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According to docs numpy's default behaviour is to index arrays first by rows then by columns:

a = numpy.arange(6).reshape(3,2)

[[0 1]
 [2 3]
 [4 5]]

print a[0][1] # is 1

I want to index the array using the geometrically oriented-convention a[x][y], as in x-axis and y-axis. How can I change the indexing order without modifying the array's shape so that a[0][1] would return 2?

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  • I borrowed the terminology from the docs (see the link): "Matrix notation uses the first index to indicate which row is being selected and the second index to indicate which column is selected. This is opposite the geometrically oriented-convention for images where people generally think the first index represents x position (i.e., column) and the second represents y position (i.e., row)" Commented Mar 3, 2011 at 7:58
  • Nevermind, was thinking something else. Commented Mar 3, 2011 at 8:00
  • 3
    Just as a note, when indexing multi-dimensional arrays you should use the notation a[i,j] rather than a[i][j]. I find on my machine that the later is 2x slower. Commented Mar 3, 2011 at 12:55
  • @Josh Thanks for the tip. It looks more readable too. Commented Mar 3, 2011 at 16:03

1 Answer 1

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8

You can write a.T[0,1] to use indices of the transpose of the array, which are the other way around in 2D.

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3 Comments

Does this create a new transposed array each time I access using this notation?
The .T only creates a view of the original array, so you are not copying data. It should be relatively fast.
Wouln't a.T[0,1] be better, because it would leave out the intermediate array avoiding to call the getitem routine twice?

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