Go vs. Python: What is the difference in 2024?

Python's clean syntax and rich ecosystem make it a popular general-purpose language. Go, on the other hand, offers a mix of simplicity and performance, positioning itself as an efficient language for systems programming and concurrent tasks.

Let's explore the features and applications of these languages to help you choose the best one for your next project.

What is GO?

Go is an open-source, statically typed, and compiled high-level programming language. It was first released in 2009 by Google. The designers of this language aimed to create a language with the following characteristics:

• Static typing and run-time efficiency (similar to C)
• Readability and usability (similar to Python)
• High-performance networking and multiprocessing

Go is used for a variety of software development purposes, including:

• Cloud & network services: Go uses a strong ecosystem of tools and APIs on major cloud providers to easily build services.
• Command-line interfaces: Go can be used to create fast CLIs using popular open-source packages and Go's standard libraries.
• Web development: Go powers fast and scalable web applications with its efficient memory management and support for several IDEs.
• DevOps & site reliability: Go has fast build times, lean syntax, and automatic formatting, making it ideal for both DevOps and SRE teams.

Go pros and cons

Let’s take a quick look at the pros and cons of Go.

Now, let's explore a brief explanation of each of the pros and cons.

Pros 👍

• Easy to learn: Go's syntax is easy to learn and understand, making it a good choice for beginners. It is also easy for developers familiar with C++ and C# to master Go.
• Speed: Go programs run faster because Golang code is directly converted into machine code before execution. This results in much faster performance compared to interpreted languages like Python.
• Concurrency: Go excels in handling multiple tasks concurrently because of its built-in concurrency model. It utilizes lightweight threads (goroutines) and channels for communication and synchronization.
• Garbage collection: Go offers automatic memory management that frees developers from manual memory allocation and deallocation

Cons 👎

• Lack of libraries: Go is a relatively new language with limited library support compared to established languages like Java or Python, which can make it challenging to find and use third-party libraries for certain tasks.
• Small community: Go has been around for a while, but the Go community is still relatively small compared to other programming languages. As a result, there are fewer tools and resources available for Go developers, which can make it more challenging to find support or collaborate on projects.
• Learning curve for advanced topics: Although Golang is a simple language, it has some unique features that may take time for developers to learn and understand. These include Golang’s concurrency model, strict typing, and error handling.

What is Python?

Python is a powerful and versatile high-level, general-purpose programming language. Python is an older yet popular language, created in 1991.

Its minimalist syntax emphasizes code readability, making it ideal for beginners and experienced developers alike. Python’s dynamic typing and its focus on simplicity enable rapid modern application development.

Here are some of the common areas where Python is extensively used:

• Data science and machine learning: Python is the most popular language for data analysis and building ML models.
• Automation and scripting: Python is used a lot in automating repetitive tasks such as file management, data processing, or web scraping.
• Artificial intelligence: Python's libraries, such as TensorFlow and PyTorch, have contributed to its popularity in developing AI applications.
• Software development: Python is a versatile language used across the software development lifecycle. It is well-suited for prototyping, developing web applications, creating desktop applications, and more.

Python pros and cons

Let’s take a quick look at the pros and cons of Python.

Now, let's explore a brief explanation of each of the pros and cons.

Pros 👍

• Easy to use: Python is well known for its simple and easy-to-read syntax, making it a great first programming language for beginners.
• Versatility: Python is incredibly versatile. The vast ecosystem of libraries and frameworks cater to various domains, including data science, machine learning, scripting, and web development.
• Fast development. Python is an interpreted language. Developers can quickly change and execute code without needing to compile it every time they make changes.
• Large community: One of the greatest strengths of Python is its large and active community, which contributes to the growing ecosystem of libraries and frameworks.

Cons 👎

• Slower execution speed: As an interpreted language, Python generally executes slower than compiled languages. It's not ideal for applications where speed is critical.
• Memory management: Python has higher memory usage due to dynamic typing and in-memory bytecode interpretation, making it less suitable for memory-intensive applications.
• Mobile development: Python is not as widely used for mobile app development as Java or Swift. Although Python can be utilized for mobile development, it has fewer libraries and some limitations compared to native mobile development tools.

What is the difference between Go and Python?

Go and Python are powerful programming languages, each with their strengths. Here's a table summarizing the key differences between Go and Python:

Go vs. Python performance and speed

Go, being a compiled language, provides superior performance. The compilation of Go code results in binary executables that can be executed directly, minimizing runtime interpretation.

Python, on the other hand, is an interpreted language, meaning that the code is interpreted at runtime, which leads to slower performance.

