1 Introduction
Python is an interpreted language, which means it doesn't need to be compiled before it can be run. When a Python program runs, it dynamically allocates memory for all variables and objects. This means that Python's memory management is handled automatically, allowing developers to focus on writing code without worrying about associated memory allocation and deallocation. This article describes Python's memory management in detail, so let's start directly without gossip!
2. Principle
The essence of Python's memory management is based on reference counting. At runtime, each object in memory has a reference count that keeps track of how many times it is referenced in code. When we create an object, its reference count will be set to 1. When the object is no longer needed, its reference count is decremented. When the reference count reaches zero, the object will be deleted from memory.
However, if we do not properly delete objects when they are no longer needed, memory leaks may result at this point. Python solves this problem through a mechanism called garbage collection, which periodically searches for and deletes objects that are no longer referenced.
3. Take a chestnut
Let's look at an example to better understand Python's memory management. Suppose we numbersassign a list to a variable:
numbers = [1, 2, 3, 4, 5]
In the above code, Python allocates memory for the list object and each integer it contains. List objects have a reference count of 1, and each integer has a reference count of 1. If we create a new variable and assign it the same list object:
new_numbers = numbers
At this point Python does not create a new list object, instead it increases the reference count of the existing list object to 2. If we remove the original variable:
del numbers
At this point the reference count of the list object will be reduced to one. The list object still exists in memory because it is still new_numbersreferenced by the variable. If at this point we new_numbersassign a new list to the variable:
new_numbers = [6, 7, 8, 9, 10]
Python creates a new list object for the new list and decrements the reference count of the old list object to zero. Then, the garbage collection mechanism is triggered to delete the old list object from memory.
4. Summary
In short, due to its efficient and concise features, Python's memory management system enables developers to focus on writing code without worrying about memory allocation and release. A reference counting system combined with garbage collection ensures that memory is used efficiently and memory leaks are avoided.
Hmm, have you lost your studies?