Why Background Python Processes Keep Running

How many programming languages do you know? If you have even the most rudimentary experience with this sector, then your answer will probably include Python in some way or form.

Python remains one of the most popular programming languages in the world, and for good reason. Its syntax is beginner-friendly, its ecosystem is massive, and you can use it for anything, including web applications and data science workflows. Very few other languages can boast of the same versatility, so it’s clear why programmers use it and why so many newbies choose to study it.

If you’re among them, you’re in for a very fulfilling experience, but there are some other, more problematic things you should know. One of them concerns the constantly running Python processes that take place in the background.

You have already completed all the tasks, so why is anything still running? Why are your resources still being consumed, and for what reason is Python so stubbornly clogging your CPU cycles? That’s what we are going to explore today.  

Signs that the Background Processes Are Still Running

The first, most obvious sign that some processes you haven’t willingly initiated keep running in the background is the quicker battery drain. It’s especially noticeable on Mac: users get used to enjoying the seemingly endless power, so when the charge is drained within an hour, it’s instantly jarring. You can read more here to learn about the Low Power Mode on Mac and see if it’s useful. When you activate it on your own, it’s perfectly safe; when your device keeps defaulting to it to save power, you’ve got a problem.

Here is how you can identify persistent background processes:

• Your main task might be complete, but the app keeps consuming CPU resources, which is evident in the activity monitor tab.
• Network ports are still open, with the services still listening for requests, fully ready to react to them.
• Memory usage is elevated because Python-related threads or processes are still holding some of your resources captive.
• When you check the logs, you notice that they keep receiving new entries, even though the app should have ceased all the activity at this point.

Another obvious sign is your operating system still displaying the Python processes in the task management bar. 

Common Reasons Background Python Processes Stay Active

There are three most common reasons explaining why your Python processes might keep running. Let’s take a look.

Daemon vs Non-Daemon Threads

Python comes with different threads, and these threads allow programmers to perform multiple tasks at the same time. The problem is that not all of them behave predictably when the main program runs its course. Here is what you need to know:

• There are daemon threads: they work in the background and get automatically terminated the moment your program is completed.    
• There are also non-daemon threads: they keep running even after the end of the program. Your operating system will wait for these threads to complete before shutting the process down once and for all.
• Daemon threads are used for supporting tasks like monitoring, while non-daemon ones help with lengthy processes like data download. 

So, if you’ve just finished a program and some non-daemon threads are still running, don’t worry. It’s perfectly normal.  

Scheduled Tasks and Background Services

One of the best features of Python is that any program written in it will function identically on any operating system. That’s a plus, but it also means that you might face the same problem of running background activities on Mac, Windows, etc. The only solution here is to understand why some processes refuse to halt when you expect them to.

Some Python applications are designed to keep running continuously. These are monitoring scripts, web servers, or chatbots; if this is what you’ve been working on, don’t worry about seeing the loose ends. They are natural; your program will keep automating repetitive actions and responding to user requests. 

Improper Exit Handling

Another common factor concerns exit handling. Sometimes, the system simply glitches: your Python script might fail to stop the interpreter properly, so the program will keep hanging around, getting stuck.

Press Ctrl+C to stop a script; if it doesn’t help, use file.close(), .terminate(), or .kill() commands depending on the magnitude and urgency of your problem. Be sure to clean your Mac regularly and use system monitoring tools to identify issues before they hit you full-force. This will reduce clutter and free up your space, which will help in case Python starts giving you trouble. 

Using the Best Practices for Managing Background Processes

In 2025, 57.9% of developers worldwide used Python as their programming language of choice. A huge number choose to study this language every year, but most of them inevitably run into a problem with running background processes.

As you already know, in some cases, it’s normal. Your non-daemon threads might be running, and they’ll get terminated once they complete the remaining part of their work. In other cases, you might be facing a cleanup or system issue: this is where you need to apply solutions like cleaning your Mac or interrupting the process manually.

If you want to work without issues like this, you need to master the best practices for managing your background processes. Here are some tips:

• Always make sure to check and double-check that you closed all database connections, file handles, and network sockets the moment you no longer need them.
• Avoid rushing to shut everything down; wait for threads to complete what they must and give them a chance to terminate safely.
• Apply monitoring tools to track process activity and identify weird resource consumption patterns: this will alert you to a problem before it harms your work.

Even following these three tips alone will help you complete your background processes properly and avoid typical Python-related issues. 

Author’s Bio

Kenneth Ayder is a programmer with lifelong experience in software development and Python training. He’s used to explaining the most complex terms in simple human language, which makes his articles suitable for experts and laymen alike. His tips and insights have already helped many users understand their work or setups better.