How to Optimize Python Code Performance

It started as a hobby project and was playfully named after a comedy persona. Now, in 2024, Python code performance efficiency, flexibility, and capabilities are the things that make it one of the go-to programming languages for multiple purposes. Nevertheless, there is a perception that Python is a “slow” language comparing to, let’s say, Java.

I’ve been using Python as my primary programming language for over 15 years, and I have a good understanding of its strengths and weaknesses compared to other languages. So, let’s figure out where all this negativity comes from and what Python performance tips and best practices I use.

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Highlights:

• Python rightly maintains its place among the most popular programming languages due to its simplicity, variety of available resources, community, and versatility, yet, it still may be perceived as “slow”
• The “speed” of software may not be defined outside of the context for the development, as it includes many conditions. Assessing the efficiency of the language can help to find the best suitable toolset for a particular case.
• It is not the only language that has issues with perceived slowness. In software development, there may be factors slowing down the performance, irrespective of the language choice. Python performance issues, in particular, also require a broader context for solutions and do not make the language itself “slow.”
• Presently available resources for Python developers and even enthusiasts essentially increase the efficiency of the language, since they simplify troubleshooting and introduce new ways to write sleek Python code.

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Throughout 16 years of Django Stars’ experience with Python for web development, the team has built applications, created websites, and released once-MVPs in virtually all industries.

As a brief example, Django Stars helped a client (under NDA) specializing in providing data for foodservice to improve data flow for higher operational efficiency and smooth business expansion after the business was acquired by Fortune 500. As a result of the partnership, the application had a restructured process flow and an improved filtering flow. These changes, due to Python code efficiency, allowed for easier lead processing and management in the sales department, as well as prepared the application for business expansion, as its platform faced the need for higher bandwidth.

Thus, let’s discuss the opportunities in Python performance optimization and what contexts it depends on.

The Art of Crafting Elaborate Python Code

Big things appear when the capabilities of Python meet creative minds that make products unseen before. As of today, there are plenty of ways paved toward the mastery by fellow developers and code enthusiasts that contributed to the way the language and the range of its use look now.

The reasons to go for Python are simple and intelligible syntax, its ecosystem, and its relatively low entry barrier. Combined, these make the language an efficient tool in the developer’s hands.

—Alexander Ryabtsev

Overview of Python’s Conciseness

The conciseness that facilitates Python performance is one of the defining attributes of the language. It allows developers to express complex logic in fewer lines of code compared to other languages. Its syntax is designed to be intuitive and clear, which not only makes the code easier to write but also easier to read.

>>> print("Hello, World!")
Hello, World!

Embracing Readability and Maintainability

This language is easy to read, maintain, and write in, and it all sums up Python efficiency, so appreciated by both engineers and stakeholders.

Its design philosophy encourages the use of English keywords instead of punctuation, resulting in code that reads almost like plain English. Plain English form elevates the code’s readability, making it easier to review and understand.

Python promotes writing clean, maintainable code by adhering to the following principle: There should be one–and preferably only one—obvious way to do it. As developers and engineers follow this guiding principle, the team makes sure Python code remains consistent and maintainable, even as it evolves or the team working on the product changes.

Read Also: What Are Descriptors in Python?

Common issues developers face when managing large Python codebases

There are several common challenges developers face when managing large Python codebases, which can be placed in these categories.

• Python performance issues are common due to its interpreted nature, which can make it slower than compiled languages. This becomes more noticeable in larger projects where efficiency is key.
• Concurrency, or the lack thereof, is another challenge. Python’s Global Interpreter Lock (GIL) ensures that only one thread executes Python bytecode at a time, which can limit the effectiveness of multithreaded programs.
• Python’s automated storage management with Garbage Collection can be useful for clearing memory from unused objects, yet it limits manual memory management.
• Finally, Python’s dynamic nature can also be a stumbling block. While a strength in many respects, it can make it harder to catch errors until runtime. Additionally, because Python is interpreted rather than compiled, it can sometimes be slower than languages like C++ or Java.

Still, the language has maintained its place among the most widely used programming languages, according to Tiobe.

Case Studies: Brief examples or case studies highlighting the successful management of large Python projects

There are many large products used by millions of people that were built with this language. Top apps made with Python include Spotify, Instagram, and Reddit are all built with the use of Python. Its syntax allows for focusing more on problem-solving rather than the intricacies of the language. Thus, such products can adapt to market changes or implement updates faster.

