Dictionary keys and values to separate numpy arrays

Question

I have a dictionary as

Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.53         


        

Answer 1

If you care about speed (Python 3.7)

rnd = np.random.RandomState(10)

for i in [10,100,1000,10000,100000]:
    test_dict = {j:j for j in rnd.uniform(-100,100,i)}
    assert len(test_dict) == i
    print(f"\nFor {i} keys\n-----------")
    
    %timeit keys = np.fromiter(test_dict.keys(), dtype=float)
    
    %timeit keys = np.array(list(test_dict.keys()))

np.fromiter is 5-7 times faster

For 10 keys
-----------
712 ns ± 4.77 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.65 µs ± 9.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

For 100 keys
-----------
1.87 µs ± 13.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
8.02 µs ± 22.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

For 1000 keys
-----------
13.7 µs ± 27.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
70.5 µs ± 251 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

For 10000 keys
-----------
128 µs ± 70.6 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
698 µs ± 455 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)

For 100000 keys
-----------
1.45 ms ± 374 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
7.14 ms ± 6.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)