# Why is 'x' in ('x',) faster than 'x' == 'x'?

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))#>>> 0.00045456900261342525min(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

`intPySequence_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 inttuplecontains(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.

`intPyObject_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 stuffTOP() # #case PyCmp_IN: # Dispatch on operation #sqm != NULL # Dispatch to builtin opsqm->sq_contains != NULL #*sqm->sq_contains # #cmp == 0 # Do comparison in loopi < Py_SIZE(a) #v == w #op == Py_EQ #++i # cmp == 0 # #res < 0 # Convert to Python-spaceres ? Py_True : Py_False #Py_INCREF(v) # #Py_DECREF(left) # Stack stuffPy_DECREF(right) #SET_TOP(res) #res == NULL #DISPATCH() #`

vs

`POP() # Stack stuffTOP() # #default: # Dispatch on operation #Py_LT <= op # Checking operationop <= Py_GE #v == NULL #w == NULL #Py_EnterRecursiveCall(...) # Recursive check #v->ob_type != w->ob_type # More operation checksf = v->ob_type->tp_richcompare # Dispatch to builtin opf != NULL # #!PyUnicode_Check(left) # ...More checks!PyUnicode_Check(right)) #PyUnicode_READY(left) == -1 #PyUnicode_READY(right) == -1 #left == right # Finally, doing comparisoncase Py_EQ: # Immediately short circuitPy_INCREF(v); # #res != Py_NotImplemented # #Py_LeaveRecursiveCall() # Recursive check #Py_DECREF(left) # Stack stuffPy_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 switchstatement 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.

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 == nFalse>>> n is nTrue`

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 loopIn [19]: %timeit 'x' == 'x' 10000000 loops, best of 3: 68 ns per loopIn [20]: %timeit 'x' in ('y', ) 10000000 loops, best of 3: 73.4 ns per loopIn [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 = 2In [50]: %timeit a is a or a == a10000000 loops, best of 3: 95.1 ns per loopIn [51]: %timeit a in (a, ) 10000000 loops, best of 3: 140 ns per loopIn [52]: %timeit a is b or a == b10000000 loops, best of 3: 177 ns per loopIn [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.