[Numpy-discussion] Help Understanding Indexing Behavior

Julian Taylor jtaylor.debian at googlemail.com
Tue Feb 25 18:15:55 EST 2014

On 26.02.2014 00:04, JB wrote:
> At the risk of igniting a flame war...can someone please help me understand
> the indexing behavior of NumPy? I will readily I admit I come from a Matlab
> background, but I appreciate the power of Python and am trying to learn more. 
>>From a Matlab user's perspective, the behavior of indexing in NumPy seems
> very bizarre. For example, if I define an array: 
> x = np.array([1,2,3,4,5,6,7,8,9,10])
> If I want the first 5 elements, what do I do? Well, I say to myself, Python
> is zero-based, whereas Matlab is one-based, so if I want the values 1 - 5,
> then I want to index 0 - 4. So I type: x[0:4]
> And get in return: array([1, 2, 3, 4]). So I got the first value of my
> array, but I did not get the 5th value of the array. So the "start" index
> needs to be zero-based, but the "end" index needs to be one-based. Or to put
> it another way, if I type x[4] and x[0:4], the 4 means different things
> depending on which set of brackets you're looking at!
> It's hard for me to see this as anything by extremely confusing. Can someone
> explain this more clearly. Feel free to post links if you'd like. I know
> this has been discussed ad nauseam online; I just haven't found any of the
> explanations satisfactory (or sufficiently clear, at any rate).

numpy indexing is like conventional C indexing beginning from inclusive
0 to exclusive upper bound: [0, 5[. So the selection length is upper
bound - lower bound.
as a for loop:
for (i = 0; i < 5; i++)

This is the same way Python treats slices.

in comparison one based indexing is usually inclusive 1 to inclusive
upper bound: [1, 4]. So the selection length is upper bound - lower
bound + 1.
for (i = 1; i <= 4; i++)

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