What is the difference between multithreading and multiprocessing in Python? When should I use one over the other?
In Python, multithreading and multiprocessing are two methods for executing multiple tasks simultaneously. The main difference between the two is that multithreading uses multiple threads within a single process, while multiprocessing uses multiple processes, each with its own memory space and CPU resources.

Multithreading is well-suited for tasks that can be broken down into smaller, parallelizable units of work and are I/O-bound (i.e., they spend more time waiting for I/O operations to complete than executing CPU-bound operations). Examples of I/O-bound tasks include reading and writing to a file, making network requests, and waiting for user input. In multithreading, all threads share the same memory space and resources, which makes it easier to share data between threads.

Multiprocessing, on the other hand, is well-suited for tasks that are CPU-bound (i.e., they spend more time executing CPU-bound operations than waiting for I/O operations to complete). Examples of CPU-bound tasks include complex computations, data processing, and image rendering. In multiprocessing, each process has its own memory space and resources, which makes it more difficult to share data between processes, but also provides greater control over resource allocation and the ability to take advantage of multiple CPU cores.

In summary, you should use multithreading when you have I/O-bound tasks that can be broken down into smaller, parallelizable units of work, and you should use multiprocessing when you have CPU-bound tasks that require significant computational resources.
Can you specify what would be some parallelizable I/O operations vs. non-parallelizable I/O operations? I'm having a hard time thinking of examples beyond the ones you already stated.
Sure! Here are some examples:

Parallelizable I/O operations:

Copying files from one location to another in parallel, for example using a tool like rsync that allows multiple threads to transfer data in parallel.
Reading from multiple disk drives at the same time to speed up data retrieval.
Downloading multiple files from the internet simultaneously, for example using a download manager that splits a file into multiple parts and downloads each part in parallel.
Non-parallelizable I/O operations:

Writing to a single file, as this typically requires the file system to ensure data is written in the correct order to maintain consistency.
Reading from a single file, as reading sequentially is generally the most efficient way to retrieve data from a file.
Writing to a single database record, as this typically requires a lock to ensure data consistency and prevent race conditions.
In general, it's important to note that while parallelizing I/O operations can help improve performance, it can also introduce new challenges and complexities, such as the need to manage and coordinate multiple threads, and the risk of overloading I/O systems and reducing performance.