaller code path, it has fewer function calls, only one switch statement and is just a bit thinner.

TL;DR:

Both dispatch to if (left_pointer == right_pointer); the difference is just how much work they do to get there. in just does less.

回答 1

这里有三个因素在起作用，共同产生这种令人惊讶的行为。

首先：in操作员使用快捷方式并检查身份（x is y），然后再检查是否等于（x == y）：

>>> n = float('nan')
>>> n in (n, )
True
>>> n == n
False
>>> n is n
True

第二：由于Python的字符串interning，两个"x"in "x" in ("x", )是相同的：

>>> "x" is "x"
True

（大警告：这是实现特定的行为！is应该永远不会被用来比较字符串，因为它会有时给了惊人的答复;例如"x" * 100 is "x" * 100 ==> False）

第三：在详细Veedrac的梦幻般的回答，tuple.__contains__（x in (y, )是大致相当于(y, ).__contains__(x)）获取进行标识检查的速度比点str.__eq__（再次，x == y就是大致相当于x.__eq__(y)）一样。

您可以看到这一点的证据，因为x in (y, )它比逻辑上等效的要慢得多x == y：

In [18]: %timeit 'x' in ('x', )
10000000 loops, best of 3: 65.2 ns per loop

In [19]: %timeit 'x' == 'x'    
10000000 loops, best of 3: 68 ns per loop

In [20]: %timeit 'x' in ('y', ) 
10000000 loops, best of 3: 73.4 ns per loop

In [21]: %timeit 'x' == 'y'    
10000000 loops, best of 3: 56.2 ns per loop

这种x in (y, )情况的速度较慢，因为在is比较失败之后，in运算符将退回到常规的相等性检查（即使用==），因此比较所需的时间与相同，因此==由于创建元组的开销，整个操作的速度变慢了。 ，遍历其成员等。

还请注意，只有在以下a in (b, )情况下才更快a is b：

In [48]: a = 1             

In [49]: b = 2

In [50]: %timeit a is a or a == a
10000000 loops, best of 3: 95.1 ns per loop

In [51]: %timeit a in (a, )      
10000000 loops, best of 3: 140 ns per loop

In [52]: %timeit a is b or a == b
10000000 loops, best of 3: 177 ns per loop

In [53]: %timeit a in (b, )      
10000000 loops, best of 3: 169 ns per loop

（为什么a in (b, )要比这快a is b or a == b？我猜想虚拟机指令会更少—  a in (b, )只有〜3条指令，其中a is b or a == b会有更多的VM指令）

Veedrac的答案- https://stackoverflow.com/a/28889838/71522 -进入每个过程具体是什么情况更多的细节==和in，是非常值得读。

点击查看英文原文

There are three factors at play here which, combined, produce this surprising behavior.

First: the in operator takes a shortcut and checks identity (x is y) before it checks equality (x == y):

>>> n = float('nan')
>>> n in (n, )
True
>>> n == n
False
>>> n is n
True

Second: because of Python’s string interning, both "x"s in "x" in ("x", ) will be identical:

>>> "x" is "x"
True

(big warning: this is implementation-specific behavior! is should never be used to compare strings because it will give surprising answers sometimes; for example "x" * 100 is "x" * 100 ==> False)

Third: as detailed in Veedrac’s fantastic answer, tuple.__contains__ (x in (y, ) is roughly equivalent to (y, ).__contains__(x)) gets to the point of performing the identity check faster than str.__eq__ (again, x == y is roughly equivalent to x.__eq__(y)) does.

You can see evidence for this because x in (y, ) is significantly slower than the logically equivalent, x == y:

In [18]: %timeit 'x' in ('x', )
10000000 loops, best of 3: 65.2 ns per loop

In [19]: %timeit 'x' == 'x'    
10000000 loops, best of 3: 68 ns per loop

In [20]: %timeit 'x' in ('y', ) 
10000000 loops, best of 3: 73.4 ns per loop

In [21]: %timeit 'x' == 'y'    
10000000 loops, best of 3: 56.2 ns per loop

The x in (y, ) case is slower because, after the is comparison fails, the in operator falls back to normal equality checking (i.e., using ==), so the comparison takes about the same amount of time as ==, rendering the entire operation slower because of the overhead of creating the tuple, walking its members, etc.

Note also that a in (b, ) is only faster when a is b:

In [48]: a = 1             

In [49]: b = 2

In [50]: %timeit a is a or a == a
10000000 loops, best of 3: 95.1 ns per loop

In [51]: %timeit a in (a, )      
10000000 loops, best of 3: 140 ns per loop

In [52]: %timeit a is b or a == b
10000000 loops, best of 3: 177 ns per loop

In [53]: %timeit a in (b, )      
10000000 loops, best of 3: 169 ns per loop

(why is a in (b, ) faster than a is b or a == b? My guess would be fewer virtual machine instructions — a in (b, ) is only ~3 instructions, where a is b or a == b will be quite a few more VM instructions)

Veedrac’s answer — https://stackoverflow.com/a/28889838/71522 — goes into much more detail on specifically what happens during each of == and in and is well worth the read.

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