# Tag: multidimensional-array

## Updating column value based on nan value of other column

I have this simple function with 2 columns. What I’m trying to do is to check what group has a number of nan and change it to a new desired value. Here’s a code snippet: Before: This is how the data looks like, you can assume numbers are sorted. In my example I know where nan and since it was

## Updating a 2D Array with the output results of a nested loop in Python

I have a nested loop where I calculate 10 distances, and store them in an array b_array. Then into a_array another array which will is to keep the results for every image. The problem is that my code overwrites a_array for every iteration even when it is outside the main function when I checked with print. Therefore, I end up

## Slice multidimensional numpy array from max in a given axis

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

## TypeError: unhashable type: ‘numpy.ndarray’, Counter rows

I’m trying to view the frequency of the elements in a 2d array as in the code: Expected result: ({(22, 66): 2, (33, 77): 1, (55, 99): 1}) But I get: Answer Create a list of tuples: (tuples are hashable) Now Counter works: numpys own unique also works

## How would I go about pasting an array over a 2D array?

The Objective I need to take an array with data that can be displayed in 2 dimensions, and paste it over any part of a 2D array as if it were an image. This function would be similar to the Python library Pillow’s paste function. Example Say I have a 2D array with a size of 5×5, with a default

## (Conv1D) Tensorflow and Jax Resulting Different Outputs for The Same Input

I am trying to use conv1d functions to make a transposed convlotion repectively at jax and tensorflow. I read the documentation of both of jax and tensorflow for the con1d_transposed operation but they are resulting with different outputs for the same input. I can not find out what the problem is. And I don’t know which one produces the correct

## How to get the all columns except last column in 3D numpy array?

I Have a 3D array composed of various columns. I just want to slice the last column. The array looks like the following: I have tried the following code. But it only shows the last column while I want to show all columns values except the last column. Answer IUUC, you can use: Example input: matching output:

## Ordering a two-dimensional array relative to the main diagonal

Given a two-dimensional array T of size NxN, filled with various natural numbers (They do not have to be sorted in any way as in the example below.). My task is to write a program that transforms the array in such a way that all elements lying above the main diagonal are larger than each element lying on the diagonal

## Divide matrix into submatrix python

The program must accept an integer matrix of size R*C and four integers X, Y, P, Q as the input. The program must divide the matrix into nine submatrices based on the following condition. The program must divide the matrix horizontally after the Xth row and Yth row. Then the program must divide the matrix vertically after the Pth column

## Smallest Submatrix in python

It contains an R*C matrix with unique digits. I have to print the submatrix having minimum and maximum integer This is my code: I need a solution without a NumPy array. I found the max and min values and also their index. After that, I don’t know what to do. Answer You can iterate over matrix again and get only