I’ve got an application where I need both the average and the standard deviation of a list of data.

The documentation says

I want to multiply two numpy arrays of different shapes along a specific axes without swapping them manually or adding “dummy” dimensions if possible.

I have noticed that with an array of completely random numbers, using np.sum vs using a for-loop to iteratively add values seems to produce different values. I have provided the code below.

Under numpy I want to perform a “usual” matrix multiplication like this:

import numpy as np I = np.array([3,2,1,4,2]) x = np.linspace(0,1,10) y = np.empty(5) for i, j in enumerate(I): y[i] = x[j:].sum() This is a simplified version of the original code. The problem is that the length of I is larger, so using loop is costly. Is there any way to avoid loop here? I wish […]

I’m trying to find the distance between a point and a given line segment.

To follow up on a previous question, I would like to extract the polynomial to calculate the result for certain values ​​in a python environment without numpy.

I’m new to NumPy and still figuring out what’s easy to do with built-ins, vs. rolling my own.

I would like to find the mathematical function that could approximately fit all of the following points :