How to get Nan values for certain values in array

Question

I am generating some random numbers and I am try to apply a condition that if values > 80 , then put None, but I am unable to get the results. My code is as follow

import pandas as pd
import numpy as np
from numpy import random

total = 200 
rand_numbers = np.random.randint(0, 100, total)
corrupt_values = np.random.randint(0, 100, total) > 80
flag = False
if flag:
    rand_numbers = [v for flag, v in zip(corrupt_values, rand_numbers)]
else:
   rand_numbers = None
print 'rand_numbers: ', rand_numbers

I am trying to get results like

rand_numbers [20, 50, Nan, Nan, 40, 10] so that values greater than 80 are replaced by Nan

I am try to generate 200 random numbers in rand_numbers and then I am making a condition that if values exceeded than 80, then it will put NaN instead of values otherwise value. I am trying to zip these two arrays and making that condition to work, but I am struggling with it. I am new to coding. Any help would be greatly appreciated.

Answer 1

It seems you need first cast values to float (because NaN is float ) and then change values by condition:

np.random.seed(100)
total = 100 
rand_numbers = np.random.randint(0, 100, total)
corrupt_values = rand_numbers > 80
print (rand_numbers)
[ 8 24 67 87 79 48 10 94 52 98 53 66 98 14 34 24 15 60 58 16  9 93 86  2 27
  4 31  1 13 83  4 91 59 67  7 49 47 65 61 14 55 71 80  2 94 19 98 63 53 27
 56 30 48 47 39 38 44 18 64 56 34 53 74 17 72 13 30 17 53 68 50 91 91 83 53
 78  0 13 57 76  3 70  3 84 79 10 87 60  3 48 52 43 36  5 71 38 86 94 98 42]

print (corrupt_values)
[False False False  True False False False  True False  True False False
  True False False False False False False False False  True  True False
 False False False False False  True False  True False False False False
 False False False False False False False False  True False  True False
 False False False False False False False False False False False False
 False False False False False False False False False False False  True
  True  True False False False False False False False False False  True
 False False  True False False False False False False False False False
  True  True  True False]

rand_numbers = rand_numbers.astype(float)
rand_numbers[corrupt_values] = None
print (rand_numbers)
[  8.  24.  67.  nan  79.  48.  10.  nan  52.  nan  53.  66.  nan  14.  34.
  24.  15.  60.  58.  16.   9.  nan  nan   2.  27.   4.  31.   1.  13.  nan
   4.  nan  59.  67.   7.  49.  47.  65.  61.  14.  55.  71.  80.   2.  nan
  19.  nan  63.  53.  27.  56.  30.  48.  47.  39.  38.  44.  18.  64.  56.
  34.  53.  74.  17.  72.  13.  30.  17.  53.  68.  50.  nan  nan  nan  53.
  78.   0.  13.  57.  76.   3.  70.   3.  nan  79.  10.  nan  60.   3.  48.
  52.  43.  36.   5.  71.  38.  nan  nan  nan  42.]

Similar solution (from deleted answer) with numpy.where :

rand_numbers = rand_numbers.astype(float)
rand_numbers = np.where(corrupt_values, np.nan, rand_numbers)
print (rand_numbers)
[  8.  24.  67.  nan  79.  48.  10.  nan  52.  nan  53.  66.  nan  14.  34.
  24.  15.  60.  58.  16.   9.  nan  nan   2.  27.   4.  31.   1.  13.  nan
   4.  nan  59.  67.   7.  49.  47.  65.  61.  14.  55.  71.  80.   2.  nan
  19.  nan  63.  53.  27.  56.  30.  48.  47.  39.  38.  44.  18.  64.  56.
  34.  53.  74.  17.  72.  13.  30.  17.  53.  68.  50.  nan  nan  nan  53.
  78.   0.  13.  57.  76.   3.  70.   3.  nan  79.  10.  nan  60.   3.  48.
  52.  43.  36.   5.  71.  38.  nan  nan  nan  42.]

Answer 2

you can use list comprehension

import numpy as np

total = 200 
rand_numbers = np.random.randint(0, 100, total)

result=[i if i<=80 else float('NaN') for i in rand_numbers]

that will give you :

 >>> result
[64, 23, 12, 8, 70, nan, 13, 19, 73, 18, 78, 25, 77, 45, nan, 6, 15, nan, nan, 47, nan, 39, 5, 9, 22, 59, 57, 71, 8, 24, 76, 33, 66, nan, 21, 39, 48, 23, 40, nan, nan, 75, 68, 17, 52, nan, 71, 55, 10, 53, 51, 21, 35, 6, 67, 10, 34, nan, 24, 11, 42, 72, 74, 40, 63, 8, 57, 10, nan, 45, nan, 18, nan, 80, 6, 21, 22, 2, 51, 54, 80, 50, 63, 40, nan, 26, 43, 65, 7, 13, 54, 69, 12, nan, nan, 40, 44, nan, 78, 45, 55, 72, 6, 46, 43, 33, 24, 69, 77, 51, 52, 51, nan, 32, 22, 54, 53, 25, 61, 32, 8, nan, 75, 9, 22, nan, nan, 54, 32, 49, nan, 8, 59, 44, 14, 62, 61, 37, 60, 56, 12, 23, 50, 76, 5, 14, 46, nan, 58, 18, 53, 18, 39, 10, 1, 17, 36, 31, 42, 71, 61, 39, 27, 79, nan, 44, 76, nan, 26, 3, 26, 19, 64, 6, 41, 65, 76, 31, nan, 12, nan, 77, 8, 49, nan, nan, nan, 5, 40, 15, nan, 42, 14, 12, 75, 54, 47, 65, 9, 12]

EDIT

It's also possible :

import numpy as np

total = 200 
rand_numbers = np.random.randint(0, 100, total)
corrupt_values = rand_numbers > 80

result=[i[0] if i[1]==False else float('nan') for i in zip(rand_numbers, corrupt_values)]