["python","performance"]
Why is a.insert(0,0) much slower than a[0:0]=[0]?

Using a list's insert function is much slower than achieving the same effect using slice assignment: > python -m timeit -n 100000 -s "a=[]" "a.insert(0,0)" 100000 loops, best of 5: 19.2 usec per loop > python -m timeit -n 100000 -s "a=[]" "a[0:0]=[0]" 100000 loops, best of 5: 6.78 usec per loop (Note that a=[] is only the setup, so a starts empty but then grows to 100,000 elements.) At first I thought maybe it's the attribute lookup or function call overhead or so, but inserting near the end shows that that's negligible: > python -m timeit -n 100000 -s "a=[]" "a.insert(-1,0)" 100000 loops, best of 5: 79.1 nsec per loop Why is the presumably simpler dedicated "insert single element" function so much slower? I can also reproduce it at repl.it: from timeit import repeat for _ in range(3): for stmt in 'a.insert(0,0)', 'a[0:0]=[0]', 'a.insert(-1,0)': t = min(repeat(stmt, 'a=[]', number=10**5)) print('%.6f' % t, stmt) print() # Example output: # # 4.803514 a.insert(0,0) # 1.807832 a[0:0]=[0] # 0.012533 a.insert(-1,0) # # 4.967313 a.insert(0,0) # 1.821665 a[0:0]=[0] # 0.012738 a.insert(-1,0) # # 5.694100 a.insert(0,0) # 1.899940 a[0:0]=[0] # 0.012664 a.insert(-1,0) I use Python 3.8.1 32-bit on Windows 10 64-bit. repl.it uses Python 3.8.1 64-bit on Linux 64-bit.
I think it's probably just that they forgot to use memmove in list.insert. If you take a look at the code list.insert uses to shift elements, you can see it's just a manual loop: for (i = n; --i >= where; ) items[i+1] = items[i]; while list.__setitem__ on the slice assignment path uses memmove: memmove(&item[ihigh+d], &item[ihigh], (k - ihigh)*sizeof(PyObject *)); memmove typically has a lot of optimization put into it, such as taking advantage of SSE/AVX instructions.