Select 50 items from a list at random to write to a file

Question

So far I have figured out how to import a file, create new files and randomize the list.

Am I having trouble choosing only 50 items from a list randomly for writing to a file?

def randomizer(input,output1='random_1.txt',output2='random_2.txt',output3='random_3.txt',output4='random_total.txt'): #Input file query=open(input,'r').read().split() dir,file=os.path.split(input) temp1 = os.path.join(dir,output1) temp2 = os.path.join(dir,output2) temp3 = os.path.join(dir,output3) temp4 = os.path.join(dir,output4) out_file4=open(temp4,'w') random.shuffle(query) for item in query: out_file4.write(item+'\n') 

So, if the shared randomization file was

 example: random_total = ['9','2','3','1','5','6','8','7','0','4'] 

I would like to get 3 files (out_file1 | 2 | 3) with the first random set of 3, the second random set of 3 and the third random set of 3 (for this example, but the one I want to create should have 50)

 random_1 = ['9','2','3'] random_2 = ['1','5','6'] random_3 = ['8','7','0'] 

Thus, the last “4” will not be included, and this is normal.

How to choose 50 from the list that I randomized?

Better yet, how could I select 50 randomly from the original list?

Answer 1

If the list is in random order, you can simply take the first 50.

Otherwise use

 random.sample(the_list, 50) 

random.sample help text:

 sample(self, population, k) method of random.Random instance Chooses k unique random elements from a population sequence. Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices). Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample. To choose a sample in a range of integers, use xrange as an argument. This is especially fast and space efficient for sampling from a large population: sample(xrange(10000000), 60) 

Answer 2

I think random.choice() is the best option.

 from numpy import random mylist = [13,23,14,52,6,23] random.choice(mylist,3) 

the function returns an array of 3 randomly selected values ​​from the list

Answer 3

One easy way to select random items is to shuffle and then slice.

 import random a = [1,2,3,4,5,6,7,8,9] random.shuffle(a) print a[:4] # prints 4 random variables