Here's one way based on np.maximum.reduceat
-
def grouby_maxY(a):
b = a[a[:,0].argsort()] # if first col is already sorted, skip this
grp_idx = np.flatnonzero(np.r_[True,(b[:-1,0] != b[1:,0])])
grp_maxY = np.maximum.reduceat(b[:,1], grp_idx)
return np.c_[b[grp_idx,0], grp_maxY]
Alternatively, if you want to bring np.unique
, we can use it to find grp_idx
with np.unique(b[:,0], return_index=1)[1]
.
Sample run -
In [453]: np.random.seed(0)
In [454]: arr = np.random.randint(0,5,(10,2))
In [455]: arr
Out[455]:
array([[4, 0],
[3, 3],
[3, 1],
[3, 2],
[4, 0],
[0, 4],
[2, 1],
[0, 1],
[1, 0],
[1, 4]])
In [456]: grouby_maxY(arr)
Out[456]:
array([[0, 4],
[1, 4],
[2, 1],
[3, 3],
[4, 0]])
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