All Questions

What is the purpose of np.meshgrid? I know it creates some kind of grid of coordinates for plotting, but I can't see the direct benefit of it. The official documentation gives the following example, ...

import numpy as np y = np.array(((1,2,3),(4,5,6),(7,8,9))) OUTPUT: print(y.flatten()) [1 2 3 4 5 6 7 8 9] print(y.ravel()) [1 2 3 4 5 6 7 8 9] Both function return the ...

What is the difference between ndarray and array in NumPy? Where is their implementation in the NumPy source code?

What is numpy.newaxis and when should I use it? Using it on a 1-D array x produces: >>> x array([0, 1, 2, 3]) >>> x[np.newaxis, :] array([[0, 1, 2, 3]]) >>> x[:, np....

I have a simple question about the .shape function, which confused me a lot. a = np.array([1, 2, 3]) # Create a rank 1 array print(type(a)) # Prints "<class 'numpy.ndarray'>" ...

Can someone explain exactly what the axis parameter in NumPy does? I am terribly confused. I'm trying to use the function myArray.sum(axis=num) At first I thought if the array is itself 3 ...

Let's say I have a row vector of the shape (1, 256). I want to transform it into a column vector of the shape (256, 1) instead. How would you do it in Numpy?

I have a 3d matrix like this np.arange(16).reshape((4,2,2)) array([[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]], [[ 8, 9], [10, 11]], [[12, 13], ...

I have a (w,h) np array in 2d. I want to make a 3d dimension that has a value greater than 1 and copy its value over along the 3rd dimensions. I was hoping broadcast would do it but it can't. This is ...

Arrays 'A' and vector 'B' below are part of pandas dataframe. I have a large array A of form: 28 39 52 77 80 66 7 18 24 9 97 68 I have a vector B of form: 32 5 42 17 How do I compare ...

If I have two numpy 1D arrays, for example x=np.array([1,2,3]) y=np.array([11,22,33]) How can I zip these into Numpy 2D coordinates arrays? If I do: x1,x2,x3=zip(*(x,y)) The results are of type list,...

Using this small reproducible example, I've so far been unable to generate a new integer array from 3 arrays that contains unique groupings across all three input arrays. The arrays are related to ...

I have the following 2D array: In [173]: arr Out[173]: array([[ 1, 2, 3, 4], # -> -> -> -> [ 5, 6, 7, 8], # <- <- <- <- [ 9, 10, 11, 12], # -> -&...

From the following array : test = np.array([[1,2,'a'],[4,5,6],[7,'a',9],[10,11,12]]) How can I delete the rows that contain 'a' ? Expected result : array([[ 4, 5, 6], [10, 11, 12]])

As in title, I need to perform numpy.exp on a very large ndarray, let's say ar, and store the result in ar itself. Can this operation be performed in-place?