indices

Why can't I index an ndarray using a list of tuple indices like so? idx = [(x1, y1), ... (xn, yn)] X[idx] Instead I have to do something unwieldy like idx2 = numpy.array(idx) X[idx2[:, 0], idx2[:, 1]] # or more generally: X[tuple(numpy.vsplit(idx2.T, 1)[0])] Is there a simpler, more pythonic way? You can use a list of tuples, but the convention is different from what you want. numpy expects a list of row indices, followed by a list of column values. You, apparently, want to specify a list of (x,y) pairs. http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#integer-array-indexing The

I noticed the "indices trick" being mentioned in the context of pretty-printing tuples. It sounded interesting, so I followed the link . Well, that did not go well. I understood the question, but could really not follow what was going on. Why do we even need indices of anything? How do the different functions defined there help us? What is 'Bare'? etc. Can someone give a play-by-play of that thing for the less-than-experts on parameter packs and variadic tuples? Barry The problem is: we have a std::tuple<T1, T2, ...> and we have some function f that we can to call on each element, where f

I'm trying to apply a function to a group of columns in a large data.table without referring to each one individually. a <- data.table( a=as.character(rnorm(5)), b=as.character(rnorm(5)), c=as.character(rnorm(5)), d=as.character(rnorm(5)) ) b <- c('a','b','c','d') with the MWE above, this: a[,b=as.numeric(b),with=F] works, but this: a[,b[2:3]:=data.table(as.numeric(b[2:3])),with=F] doesn't work. What is the correct way to apply the as.numeric function to just columns 2 and 3 of a without referring to them individually. (In the actual data set there are tens of columns so it would be

I had a generic question about JavaScript arrays. Are array indices in JavaScript internally handled as strings? I read somewhere that because arrays are objects in JavaScript, the index is actually a string. I am a bit confused about this, and would be glad for any explanation. That is correct so: > var a = ['a','b','c'] undefined > a [ 'a', 'b', 'c' ] > a[0] 'a' > a['0'] 'a' > a['4'] = 'e' 'e' > a[3] = 'd' 'd' > a [ 'a', 'b', 'c', 'd', 'e' ] Formally, all property names are strings. That means that array-like numeric property names really aren't any different from any other property names.

I have a 2 dimensional NumPy array. I know how to get the maximum values over axes: >>> a = array([[1,2,3],[4,3,1]]) >>> amax(a,axis=0) array([4, 3, 3]) How can I get the indices of the maximum elements? So I would like as output array([1,1,0]) >>> a.argmax(axis=0) array([1, 1, 0]) >>> import numpy as np >>> a = np.array([[1,2,3],[4,3,1]]) >>> i,j = np.unravel_index(a.argmax(), a.shape) >>> a[i,j] 4 SevakPrime argmax() will only return the first occurrence for each row. http://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html If you ever need to do this for a shaped array, this

I have a tbl_df where I want to group_by(u, v) for each distinct integer combination observed with (u, v) . EDIT: this was resolved by adding group_indices() back in dplyr 0.4.0 a) I then want to assign each distinct group some arbitrary distinct number label=1,2,3... e.g. the combination (u,v)==(2,3) could get label 1, (1,3) could get 2, and so on. How to do this with one mutate() , without a three-step summarize-and-self-join? dplyr has a neat function n() , but that gives the number of elements within its group, not the overall number of the group . In data.table this would simply be called