Python Benchmark: Unique values in array

Looking for a fast way to "Unique values in array" in python? Check below to see see which method is the fastest and uses less memory.

Packages

List packages as it's done in a requirements.txt file. Learn more

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numpy
 
 
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Setup

Runs before the benchmark, and it is not measured. Good place to import libraries and prepare data. Learn more

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import numpy as np
import pandas as pd
values = np.random.randint(0, 100, 100_000, dtype=np.int)
 
 
הההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההה
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Code 1

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unique_values = np.unique(values)
 
 
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Code 2

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unique_values = pd.unique(values)
 
 
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Code 3

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unique_values = np.array(sorted(pd.unique(values)))
 
 
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Code 4

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unique_values = np.array(list(set(values)))
 
 
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Teardown

Runs after the benchmark, and it is not measured. Good place to print out results. Learn more

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print(unique_values)
 
 
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Results

Fastest

pandas.unique

1.22 ms

822 op/s

Speed

Benchmark	Runtime	Operations	VS	Visual
pandas.unique	1.22 ms	822 op/s	1×	0.0012159861494288888
pandas.unique - sorted	1.25 ms	801 op/s	1.03×	0.0012483375298010337
numpy.unique	4.86 ms	206 op/s	4×	0.004862794652581215
set	24.3 ms	41.2 op/s	20×	0.02425069222226739

Memory

Benchmark	Peak Memory	Score	VS	Visual
set	< 1 KB	9.62	1×	0
pandas.unique - sorted	544 KB	8.17	544×	544
pandas.unique	544 KB	8.18	544×	544
numpy.unique	1.10 MB	7.27	1100×	1100

Output

numpy.unique

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]

pandas.unique

[25 72  6 93 70  3 24 71 81 21 65 60 50 11  2 55 92 41 87 45 39 90 30 61
 76 14 20 22 38 17 42  9 49 79  0 35 10 40  1 82 28 97 89 52 68 83 43  5
 73 58 48  8 67 75 31 94 57 59 74 98 66 16 86 51 53 78 23 15 69 19 99 18
 47 77 84 46 29 96 26 85 32 91 95 33 54 62 64 27 13  4 34 44 63 12 56 88
 37 80  7 36]

pandas.unique - sorted

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]

set

[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
 96 97 98 99]