Counting number of elements greater than a certain value in a numy.ndarray

Question

I want to calculate the count of number of elements in a numpy.ndarry which is greater than a certain value. How do I get the required results?

For example:

[[0.25656927 0.31030828 0.23430803 0.25999823 0.20450112 0.19383106
  0.35779405 0.36355627 0.16837767 0.1933686  0.20630316 0.17804974
  0.06902786 0.26209944 0.21310201 0.12016498 0.14213449 0.16639964
  0.33461425 0.15897344 0.20293266 0.14630634 0.2509769  0.17211646
  0.3922994  0.14036047 0.12571093 0.25565785 0.18216616 0.0728473
  0.25328827 0.1476636  0.1873344  0.12253726 0.16082433 0.20678088
  0.33296013 0.03104548 0.14949016 0.05495472 0.1494042  0.32033417
  0.05361898 0.14325878 0.16196126 0.15796155 0.10990247 0.14499696]]

is the array and I want the count of number of elements greater than 0.19214945092486838 . Here the value will be 21. How to calculate it?

Answer 1

You can simply do:

import numpy

arr = numpy.asarray([0.25656927, 0.31030828, 0.23430803, 0.25999823, 0.20450112, 0.19383106, 0.35779405, 0.36355627, 0.16837767, 0.1933686,  0.20630316, 0.17804974, 0.06902786, 0.26209944, 0.21310201, 0.12016498, 0.14213449, 0.16639964, 0.33461425, 0.15897344, 0.20293266, 0.14630634, 0.2509769,  0.17211646, 0.3922994,  0.14036047, 0.12571093, 0.25565785, 0.18216616, 0.0728473, 0.25328827, 0.1476636,  0.1873344,  0.12253726, 0.16082433, 0.20678088, 0.33296013, 0.03104548, 0.14949016, 0.05495472, 0.1494042,  0.32033417, 0.05361898, 0.14325878, 0.16196126, 0.15796155, 0.10990247, 0.14499696])

print((arr > 0.19214945092486838).sum())

The output is: 21

Answer 2

ar[ar>0.19214945092486838] will provide you list of elements which are higher than the current values. You can take len to get the length

>>> import numpy as np
>>> ar = np.array([0.25656927,0.31030828,0.23430803,0.25999823,0.20450112,0.19383106,0.35779405,0.36355627,0.16837767,0.1933686,0.20630316,0.17804974    ,0.06902786,0.26209944,0.21310201,0.12016498,0.14213449,0.16639964,0.33461425,0.15897344,0.20293266,0.14630634,0.2509769,0.17211646    ,0.3922994,0.14036047,0.12571093,0.25565785,0.18216616,0.0728473,0.25328827,0.1476636,0.1873344,0.12253726,0.16082433,0.20678088    ,0.33296013,0.03104548,0.14949016,0.05495472,0.1494042,0.32033417,0.05361898,0.14325878,0.16196126,0.15796155,0.10990247,0.14499696])

>>> print(len(ar[ar>0.19214945092486838]))
>>> 21

Answer 3

Here's one way

my_array = ... the target array ...
result = sum(0.19214945092486838 < x for x in my_array)

Answer 4

With numpy you can try:

Myarray= [ [ your array]]
Value_to_search=0.19214945092486838

Array_greater_than=Myarray>Value_to_search
Nb_Val_greater_than=Array_greater_than.sum()
print(Nb_Val_greater_than)

Answer 5

To get an array of which the item is greater than / less than:

>>> import numpy as np
>>> data = np.arange(12)
>>> data > 5
array([False, False, False, False, False, False,  True,  True,  True,
        True,  True,  True])

Then you just have to find the sum of the array:

>>> (data > 5).sum()
6

Now substitude data with your values, and use (data > 0.19214945092486838) instead.

Answer 6

The following snippet of code will achieve what you desire :)

import numpy as np
arrayToCheck=np.array([0.25656927, 0.31030828, 0.23430803, 0.25999823, 0.20450112, 0.19383106,
  0.35779405, 0.36355627, 0.16837767, 0.1933686,  0.20630316, 0.17804974,
  0.06902786, 0.26209944, 0.21310201, 0.12016498, 0.14213449, 0.16639964,
  0.33461425, 0.15897344, 0.20293266, 0.14630634, 0.2509769,  0.17211646,
  0.3922994,  0.14036047, 0.12571093, 0.25565785, 0.18216616, 0.0728473,
  0.25328827, 0.1476636,  0.1873344,  0.12253726, 0.16082433, 0.20678088,
  0.33296013, 0.03104548, 0.14949016, 0.05495472, 0.1494042,  0.32033417,
  0.05361898, 0.14325878, 0.16196126, 0.15796155, 0.10990247, 0.14499696])
print ("The number of float numbers above your threshold is " + str(np.sum(a>0.19214945092486838)))

Answer 7

Cleanest way (IMHO):

x > 1 will transform your array x into a boolean one, where elements larger than 1 are True. Then you can count the True values by np.count_nonzero()

Thus, np.count_nonzero(x > 1)

Answer 8

arr=np.array([0.25656927,0.31030828,0.23430803,0.25999823,0.20450112,0.19383106,
  0.35779405, 0.36355627, 0.16837767, 0.1933686,  0.20630316, 0.17804974,
  0.06902786, 0.26209944, 0.21310201, 0.12016498, 0.14213449, 0.16639964,
  0.33461425, 0.15897344, 0.20293266, 0.14630634 ,0.2509769 , 0.17211646,
  0.3922994 , 0.14036047, 0.12571093, 0.25565785, 0.18216616, 0.0728473,
  0.25328827, 0.1476636 , 0.1873344 , 0.12253726, 0.16082433, 0.20678088,
  0.33296013, 0.03104548, 0.14949016, 0.05495472, 0.1494042 , 0.32033417,
  0.05361898, 0.14325878 ,0.16196126, 0.15796155, 0.10990247, 0.14499696])

Count:

arr[np.where(arr>0.19214945092486838)].shape[0]
    

Answer 9

you can use len to count results like this example:

import numpy as np

matrix = np.array([[0.25656927,0.31030828,0.23430803,0.25999823,0.20450112,0.19383106,
  0.35779405, 0.36355627, 0.16837767, 0.1933686, 0.20630316, 0.17804974,
  0.06902786, 0.26209944, 0.21310201, 0.12016498, 0.14213449, 0.16639964,
  0.33461425, 0.15897344, 0.20293266, 0.14630634, 0.2509769,  0.17211646,
  0.3922994,  0.14036047, 0.12571093, 0.25565785, 0.18216616, 0.0728473,
  0.25328827, 0.1476636,  0.1873344,  0.12253726, 0.16082433, 0.20678088,
  0.33296013, 0.03104548, 0.14949016, 0.05495472, 0.1494042,  0.32033417,
  0.05361898, 0.14325878, 0.16196126, 0.15796155, 0.10990247, 0.14499696]])

n = len(matrix[matrix > 0.18])
print(n)