On Thu, May 29, 2008 at 4:36 PM, Raul Kompass <rkompass@gmx.de> wrote:
I'm new to using numpy. Today I experimented a bit with indexing motivated by the finding that although a[a>0.5] and a[where(a>0.5)] give the same expected result (elements of a greater than 0.5) a[argwhere(a>0.5)] results in something else (rows of a in different order).
I tried to figure out when indexing will yield rows and when it will give me an element and I could not find a simple rule.
I systematically tried and got the follwing: ----------------------------------
from scipy import * a = random.rand(10).reshape(2,5) a array([[ 0.87059263, 0.76795743, 0.13844935, 0.69040701, 0.92015062], [ 0.97313123, 0.85822558, 0.8579044 , 0.57425782, 0.57355904]])
a[0,1] # shape([0,1]) = (2,) 0.767957427399
a[[0],[1]] # shape([[0],[1]]) = (2, 1) array([ 0.76795743])
a[[0,1]] # shape([[0,1]]) = (1, 2) array([[ 0.87059263, 0.76795743, 0.13844935, 0.69040701, 0.92015062], [ 0.97313123, 0.85822558, 0.8579044 , 0.57425782, 0.57355904]])
a[[[0,1]]] # shape([[[0,1]]]) = (1, 1, 2) array([[ 0.87059263, 0.76795743, 0.13844935, 0.69040701, 0.92015062], [ 0.97313123, 0.85822558, 0.8579044 , 0.57425782, 0.57355904]])
a[[[0],[1]]] # shape([[[0],[1]]]) = (1, 2, 1) array([ 0.76795743])
a[[[0]],[[1]]] # shape([[[0]],[[1]]]) = (2, 1, 1) array([[ 0.76795743]])
a[[[[0,1]]]] # shape([[[[0,1]]]]) = (1, 1, 1, 2) array([[[ 0.87059263, 0.76795743, 0.13844935, 0.69040701, 0.92015062], [ 0.97313123, 0.85822558, 0.8579044 , 0.57425782, 0.57355904]]])
a[[[[0],[1]]]] # shape([[[[0],[1]]]]) = (1, 1, 2, 1) array([[[ 0.87059263, 0.76795743, 0.13844935, 0.69040701, 0.92015062]],
[[ 0.97313123, 0.85822558, 0.8579044 , 0.57425782, 0.57355904]]])
a[[[[0]],[[1]]]] # shape([[[[0]],[[1]]]]) = (1, 2, 1, 1) array([[ 0.76795743]])
a[[[[0]]],[[[1]]]] # shape([[[[0]]],[[[1]]]]) = (2, 1, 1, 1) array([[[ 0.76795743]]])
Looks confusing to me too. I guess it's best to take it one step at a time.
import numpy as np a = np.arange(6).reshape(2,3) a[0,1] 1
That's not surprising.
a[[0,1]]
That one looks odd. But it is just shorthand for:
a[[0,1],:]
So rows 0 and 1 and all columns. array([[0, 1, 2], [3, 4, 5]]) This gives the same thing:
a[0:2,:]
array([[0, 1, 2], [3, 4, 5]]) Only it's not quite the same thing. a[[0,1],:] returns a copy and a[0:2,:] returns a view
a[[0,1],:].flags.owndata True a[0:2,:].flags.owndata False