Is shared readonly data copied to different processes for multiprocessing?
The piece of code that I have looks some what like this:
numpy
Rank items in an array using Python/NumPy, without sorting array twice
I have an array of numbers and I’d like to create another array that represents the rank of each item in the first array. I’m using Python and NumPy.
Why does multiprocessing use only a single core after I import numpy?
I am not sure whether this counts more as an OS issue, but I thought I would ask here in case anyone has some insight from the Python end of things.
How to access the ith column of a NumPy multidimensional array?
Suppose I have:
Store different datatypes in one NumPy array?
I have two different arrays, one with strings and another with ints. I want to concatenate them, into one array where each column has the original datatype. My current solution for doing this (see below) converts the entire array into dtype = string, which seems very memory inefficient.
NumPy selecting specific column index per row by using a list of indexes
I’m struggling to select the specific columns per row of a NumPy matrix.
Using strides for an efficient moving average filter
I recently learned about strides in the answer to this post, and was wondering how I could use them to compute a moving average filter more efficiently than what I proposed in this post (using convolution filters).
Best way to join / merge by range in pandas
I’m frequently using pandas for merge (join) by using a range condition.
Replace all elements of Python NumPy Array that are greater than some value
I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:
numpy: most efficient frequency counts for unique values in an array
In numpy / scipy, is there an efficient way to get frequency counts for unique values in an array?