How to join arrays obtained with different numpy functions?

Question

I am writing a program to show the age of 100 people whose ages range between 18 and 110. I want to set a negative value for a couple of them and an age bigger than 120 for other 3 people, then clean the dataset setting 0 in these 5 "wrong" ages, and then check the minimum and the maximum of the ages after the cleaning.

I have a solution by setting the wrong ages individually, but I would like to be able to do this by creating one array with 95 elements (the correct ages), another with 5 elements (the wrong ages), and then concatenate them. However I don´t know how to concatenate 1-D arrays that were generated differently.

Here is my existing solution:

peopleAge = np.random.randint(18,110,100)
peopleAge[12] = -6
peopleAge[15] = -23
peopleAge[29] = 132
peopleAge[80] = 155
peopleAge[99] = 200
peopleAge = np.where((peopleAge < 0) | (peopleAge > 120), 0, peopleAge)

minAge = min(peopleAge[peopleAge > 0])
maxAge = max(peopleAge[peopleAge < 111])

And here is what I am trying to do:

peopleAge = np.random.randint(18,110,95)
peopleAgeSpecial = np.array([-6,-23,132,155,200])

How can I join peopleAge and peopleAgeSpecial into one array?

What do you want the result to look like? One option produces a (100,) shape array. — hpaulj
– hpaulj, Commented Feb 15, 2019 at 17:31
The result will look the same as shown in the existing solution, but the way to build the 100-item array will be different. I would like to know how I can arrive at the same result by concatenating two arrays. — Madrid_datalady
– Madrid_datalady, Commented Feb 15, 2019 at 17:33
You could either use a binary vector that selects the elements where you want to change you array or use the positions of the array directly wrong_binary = np.array([0, 0, 0, 1, 1, 0, 0, ... ,1, 0]) # something like that peopleAge = np.random.randint(18,110,95) peopleAge[wrong_binary] = np.array([-6,-23,132,155,200]) or wrong = np.array([12, 15, 29, 80, 99]) peopleAge = np.random.randint(18,110,95) peopleAge[wrong] = np.array([-6,-23,132,155,200]) — p13rr0m
– p13rr0m, Commented Feb 15, 2019 at 20:13

iGian · Accepted Answer · 2019-02-15 20:02:25Z

1

If you don't want to set the exact same index but just have five strange ages randomly distributed:

peopleAge = np.random.randint(18,110,95)
peopleAgeSpecial = np.array([-6,-23,132,155,200])

peopleAge = np.append(peopleAge, peopleAgeSpecial, axis = 0)
np.random.shuffle(peopleAge)

answered Feb 15, 2019 at 20:02

iGian

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