Random Numbers at Matrix's output

Question

Input:

import random
import numpy as np

gladList = np.empty((128,5))

for i in range(128):
    gladList[i]=(i,random.randint(1000,1500),random.randint(40,80),random.randint(0,100),random.randint(60,100))
    print(gladList[i])

expected output: [[ 49. 1169. 63. 70. 74.]] [[ 50. 1134. 80. 23. 70.]] . . .

error at output(at line 2): [[ 0. 1116. 72. 72. 69.]] [[1.000e+00 1.475e+03 6.800e+01 8.600e+01 6.900e+01]] [[ 2. 1443.
60. 53. 75.]] [[ 3. 1309. 80. 34. 91.]] [[ 4. 1020. 45. 32. 61.]]

I dont know why im getting this random numbers at line 2, if i run code again im getting even more errors in arrays. But first error is always at line 2(gladList[1]) its like a virus at my output.

Why do you think this is an error. It seems to be giving the random numbers provided by you for the range you provided. — dewDevil
– dewDevil, Commented Jun 12, 2020 at 10:12
Im expecting numbers like [1, 1000, 40, 0, 60] at output but getting instead [1.000e+00 1.475e+03 6.800e+01 8.600e+01 6.900e+01] where did they come from? — justwannalearn
– justwannalearn, Commented Jun 12, 2020 at 10:15
You mean you have scientific notation on the second line? Actually I'm seeing that too in all runs, precisely in the second line, not sure why. — yatu
– yatu, Commented Jun 12, 2020 at 10:15
Yeah im trying to make something like a game on Python to train coding. Its my generate random gladiator code, generating a gladiator with scientific numbers making me sad, i guess its something about numpy but im not sure. — justwannalearn
– justwannalearn, Commented Jun 12, 2020 at 10:20
Note that tese are not errors at all. Its just a different notation for those integers. What I'm not sure is of why the second row is always printed using that notation — yatu
– yatu, Commented Jun 12, 2020 at 10:20

dewDevil · Accepted Answer · 2020-06-12 10:19:20Z

1

If you think you are getting scientific notation, that is a problem for you, then you can do the following to remove it.

np.set_printoptions(suppress=True)

I hope this helps you, so whenever you will print the array you won't get exponential notation.

answered Jun 12, 2020 at 10:19

dewDevil

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hpaulj · Accepted Answer · 2020-06-12 14:55:08Z

np.random.randint lets us specify different integer ranges. Using that we can generate (128,4) points with one call. Here I'll use a smaller 10:

In [35]: arr = np.random.randint((1000,40,0,60),(1500,80,100,100),(10,4))                                       
In [36]: arr                                                                                                    
Out[36]: 
array([[1003,   42,   66,   74],
       [1421,   42,   54,   66],
       [1212,   46,   22,   61],
       [1048,   58,   52,   94],
       [1487,   50,   20,   76],
       [1249,   45,   80,   74],
       [1488,   45,   54,   74],
       [1105,   69,   53,   67],
       [1341,   47,   36,   84],
       [1161,   48,   81,   86]])

and adding the index column:

In [37]: arr = np.concatenate((np.arange(10)[:,None],arr),axis=1)                                               
In [38]: arr                                                                                                    
Out[38]: 
array([[   0, 1003,   42,   66,   74],
       [   1, 1421,   42,   54,   66],
       [   2, 1212,   46,   22,   61],
       [   3, 1048,   58,   52,   94],
       [   4, 1487,   50,   20,   76],
       [   5, 1249,   45,   80,   74],
       [   6, 1488,   45,   54,   74],
       [   7, 1105,   69,   53,   67],
       [   8, 1341,   47,   36,   84],
       [   9, 1161,   48,   81,   86]])

Doing the same row by row. Note create arr1 as integer dtype:

In [42]: arr1 = np.zeros((10,5),int)                                                                            
In [43]: for i in range(10): 
    ...:     arr1[i,0]=i 
    ...:     arr1[i,1:] = np.random.randint((1000,40,0,60),(1500,80,100,100),(1,4)) 
    ...:                                                                                                        
In [44]: arr1                                                                                                   
Out[44]: 
array([[   0, 1014,   62,   15,   77],
       [   1, 1046,   41,    6,   80],
       [   2, 1198,   67,   77,   67],
       [   3, 1306,   76,   25,   86],
       [   4, 1194,   60,   57,   62],
       [   5, 1068,   75,   32,   76],
       [   6, 1468,   74,   39,   67],
       [   7, 1268,   54,   47,   79],
       [   8, 1191,   75,    0,   71],
       [   9, 1164,   42,   27,   90]])

The same thing a float uses scientific notation because values range from 1 to 1000+:

In [45]: arr1.astype(float)                                                                                     
Out[45]: 
array([[0.000e+00, 1.014e+03, 6.200e+01, 1.500e+01, 7.700e+01],
       [1.000e+00, 1.046e+03, 4.100e+01, 6.000e+00, 8.000e+01],
         ...
       [9.000e+00, 1.164e+03, 4.200e+01, 2.700e+01, 9.000e+01]])

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