I want to upload a huge number of entries (~600k) into a simple table in a PostgreSQL DB, with one foreign key, a timestamp and 3 float per each entry. However, it takes 60 ms per each entry to execute the core bulk insert described here, thus the whole execution would take 10 h. I have found out, that it is a performance issue of executemany() method, however it has been solved with the execute_values() method in psycopg2 2.7.
I have experience in coding OpenMP for Shared Memory machines (in both C and FORTRAN) to carry out simple tasks like matrix addition, multiplication etc. (Just to see how it competes with LAPACK). I know OpenMP enough to carry out simple tasks without the need to look at documentation.
I have found lots of information on the internet about adding Members to a Group in Googlegroups, but I cant manage to get any of it to work.
I’m trying to fit the distribution of some experimental values with a custom probability density function. Obviously, the integral of the resulting function should always be equal to 1, but the results of simple scipy.optimize.curve_fit(function, dataBincenters, dataCounts) never satisfy this condition.
What is the best way to solve this problem?
Does python support chaining is operators, such as the following?
I am trying to create a column (“consec”) which will keep a running count of consecutive values in another (“binary”) without using loop. This is what the desired outcome would look like:
I have the following test (formatted just like below):
As we all know, the pythonic way to swap the values of two items a and b is
I am using python-tesseract to extract words from an image. This is a python wrapper for tesseract which is an OCR code.
Is there a Python way without using a subprocess to clone a git repository? I’m up for using any sort of modules you recommend.