Difference Between Golang Vs. Python
Python is a general-purpose programming language. Go is the programming language, also known as Golang, created by Google in 2009. Python is an excellent language with an easy to understand syntax and hence more readable, flexible. In contrast, Go is also in the prime league when it comes to clear syntax, which holds zero unnecessary components.
Difference Between Golang Vs. Python
Golang inculcates many features of other modern languages, such as method and operator overloading, pointer arithmetic, concurrency, channels, and type inheritance. The product, in the end, was the statically typed language with a refined and robust library, and unmatched performance and speed.
Python is that it is the interpreted language, which means that the written code is not translated to the computer-readable format at runtime; most programming languages do this conversion when a program is being compiled. This kind of language is also referred to as the “scripting language” because it was initially meant to be used for small projects.
You don’t have to compile your Golang code to run it because It will be automatically compiled and run. The main strength of Golang is that it’s minimalistic and fast. Golang is not a scripting language like Python, people do write the lot of scripts with it.
Go can act as a potent tool when it comes to micro-services, web-programming, or mobile application development. In many use cases, Golang web development has proved to be more rapid than Python.
Let’s compare two languages on the following points.
Go is extremely fast when it comes to performance. The performance is similar to that of Java or C++. Go is typically 30 times and in sometimes 40 faster than Python. Here’s a small benchmark game comparing Go vs. Python.
Python is the great language, but its performance is pretty sluggish for use cases such as serialization or deserialization, ranking, and aggregation.
As you know, Golang was built by Google, which has hundreds of thousands of servers. That is why Golang has inbuilt support for concurrent process channeling i.e., concurrency. Golang is built for scalability since its product of Google and proven the scalability in their projects. Python, on the other side, has a tough time with a concurrency but can implement parallelism through threads.
Concurrency and Channels
Concurrency means that the app is making progress on more than one task at the same time. Golang is innovative with Goroutines and channels. Goroutines are Go’s lightweight approach to threading, and channels are the favored way to communicate between goroutines. Goroutines are very inexpensive to create and only take a few KBs of additional memory. Because Goroutines are so light, it is possible to have hundreds or even thousands of them running at the same time. You can communicate between the goroutines using channels. The Go runtime handles all the complexity.
The goroutines and channel-based approach to concurrency make it very easy to use all available CPU cores and handle concurrent IO: all without complicating development. Compared to Python or Java, running a function on a goroutine requires minimal boilerplate code.
Golang is mostly used for systems programming. Due to its support for concurrency, it has also found a reasonable amount of use and acceptance in the cloud computing or cluster computing field.
Python has been widely used in a field of AI, data analytics, deep learning, and web development. This can be most recognized by the crazy libraries that are available in Python that make life in those fields a whole lot easier.
Let’s see how Go and Python code is executed. So first of all, Python is the dynamically typed language, and Golang is a statically typed language. Python and Golang use an interpreter and a compiler, respectively.
Golang is a statically typed language one where variable types are declared explicitly for the compiler; hence even trivial bugs are caught easily while in a dynamically typed language type inference is implemented by the interpreter thus some bugs may remain, due to the interpreter misinterpreting something.
Packages and Libraries
Python takes the cake in the sheer amount of available libraries. There are packages like Numpy that help you with array handling and complex matrix functions, Tensorflow, and Scikit Learn for Deep Learning, for image processing, Pandas for Data Analysis, matplotlib for visualization and the list goes on and on. There are millions of libraries available in Python.
Golang does not have as many libraries as Python, but they do provide insane libraries for web development, concurrent programming, database handling, and encryption too. Golang has excellent support for the tools we use. Robust libraries were already available for Redis, RabbitMQ, PostgreSQL, Template parsing, Task scheduling, Expression parsing, and RocksDB.