how to assign non contiguous labels of one numpy array to another numpy array and add accordingly?

Question

I have the following labels

>>> lab
array([3, 0, 3 ,3, 1, 1, 2 ,2, 3, 0, 1,4])

I want to assign this label to another numpy array i.e

>>> arr
array([[81,  1,  3, 87],  # 3
       [ 2,  0,  1,  0],  # 0
       [13,  6,  0,  0],  # 3
       [14,  0,  1, 30],  # 3
       [ 0,  0,  0,  0],  # 1
       [ 0,  0,  0,  0],  # 1
       [ 0,  0,  0,  0],  # 2
       [ 0,  0,  0,  0],  # 2
       [ 0,  0,  0,  0],  # 3
       [ 0,  0,  0,  0],  # 0 
       [ 0,  0,  0,  0],  # 1
       [13,  2,  0, 11]]) # 4

and add all corresponding rows with same labels.

The output must be

([[108, 7, 4,117]--3
[  0, 0, 0,  0]--0
[  0, 0, 0,  0]--1
[  0, 0, 0,  0]--2
[13,  2,  0, 11]])--4

Use the labels to sort, I.e. reorder, the rows, and add.reduceat to sum them by groups. — hpaulj
– hpaulj, Commented Dec 4, 2015 at 13:29

Lee · Accepted Answer · 2015-12-04 14:15:28Z

1

You could use groupby from pandas:

import pandas as pd

parr=pd.DataFrame(arr,index=lab)
pd.groupby(parr,by=parr.index).sum()
     0  1  2    3
0    2  0  1    0
1    0  0  0    0
2    0  0  0    0
3  108  7  4  117
4   13  2  0   11

answered Dec 4, 2015 at 14:15

Lee

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Comments

hpaulj · Accepted Answer · 2015-12-04 18:12:43Z

numpy doesn't have a group_by function like pandas, but it does have a reduceat method that performs fast array actions on groups of elements (rows). But it's application in this case is a bit messy.

Start with our 2 arrays:

In [39]: arr
Out[39]: 
array([[81,  1,  3, 87],
       [ 2,  0,  1,  0],
       [13,  6,  0,  0],
       [14,  0,  1, 30],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [13,  2,  0, 11]])
In [40]: lbls
Out[40]: array([3, 0, 3, 3, 1, 1, 2, 2, 3, 0, 1, 4])

Find the indices that will sort lbls (and rows of arr) into contiguous blocks:

In [41]: I=np.argsort(lbls)
In [42]: I
Out[42]: array([ 1,  9,  4,  5, 10,  6,  7,  0,  2,  3,  8, 11], dtype=int32)
In [43]: s_lbls=lbls[I]
In [44]: s_lbls
Out[44]: array([0, 0, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4])
In [45]: s_arr=arr[I,:]
In [46]: s_arr
Out[46]: 
array([[ 2,  0,  1,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [ 0,  0,  0,  0],
       [81,  1,  3, 87],
       [13,  6,  0,  0],
       [14,  0,  1, 30],
       [ 0,  0,  0,  0],
       [13,  2,  0, 11]])

Find the boundaries of these blocks, i.e. where s_lbls jumps:

In [47]: J=np.where(np.diff(s_lbls))
In [48]: J
Out[48]: (array([ 1,  4,  6, 10], dtype=int32),)

Add the index of the start of the first block (see the reduceat docs)

In [49]: J1=[0]+J[0].tolist()
In [50]: J1
Out[50]: [0, 1, 4, 6, 10]

Apply add.reduceat:

In [51]: np.add.reduceat(s_arr,J1,axis=0)
Out[51]: 
array([[  2,   0,   1,   0],
       [  0,   0,   0,   0],
       [  0,   0,   0,   0],
       [108,   7,   4, 117],
       [ 13,   2,   0,  11]], dtype=int32)

These are your numbers, sorted by lbls (for 0,1,2,3,4).

With reduceat you could take other actions like maximum, product etc.

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