I do this all the time. Post the results for each row to a multiprocessing.Queue, and spawn a single process that gets from the queue and writes to the file. It'll post some code when I get to work.

Parallel processing is a technique that allows you to run multiple tasks simultaneously on different cores or machines, speeding up your data mining code and making the most of your computing ...

In this example we will take input files from raw folder and process these files in parallel in 4 steps. Each step will be executed in a separate function and output is saved in respective folders.

To enhance the speed of reading and writing CSV files in Python, I recommend ... It is not recommended when you need to process extremely large data sets, for example, data with a few hundred ...

This repository contains scripts to benchmark the performance of Golang, NestJS, PHP, and Python in processing large CSV files. The benchmark compares the execution time, memory usage, and ease of ...

DVC.ai has announced the release of DataChain, a revolutionary open-source Python ... data processing workflow, making it invaluable for data scientists and developers. DataChain is designed to ...