How can Python randomly shuffle a list of 100,000 items?
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I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.
NB.
Fairness trumps efficiency or speed.
I have access to /dev/urandom.
The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.
python random shuffle
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up vote
-1
down vote
favorite
I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.
NB.
Fairness trumps efficiency or speed.
I have access to /dev/urandom.
The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.
python random shuffle
1
What have you tried so far?
– BernardL
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40
add a comment |
up vote
-1
down vote
favorite
up vote
-1
down vote
favorite
I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.
NB.
Fairness trumps efficiency or speed.
I have access to /dev/urandom.
The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.
python random shuffle
I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.
NB.
Fairness trumps efficiency or speed.
I have access to /dev/urandom.
The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.
python random shuffle
python random shuffle
edited Nov 22 at 17:05
asked Nov 22 at 16:36
Paul Uszak
209414
209414
1
What have you tried so far?
– BernardL
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40
add a comment |
1
What have you tried so far?
– BernardL
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40
1
1
What have you tried so far?
– BernardL
Nov 22 at 16:38
What have you tried so far?
– BernardL
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40
add a comment |
2 Answers
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0
down vote
accepted
You can easily shuffle millions of numbers in NumPy:
import numpy as np
data = np.arange(1e6)
%timeit np.random.shuffle(data)
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
add a comment |
up vote
0
down vote
Have you tried using numpy's shuffle
?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html
or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
You can easily shuffle millions of numbers in NumPy:
import numpy as np
data = np.arange(1e6)
%timeit np.random.shuffle(data)
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
add a comment |
up vote
0
down vote
accepted
You can easily shuffle millions of numbers in NumPy:
import numpy as np
data = np.arange(1e6)
%timeit np.random.shuffle(data)
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
You can easily shuffle millions of numbers in NumPy:
import numpy as np
data = np.arange(1e6)
%timeit np.random.shuffle(data)
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
You can easily shuffle millions of numbers in NumPy:
import numpy as np
data = np.arange(1e6)
%timeit np.random.shuffle(data)
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
edited Nov 22 at 17:09
answered Nov 22 at 16:41
Nils Werner
17.3k13859
17.3k13859
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
add a comment |
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
– Paul Uszak
Nov 22 at 17:05
add a comment |
up vote
0
down vote
Have you tried using numpy's shuffle
?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html
or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html
add a comment |
up vote
0
down vote
Have you tried using numpy's shuffle
?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html
or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html
add a comment |
up vote
0
down vote
up vote
0
down vote
Have you tried using numpy's shuffle
?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html
or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html
Have you tried using numpy's shuffle
?
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html
or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html
answered Nov 22 at 16:39
Dan
36.8k95199
36.8k95199
add a comment |
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1
What have you tried so far?
– BernardL
Nov 22 at 16:38
Can you use NumPy?
– Nils Werner
Nov 22 at 16:38
@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40
No, of course not!
– Nils Werner
Nov 22 at 16:40