calculate mean of numpy array based on unique element of another array

Question

m = np.array([[[1,2],[2,1]], [[1,1],[2,1]]])

array([[[1, 2],
        [2, 1]],

       [[1, 1],
        [2, 1]]])



m2 = np.array([[[0.4,0.5],[0.2,0.3]], [[0.3,0.4],[0.5,0.5]]])

array([[[ 0.4,  0.5],
        [ 0.2,  0.3]],

       [[ 0.3,  0.4],
        [ 0.5,  0.5]]])

How can I calculate the mean of m2 for each unique element of m? I could write a list comprehension to loop through the elements of m, but I was wondering if there was a more straightforward way to do this. Is np.ma.masked_array appropriate in this case?

This is what I'm trying to achieve

1: np.mean(m2[m==1])
2: np.mean(m2[m==2])

this is fine for a couple of elements, but if I have 100, then it becomes cumbersome. I could write a list comprehension, but was hoping for a better alternative.

Could you include an example code of what you are trying to achieve here? — Istvan
– Istvan, Commented Mar 26, 2019 at 19:45
It's unclear what you're asking. Please provide a minimal reproducible example — G. Anderson
– G. Anderson, Commented Mar 26, 2019 at 19:45
To further the previous comments, it's not clear to me at all how m and m2 are related — roganjosh
– roganjosh, Commented Mar 26, 2019 at 19:53
sorry for not being clear. I have updated the question with an example — HappyPy
– HappyPy, Commented Mar 26, 2019 at 19:59

mujjiga · Accepted Answer · 2019-03-26 20:05:24Z

1

[np.mean(m2[m==i]) for i in np.unique(m)]

answered Mar 26, 2019 at 20:05

mujjiga

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Comments

B. M. · Accepted Answer · 2019-03-26 20:55:21Z

0

Pandas give you a fast and elegant solution :

import pandas as pd    
df = pd.DataFrame(index=m.ravel(),data=m2.ravel())    
df.groupby(level=0).mean() 

#      0
#1  0.38
#2  0.40

it runs in 30 ms with:

m = np.random.randint(100,size=(100,100,100))
m2 = np.random.random((100,100,100))

edited Mar 26, 2019 at 20:55

answered Mar 26, 2019 at 20:47

B. M.

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