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Function: require_once

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

I'm having trouble selecting only 50 items from the list randomly to write 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 total randomization file was

example:

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

I would want 3 files (out_file1|2|3) with the first random set of 3, second random set of 3, and 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']

So the last '4' will not be included which is fine.

How can I select 50 from the list that I randomized ?

Even better, how could I select 50 at random from the original list ?

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

Otherwise, use

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

import random
a = [1,2,3,4,5,6,7,8,9]
random.shuffle(a)
print a[:4] # prints 4 random variables
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    • @MonicaHeddneck Why would random shuffling and slicing be better? Wouldn't selecting a number of sample by randomizing the selection have the same merits as random shuffling and then taking a slice of the shuffled samples? Can you please explain? Thanks.
    • I used this to easily create a test / train set for a machine learning project. Using random.choice(mylist,3) wouldn't create two disjoint sets as this did.

I think random.choice() is a better option.

import numpy as np

mylist = [13,23,14,52,6,23]

np.random.choice(mylist, 3, replace=False)

the function returns an array of 3 randomly chosen values from the list

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Say your list has 100 elements and you want to pick 50 of them in a random way. Here are the steps to follow:

  1. Import the libraries
  2. Create the seed for random number generator, I have put it at 2
  3. Prepare a list of numbers from which to pick up in a random way
  4. Make the random choices from the numbers list

Code:

from random import seed
from random import choice

seed(2)
numbers = [i for i in range(100)]

print(numbers)

for _ in range(50):
    selection = choice(numbers)
    print(selection)
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