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Go vs Python: when do you use which language? We look at the differences and use cases for the two languages.(In Detail)

For a development job, can’t you decide whether you should go for the speed of Go or the ease of Python? No problem, that is not always immediately clear. We go through the pros and cons of each with you.

Regarding ease for developers and rapid development, there are two programming languages ​​that stand head and shoulders above the rest: Python and Go. Python is a welcome guest at development that deals with scripting, devops, machine learning and testing, while Go is used for a new wave of container-based cloud-native applications. Sometimes the choice is obvious: Python for a rich ecosystem, Go for fast execution speed. But sometimes it is more subtle and not immediately clear. In this article we go through the differences and highlight the plus and minus points to help you make an informed choice if you want to perform a task.

Go vs. Python: Ease of development Python and Go both have a reputation for simplicity. They both have a simple and easy syntax and a feature set that is small and easy to remember. Python and Go both have a short cycle of write-compile-run. There is no compilation phase in Python – it has an interpreter – so scripts are executed almost immediately. Go compiles in advance, but the compilation phase is a lot faster than with languages ​​such as C ++. Go feels more like a scripting language than a language that is per-compiled.

Python uses dynamic typing, which makes it easy to quickly create prototypes of applications. Typing elements with descriptions is optional and can be added to ensure that the program interprets correctly (which is a good idea for large projects) but it is not required. However, large code bases become unmanageable if you do not specify any types. In the case of Go, typing is strict, but is easily distracted in most cases, so it is less cumbersome. That also means that larger codebases are easier to manage from the outset, because Go developers have the tradition of using typing skills. The flip side is that Go lacks genericity, so code that could be described in shorter languages ​​in other languages ​​- including Python – becomes more extensive and more descriptive in Go.

Go vs. Python: Speed ​​of the runtime If there is one area where Go easily beats Python, it’s the speed. Go runs much faster than Python, even without developers’ optimizations. Go compiles directly to native machine language, while the Python runtime is more difficult to optimize for speed. This does not alter the fact that Python is quick enough for typical tasks, so it is worth making a benchmark of a Python implementation of your use case. The most performance-intensive tasks that Python is used for are not performed in Python itself, but via libraries written in C or C ++. The PyPy runtime, an alternative to the more traditional CPython runtime, can also provide a speed boost to applications such as web servers, even in places where Python’s scripting-like elements are heavily used.

Go vs. Python: Roll out Go was designed from the start to be able to roll out compiled apps as stand-alone binaries for multiple platforms. This is in sharp contrast to Python; Designed as a scripting language, the Python runtime is required to run programs. Python does not have a native solution for running a script as a stand-alone executable, but you can go to a third party library such as PyInstaller for this functionality. Container solutions such as Docker also make it easier to package a Python app together with the runtime.

Go vs. Python: Project management Another thing that immediately goes into Go: modern software management. Quick commands in the command line create a new Go project repository and manage the dependencies. It should be noted that Go has not always provided the best support for dependencies and reproducible builds, but the module system introduced in Go 1.11 provides a shared mechanism for working with different versions of libraries. Python has the exact opposite problem in a number of areas: a wide range of project management and version tools creates confusion about which methods are best for a given task. The positive side is that you are flexible enough that you do not have to work in the way that is enforced by specific tooling.

Go vs. Python: Asynchronous programming Asynchronous operations – performing one task while waiting for another – help I / O-focused code, such as network services, to run more efficiently. Go supports async native in its goroutines, a syntax feature. With goroutines you can perform small operations in parallel with a native communication data type, channels, to synchronize the operations. Go also has tooling to prevent accidental misuse of the features: you can still write code with a race condition or deadlock, but it is easy to catch the most common errors. Python also recently received language-level support for asynchronous behavior with the keywords async / wait. Asynchronous programming was possible before, but not so clear. That means that library support for modern async use is not as advanced as it could have been because it is such a late arrival in the language. But more libraries are now becoming aync compatible and the versions of Python that do not have it will soon lose their support.

Go versus Python: Dealing with errors and debugging The two languages ​​have fundamentally different philosophies about how to deal with errors. In Python, errors are first-call objects and they pop up when the application encounters an exception. Making an exception is therefore optional and the developer must determine whether these cases are collected and dealt with. That means flexible error handling that doesn’t make every call messy with possible exceptions. In Go it works very differently: each function gives the value of the function itself and a possible error object. Go programs usually describe very specifically which cases can cause errors in the function itself, so that its handling is clear in advance. The disadvantage is that you write more code. Go also has panic / recover to deal with extreme cases that should not cause the program to crash, but you should not use them as extensively as exceptions in Python. In Go 2.0, new methods could be added that yield less descriptive code, but that revision is not nearly finished yet. Go versus Python: Testing Not all modern software development uses unit and integration tests, but projects that do are generally more robust. Python and Go both offer native mechanisms for unit testing. At Go there is the native package testing and at Python there is the framework unit test. At Go you can set what is included in testing, while at Python you need third party package coverage for that. On the other hand, Python has very flexible built-in test options – for example, assertions that check a very wide range of common cases, including emerging exceptions. Python uses a special class to distinguish test code from application code, while Go uses common functions and file names. Go versus Python: Ecosystems Over the years, both languages ​​have collected an impressive number of libraries of third-party software that illustrate their strengths and weaknesses. Python is often the go-to for scripting and automation and is used for building web services and simple interfaces between complex systems. Because of the latter application, the language has become so large in machine learning and data science: Python makes it easier to glue together large, complex libraries and workflows that are used in data analytics and machine learning models.

At Go, the success story is in asynchronous programming and its native speed. Web servers, network applications, CPU-bound micro services and system tools are all wonderful candidates for Go. Most software that supports modern, container-based application development – including Docker and Kubernetes – are written in Go. One way to look at which of the two you need is to be informed by existing Python and Go projects that are similar to what you are doing. There is a good chance that much of what you are trying to do has already been developed, so you choose not only a language, but also the supporting libraries that exist for it.

Go vs Python: when do you use which language? We look at the differences and use cases for the two languages.(In Detail)
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