I have an np.ndarray of shape (5, 5, 2, 2, 2, 10, 8) named table. I can succesfully slice it like this: But for some reason when I try to specify three values for dimension 5 (of length 10) like this: I get: The same is for: This does not happen with: which output the correct result. I tried to
I have a 3d numpy array like this (as an example) I want to apply the following operations only to elements within the column with index 1 in the inner dimension. The elements are [-2,6,10,14] for the example above. The operations would be: Can someone help me? I have looked into several NumPy methods but can’t seem to adapt to
I have two ndarrays of size (m x n), and two lists of length m and n respectively. I want to convert the two matrices to a dataframe with four columns. The first two columns correspond to the m and n dimensions, and contain the values from the lists. The next two columns should contain the values from the two
Say I have a multi-dimensional array, for example: And say I have a one-dimensional array (b), with some values which may be seen in my multi-dimensional array (a): Now I want to replace all of the non-intersecting values in array a with zero. I want to get the following result: How could I achieve this? I tried several ways of
Given a dummy heightmap (or digital elevation model) stored as a Numpy array like this: I can calculate its slope and aspect like this: And visualise it: But how would I go the other way? I’d like to generate a blank 2D Numpy array of a fixed size, then fill it with values that follow a known slope and aspect
Let’s say I have the following numpy array, of 1’s and 0’s exclusively: I want to group all elements into chunks of 3, and replace the chunks by a single value, based on a condition. Let’s say I want [0,1,1] to become 5, and [0,1,0] to become 10. Thus the desired output would be: All possible combinations of 1’s and
I have an array of probabilities: l = [0.9, 0.2, 0.4] and I want to make it 2d array: l = [0.9 0.1 0.2 0.8 0.4 0.6] What is the best way to do so? Thanks!
I am having trouble reading the binary file. I have a NumPy array as, data = array([[ 0. , 0. , 7.821725 ], [ 0.05050505, 0. , 7.6358337 ], [ 0….
I was reading this question, about immutable numpy arrays, and in a comment to one of the answers someone shows that the given trick does not work when y = x[:] is used rather than y = x. >>> …
I have a pandas DataFrame containing two columns [‘A’, ‘B’]. Each column is made up of integers. I want to construct a sparse matrix with the following properties: row index is all integers from 0 to …