CODEWITHSUNDEEP
pythonarraysperformancenumpy
Blind0ne
Blind0ne

Reputation: 1037

Calculate mean of array with specific value from another array

I have these numpy arrays:

array1 = np.array([-1, -1, 1, 1, 2, 1, 2, 2])
array2 = np.array([34.2, 11.2, 22.1, 78.2, 55.0, 66.87, 33.3, 11.56])

Now I want to return a 2d array in which there is the mean for each distinctive value from array1 so my output would look something like this:

array([[-1, 22.7],
       [ 1, 55.7],
       [ 2, 33.3]])

Is there an efficient way without concatenating those 1D arrays to one 2D array? Thanks!

Upvotes: 2

Views: 983

Answers (2)

Eelco Hoogendoorn
Eelco Hoogendoorn

Reputation: 10759

This is a typical grouping operation, and the numpy_indexed package (disclaimer: I am its author) provides extensions to numpy to perform these type of operations efficiently and concisely:

import numpy_indexed as npi
groups, means = npi.group_by(array_1).mean(array_2)

Note that you can in this manner easily perform other kind of reductions as well, such as a median for example.

Upvotes: 3

Divakar
Divakar

Reputation: 221504

Here's an approach using np.unique and np.bincount -

# Get unique array1 elems, tag them starting from 0 and get their tag counts
unq,ids,count = np.unique(array1,return_inverse=True,return_counts=True)

# Use the tags/IDs to perform ID based summation of array2 elems and 
# thus divide by the ID counts to get ID based average values
out = np.column_stack((unq,np.bincount(ids,array2)/count))

Sample run -

In [16]: array1 = np.array([-1, -1, 1, 1, 2, 1, 2, 2])
    ...: array2 = np.array([34.2, 11.2, 22.1, 78.2, 55.0, 66.87, 33.3, 11.56])
    ...: 

In [18]: out
Out[18]: 
array([[ -1.        ,  22.7       ],
       [  1.        ,  55.72333333],
       [  2.        ,  33.28666667]])

Upvotes: 1

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