If as the input you provide the (integer) power, what is the fastest way to create the corresponding power of ten? Here are four alternatives I could come up with, and the fastest way seems to be using an f-string:

from functools import partial
from time import time
import numpy as npdef fstring(power):return float(f'1e{power}')def asterisk(power):return 10**powermethods = {'fstring': fstring,'asterisk': asterisk,'pow': partial(pow, 10),'np.pow': partial(np.power, 10, dtype=float)
}# "dtype=float" is necessary because otherwise it will raise: 
# ValueError: Integers to negative integer powers are not allowed.
# see https://stackoverflow.com/a/43287598/5472354
powers = [int(i) for i in np.arange(-10000, 10000)]
for name, method in methods.items():start = time()for i in powers:method(i)print(f'{name}: {time() - start}')

Results:

fstring: 0.008975982666015625
asterisk: 0.5190775394439697
pow: 0.4863283634185791
np.pow: 0.046906232833862305

I guess the f-string approach is the fastest because nothing is actually calculated, though it only works for integer powers of ten, whereas the other methods are more complicated operations that also work with any real number as the base and power. So is the f-string actually the best way to go about it?

You're comparing apples to oranges here. 10 ** n computes an integer (when n is non-negative), whereas float(f'1e{n}') computes a floating-point number. Those won't take the same amount of time, but they solve different problems so it doesn't matter which one is faster.

But it's worse than that, because there is the overhead of calling a function, which is included in your timing for all of your alternatives, but only some of them actually involve calling a function. If you write 10 ** n then you aren't calling a function, but if you use partial(pow, 10) then you have to call it as a function to get a result. So you're not actually comparing the speed of 10 ** n fairly.

Instead of rolling your own timing code, use the timeit library, which is designed for doing this properly. The results are in seconds for 1,000,000 repetitions (by default), or equivalently they are the average time in microseconds for one repetiton.

Here's a comparison for computing integer powers of 10:

>>> from timeit import timeit
>>> timeit('10 ** n', setup='n = 500')
1.09881673199925
>>> timeit('pow(10, n)', setup='n = 500')
1.1821871869997267
>>> timeit('f(n)', setup='n = 500; from functools import partial; f = partial(pow, 10)')
1.1401332350014854

And here's a comparison for computing floating-point powers of 10: note that computing 10.0 ** 500 or 1e500 is pointless because the result is simply an OverflowError or inf.

>>> timeit('10.0 ** n', setup='n = 200')
0.12391662099980749
>>> timeit('pow(10.0, n)', setup='n = 200')
0.17336435099969094
>>> timeit('f(n)', setup='n = 200; from functools import partial; f = partial(pow, 10.0)')
0.18887039500077663
>>> timeit('float(f"1e{n}")', setup='n = 200')
0.44305286100097874
>>> timeit('np.power(10.0, n, dtype=float)', setup='n = 200; import numpy as np')
1.491982370000187
>>> timeit('f(n)', setup='n = 200; from functools import partial; import numpy as np; f = partial(np.power, 10.0, dtype=float)')
1.6273324920002779

So the fastest of these options in both cases is the obvious one: 10 ** n for integers and 10.0 ** n for floats.