Go vs Python: Which to Pick for ML and Web Development Project

Quick Summary

Navigating the landscape of programming languages can be a game-changer for businesses striving to elevate their technical capabilities. The task becomes even more complex when your development team is unfamiliar with the technology at hand. In this guide, we will delve into a comparison of two prominent languages: Golang and Python. We’ll examine their web development and machine learning roles, highlighting their distinctive features and benefits. This analysis will empower you to decide which language aligns best with your project objectives and business aspirations.

What is Go?

Go, commonly known as Golang, was developed in response to challenges faced by Google in the early 2000s. As more users connected to their servers, Google found it increasingly difficult to manage the rising demand. Building large applications like Chrome became time-consuming, often taking hours to compile. Additionally, Google needed a programming language that could efficiently handle vast amounts of text data for web crawling. These needs led to the creation of Go.

Released in 2009, Go is a modern programming language designed to simplify the development of efficient servers and applications that can support high-traffic loads. Drawing inspiration from the C programming language, Go emphasizes ease of use and scalability. With its low memory usage, Go focuses on delivering a straightforward yet powerful tool for developers in search of robust solutions.

Pros and Cons of Go for Web Development and Machine Learning

It’s no surprise that Google developed its programming language, Go. However, like any tool, it comes with its own set of advantages and disadvantages.

Pros of using Go

Go offers several benefits that make it an appealing choice for businesses, especially when compared to other programming languages:

• Easy to Learn: Go is known for its straightforward syntax, making it accessible for beginners. While it may not be as widely recognized as some other languages, its simplicity allows new developers to pick it up quickly.
• Robust Standard Library: Go includes an official library that helps streamline development. This built-in support reduces the likelihood of errors and simplifies many common programming tasks.
• Performance: Go is designed for speed. Its efficient compilation and execution times often outpace many other languages, making it suitable for high-performance applications.
• Growing Community: As Go gains popularity, its community continues to expand. This growing ecosystem provides developers with resources, libraries, and support that can enhance their projects.

Cons of Go

Despite its advantages, Go also has some drawbacks that potential users should consider:

• Learning Curve: While Go is easy to learn, mastering its more advanced features can take time and effort. Developers may find themselves writing more lines of code compared to more concise languages in some scenarios.
• Relative Newness: As a relatively young language, Go might not yet match the capabilities of more established programming languages in certain contexts. This can be a limitation for projects requiring advanced features or libraries.
• Finding Its Niche: Unlike languages such as Python or Java, which have clearly defined roles in the programming landscape, Go is still carving out its niche. This means that it may not be the best fit for every project or application type.

What is Python?

Python is one of the most popular programming languages today, and its popularity is well-earned. It stands out because it allows users to write clear, concise code while also supporting the creation of complex, multi-layered programs. Its ease of use makes it somewhat comparable to Go.

Python first appeared in the mid-1990s and was designed to simplify coding. It focuses on reducing the amount of code needed to develop efficient and reliable software. Its simplicity has made it accessible not just for programmers but also for professionals from other fields. For example, data scientists, network engineers, and accountants use Python to analyze large datasets and perform various tasks, showcasing its broad applicability and user-friendly nature. As a result, many businesses turn to a Python development company to harness these advantages and create tailored software solutions.

Pros and Cons of Python for Web Development and Machine Learning

Python continues to be a top choice among developers, but like any programming language, it comes with its own set of advantages and challenges.

Pros of using Python

Here are some of the main benefits of using Python for your projects:

• Easy to Learn and Use: Python’s straightforward syntax makes it beginner-friendly, enabling developers to write code quickly and efficiently.
• Extensive Libraries and Strong Community Support: Python offers a wide range of libraries for various needs, from web development to machine learning, along with an active community that provides ample support.
• Ideal for Prototyping: Python’s simplicity makes it perfect for rapidly building prototypes and testing new ideas.
• Improved Efficiency: Python is continually improving in terms of performance and, in some cases, is becoming more efficient than traditional languages like Java.

Cons of using Python

However, Python does have its drawbacks:

• Slower Execution: Compared to other programming languages, Python can be slower, which might not be ideal for performance-heavy applications.
• Not Mobile-Friendly: Python isn’t the best option for mobile development, and getting it to work for mobile platforms requires additional effort.
• High Memory Usage: Python can consume more memory than some other languages, which is worth considering when working with memory-intensive applications.
• Limitations with Multi-threading: The Global Interpreter Lock (GIL) restricts Python’s ability to handle multiple threads effectively, which can be a bottleneck for certain projects requiring concurrency.

Why Machine Learning?

AI and machine learning are seeing increased use across various industries. According to the 2024 Stack Overflow Developer Survey, only about 6.5% of developers specialize in areas like big data and machine learning, even though interest in these fields continues to grow.

The demand for machine learning expertise is expected to increase even more. As more businesses incorporate ML technology, they’ll be seeking skilled Machine Learning Engineers, leading to numerous job opportunities and competitive salaries for those with the right skills. It’s a promising field for anyone looking to build a career in this area.

