On Tue, 2018-01-09 at 12:27 +0000, martin.gfel...@swisscom.com wrote: > Hi Derek > > I have a related question: > > Given: > > a = numpy.array([[0,1,2],[3,4]]) > assert a.ndim == 1 > b = numpy.array([[0,1,2],[3,4,5]]) > assert b.ndim == 2 > > Is there an elegant way to force b to remain a 1-dim object array? >
You will have to create an empty object array and assign the lists to it. ``` b = np.empty(len(l), dtype=object) b[...] = l ``` > I have a use case where normally the sublists are of different > lengths, but I get a completely different structure when they are > (coincidentally in my case) of the same length. > > Thanks and best regards, Martin > > > Martin Gfeller, Swisscom / Enterprise / Banking / Products / Quantax > > Message: 1 > Date: Sun, 31 Dec 2017 00:11:48 +0100 > From: Derek Homeier <de...@astro.physik.uni-goettingen.de> > To: Discussion of Numerical Python <numpy-discussion@python.org> > Subject: Re: [Numpy-discussion] array - dimension size of 1-D and 2-D > examples > Message-ID: > <cc548593-308b-4561-a03c-d3017c707...@astro.physik.uni-goetting > en.de> > Content-Type: text/plain; charset=utf-8 > > On 30 Dec 2017, at 5:38 pm, Vinodhini Balusamy <me.vi...@gmail.com> > wrote: > > > > Just one more question from the details you have provided which > > from > > my understanding strongly seems to be Design [DEREK] You cannot > > create > > a regular 2-dimensional integer array from one row of length 3 > > > and a second one of length 0. Thus np.array chooses the next > > > most > > > basic type of array it can fit your input data in > > Indeed, the general philosophy is to preserve the structure and type > of your input data as far as possible, i.e. a list is turned into a > 1d-array, a list of lists (or tuples etc?) into a 2d-array,_ if_ the > sequences are of equal length (even if length 1). > As long as there is an unambiguous way to convert the data into an > array (see below). > > > Which is the case, only if an second one of length 0 is given. > > What about the case 1 : > > > > > x12 = np.array([[1,2,3]]) > > > > > x12 > > > > array([[1, 2, 3]]) > > > > > print(x12) > > > > [[1 2 3]] > > > > > x12.ndim > > > > 2 > > > > > > > > > > > > > > This seems to take 2 dimension. > > Yes, structurally this is equivalent to your second example > > > also, > > > > x12 = np.array([[1,2,3],[0,0,0]]) > > > > print(x12) > > [[1 2 3] > [0 0 0]] > > > > x12.ndim > > 2 > > > I presumed the above case and the case where length 0 is provided > > to be treated same(I mean same behaviour). > > Correct me if I am wrong. > > > > In this case there is no unambiguous way to construct the array - you > would need a shape (2, 3) array to store the two lists with 3 > elements in the first list. Obviously x12[0] would be > np.array([1,2,3]), but what should be the value of x12[1], if the > second list is empty - it could be zeros, or repeating x12[0], or > simply undefined. np.array([1, 2, 3], [4]]) would be even less > clearly defined. > These cases where there is no obvious ?right? way to create the array > have usually been discussed at some length, but I don?t know if this > is fully documented in some place. For the essentials, see > > https://docs.scipy.org/doc/numpy/reference/routines.array-creation.ht > ml > > note also the upcasting rules if you have e.g. a mix of integers and > reals or complex numbers, and also how to control shape or data type > explicitly with the respective keywords. > > Derek > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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