An alternative is to use np.ravel: >>> np.zeros((3,3)).ravel() array([ 0., 0., 0., 0., 0., 0., 0., 0., 0.]) The importance of ravel over flatten is ravel only copies data if necessary and usually returns a view, while flatten will always return a copy of the data. To use reshape to flatten the array: tt = … Read more
You have some errors in your code: Use myArray[i].push( 0 ); to add a new column. Your code (myArray[i][j].push(0);) would work in a 3-dimensional array as it tries to add another element to an array at position [i][j]. You only expand (col-d)-many columns in all rows, even in those, which haven’t been initialized yet and … Read more
I don’t know of a clean way to do something like this: mask = np.ones(a.shape,dtype=bool) #np.ones_like(a,dtype=bool) mask[indices] = False a[~mask] = 999 a[mask] = 888 Of course, if you prefer to use the numpy data-type, you could use dtype=np.bool_ — There won’t be any difference in the output. it’s just a matter of preference really.
You’ve gotten a handful of nice examples of how to do what you want. However, it’s also useful to understand the what’s happening and why things work the way they do. There are a few simple rules that will help you in the future. There’s a big difference between “fancy” indexing (i.e. using a list/sequence) … Read more
Cay S. Horstmann has stated within his book Core Java for the impatient: There are no two-dimensional array lists in Java, but you can declare a variable of type ArrayList<ArrayList<Integer>> and build up the rows yourself. due to the fact that ArrayLists can expand and shrink and become jagged rather than multi-dimensional, one could say … Read more
I would try this: var array:[[Person]] = [[person1, person2, person3], [person9, person10, person11]] /*Casting it like this should keep from getting an error later and/or having to recast the objects*/ for people in array { /*This is going to look at each array in arrays, and call each one ‘people’ within this loop*/ for person … Read more