I have an array like this and need to replace every 1 with 2, every 3 with 4, every 4 with 1. Is there a way to do this just with np and not loops?
import numpy as np
np.random.seed(2)
arr=np.random.randint(1,5,(3,3),int)
arr
array([[1, 4, 2],
[1, 3, 4],
[3, 4, 1]])
If I use array mask sequentially, it doesn't give the expected outcome:
array([[2, 1, 2],
[2, 4, 1],
[4, 1, 2]])
It is based on a conditional logic and not maths formula
If the array values don't necessarely range between 1 and 4 you can use np.select:
import numpy as np
a = np.random.randint(1,5, (3,3))
condlist = [np.logical_or(a==1, a==2), a==3, a==4]
choicelist= [2, 4, 1]
b = np.select(condlist, choicelist)
which does not care about the order of the conditions
Here's one with np.searchsorted for performance efficiency -
def map_values(arr, old_val, new_val):
sidx = old_val.argsort()
idx = np.searchsorted(old_val,arr,sorter=sidx)
return np.where(old_val[idx]==arr, new_val[sidx[idx]], arr)
Sample run -
In [40]: arr
Out[40]:
array([[1, 4, 2],
[1, 3, 4],
[3, 4, 1]])
In [41]: old_val = np.array([1,3,4])
...: new_val = np.array([2,4,1])
In [42]: map_values(arr, old_val, new_val)
Out[42]:
array([[2, 1, 2],
[2, 4, 1],
[4, 1, 2]])
Could do this with a lambda function and np.vectorize():
import numpy as np
np.random.seed(2)
arr=np.random.randint(1,5,(3,3),int)
f = lambda x: x%4 + 1 if x in [1,3,4] else x
vfunc = np.vectorize(f)
Usage:
>>> vfunc(arr)
array([[2, 1, 2],
[2, 4, 1],
[4, 1, 2]])
You have to be careful about the order of assignments. For example, if you do
arr[arr == 4] = 1
arr[arr == 1] = 2
Now all of the elements that were originally 4 will be 2, not 1 as you intend.
One solution is to carefully craft the order of assignments:
arr[arr == 1] = 2
arr[arr == 4] = 1
However, this is very brittle and will fall apart as you introduce more of them. It would be better to create the masks up front from the original array:
ones = arr == 1
fours = arr == 4
arr[ones] = 2
arr[fours] = 1
Now the order of the assignments won't matter because the masks are determined before modifying the array.
You want arr % 4 + 1, except in the case of 2, which stays the same. So use np.where to find all the 2s. Then do arr % 4 + 1, then reset all the 2s.
import numpy as np
np.random.seed(2)
arr=np.random.randint(1,5,(3,3),int)
twos = np.where(arr == 2)
arr = arr % 4 + 1
arr[twos] = 2
print(arr)
