Filter integers in numpy float array
Question:
Is there any built in function to discard integer and keep only float number in numpy.
import numpy as np
input = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
desired_ouput = some_function(input)
# Expected ouput
# desired_output = np.array([0.01, 2.001, 2.002])
Answers:
I know of no in-built function. But you can create one yourself:
import numpy as np
A = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
def remove_ints(arr):
return arr[~(arr == arr.astype(int))]
res = remove_ints(A)
array([ 0.01 , 2.001, 2.002])
Aside, you should not use a built-in class such as input as a variable name.
I don’t think so. My approach would be
import numpy as np
a = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
mask = np.isclose(a, a.astype(int))
print(a[~mask])
#[ 0.01 2.001 2.002]
I don’t know any builtin for this but you can filter those floats using:
filter(lambda x: int(str(x).split('.')[1]) != 0, input)
The lambda expression here checks if the decimal places are zero which I interpret as the number being an int.
Mask with whether each element is equal to it as an integer.
arr = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
out = arr[arr != arr.astype(int)]
#np.array([0.01, 2.001, 2.002])
If you do not have to much data (short list), maybe do not need numpy:
>>> i = [0.0, 0.01, 1.0, 2.0, 2.001, 2.002]
>>> a=[j for j in i if not j.is_integer()]
>>> a
['0.01', '2.001', '2.002']
Otherwise see Joe Iddon answer
I’ve always used np.equal with np.mod:
>>> A[~np.equal(np.mod(A, 1), 0)]
array([0.01 , 2.001, 2.002])
I had a similar question a while back: Numpy: Check if float array contains whole numbers. The simplest way to mask fractions that I am currently aware of is
mask = ((input % 1) != 0)
You can then apply the mask directly with
output = input[mask]
It bothered me that there is no built-in function to determine the integerness of a float quickly, so I wrote a fast ufunc that provides the functionality of float.is_integer for numpy. You can download from github and compile if you’re interested:
from is_integer_ufunc import is_integer
output = input[~is_integer(input)]
I’ll see if the numpy community wants to consider adding something like that to the core library. The question seems to come up often enough to justify it.
Is there any built in function to discard integer and keep only float number in numpy.
import numpy as np
input = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
desired_ouput = some_function(input)
# Expected ouput
# desired_output = np.array([0.01, 2.001, 2.002])
I know of no in-built function. But you can create one yourself:
import numpy as np
A = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
def remove_ints(arr):
return arr[~(arr == arr.astype(int))]
res = remove_ints(A)
array([ 0.01 , 2.001, 2.002])
Aside, you should not use a built-in class such as input as a variable name.
I don’t think so. My approach would be
import numpy as np
a = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
mask = np.isclose(a, a.astype(int))
print(a[~mask])
#[ 0.01 2.001 2.002]
I don’t know any builtin for this but you can filter those floats using:
filter(lambda x: int(str(x).split('.')[1]) != 0, input)
The lambda expression here checks if the decimal places are zero which I interpret as the number being an int.
Mask with whether each element is equal to it as an integer.
arr = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002])
out = arr[arr != arr.astype(int)]
#np.array([0.01, 2.001, 2.002])
If you do not have to much data (short list), maybe do not need numpy:
>>> i = [0.0, 0.01, 1.0, 2.0, 2.001, 2.002]
>>> a=[j for j in i if not j.is_integer()]
>>> a
['0.01', '2.001', '2.002']
Otherwise see Joe Iddon answer
I’ve always used np.equal with np.mod:
>>> A[~np.equal(np.mod(A, 1), 0)]
array([0.01 , 2.001, 2.002])
I had a similar question a while back: Numpy: Check if float array contains whole numbers. The simplest way to mask fractions that I am currently aware of is
mask = ((input % 1) != 0)
You can then apply the mask directly with
output = input[mask]
It bothered me that there is no built-in function to determine the integerness of a float quickly, so I wrote a fast ufunc that provides the functionality of float.is_integer for numpy. You can download from github and compile if you’re interested:
from is_integer_ufunc import is_integer
output = input[~is_integer(input)]
I’ll see if the numpy community wants to consider adding something like that to the core library. The question seems to come up often enough to justify it.