I'm looking for the most memory-efficient way to compute the absolute squared value of a complex numpy ndarray arr = np.empty((250000, 150), dtype='complex128') # common size I haven't found a...

I'd like to know if there is a more efficient/pythonic way to add multiple numpy arrays (2D) rather than: def sum_multiple_arrays(list_of_arrays): a = np.zeros(shape=list_of_arrays[0].shape) #init...

Is it generally safe to provide the input array as the optional out argument to a ufunc in numpy, provided the type is correct? For example, I have verified that the following works: >>> ...

I am trying to convert a csv into numpy array. In the numpy array, I am replacing few elements with NaN. Then, I wanted to find the indices of the NaN elements in the numpy array. The code is : im...

How do I convert a np.add.at statement into tensorflow? np.add.at(dW, self.x.ravel(), dout.reshape(-1, self.D)) Edit self.dW.shape is (V, D), self.D.shape is (N, D) and self.x.size is N

I'm working on cs231n and I'm having a difficult time understanding how this indexing works. Given that x = [[0,4,1], [3,2,4]] dW = np.zeros(5,6) dout = [[[ 1.19034710e-01 -4.65005990e-01 8.93743...

Running Numpy version 1.19.2, I get better performance cumulating the mean of every individual axis of an array than by calculating the mean over an already flattened array. shape = (10000,32,32,3)...

There is a positive function in numpy (version 1.13+), which seemingly does nothing: In [1]: import numpy as np In [2]: A = np.array([0, 1, -1, 1j, -1j, 1+1j, 1-1j, -1+1j, -1-1j, np.inf, -np.inf...

Relating to this answer, is there a fast way to compute medians over an array that has groups with an unequal number of elements? E.g.: data = [1.00, 1.05, 1.30, 1.20, 1.06, 1.54, 1.33, 1.87, 1.6...

I'm working on some code where I have several matrices and want to subtract a vector $v$ from each row of each matrix (and then do some other stuff with the result). As I'm using NumPy and want to ...

I wonder if it is possible at all to perform element-wise sum (or other operations) of two structured numpy arrays of an identical shape. arr1 = np.array([[1,2,3],[2,3,4]], dtype=[("x", "f8"),("y"...

Is it possible to use ufuncs https://docs.scipy.org/doc/numpy/reference/ufuncs.html In order to map function to array (1D and / or 2D) and scalar If not what would be my way to achieve this? ...

I am trying to broadcast a simple operation of ">" over two 3D arrays. One has dimensions (m, 1, n) the other (1, m, n). If I change the value of the third dimension (n), I would naively expect tha...

I measured performance of ufuncs like np.cumsum over different axes: In [51]: arr = np.arange(int(1E6)).reshape(int(1E3), -1) In [52]: %timeit arr.cumsum(axis=1) 2.27 ms ± 10.5 µs per loop (mean ±...

I've read a lot "avoid for loops with numpy". So, I tried. I was using this code (simplified version). Some auxiliary data: In[1]: import numpy as np resolution = 1000 # this parameter varies t...

From an array like db (which will be approximately (1e6, 300)) and a mask = [1, 0, 1] vector, I define the target as a 1 in the first column. I want to create an out vector that consists of ones w...