Sebastian Haase wrote: > On Friday 25 August 2006 07:01, Travis Oliphant wrote: > >> Keith Goodman wrote: >> >>> How do I delete a row (or list of rows) from a matrix object? >>> >>> To remove the n'th row in octave I use x(n,:) = []. Or n could be a >>> vector of rows to remove. >>> >>> In numpy 0.9.9.2813 x[[1,2],:] = [] changes the values of all the >>> elements of x without changing the size of x. >>> >>> In numpy do I have to turn it around and construct a list of the rows >>> I want to keep? >>> >> Basically, that is true for now. >> >> I think it would be worth implementing some kind of function for making >> this easier. >> >> One might think of using: >> >> del a[obj] >> >> But, the problem with both of those approaches is that once you start >> removing arbitrary rows (or n-1 dimensional sub-spaces) from an array >> you very likely will no longer have a chunk of memory that can be >> described using the n-dimensional array memory model. >> >> So, you would have to make memory copies. This could be done, of >> course, and the data area of "a" altered appropriately. But, such >> alteration of the memory would break any other objects that have a >> "view" of the memory area of "a." Right now, there is no way to track >> which objects have such "views", and therefore no good way to tell >> (other than the very conservative reference count) if it is safe to >> re-organize the memory of "a" in this way. >> >> So, "in-place" deletion of array objects would not be particularly >> useful, because it would only work for arrays with no additional >> reference counts (i.e. simple b=a assignment would increase the >> reference count and make it impossible to say del a[obj]). >> >> However, a function call that returned a new array object with the >> appropriate rows deleted (implemented by constructing a new array with >> the remaining rows) would seem to be a good idea. >> >> I'll place a prototype (named delete) to that effect into SVN soon. >> >> -Travis >> >> > Now of course: I often needed to "insert" a column, row or section, ... ? > I made a quick and dirty implementation for that myself: > def insert(arr, i, entry, axis=0): > """returns new array with new element inserted at index i along axis > if arr.ndim>1 and if entry is scalar it gets filled in (ref. broadcasting) > > note: (original) arr does not get affected > """ > if i > arr.shape[axis]: > raise IndexError, "index i larger than arr size" > shape = list(arr.shape) > shape[axis] += 1 > a= N.empty(dtype=arr.dtype, shape=shape) > aa=N.transpose(a, [axis]+range(axis)+range(axis+1,a.ndim)) > aarr=N.transpose(arr, [axis]+range(axis)+range(axis+1,arr.ndim)) > aa[:i] = aarr[:i] > aa[i+1:] = aarr[i:] > aa[i] = entry > return a >
Sure, it makes sense to parallel the delete function. -Travis ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
