(JIT) Compilation of Python code with FFI library calls

I’m using Python with a library that uses cppyy to get access to a ton of C++ functions natively in Python. However, the calls to cppyy methods take a lot of time, and looping in Python with a library …

How to skim itertools permutations?

Initial code: from itertools import permutations ListX = [“A”,”B”,”C”,”(“,”)”,”#”] perm_iterator = list(permutations(ListX)) print(list(…

Fastest way to split a list into a list of lists based on another list of lists

Say I have a list that contains 5 unique integers in the range of 0 to 9. import random lst = random.sample(range(10), 5) I also have a list of lists, which is obtained by splitting integers from 0 …

Pandas average of previous rows fulfilling condition

I have a huge data-frame (>20m rows) with each row containing a timestamp and a numeric variable X. I want to assign a new column where for each row the value in this new column is the average of X …

Performance comparison: Why is it faster to copy an entire numpy Matrix and then change one column than to just use numpy.column_stack?

I am trying to improve the performance of some Python code. In that code, one column of a matrix (numpy-array) has to be changed temporarily. The given code looks as follows: def get_Ai_copy(A, b, i): …

How to improve the performance of traversing a large dataset

I want to improve the logic for my task that is defined as following, the tasks is implemented in Python 3 with Django framwork: The source data is onboard to our system, the Job entity defines what …

Most efficient way to combine large Pandas DataFrames based on multiple column values

I am processing information in several Pandas DataFrames with 10,000+ rows. I have… df1, student information Class Number Student ID 0 13530159 201733468 1 13530159 201736271 2 …

For loop is several times faster in R than in Python using the rpy2 library

The following simply for block takes about ~3 sec to complete in R: The same code run in Python through the rpy2 library takes between 4-5 times more: Is this just because I’m using the rpy2 library to communicate with R or is there something else at play? Can this be improved in any way (while still running the code in Python)? Answer 4 to 5 times slower seems a little much, but this might be the case if you are using custom conversion (rpy2 can convert R objects to arbitrary Python objects on the fly – see the doc).

IPython (jupyter) vs Python (PyCharm) performance

Are there any performance difference between a code run on a IPython (Jupyter for example) and the same code run on “standard” Python (PyCharm for example)? I’m working on a neural network …

Binary Insertion Sort vs. Quicksort

I was looking at different sorting algorithms and their performance (link) and then I tried to implement some sorting algorithms myself. I wanted to improve them as well and so, as I was coding the …