Memory is important to any program’s efficiency. Similarly, memory storage in Python is critical, but memory leaks cause errors. Unused or unreferenced data in the Python is not cleaned up by the garbage collector. So, if you are wondering if Python has memory leaks, the answer is yes. Yes, Python has memory leaks. So, dealing with memory leaks in Python has become a difficulty for all developers and programmers.






In computer science, a memory leak occurs when objects are saved in a program’s memory but are not used, causing them to accumulate in the memory. It’s a memory leak. It clogs up the program’s storage, causing space issues, program deletion, and sluggish performance.


Python Memory Leak


There is a memory in Python. So when Python’s RAM is full of unneeded objects that haven’t been deleted. Unused items leak into used memory and cannot be erased. Python calls it memory leaks.




Memory management in Python is an application to prevent memory leaks when reading and writing data. It cleans unnecessary data from memory to ensure memory efficiency. Cpython is built-in Python garbage collector that collects unreferenced and unneeded data.


In other words, Python programmers do not have to worry about memory allocation and deallocation because Cpython will automatically tell the garbage collector to delete the unreferenced data from memory.


But in practice, it is not as simple as it seems. Garbage collectors sometimes fail to check for unreferenced objects, causing memory leaks in Python. Python scripts eventually run out of memory due to memory leaks. Finding and fixing memory leaks in Python is difficult.


So, in Python, a memory leak occurs when useless data accumulates and the programmer forgets to erase it. To find memory leaks in Python, we need to run memory profiling, which measures how much memory each portion of the code uses.


Don’t be scared by the word Memory profiling, it’s simple.


Memory leaks in Python




To linger all large objects not released




Code reference cycles can cause memory leaks.




Libraries can also cause memory leaks.




First, use the built-in gc module to debug memory usage. It will list all items known to the garbage collector. It will show you where the RAM is being used. Then you can filter it by use. Even if the object is referenced. Then delete them to prevent Python memory leaks.


It will output all objects and data created during execution. However, the built-in module gc does not explain how objects are allocated. In the end, it won’t help you find the code responsible for allocating the objects causing memory leaks.




Python’s new Tracemalloc built-in module is the finest. Because it is the best solution for memory leaks in Python. It will let you link an object to its original location.




It features a stack trace that shows which application of a shared function is eating memory. Tracemalloc keeps track of an object’s memory usage. Memory leaks in Python can be tracked down. To correct or clean memory leaks, you must first identify them.


It will efficiently reduce a program’s memory footprint. That’s why Tracemalloc is known as the memory tracker in Python.




Python is a world-class object-oriented programming language. Many prominent organizations like Google and YouTube use it for their initiatives. It is efficient. But, like other programs, it has memory leaks. Python’s Cpython helps allocate and deallocate memory.