Summary
Tuples tend to perform better than lists in almost every category:
1) Tuples can be constant folded.
2) Tuples can be reused instead of copied.
3) Tuples are compact and don't over-allocate.
4) Tuples directly reference their elements.
Tuples can be constant folded
Tuples of constants can be precomputed by Python's peephole optimizer or AST-optimizer. Lists, on the other hand, get built-up from scratch:
>>> from dis import dis
>>> dis(compile("(10, 'abc')", '', 'eval'))
1 0 LOAD_CONST 2 ((10, 'abc'))
3 RETURN_VALUE
>>> dis(compile("[10, 'abc']", '', 'eval'))
1 0 LOAD_CONST 0 (10)
3 LOAD_CONST 1 ('abc')
6 BUILD_LIST 2
9 RETURN_VALUE
Tuples do not need to be copied
Running tuple(some_tuple) returns immediately itself. Since tuples are immutable, they do not have to be copied:
>>> a = (10, 20, 30)
>>> b = tuple(a)
>>> a is b
True
In contrast, list(some_list) requires all the data to be copied to a new list:
>>> a = [10, 20, 30]
>>> b = list(a)
>>> a is b
False
Tuples do not over-allocate
Since a tuple's size is fixed, it can be stored more compactly than lists which need to over-allocate to make append() operations efficient.
This gives tuples a nice space advantage:
>>> import sys
>>> sys.getsizeof(tuple(iter(range(10))))
128
>>> sys.getsizeof(list(iter(range(10))))
200
Here is the comment from Objects/listobject.c that explains what lists are doing:
/* This over-allocates proportional to the list size, making room
* for additional growth. The over-allocation is mild, but is
* enough to give linear-time amortized behavior over a long
* sequence of appends() in the presence of a poorly-performing
* system realloc().
* The growth pattern is: 0, 4, 8, 16, 25, 35, 46, 58, 72, 88, ...
* Note: new_allocated won't overflow because the largest possible value
* is PY_SSIZE_T_MAX * (9 / 8) + 6 which always fits in a size_t.
*/
Tuples refer directly to their elements
References to objects are incorporated directly in a tuple object. In contrast, lists have an extra layer of indirection to an external array of pointers.
This gives tuples a small speed advantage for indexed lookups and unpacking:
$ python3.6 -m timeit -s 'a = (10, 20, 30)' 'a[1]'
10000000 loops, best of 3: 0.0304 usec per loop
$ python3.6 -m timeit -s 'a = [10, 20, 30]' 'a[1]'
10000000 loops, best of 3: 0.0309 usec per loop
$ python3.6 -m timeit -s 'a = (10, 20, 30)' 'x, y, z = a'
10000000 loops, best of 3: 0.0249 usec per loop
$ python3.6 -m timeit -s 'a = [10, 20, 30]' 'x, y, z = a'
10000000 loops, best of 3: 0.0251 usec per loop
Here is how the tuple (10, 20) is stored:
typedef struct {
Py_ssize_t ob_refcnt;
struct _typeobject *ob_type;
Py_ssize_t ob_size;
PyObject *ob_item[2]; /* store a pointer to 10 and a pointer to 20 */
} PyTupleObject;
Here is how the list [10, 20] is stored:
PyObject arr[2]; /* store a pointer to 10 and a pointer to 20 */
typedef struct {
Py_ssize_t ob_refcnt;
struct _typeobject *ob_type;
Py_ssize_t ob_size;
PyObject **ob_item = arr; /* store a pointer to the two-pointer array */
Py_ssize_t allocated;
} PyListObject;
Note that the tuple object incorporates the two data pointers directly while the list object has an additional layer of indirection to an external array holding the two data pointers.
Best Answer
The standard way of comparing different algorithms is by comparing their complexity using Big O notation. In practice you would of course also benchmark the algorithms.
As an example the sorting algorithms bubble sort and heap sort has complexity O(n2) and O(n log n) respective.
As a final note it's very hard to construct representative benchmarks, see this interesting post from Christer Ericsson on the subject.