Go vs Python: Which Language Should You Use for Your Next Project?

Go vs. Python? Which programming language should you use for your next project? It depends. To help you decide, we’ll break down their syntax, use cases, features, and more. But first, let’s focus on what they are.

What is Go?

In the mid-2000s, Google was running into a problem. An increasing number of users were accessing their search servers, and Google was having a hard time keeping up the pace. Google was also building massive applications like Chrome that took hours to compile. If that were not enough, Google crawls through a LOT of web pages—and they needed a language that could efficiently process strings of text. Those challenges led them to develop Golang, also known as “Go.” 

Go was released back in 2009—so it’s relatively new. It was created based on the C programming language, with the express purpose of programming scalable servers and applications with huge amounts of throughput. In addition to scalability, Go emphasizes ease of use and low memory overhead. In short, when you think of Go, think of scalability, simplicity, and efficiency.

What is Python?

Hands down, Python is one of the most popular languages out there—and there is good reason for that. Python has the unique ability to create concise and readable code, yet still allows users to create complex, multi-tiered applications. In many ways, it’s similar to Go.

Python dates back to the mid-’90s. It is an object-oriented programming language designed to minimize the overall code required to write fast and robust software. Due to the fact Python is so easy to learn, many non-programmers will use it for their profession. For example, data scientists, network engineers, and even accountants will use this language to parse and analyze huge amounts of data. 

Go vs. Python: Syntax

Let’s first look at how to write for a loop in Go, then we’ll do the same thing in Python. Executing either of these methods will do the same thing: It will print numbers 1 through 6. After that, we’ll compare and contrast the two syntaxes.

Go Function Example

First, let's look at an example of a function in Go.

func main() {
    const s string = “the number is “
    i := 1
    for i <= 3 {
        fmt.Println(s + i)
        i = i + 1
    }
}

Python Function Example

Here is the same thing in Python:

def main(): 
    s = “the number is ”
    for x in range(1,7):
          print(s + x)

Since Go is based on the C programming language, it will have much of the same syntax as C. That’s why Go relies on demarcating segments of code with a curly bracer. Python, on the other hand, relies on spaces. Generally, each segment of a code is four spaces (or a tab) to indicate it is a new line. 

For large amounts of code, Python’s lack of syntax can make it easier to read for new programmers. (Check here for a closer look at Python syntax). However, a negative is that often, code will not compile because the lines are not properly indented, and that can quickly become a pain in the neck. 

Another thing to notice is that in Go, the user is able to declare a variable as a constant, which makes it immutable. This increases Go’s complexity slightly but assists in code readability—because now a developer reading the code knows for a fact that a value will never change. Python, by contrast, does not have the concept of a “constant” variable; they are all mutable. This idea makes for cleaner code, but it makes it more difficult to know what the code is doing.

This barely skims the surface of the syntactic differences between Go and Python. However, the big takeaway is this: Python is more readable, but less concise than Go. Go has a higher learning curve, but provides a couple more syntactic features to make the code easier to troubleshoot.

Go vs. Python: Features

Let’s take a look at three major features of each language. Let’s first look at the three broad features of Python first, and then Go.

Python Features

• Object-oriented. Everything in Python is an object. This makes coding Python modules, reusable and easily imported into different applications. It is far easier to understand how data is represented in an object-oriented environment.

• Easy to learn and readable. Unlike Go, Python is a dynamically typed language. There is no need to specify a data type, which allows for more flexibility when writing code and allows a variable’s type to change on the fly.

• Massive standard library. The Python library is seriously huge. Not only that, but it has an extensive dependency manager called pip that allows you to use vast amounts of libraries for data analysis, graphing, and much more.

Go Features

• Concurrency. Hands down, the best feature of Go is its advanced scalability and concurrency features. Go is designed to be lightweight and to take advantage of Goroutines and channels to create sleek, responsive streaming services and web applications. 

• Simple syntax. One of the best features of Go is one most people will notice immediately—it is surprisingly simple to read. This reduces developer ramp-up time and the time required to maintain the software.

• Cross-platform support. Go can be used anywhere it needs to be, whether it is Linux, Mac or Windows. This is as opposed to other languages like Swift, PowerShell, or Visual-Basic. These languages are pretty much restricted to their respective architecture.

Go vs. Python: Use Cases

Python is a versatile programming language with a wide breadth of uses. Let’s go over three great use cases for each language, starting with Python.

Python Use Cases

Let's delve into the best cases for utilizing Python.

• Web development. Web applications are one of the most popular use cases for Python. Web frameworks, such as Django and Flask, make it easier than ever to create a cohesive front-end and back-end for any web app. Python's ease of use, fast development cycle and large community of developers make it a great choice for web development.

• Data science and machine learning. If your use case is big data, no language is better than Python. Its in-depth libraries for data manipulation and analysis make it a great choice for data science and machine learning. For example, Pandas and NumPy are used extensively in multiple industries for data analysis. Python’s machine learning libraries like TensorFlow, Keras, and PyTorch are widely used by developers for building and training machine learning models.

• Scientific computing. Python's libraries like SciPy, Matplotlib, and Pandas make it a popular choice for scientific computing. Python's ease of use and rich set of data manipulation and visualization libraries make it a great tool for researchers and scientists who need to analyze and visualize data.

Python Use Cases

Now, let’s take a look at Go.

• Web servers: One of the great features of Go is that it has a built-in library solely for HTTP interactions. Combine this with its hallmark concurrency and lightweight framework, and you have the perfect language for writing a backend server.

• ETL (Extract, Transform, Load): An ETL application takes data from several existing sources and loads them into a central data lake for analysis. This operation will handle billions of records potentially. The use of channels and Goroutines make this the perfect project to tackle using Go.

• System Utilities: Go is a great language for low-level manipulation. For example, if your organization needs to write a command-line interface to analyze network traffic, build code scaffolding, or more, Go is the way to go. There is already plenty of precedent for doing so, such as GoTTY, ngRok, and Caddy, all of which are CLI’s written in Go.

Go vs. Python: Future Prospects

Both of these languages have a very bright future. Both Go and Python have a vibrant community and ever-growing libraries to meet your needs. However, if you want to go where the money is, then Go is probably your best bet. 

The average Go developer makes about $89,000 a year, while the average Python developer makes around $71,000 a year. A big part of this is because Python is used so often in academic studies, such as machine learning, AI, and data analytics. Go, on the other hand, is generally used to create highly scalable servers to process millions of requests.

Final Thoughts

As you can see, we covered a lot of ground here. So let’s sum up what we have learned.

Go is a programming language developed by Google to be simple to use and highly scalable. Go is perfect for creating backend servers, ETL operation, system utilities, and more. The rule of thumb is this: If you expect your app to need a great deal of data processing, string manipulation, concurrency, or all of the above, go with Go.

Python was created to be readable and simple and to serve as a general-purpose programming language. Python is incredible at building simple scripts to manipulate data. It is the perfect language for beginners too. There are plenty of resources out there to get you started on data analytics, which is one of Python’s many specialties. For Python, the rule of thumb is this: If you want to create a web application, an AI app, machine learning, data analytics, or you just want to learn how to program, then Python is the best language there is.

If you are ready to get started with either or both, check out these programming and development courses.

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