numpy unique vs set

strs = np.repeat(np.array(['a', 'b', 'c'], dtype='O'), 10000)

In [11]: %timeit pd.unique(strs)
558 µs ± 16.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [12]: %timeit list(set(strs))
531 µs ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

nums = np.repeat(np.array([1, 2, 3]), 10000)

In [13]: %timeit pd.unique(nums)
230 µs ± 9.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [14]: %timeit list(set(nums))
2.16 ms ± 71 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)