Python is also known for its extensive standard library and extensive ecosystem of third-party packages. It allows engineers to elevate pre-existing solutions rather than building everything from scratch, thereby improving productivity and code quality. For instance, Spotify uses Python for data analysis and backend services, making good use of its scientific libraries like NumPy and Pandas.

Finally, it has a vast and active community that contributes to its tips on software development with Python and code enhancement all the time. This way, the language stays updated with the latest trends and provides resources for learning and troubleshooting.

Python’s Perceived Slowness

For decades, we’ve seen a lot of articles with titles like “Why is Python slow?”, “How to speed up Python code?”, “How to make Python run faster?”, “How to optimize Python code for speed”, etc. These concerns view Python in a bad light but also show a lack of understanding of the language. Unfortunately, some people believed it. It has often been compared to other programming languages. Such stereotypes may be harmful when it comes to choosing the language to make a product, as, in fact, there is no  “silver bullet” – the coolest, fastest, and most powerful programming language. If such a language existed, we would all already use it.

Read more: Asynchronous paradigm and Python AsyncIO

So, let’s figure out where all this negativity comes from, and what are the reasons.

Myth vs. Reality: Clarifying Misconceptions about Python Speed and Flexibility

First, we need to define the terms “slow” and “fast”. Both of these concepts characterize “speed”.

Python is a multi-purpose programming language, so it can be used to create all types of projects listed above and many more. Such a notion as software speed has its specifics, nomenclature, vocabulary, and metrics. In this context, it is possible to define software speed as the time for which the algorithm of the given program is executed. However, a more specific definition of speed may be applied in a particular context or case.

If a broader context of development and product were irrelevant, there would be no diversity in programming languages along with the need for it. But there is no “one-code-fits-all” solution for the universal problem of speed since the issues vary based on the context.

Is it a Python problem?

It is not the only language that faces issues to do with speed one way or another. Hence, before delving into particular issues around “speed” with Python, it is important to acknowledge the challenges that do not depend on the language.

1. RPS is not a sufficient metric to assess speed universally. Should RPS be chosen as a speed metric, its specifics, such as nature, synchronicity, database queries, and processes serving, may only add up to confusion. On top of that, this metric is isolated from the product or project purpose, which makes RPS hard to use for efficiency assessment.
2. Bottlenecks may come from the application components rather than the code itself. Usually, web applications consist of many components, each of which can be a point of failure. In most cases, the bottleneck of web applications is the database, not the application code itself.
3. Some “speed” issues are solved with code refactoring, and it’s another shared issue for different languages. As of today, there are different implementations of Python aiming at performance, which are applicable based on the situation.
4. Not all projects need “speed” at all, as the assessment of speed adequacy comes from the context of the product itself.

As the non-specific issues around speed are clear, let’s look closer at the issues to do with Python and see what they are about.

Global Interpreter Lock (GIL) and Multithreading Challenges

The Global Interpreter Lock (GIL) is often cited as a flaw in discussions about “slow” Python. In this language, the GIL ensures Thread Safety by preventing conflicts during memory access (race condition). Despite criticism, the GIL significantly contributes to Python’s popularity, as it simplifies multithreaded programming and reduces the need for an in-depth understanding of complex Computer Science concepts. This simplification, combined with Python’s readable syntax, lowers the entry barrier and allows for a greater focus on solving business challenges rather than on manual memory management. While the GIL imposes limitations, Python still supports multithreading through its standard library and frameworks for writing asynchronous web applications, such as Aiohttp and FastAPI.

Python is an interpreted programming language, meaning that to execute a program, the Python interpreter runs the code on the fly. In contrast, languages like C, C++, and Go compile programs into independent applications that execute without needing an interpreter. Compiled programs are inherently faster due to the absence of this additional processing step. However, interpreted languages offer benefits, such as easier debugging and immediate script execution without the need for compilation.

Dynamic Typing vs. Static Typing. Python employs dynamic typing, where variable types are determined at runtime. This aspect is often discussed in terms of program “correctness” rather than speed. In my opinion, the debate between dynamic and static typing parallels the discussion about interpreters vs. compilers: compilers in statically typed languages can apply optimizations that potentially enhance performance. It’s challenging to directly compare the impact of typing systems due to the multitude of factors unique to different languages. With Python 3.8 and onwards, Type Hinting and the mypy utility partially address “correctness” issues by emulating static typing, although it’s not the same as “honest” static typing at the language level.

