Calculating the averages of elements in one array based on data in another array

I need to average the Y values corresponding to the values in the X array…

X=np.array([  1,  1,  2,  2,  2,  2,  3,  3 ... ])

Y=np.array([ 10, 30, 15, 10, 16, 10, 15, 20 ... ])

In other words, the equivalents of the 1 values in the X array are 10 and 30 in the Y array, and the average of this is 20, the equivalents of the 2 values are 15, 10, 16, and 10, and their average is 12.75, and so on…

How can I calculate these average values?

>Solution :

One option is to use a property of linear regression (with categorical variables):

import numpy as np

x = np.array([  1,  1,  2,  2,  2,  2,  3,  3 ])
y = np.array([ 10, 30, 15, 10, 16, 10, 15, 20 ])

x_dummies = x[:, None] == np.unique(x)
means = np.linalg.lstsq(x_dummies, y, rcond=None)[0]
print(means) # [20.   12.75 17.5 ]