Benchmarks show that Go code can be 4-10x faster than similar Python code, particularly for CPU-bound tasks. This performance gap widens with larger codebases because Go compiles the entire program into machine code, while Python interprets code line by line.

Go vs. Python syntax

While Go offers better performance, Python is one of the easiest programming languages to learn. Python also has an extensive community and plenty of learning resources, which makes it a great choice for beginners.

Go uses curly braces ({}) to define code blocks. Variables need to be explicitly declared with their data types, which promotes static typing and helps catch errors early in the development process. However, this can make the code more verbose compared to Python.

Python, on the other hand, uses indentation to define code blocks. This approach is concise and promotes readability, but it can introduce errors if not strictly followed. Python is dynamically typed, meaning variable types are inferred at runtime. This provides more flexibility but can potentially lead to runtime errors.

Go vs. Python use cases

Go excels in building performant and scalable backend systems. Its speed, concurrency features (goroutines), and built-in networking tools make it ideal for developing microservices, command-line interfaces (CLIs), network programming, and more.

Python, on the other hand, is popular across various fields due to its rich libraries and frameworks. It is widely used in data science, machine learning, artificial intelligence, scripting, automation, web development, and more.

Go vs. Python in web scraping

Python provides many libraries that simplify the scraping process. Some of the most popular libraries are Requests, BeautifulSoup, and Scrapy.

While Go also has libraries for web scraping, such as colly for scraping and goquery for parsing HTML, they are generally less mature and have fewer features compared to their Python counterparts. However, Go libraries are improving rapidly and can be a good choice for performance-critical applications.

Additional libraries and tools are necessary to enable Go and Python to render JavaScript. Python libraries like Selenium, Playwright, and Go's Chromedp, allow you to render web pages and execute JavaScript.

Python's rich libraries, clear code, and abundant resources make it the best choice for beginners or projects where speed isn't crucial.

Go, on the other hand, is suitable for large-scale scraping because of its great performance and memory management.

Go vs. Python in machine learning

Python is considered the top machine-learning language. So, why should you consider Golang for ML? The Go programming language offers several advantages: performance, concurrency, and the ability to compile the code into a single binary.

When you compile a Go program, it creates a single, self-contained executable that includes all necessary dependencies, eliminating the need for dependency management during deployment.

Go's machine learning ecosystem is still under development, with libraries like GoLearn and TFGo lagging behind Python's established tools in terms of features and community support.

Python's ease of use, a large ecosystem of libraries and tools, and strong support for data science tasks make it the go-to choice for most machine-learning projects.

Go, on the other hand, is a faster and more performant language and can be a better choice for projects that require high performance or concurrency.

When to use Go and when Python?

Choosing the right programming language for a project can significantly impact the efficiency of development, scalability, and performance. Here's a quick guide to help you decide between Go and Python based on their strengths and ideal use cases.

When to use Go

• When a static, strongly typed language is preferred.
• When your project involves heavy concurrency or requires high-performance parallel processing.
• If your project involves building APIs, microservices, or networked applications.
• When you need low-level features and efficient runtime for system programming tasks.
• When your project requires simplicity and easy deployment with minimal dependencies.

When to use Python

• When an easy-to-learn language with a simple and readable syntax is preferred.
• When you need abundant community support and readily available resources.
• If performance is not a critical factor and you prioritize development speed and ease of use.
• If the focus is on applications involving data analysis, visualization, and AI/ML projects.
• In projects that require extensive libraries and frameworks for web development, data science, and machine learning.

Frequently asked questions

Is Go faster than Python?

Yes, Go is faster than Python due to Go's statically typed compiled nature, offering great performance. Python, on the other hand, is typically slower because of its interpreted nature and dynamic typing.

Is Go better than Python?

No, Go is not necessarily better than Python. They excel in different areas. Go is excellent in performance and concurrency, while Python excels in readability, ease of learning, and rich libraries for various tasks.

Is Go similar to Python?

No, Go and Python are not similar. They have significant differences in syntax, paradigms, and use cases. Python focuses on readability and simplicity, supporting a wide range of applications, while Go is faster and better for systems-level programming and large-scale distributed systems.

The verdict

Python's clean syntax, large ecosystem, and rapid development make it an excellent choice for data analysis, machine learning, web development, scripting, or system administration tasks. Its vast libraries and large community support help developers efficiently achieve their goals.

Go, on the other hand, is excellent for concurrency and system-level programming. Its compiled nature and concurrency support make it an ideal choice for developing high-performance applications and microservices.

Ultimately, the best language choice depends on your specific project requirements. By evaluating the characteristics outlined above, you can make an informed decision on which one to use for your next project.