Dynamic Memory Management. Python dynamically allocates and frees memory during program execution, utilizing garbage collection to manage memory release. This process consumes additional time and resources, contributing to a “slowdown.” However, let me note that with each new release, these mechanisms are refined, reducing their impact on performance and contributing to Python performance improvements. Today, Python as an interpreter is very different from what it was 10 or 20 years ago. There are many ways to configure garbage collection or completely disable it for a specific case.

There are also other nuances including Python’s extensive use of high-level abstractions, reflection, and the overhead associated with constructs like generators, decorators, and context managers. Indeed, these features enable more compact and expressive code at the expense of performance. Yet, they significantly reduce development time. While these features expedite development rather than execution, they offer an indirect advantage:

Use-case and speed: Better operational flexibility for high-volume video streaming

One of the perfect examples of approaches that make it easier to optimize Python code for speed and enhance performance is Django Stars’ partnership with Multi Media. This streaming streaming and community platform approached the tech team regarding the issues emerging with more than 40k users on the broadcast. The company had a specific problem to solve, documentation, and technology stack idea. At that moment, the company reached the limit of its capabilities in vertical scaling, so it was also necessary to elaborate a new, scaling-friendly, horizontal one.

As a result, the tech team redesigned the solution architecture and introduced smart load balancing and the ability to parallelize the workflow. The technology stack was also enhanced with tools that suit high-load solutions. The platform, thus, is capable of dynamic scaling and handling peak loads better.

Python’s Radiance in Software Development

Despite the perceived slowness, there are clear benefits businesses and developers recognize about Python, which explains its popularity. Here are the highlights by Django Stars on the advantages of working with Python.

Web Development Wonders: Showcasing Django and Flask Success Stories

Web development has witnessed massive growth and evolution, with Python-based frameworks like Django and Flask leading the way. They created a niche for themselves in the realm of web development, each with its own strengths.

Django, a high-level Python web framework, allows for rapid development and clean, pragmatic design. It’s used by some big names in the tech industry, including YouTube and DropBox.

On the other hand, Flask, a micro web framework also written in Python, is celebrated for its simplicity. It doesn’t include any tools or libraries unless explicitly asked for, making it a lightweight and efficient option. Flask has been instrumental in building LinkedIn’s email infrastructure and powering Netflix’s backend services.

Whether it’s building a social media behemoth like Instagram or streamlining email services for a professional network like LinkedIn, these Python frameworks have proven to be reliable allies in the evolving landscape of web development.

Data Science and Machine Learning Marvels: NumPy, Pandas, and TensorFlow, PyTorch

When it comes to data science and machine learning, four Python libraries have become indispensable tools: NumPy, Pandas, TensorFlow, and PyTorch.

• NumPy, short for Numerical Python, is a foundational package that provides support for large, multi-dimensional arrays and matrices. It also offers a broad collection of mathematical functions to operate on these arrays, making it an essential tool for numerical computations.
• Pandas, another powerful library, excels in data manipulation and analysis. It introduces two key data structures – Series and DataFrame – which are instrumental in handling and organizing data in a tabular format. Its robust I/O capabilities make it easy to import data from diverse sources and export results in various formats.
• TensorFlow, developed by the Google Brain team, is a popular framework for creating deep learning models. It allows developers to build and train complex neural networks, facilitating tasks like image and speech recognition, and natural language processing.
• PyTorch, backed by Facebook’s AI Research lab, is a dynamic deep-learning framework that emphasizes flexibility and speed. It is popular due to its intuitive interface and ease of use, making it a favorite among researchers and developers alike.

Scripting and Automation: Revolutionizing Business Processes

Python can simplify the completion of many repetitive tasks. Its simple syntax and readability make it an ideal choice for scripting and automation. Developers can write clear, maintainable code, making Python scripts easy to understand and modify. Some examples of commonplace tasks that can be automated with Python are:

• Managing emails
• Populating PDF and Excel files with data from a specific source
• Converting image files and downloading images in bulk
• Quick math equations or calculating exchange rates
• Accessing and saving data from the internet on a hard drive

Accelerating Time-to-Market

The simplification of processes that come with Python as automation also reduces the time needed to develop an MVP, which makes Python for startups such a popular option. Plus, its support for automation aids in reducing manual intervention, especially in areas like testing and data analysis. Automating these tasks not only saves time but also minimizes the risk of human errors, leading to Python code performance optimization with more reliable product outcomes.

