In Python, memory management refers to the process of allocating, using, and releasing memory for the objects created during the program’s execution. Python uses an automatic memory management system that relies on a combination of reference counting and a garbage collector.
Thank you for reading this post, don't forget to subscribe!1. Reference counting: Python uses reference counting as the primary method for memory management. Each object in Python has a reference count, which represents the number of references pointing to that object. When an object’s reference count drops to zero, meaning there are no references to it, the memory occupied by the object is deallocated immediately.
2. Garbage collector: While reference counting handles most memory deallocation, it has some limitations, such as circular references. To handle these cases, Python employs a cyclic garbage collector. The garbage collector periodically runs in the background, identifying and collecting objects that are no longer accessible, even if they have circular references. This ensures that memory is effectively released, preventing memory leaks.
Memory management in Python is automatic and transparent to the programmer. When an object is no longer in use (i.e., its reference count reaches zero), Python automatically reclaims the memory used by that object through its garbage collection mechanism. This dynamic memory management simplifies memory handling for developers, as they don’t need to manually allocate and deallocate memory like in low-level languages.
However, it’s essential to be mindful of certain practices to avoid memory issues, such as creating large data structures unnecessarily, managing file handling properly, and being aware of circular references that may not be promptly identified by the garbage collector. Proper memory management helps ensure efficient execution and prevents memory leaks, resulting in more stable and reliable Python applications.
Example:
# Define a simple class
class Person:
def __init__(self, name):
self.name = name
# Create objects and assign them to variables
person1 = Person(“Alice”)
person2 = Person(“Bob”)
# Assign the same object to another variable
person3 = person1
# Now let’s delete some references
del person1
del person2
# At this point, the only reference to the “Alice” object is person3
# When we delete this reference as well, the memory occupied by “Alice” is freed.
del person3
# Now, let’s create a circular reference
person4 = Person(“Charlie”)
person5 = Person(“David”)
person4.friend = person5
person5.friend = person4
# Deleting references to “Charlie” and “David” won’t immediately free the memory,
# as they still have a circular reference to each other.
# Run garbage collection explicitly to collect the objects with circular references.
import gc
gc.collect()
# Now the objects “Charlie” and “David” have no references, and their memory is freed.
# Note that Python’s garbage collector will handle most memory deallocation automatically,
# so you don’t usually need to explicitly call gc.collect(). It’s done here for illustration purposes.
# Let’s print a message to show when the objects’ __del__ method is called.
class Person:
def __init__(self, name):
self.name = name
def __del__(self):
print(f”{self.name} object is being deleted.”)
# Create objects and assign them to variables
person1 = Person(“Alice”)
person2 = Person(“Bob”)
person3 = Person(“Charlie”)
# Deleting the references, the __del__ method is called when the object is garbage collected.
del person1
del person2
del person3
# Output:
# Alice object is being deleted.
# Bob object is being deleted.
# Charlie object is being deleted.
Flow diagram:
“`
+——————–+
| Python Code |
+——————–+
|
v
+———————+
| Object Creation |
| and Memory Allocation |
+———————+
|
v
+———————+
| Reference Counting |
+———————+
|
v
+———————-+
| Garbage Collection |
+———————-+
|
v
+——————-+
| Memory Deallocation |
+——————-+
“`
Explanation:
1. Python code is executed, which involves object creation and manipulation.
2. Memory is allocated for each object created during the program’s execution.
3. Reference counting keeps track of the number of references to each object.
4. When references go out of scope or are explicitly deleted, the reference count decreases.
5. The garbage collector periodically runs in the background to identify and collect objects with zero reference counts (unreachable objects), including those with circular references.
6. Memory deallocation occurs when an object’s reference count reaches zero, and the object is no longer accessible.
7. The memory is then available for reuse for future objects or operations.
Please note that this is a simplified representation, and Python’s memory management system involves more complexity and optimization under the hood. The diagram gives a basic idea of the high-level flow of memory management in Python.
