I am trying to use np.where to swap one element for another, in the same row (but different column).
Here is what I have so far:
arr = np.random.random_integers(0,10,size=(10,10))
split = np.random.random_integers(0,10,size=arr[1,:].shape)
newloc = np.random.random_integers(0,10,size=arr[1,:].shape)
arr2 = np.where(arr>split,arr[0,newloc],arr)
My problem is that 0 in arr[0,newloc] means that it always pulls from row 0 off arr. But I want something like "same_row" if that makes sense.
I think what you're looking for is arr[:, newloc]. This will allow you to index arr's second axis (i.e. the columns) with newloc's values.
np.where(arr > split, arr[:, newloc], arr)
Here is an example with two rows:
>> arr = np.random.randint(0, 10, size=(2,10))
>> split = np.random.randint(0, 10, size=arr.shape[1])
>> newloc = np.random.randint(0, 10, size=arr.shape[1])
>> arr
array([[2, 3, 0, 0, 1, 4, 6, 5, 1, 9],
[9, 2, 6, 3, 2, 6, 3, 6, 2, 5]])
>> split
array([5, 7, 3, 5, 1, 6, 0, 8, 8, 6])
>> newloc
array([1, 7, 0, 5, 0, 7, 3, 6, 4, 8])
>> np.where(arr > split, 1, 0) # mask
array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 1, 0, 1, 0, 1, 0, 0, 0]])
>> np.where(arr > split, arr[:, newloc], -1)
array([[-1, -1, -1, -1, -1, -1, 0, -1, -1, 1],
[ 2, -1, 9, -1, 9, -1, 3, -1, -1, -1]])
There, you can check if the columns are being replaced correctly.
I have used np.random.randint in the above snippet since np.random.random_integers is deprecated.
newlocis an array, what do intend to do here:arr[0,newloc]?arr[0,newloc[same_col_asarr]]I just don't know how to do it. My goal is basically if: arr>split, return a different element in the same row as arr, the location of which is held in newloc.