Market Statistics of Go vs Python

According to the 2024 Stack Overflow Developer Survey, Python remains a top language, ranking 3rd with 51% of developers actively using it. Its versatility in web development, data science, and machine learning continues to drive its popularity.​

Go, on the other hand, ranks 13th, is used by 9.5% of developers, and is rapidly gaining traction in cloud infrastructure and backend development. Go’s appeal lies in its efficiency and performance for scalable systems.​

While Python is a general-purpose favorite, Go’s strengths in concurrency and cloud projects make it a growing choice for specific domains.

Go vs. Python for Web Development and Machine Learning: Features

Both Go and Python have specific strengths for web development and machine learning, making them popular for different types of projects. Here’s a unique breakdown of their standout features:

Go Features

• Concurrency for Scalability: One of Go’s standout capabilities is how it handles multiple tasks simultaneously using tools like goroutines and channels. This makes Go perfect for developing highly scalable, efficient applications, especially in areas like cloud computing and microservices.
• Straightforward and Readable Syntax: Go’s simplicity is a major plus. Its syntax is clear and easy to pick up, enabling developers to write and maintain code quickly, minimizing errors, and reducing complexity.
• Cross-Platform Functionality: Go offers excellent cross-platform support, allowing developers to seamlessly run their programs on systems like Linux, macOS, and Windows. This adaptability makes Go a strong choice for teams working across diverse environments.

Python Features

• Object-Oriented Structure: Python uses object-oriented programming, which makes the code reusable and easy to adapt for different applications. This is especially useful in complex software projects where modularity is key.
• Dynamic Typing for Simplicity: One of Python’s core strengths is its dynamic typing, meaning you don’t need to specify data types explicitly. This feature allows developers to focus on the functionality of their code, making it easier and faster to write.
• Massive Standard Library: Python offers a rich ecosystem of libraries that cover everything from web development to machine learning, making it a versatile choice for a wide range of projects. With tools like pip, developers can quickly install and manage packages to expand functionality.

When deciding between Python and Go, it’s essential to consider your project’s goals: Python is highly flexible and simple, while Go excels in performance and scalability, making it ideal for cloud-based and large-scale applications.

Python vs Go for Web Development and Machine Learning: Deep Comparison

When comparing Go and Python, it’s essential to look beyond the technical specifications and dive into how these languages perform in real-world scenarios. Factors like speed, scalability, execution libraries, and overall project efficiency all play a role in determining the best fit for your needs.

Popularity  

Python has been a versatile programming language for over two decades, and it is used across various industries. Nearly 50% of developers globally use it regularly, making it one of the top five programming languages.

In comparison, Go (Golang) has yet to achieve similar adoption. By 2021, about 2.5 million developers had chosen Go, but its popularity declined slightly in 2022.

Python is particularly dominant in North America, India, and Western Europe, while Go is more popular in Asia and Oceania, with over 700,000 developers using it.

Choosing between the two depends on performance, community support, and specific project goals.

Performance 

To compare Python and Go’s performance, complex mathematical equations are often used to test memory management and processing time.

Go, designed with speed and efficiency in mind, outperforms Python in such tests. Its use of “goroutines” allows for lightweight, efficient handling of multiple tasks simultaneously, making it ideal for high-performance applications.

This focus on reliability and speed is why many developers choose Go over Python when building resource-heavy software. While Python remains simpler and versatile, Go excels in tasks where performance is key.

Framework 

Frameworks are essential tools that help speed up the development of websites and apps. Python stands out with its vast selection of open-source, cross-platform frameworks like Django and Flask, which simplify the development process. In contrast, Go doesn’t have a built-in framework specifically for software development.

The use of Golang Web Frameworks depends heavily on the project’s technical requirements. Ultimately, the best choice between Go and Python depends on the project’s needs and the client’s preferences.

Scalability

As applications grow, scalability becomes crucial. Python is popular in both large-scale and startup projects but struggles with parallel processing, affecting performance at massive scales. It breaks tasks into subtasks, which can be resource-intensive.

Go, however, excels with its built-in concurrency, enabling simultaneous task execution, making it ideal for large-scale projects where performance and scalability are key.

In terms of scalability, Go is the stronger choice, especially in resource-demanding environments.

Application Areas

Each programming language has its strengths in specific fields, like web development, AI, and cloud computing. Go and Python differ in their application areas based on their capabilities and supporting libraries.

Python is widely used in:

• Data Analytics
• Machine learning (ML) and artificial intelligence (AI)
• Deep learning
• Web development  

Python’s extensive libraries make it a popular choice in these fields, allowing developers to build complex solutions with ease.

On the other hand, Golang (Go) excels in systems programming but is also used in:

• Cloud computing and cluster computing
• Web development
• Server-side applications
• DevOps  

Go’s strengths lie in its concurrency support, fast performance, and easy-to-use libraries, making it ideal for projects that require speed and efficiency.

Overall, Go’s speed and concurrency make it a solid choice for server-side and cloud-based applications, while Python remains dominant in data-driven and AI-related fields.

Execution

A key difference between Go and Python is how they execute code. Go is statically typed, requiring explicit variable declarations, which helps catch type-related errors and simplifies debugging. In contrast, Python is dynamically typed, allowing types to be inferred at runtime, which increases flexibility but also the risk of undetected bugs.

