Скачать или смотреть Better way to execute iterative function for very large dataset in python

Working with large datasets in Python can be challenging, especially when you need to apply iterative functions to process the data efficiently. In this tutorial, we'll explore some strategies and techniques to improve the performance of your code when dealing with very large datasets. We'll cover topics like memory optimization, parallel processing, and efficient iteration techniques.
Generators are a great way to iterate over large datasets without loading the entire dataset into memory. Instead of creating a list or other data structure to store your data, you can use a generator to yield one item at a time, allowing you to process the data efficiently.
This code will read and process each line of the file one at a time, without storing the entire file in memory.
Memory mapping is a technique that allows you to map a file into memory, making it easier to access large datasets efficiently. The mmap module in Python provides a way to do this.
This approach is especially useful when you need random access to different parts of the file because it avoids reading the entire file into memory.
For CPU-bound tasks, you can take advantage of multiprocessing to parallelize the processing of your dataset. The multiprocessing module