O(n^2) time and O(1) space algorithm ( without any workarounds and hanky-panky stuff! ) Rotate by +90: Transpose Reverse each row Rotate by -90: Method 1 : Transpose Reverse each column Method 2 : Reverse each row Transpose Rotate by +180: Method 1: Rotate by +90 twice Method 2: Reverse each row and then reverse … Read more
It actually is possible to do what you want, but you have to use the functional form of the indexing operator. When you perform an indexing operation using (), you are actually making a call to the subsref function. So, even though you can’t do this: value = magic(5)(3, 3); You can do this: value … Read more
1. The meaning of shapes in NumPy You write, “I know literally it’s list of numbers and list of lists where all list contains only a number” but that’s a bit of an unhelpful way to think about it. The best way to think about NumPy arrays is that they consist of two parts, a … Read more
Numpy matrices are strictly 2-dimensional, while numpy arrays (ndarrays) are N-dimensional. Matrix objects are a subclass of ndarray, so they inherit all the attributes and methods of ndarrays. The main advantage of numpy matrices is that they provide a convenient notation for matrix multiplication: if a and b are matrices, then a*b is their matrix … Read more
zip is its own inverse! Provided you use the special * operator. >>> zip(*[(‘a’, 1), (‘b’, 2), (‘c’, 3), (‘d’, 4)]) [(‘a’, ‘b’, ‘c’, ‘d’), (1, 2, 3, 4)] The way this works is by calling zip with the arguments: zip((‘a’, 1), (‘b’, 2), (‘c’, 3), (‘d’, 4)) … except the arguments are passed to … Read more