I have a 3-dimensional array a of shape (n, m, l). I extract one column j from it’s last axis and compute the maximum index along the first axis like follows: Now I’d like to slice the original array a to get all the information based on the index where the column j is maximal. I.e. I’d like an numpythonic
Tag: array-broadcasting
np.where for 2d array, manipulate whole rows
I want to rebuild the following logic with numpy broadcasting function such as np.where: From a 2d array check per row if the first element satisfies a condition. If the condition is true then return the first three elements as a row, else the last three elements. A short MWE in form of a for-loop which I want to circumvent:
Broadcasting over two arrays with different shapes (Numpy-Python)
Suppose I have the following arrays: So an array with shape (5, 5) Now I have a second array, which is a slice of the first: An array with shape (2, 5). Now I want to subtract every vector of the first array by the vectors of the second array subsequently(excluding- in the first array- the vector of the second
Get multiplication table generalized for n dimensions with numpy
Let a = np.arange(1, 4). To get the 2 dimensional multiplication table for a, I do: For 3 dimensions, I can do the following: How could I write a function that takes a numpy array a and a number of dimensions n as input and ouputs the n dimensional multiplication table for a? Answer This should do what you need:
How does pytorch broadcasting work?
produces a Tensor with size: torch.Size([4,4]). Can someone provide a logic behind this? Answer PyTorch broadcasting is based on numpy broadcasting semantics which can be understood by reading numpy broadcasting rules or PyTorch broadcasting guide. Expounding the concept with an example would be intuitive to understand it better. So, please see the example below: Now for torch.add(t_rand, t_ones), visualize it
how to multiply pandas dataframe with numpy array with broadcasting
I have a dataframe of shape (4, 3) as following: I want to multiply each column of the dataframe with a numpy array of shape (4,): In numpy, the following broadcasting trick works: However, it doesn’t work in the case of pandas dataframe, I get the following error: Any suggestions? Answer I find an alternative way to do the multiplication