How can I select specific rows, where these rows equal to another row in another parallel array? in other words; can I vectorize code? here p, y are ndarray withe the same shape
for inx,val in enumerate(p):
if val ==y[inx]:
pprob.append(1)
else:
pprob.append(0)
I just ran this in a python shell in Python 3.9.4
import numpy as np
x = np.array([1,2,3,4,5])
y = np.array([1,1,3,3,5])
matching_idx = np.where(x == y) # (array([0, 2, 4]),)
x[matching_idx] # array([1, 3, 5])
Seems like x[matching_idx] is what you want
The key to this is np.where(), explained here
import numpy as np
a = np.random.normal(size=(10, 5))
b = np.random.normal(size=(10, 5))
a[1] = b[1]
a[7] = b[7]
rows = np.all(a == b, axis=1).astype(np.int32)
rows = rows.tolist() # if you really want the result to be a list
print(rows)
Result as expected
[0 1 0 0 0 0 0 1 0 0]
If you could be dealing with more than two dimensions, change the following (works for 2d as well):
# this
np.all(a == b, axis=1)
# to this
np.all(a == b, axis=tuple(range(len(a.shape)))[1:])
