Update audb.Dependencies methods benchmark

[hagenw]

This updates the benchmark of audb.Dependencies methods to be more independent of the actual implementation of the dtypes used, and include different implementations in the results.

Unfortunately, the results show that using the object dtype for strings is still the fastest way, which seems to be mainly influenced by degraded row based performance for string and string[pyarrow]:

import pandas as pd
import timeit

points = 1000000
data = [f"data-{n}" for n in range(points)]
for dtype in ["object", "string", "string[pyarrow]"]:
    index = pd.Index([f"index-{n}" for n in range(points)], dtype=dtype)
    df = pd.DataFrame(data, index=index, dtype=dtype)
    print(dtype)
    %timeit df.loc['index-2000']

which returns

object
9.78 µs ± 18.9 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
string
15.7 µs ± 36.5 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
string[pyarrow]
17.6 µs ± 66.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)