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I'm using numpy and want to index a row without losing the dimension information.
import numpy as np X = np.zeros((100,10)) X.shape # >> (100, 10) xslice = X[10,:] xslice.shape # >> (10,) In this example xslice is now 1 dimension, but I want it to be (1,10). In R, I would use X[10,:,drop=F]. Is there something similar in numpy. I couldn't find it in the documentation and didn't see a similar question asked.
Thanks!
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Sliced numpy array does not modify original array.
None is an alias for NP. newaxis. It creates an axis with length 1. This can be useful for matrix multiplcation etc. >>>> import numpy as NP >>>> a = NP.arange(1,5) >>>> print a [1 2 3 4] >>>> print a.shape (4,) >>>> print a[:,None].shape (4, 1) >>>> print a[:,None] [[1] [2] [3] [4]]
Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects. As mentioned earlier, items in ndarray object follows zero-based index. Three types of indexing methods are available − field access, basic slicing and advanced indexing.
Slicing in python means taking elements from one given index to another given index. We pass slice instead of index like this: [start:end] . We can also define the step, like this: [start:end:step] .
Another solution is to do
X[[10],:] or
I = array([10]) X[I,:] The dimensionality of an array is preserved when indexing is performed by a list (or an array) of indexes. This is nice because it leaves you with the choice between keeping the dimension and squeezing.
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It's probably easiest to do x[None, 10, :] or equivalently (but more readable) x[np.newaxis, 10, :].
As far as why it's not the default, personally, I find that constantly having arrays with singleton dimensions gets annoying very quickly. I'd guess the numpy devs felt the same way.
Also, numpy handle broadcasting arrays very well, so there's usually little reason to retain the dimension of the array the slice came from. If you did, then things like:
a = np.zeros((100,100,10)) b = np.zeros(100,10) a[0,:,:] = b either wouldn't work or would be much more difficult to implement.
(Or at least that's my guess at the numpy dev's reasoning behind dropping dimension info when slicing)
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