What’s the easiest way to generate a numpy array of index values in Python? [closed]
Closed 3 days ago.
Multiplying a 2D array with a 3D array of vectors in Numpy
I have a dxd matrix that I would like to multiply with a dx1xn matrix. The idea is to get back another dx1xn matrix, so I would essentially like to multiply each dxd matrix with each dx1 matrix n times along that third axis. I have no idea how to go about this though. I have tried np.apply_along_axis
and np.apply_over_axes
, but the first only selects one axis rather than the dx1 matrices, and the second doesn’t do what I want either.
Create new matrix with sum of all n*n submatrixes sum without using loops
I have a starting array (a) and I’m using loops to create a new matrix (b) where every element represents the sum of every unique subarray n*n (0+1+5+6=12, 1+2+6+7=16, …). Can I achieve the same result without using loops by inbuilt Numpy methods? I tried np.reshape, but I can’t count intersecting subarrays with it.
Multiply array of matrices with array of vectors in Numpy
I have an array of matrices A
of shape A.shape = (N, 3, 3)
and an array of vectors V
of shape V.shape = (N, 3)
. I want to get an (N, 3) array, where each vector is the result of multiplying the n-th matrix with the n-th vector. Such as:
A more efficient way to add values inplace at colums of a 2d matrix, using a 2d array for the colum indices
I would like to do a +=
operation at specified columns of a 2d matrix, where the column indices are in another 2D matrix.
Creating a dynamic numpy array using two existing arrays
I’m looking to multiply out two arrays (not their values). In this instance, I have an array of sequential dates which can vary and another array of values 0 to 23 (i.e. hours)
Apply permutation array on multiple axes in numpy
Let’s say I have an array of permutations perm
which could look like:
How to transform nested arrays by extending the dimension of the array
Suppose I have an array X with N elements. I would like then to input this array into a 2D array A as follows
An ordinary numpy array produces a type error?
Could someone explain why I get this warning
How to quickly find the minimum element to the right for every element of a Numpy array?
Let’s say I have an array: