On Oct 18, 9:27 pm, elsa <kerensael...@hotmail.com> wrote:
> Hello,
>
> I'm trying to find a way to collect a set of values from real data,
> and then sample values randomly from this data - so, the data I'm
> collecting becomes a kind of probability distribution. For instance, I
> might have age data for some children. It's very easy to collect this
> data using a list, where the index gives the value of the data, and
> the number in the list gives the number of times that values occurs:
>
> [0,0,10,20,5]
>
> could mean that there are no individuals that are no people aged 0, no
> people aged 1, 10 people aged 2, 20 people aged 3, and 5 people aged 4
> in my data collection.
>
> I then want to make a random sample that would be representative of
> these proportions - is there any easy and fast way to select an entry
> weighted by its value? Or are there any python packages that allow you
> to easily create your own distribution based on collected data? Two
> other things to bear in mind are that in reality I'm collating data
> from up to around 5 million individuals, so just making one long list
> with a new entry for each individual won't work. Also, it would be
> good if I didn't have to decide before hand what the possible range of
> values is (which unfortunately I have to do with the approach I'm
> currently working on).
My suggestion is to sample into a cumulative sum list and find the
index by binary search:
import bisect
import random
data = [0, 0, 10, 20, 5]
cumsum = []
for x in data:
cumsum.append(cumsum[-1] + x if cumsum else x)
virtual_index = random.randrange(cumsum[-1])
actual_index = bisect.bisect_right(cumsum, virtual_index)
HTH,
Ian
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