Additionally, Python’s compatibility with other technologies enables seamless integration within many technology stacks. It eliminates the need for time-consuming adjustments when incorporating Python-based solutions into existing systems.

Cross-disciplinary Integration: The Role of Python in Diverse Technology Stacks

It fits into diverse technology stacks with ease, making it a preferred choice for engineers working in varied fields such as web development, data science, artificial intelligence, etc. The simplicity and readability of Python, along with its extensive standard libraries, make it adaptable to different technological environments.

Integration Capabilities: Highlight Python’s ease of integration with other languages and technologies

It easily integrates with other languages and technologies, thus streamlining the development process. Python provides numerous interfaces to libraries and systems written in other languages including C, Java, and .NET. This way, businesses have all the tools to create multi-component systems without significant hurdles.

Such integration capabilities not only simplify the development process but also enhance the efficiency and effectiveness of the final product.

Optimal Strategies to Improve Python Performance

Despite the perceived slowness, this language supports many outstanding products. If it becomes an issue, there are several strategies for Python performance improvement.

What tools and best practices can be used for diagnosing Python performance issues

Typically, the database is the main bottleneck in web applications, not the code itself, and this issue isn’t tied to any particular programming language. Numerous architectural approaches and patterns exist to mitigate this, applicable across any language.

If still, the core issue lies within the application code, refactoring — revising the code — can yield benefits without necessitating a switch in programming language or other tools. Should refactoring fall short, consider performance-focused Python implementations, such as PyPy or Cython.

Usually, issues in code accumulate gradually as a product grows and incorporates new features, resulting in a slow and unwieldy legacy system. This scenario isn’t unique to any specific programming language or toolset. The reason is the absence of all requirements at the beginning, or changes in requirements when the product is in progress.

Python Code Optimization

Code Profiling

The first step in optimizing Python code is profiling. It involves measuring the performance of the code to identify any bottlenecks or inefficiencies. By pinpointing where the code spends most of its time, engineers can focus their optimization efforts on those areas.

Selecting the right data structures and algorithms

The choice of data structures and algorithms can significantly impact the speed and efficiency of your code. Therefore, it is vital to understand the complexity of different data structures and algorithms and choose the most suitable ones for your specific needs.

Collecting metrics of your app to identify real user behavior patterns

Analyze real user behavior patterns, to identify areas of your application that are frequently used or have Python performance issues, and prioritize them for optimization.

Concurrency, Parallelism, and Asynchronous Programming

Concurrency allows multiple parts of the program to run simultaneously, while asynchronous programming lets the code perform other tasks while waiting for a task to complete. These techniques can significantly improve the overall performance of Python applications.

C Extensions, Cython, and Other Acceleration Techniques

These techniques allow you to write critical parts of your code in C or Cython, which can be significantly faster than Python. However, these should be used judiciously as they can make your code more complex.

Real-world Case Studies: Achieving Optimal Performance in Various Development Scenarios

Such approaches were implemented in various projects by Django Stars, like the travel app for PADI. The tech team was going for stable, and easy-to-upgrade solutions with scalability in mind. So, the approach was chosen to ensure the website will easily handle the increase in the amount of users, visitors, requests, and transactions.

Best Practices in Python Development

Keeping the code clean helps to improve Python performance, ensures readability, and simplifies debugging and maintenance processes. In this context, PEP 8 serves as an essential guide for Python developers, as it gives clear coding conventions that promote the production of clean, consistent, and easily understandable code.

Developers emphasize diligent error handling, using built-in functions wherever possible, and avoiding global variables, especially in a long Python code. When it comes to tools and Integrated Development Environments (IDEs) that can enhance productivity in Python development, PyCharm, and Visual Studio Code are two of the most popular choices. These IDEs come with features like intelligent code completion, linting for potential errors, and debugging tools and modules that can significantly boost a developer’s efficiency. Other useful tools for Python for software development include Jupyter Notebooks for data analysis and visualization and Git for version control. The right combination of the available practices allows for creating an optimal toolset for a particular case.

Read more on debugging commands in Python and Django Pdb.

Conclusion

As a Python software development company with many years of experience, Django Stars knows how to optimize Python code for speed or improve the performance of the existing long code. It has all the reasons to choose it for the projects it handles. Despite the seeming “slowness” of the language, tech professionals acknowledge that it is a matter of finding the right solution to a particular problem or request.

If you want a consultation on your Python code performance or need a digital solution for your business, be sure to contact us and get a custom approach for achieving your goals using this now legendary programming language.