a = ['1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '7', '7', '7', '10', '10']
print a.count("1")
It's probably optimized heavily at the C level.
Edit: I randomly generated a large list.
In [8]: len(a)
Out[8]: 6339347
In [9]: %timeit a.count("1")
10 loops, best of 3: 86.4 ms per loop
Edit edit: This could be done with collections.Counter
a = Counter(your_list)
print a['1']
Using the same list in my last timing example
In [17]: %timeit Counter(a)['1']
1 loops, best of 3: 1.52 s per loop
My timing is simplistic and conditional on many different factors, but it gives you a good clue as to performance.
Here is some profiling
In [24]: profile.run("a.count('1')")
3 function calls in 0.091 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.091 0.091 <string>:1(<module>)
1 0.091 0.091 0.091 0.091 {method 'count' of 'list' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Prof
iler' objects}
In [25]: profile.run("b = Counter(a); b['1']")
6339356 function calls in 2.143 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 2.143 2.143 <string>:1(<module>)
2 0.000 0.000 0.000 0.000 _weakrefset.py:68(__contains__)
1 0.000 0.000 0.000 0.000 abc.py:128(__instancecheck__)
1 0.000 0.000 2.143 2.143 collections.py:407(__init__)
1 1.788 1.788 2.143 2.143 collections.py:470(update)
1 0.000 0.000 0.000 0.000 {getattr}
1 0.000 0.000 0.000 0.000 {isinstance}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Prof
iler' objects}
6339347 0.356 0.000 0.356 0.000 {method 'get' of 'dict' objects}