numpy ignores “casting” argument
Numpy version is 1.26.4
matching numpy conditions row and column without iteration
Given a dataframe condition defined as:
Create bimodal distribution from two uniform distribution using Numpy
I am trying to reproduce the bimodalSample function of this blog in Python.
what does mean + + operator in python numpy
I’ve met following statement in python numpy library
How to make a double array from two lists using np.zeros
I have these two lists:
requested array has an inhomogeneous shape
I want to save the results of the game in the folder which is located here C:123train_data (i.e. data should be saved in the folder train_data).
LArge array average
I have an array of size (1000, 128, 128) where 1472 are total array of 128x128. Now I want to take an average of every 10th array (axis=0) so that I get final array of the size (100,128,128). I can take all array average with np.mean(data,axis=0), but I don’t know how to take average of every 10 array in this case.
Handling a large matrix with numpy efficiently
I am trying to run some simple calculations on a quite large matrix; roughly, 200Kx200K or real numbers. I need to obtain:
return diag elements of nxn matrix without using a for-loop
Imagine having an array of matrices (nxkxk), how would I return the diagonal entries while keeping the original shape without using a for-loop.
numpy.random.randn generates the same values every time
My code is very basic
import numpy as np