Tag: arrays

Iterate and loop through certain nested values to use within JSON Data and Request URLs

I have an nested list that works as [[id, price], [id, price], [id, price], [id, price]] and I need to iterate through the [0] and the [1] value to use in the request url and json data respectively. I can’t get the variable to be used in the request url but the json data seems to work fine Answer have

Get the index of the largest two values per row

arrays numpy python sorting

For each row, I would like to get the index of the largest two values. Here’s the result I would like to obtain: Answer Use np.argsort along the second axis and take last two values reversed.

Difference of Numpy Dimension Expand code

arrays numpy python

I got confuse between two Numpy dimension expand code. First code is X[:, np.newaxis]. Second code is X[:, np.newaxis, :]. I ran this code on Jupyter, But it returns same shape and result. What is the difference? Answer Refer numpy documentation for newaxis. https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis x[newaxis, :] is equivalent to x[newaxis] and x[None] Any dimension after np.newaxis is still present in

Print array value without brackets in Python

arrays json python script

So, I have an array value like this with print(Items) it’s returning [‘*.abc.com’, ‘*.xyz.me’] but, I want to print everything without brackets like ‘*.abc.com’, ‘*.xyz.me’ Followed some way, but those are returning different not exactly what I want. Answer what about this? Output: i add a short description like mozway suggests: you convert your python list to a string (at

Scale/resize a square matrix into a larger size whilst retaining the grid structure/pattern (Python)

arrays matrix numpy python

Like above, I want to retain the same ‘grid’ format whilst increasing the dimensions of the 2D array. How would I go about doing this? I assume the original matrix can only be scaled up by an integer n. Answer You can use numba if performance is of importance (similar post) with no python jitting and in parallel mode if

Dask Distributed: Reducing Multiple Dimensions into a Distance Matrix

arrays dask dask-distributed distance python

I want to calculate a large distance matrix, based on a higher dimensional vector. For instance, I have 1000 instances each represented by 20 vectors of length 10. The distance between each two instances is given by the mean distance between each of the 20 vectors associated to each vector. So I want to go from a 1000 by 20

remove background of RGB image (3D array) based on another array (boolean mask)

arrays image numpy python

I’m stuck on what should be fairly straight forward, but other opened questions don’t see to address exactly the same issue i’m having. I’m trying to crop an image based on boolean mask. I can do this with: (This function inputs RGB image (w, h, c) and True/False mask of shape (w, h, n) and crops a bounding box around

In Python, why my variable StartingU get updated when I only update the variable AverageU in my for loops? What is wrong with the code?

arrays numpy python variables

The purpose is to calculate the average value (AverageU) from a starting array (StartingU) The for loops that calculate the average values for AverageU Output: The problem is why StartingU gets updated? It should be unchanged Answer AverageU changed since this code, not after for loop. AverageU and StartingU are the same instances. You can check it with is function.