How to apply conditional statement in numpy array?

Question

I am trying to apply conditional statements in a numpy array and to get a boolean array with 1 and 0 values.

I tried so far the np.where(), but it allows only 3 arguments and in my case I have some more.

I first create the array randomly:

numbers = np.random.uniform(1,100,5)

Now, if the value is lower then 30, I would like to get a 0. If the value is greater than 70, I would like to get 1. And if the value is between 30 and 70, I would like to get a random number between 0 and 1. If this number is greater than 0.5, then the value from the array should get 1 as a boolean value and in other case 0. I guess this is made again with the np.random function, but I dont know how to apply all of the arguments.

If the input array is:

[10,40,50,60,90]

Then the expected output should be:

[0,1,0,1,1]

where the three values in the middle are randomly distributed so they can differ when making multiple tests.

Thank you in advance!

jezrael · Accepted Answer · 2019-07-13 08:13:05Z

3

Use numpy.select and 3rd condition should should be simplify by numpy.random.choice:

numbers = np.array([10,40,50,60,90])
print (numbers)
[10 40 50 60 90]

a = np.select([numbers < 30, numbers > 70], [0, 1], np.random.choice([1,0], size=len(numbers)))
print (a)
[0 0 1 0 1]

If need 3rd condition with compare by 0.5 is possible convert mask to integers for True, False to 1, 0 mapping:

b = (np.random.rand(len(numbers)) > .5).astype(int)
#alternative
#b = np.where(np.random.rand(len(numbers)) > .5, 1, 0)

a = np.select([numbers < 30, numbers > 70], [0, 1], b)

Or you can chain 3 times numpy.where:

a = np.where(numbers < 30, 0,
    np.where(numbers > 70, 1, 
    np.where(np.random.rand(len(numbers)) > .5, 1, 0)))

Or use np.select:

a = np.select([numbers < 30, numbers > 70, np.random.rand(len(numbers)) > .5], 
               [0, 1, 1], 0)

edited Jul 13, 2019 at 8:13

answered Jul 13, 2019 at 7:49

jezrael

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2 Comments

Nick Over a year ago

Thanks you! I have never used the np.select so far. Will take a closer look to this function :)

Nick Over a year ago

Well, and for numbers between 30 and 70, we use the np.random function. So far, so good. But then if they get a value lower or equal than 0.5, the boolean value should be 0. If they get a value greater than 0.5, then they obtain the value 1.

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