For large applications, Python’s dynamic nature can lead to errors, while Go’s static typing provides a more structured approach, making it better suited for developers focused on performance and accuracy.

Libraries

Golang, as a newer player in web development, has a more limited library ecosystem compared to Python, which has a long-standing presence. However, Go’s core packages support rapid and efficient development for web applications, lightweight apps, and cloud solutions.

If your project involves large datasets or requires a variety of libraries, Python is preferable. With over 140,000 libraries, Python excels in data science, machine learning, data visualization, and image processing.

In terms of library offerings, Python 3.11 surpasses Go, providing extensive resources for diverse challenges.

Readability

Code readability is vital, especially in team environments. Python is known for its readability, but its flexibility can sometimes confuse due to multiple ways to achieve the same result.

In contrast, Go emphasizes clarity with stricter coding standards, minimizing misinterpretation and providing a specific way to complete tasks.

While Go is primarily used by experienced developers, Python ranks among the top five programming languages, offering substantial community support and resources. This gives Python an advantage, but Go’s structured approach enhances readability for those familiar with its conventions.

Prototyping

Python is an excellent choice for creating software prototypes due to its flexibility and simplicity. Its user-friendly nature enables developers to build a Minimum Viable Product (MVP) quickly, even with limited programming skills or resources.

On the other hand, Go, being a statically typed language, requires more programming expertise. This can make the prototyping process more challenging, even for simpler projects like lightweight authentication microservices.

Machine Learning

Both Python and Go can be utilized for machine learning development, but Python is often seen as the better option in this domain. This is primarily due to its extensive libraries, which provide a diverse range of machine-learning algorithms, model implementations, and prediction tools.

While Go is a robust language, it still falls short in terms of available resources and libraries for machine learning. Additionally, Go’s more complex debugging and stricter typing can make it less attractive for machine learning projects compared to Python.

Deep Learning

When it comes to deep learning, Golang is not as commonly used as other languages. While there are some libraries like GoLearn and Goml, they lack the comprehensive structures needed to handle complex projects effectively.

The limited availability of Go-based frameworks and libraries makes deep learning development more challenging, especially when compared to the rich ecosystem provided by Python.

Data Science

Historically, Python for Data Analysis and Data Science has been the go-to language. Its extensive frameworks and libraries support various fields, including mathematics, artificial intelligence, statistics, and distributed processing, making it ideal for launching data-driven projects.

Although Golang is starting to make inroads into AI technologies, it is still relatively immature in the realm of data science and lacks the necessary community support to compete with Python’s established presence.

When to choose Go and When to choose Python?

Both Go and Python have unique strengths and weaknesses. Here’s when to choose each:

• Use Python for People:

Python is perfect for user-focused applications, such as web apps and data analysis tools. Its simplicity and readability enable quick development and iteration, making it ideal for projects involving user interaction.

• Use Go for Servers: 

Go excels in server-side applications, particularly for high-performance systems like microservices. Its efficiency and concurrency make it suitable for projects requiring speed and scalability.

Companies using Python vs Golang

Companies Utilizing Python

• Google: In addition to developing Go, Google employs Python for various internal projects and services.
• Instagram: The platform relies on Python for its backend, benefiting from its straightforward syntax and rapid development capabilities.
• Spotify: Uses Python extensively for data analysis and backend operations, taking advantage of its extensive library ecosystem.
• Netflix: Leverages Python for tasks involving data processing and machine learning across its services.
• Dropbox: Built its desktop client entirely in Python, valuing its ease of use and speed in development.

Companies Utilizing Go

• Google: As the creator of Go, Google incorporates it into numerous high-performance applications.
• Uber: Employs Go for its microservices architecture, allowing efficient management of large volumes of data and requests.
• Trello: Chooses Go for its backend infrastructure, capitalizing on its performance and scalability.
• SoundCloud: Utilizes Go for backend systems, focusing on efficiency and concurrent processing.
• Docker: Developed using Go, which suits its need for creating containerized applications effectively.

Python is often chosen by businesses that prioritize web development, data analysis, and quick prototyping due to its rich library support and user-friendly nature. Go is preferred for applications that require high performance, scalability, and efficient concurrency, making it suitable for microservices and systems programming. The decision between Python and Go ultimately hinges on the specific requirements of your project and the development ecosystem you aim to work within.

Conclusion

Both Go and Python are strong contenders for web app development, each offering distinct advantages. Go is known for its reliability and efficiency, making it perfect for high-performance applications. On the other hand, Python excels in flexibility, particularly for developing comprehensive solutions like ERP systems that manage everything from business operations to data analytics.

To find the right technology stack for your project, start by defining your software architecture based on your specific needs. This will help you decide whether Go or Python is the better fit for your goals.

If you’re tackling a machine learning project and debating whether to use Go or Python, our team of experienced Machine Learning Engineers is here to help. We can guide you in making the best choice and achieving exceptional results. For expert support, consider Hiring Python Developers from Tagline Infotech, a leading Python Development Company to ensure your project’s success.

FAQ’S: