What Is Python Memory Management?

Since the Python programming language is capable of handling memory automatically, unlike some others, its memory manager provides space for new objects while removing unused objects to open space. Because it is suitable for beginners, Python is one of the most popular programming languages for artificial intelligence and machine learning. If you’re building AI applications and machine learning models, you will most likely be working with massive data sets. Without proper memory management, you can face challenges while building these applications, as effective memory allocation is necessary in order to avoid problems that can arise when you run out of memory, such as memory leaks.

Having information stored in an AI’s memory allows the system to continue to learn from data to make better predictions and decisions in the future in an adaptable and dynamic way. In this article, you’ll discover more about Python memory management, including Python memory management techniques, the pros and cons of this system, and tips for successfully implementing this technology.

What is Python memory management?

Python memory management is the process of allocating and dealing with memory so that your programs can run efficiently. One advantage of Python, compared to other programming languages, is that it can perform memory management tasks automatically.

You can classify memory management in Python in one of two ways: dynamic allocation or static allocation. Dynamic allocation occurs as the program is running. This means that as the program operates, it can dynamically determine where to allocate memory while reusing and releasing it. Static memory allocation happens before the running of a program, predetermining the amount and distribution of the memory, and without the ability to reuse memory.

Python memory management techniques

Python has a wide variety of methods for effective memory management and allocation. Here’s a look at these different processes and how they work.

Garbage collection

When previously allocated memory no longer serves a purpose in your system, Python programs implement a process known as garbage collection. Garbage collection is an automatic process that detects memory the system isn’t using and frees up that space. This allows you to avoid any problems with forgetting to deallocate memory. Although garbage collection is an automatic process, you do have the ability to make some modifications. For example, you can customize the time intervals in which the deallocation of memory activates.

Reference counting

A key part of the garbage collection process is the reference counting algorithm. When you’re working with Python, the Python memory manager uses reference counting to track how frequently a program uses an object. An object is a variable containing data, such as an integer or a string, and interacts directly with Python libraries and frameworks, providing instructions for a program's running. During garbage collection, the reference counting algorithm counts how often the program references an object, and when the reference counter reaches zero, the object is automatically removed. 

Generators and iterators

Generators and iterators allow you to create data streams that load one at a time rather than the entire data sequence being stored automatically within the memory. This is advantageous when working with large data sets, helping to maximize the memory you save along the way.

GIL

GIL, or global interpreter lock, helps address some of the challenges of reference counting. For example, Python programs that run multiple threads simultaneously can struggle to get an accurate reference count, as more than one thread can use the objects. However, GIL allows you to prevent threads from interfering with the others' reference counts. This is important because it helps mitigate the risk of crashes and errors in your code.

Pros and cons of Python memory management

Python’s memory management, a pro being memory-efficient code, helps contribute to its widespread use. However, some minor challenges exist as well. Here’s a look at the pros and cons.

Pros

• Code written in Python is efficient with its memory use, allowing you to minimize the demand on other system resources and increase processing speeds.

• Python generators effectively handle memory for large data sets by creating data streams that prevent you from saving the whole data set in the memory.

• Over-allocating memory leads to memory leaks, which cause programs to operate slower, but garbage collection helps prevent this.

Cons

• Python memory management doesn’t offer the same level of manual customizability compared to other programming languages.

• Program runtimes can potentially experience slower rates as sometimes the program holds freed memory in the interpreter rather than freeing it up for use by the operating system.

Who uses Python memory management?

Python is a diverse programming language used in a variety of fields such as web development, scientific computing, finance, and gaming. Here are several positions where you can benefit from these techniques, along with average annual salaries:

• Data scientist: $129,597

• Machine learning engineer: $126,293

• Artificial intelligence engineer: $127,904

• Web developer: $84,555

• Software engineer: $118,670

• Data analyst: $76,952

• Database administrator: $105,843

• Automation engineer: $94,093

*All annual base salary data is sourced from Glassdoor as of February 2024 and does not include additional pay, such as commission and benefits.

Tips for effective Python memory management

You can use a memory management library or package to improve the ease with which you can manage your memory usage in Python. This will allow you to monitor the memory usage of individual objects or the amount of memory in the interpreter. For example, memory_profiler is a package that illustrates the memory usage of your application over time, allowing you to see if you’re using it efficiently. Additionally, implementing generators whenever possible is another effective way to minimize memory demands while working with data sets. You should also manually remove unreferenced objects and any other unnecessary copies of objects.

Getting started with Coursera

On Coursera, you can find highly-rated courses to learn more about programming with Python and using Python for AI and machine learning. Get more comfortable with Python programming basics by enrolling in the Python for Everybody Specialization from the University of Michigan. With this Specialization, you can learn fundamental Python concepts, including data processing, visualization, and data structures.

If you’re interested in learning more about machine learning on Coursera, Machine Learning with Python from IBM will introduce you to various machine learning and deep learning algorithms, classification techniques, and Python libraries for machine learning.