indexing numpy multidimensional arrays

Question

I need to access this numpy array, sometimes with only the rows where the last column is 0, and sometimes the rows where the value of the last column is 1.

y = [0  0  0  0
     1  2  1  1 
     2 -6  0  1
     3  4  1  0]

I have to do this over and over, but would prefer to shy away from creating duplicate arrays or having to recalculate each time. Is there someway that I can identify the indices concerned and just call them? So that I can do this:

>>print y[LAST_COLUMN_IS_0] 
[0  0  0  0
3  4  1  0]

>>print y[LAST_COLUMN_IS_1] 
[1  2  1  1 
2 -6  0  1]

P.S. The number of columns in the array never changes, it's always going to have 4 columns.

Glorfindel · Accepted Answer · 2022-12-30 21:03:21Z

6

You can use numpy's boolean indexing to identify which rows you want to select, and numpy's fancy indexing/slicing to select the whole row.

print y[y[:,-1] == 0, :]
print y[y[:,-1] == 1, :]

You can save y[:,-1] == 0 and ... == 1 as usual, since they are just numpy arrays.

(The y[:,-1] selects the whole of the last column, and the == equality check happens element-wise, resulting in an array of booleans.)

edited Dec 30, 2022 at 21:03

Glorfindel

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answered Sep 1, 2012 at 17:49

huon

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3 Comments

Zach Over a year ago

+1, It works. But I'm having trouble understanding what y[:,-1] == 0 is. It's a numpy array, but not a range? How can you then use it to index?

huon Over a year ago

@Zach, scipy.org/…

Zach Over a year ago

Thanks. Quick additional question, how do I then select a column from the result. For example, to select the 1st column, this doesn't work: y[y[:,-1] == 0, :][0]

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