Is Go a Better Data Mining Language Than Python?
Let’s examine Go and Python to see how they match different data mining applications and what makes people disagree about which is superior.
Both Go and Python have widely used programming languages for data mining. They both have their own set of advantages and disadvantages. Nonetheless, there is always the question of which of these languages is superior. So let’s compare them to see how they match different data mining applications and what it is that has folks split on which is better.
What Is Go?
Google introduced Go in 2007 as a functional, streamlined alternative to the more difficult C++ programming language. Go was built from the ground up to support concurrency on multi-core computers, making it ideal for networking and infrastructure applications. Go is an open-source programming language with built-in memory safety, garbage collection, and CSP-style parallelism, making it a better alternative to Python, Java, and other programming languages.
Data scientists who need to design applications for large-scale infrastructure use the language frequently. Go is also utilized in DevOps and site reliability automation, and developers frequently use it for robotics and gaming software. All of this makes Go a stronger platform for cloud-enabled APIs and server-side development. Go is also fantastic for efficient dependency management since it offers concurrent functions like goroutines and channels that allow the remainder of the program to compute while they execute.
Furthermore, because Go is a statically typed language, you must declare your variable data types before using them. A statically typed language will not compile unless all variable types are defined correctly. This is why conversions and compatibility are much easier with Go, and you won’t have run-type problems.
What Is Python?
Python is an easy-to-learn procedural language that’s ideal for beginners who wish to master the fundamentals of coding.
Python, which was created by Guido van Rossum in 1991, has been around longer than Go. It has a wide range of syntax, large libraries, and a variety of frameworks. Because it’s been around for so long, it’s experienced various iterations, including Python 2 and Python 3. The transition from Python 2 to Python 3 was a shambles, with numerous backward compatibility concerns. However, any new project should be written in Python 3 today, as practically all third-party libraries have been updated to Python 3.
Python has really made a name for itself in the field of machine learning. Pandas, TensorFlow, Scikit-Learn, and PyTorch, among other specialized libraries and Deep Learning frameworks, have emerged as the de facto tool for ML researchers.
Comparing Go and Python
Most data scientists will tell you that Go is fantastic, but even if Python were flawless, they would never select Go over Python for anything. This is due to a variety of factors. Python is easier to learn, has a large ecosystem of third-party libraries, and a large community of users.
When you need speed, though, you turn to Go.
Python is a fantastic programming language for working with websites. However, if you require an application that relies on concurrency to improve throughput, Go is the language to choose.
A short diagram provides an overview:
Is speed the most important factor for you? Go is quick, but there are other factors to consider before deciding which language is best for you. Let’s take a look at a couple of them.
1. When it comes to emotion mining, we use ML software in real-world, practical commercial scenarios to analyze sentiment in data. You’ll need a programming language that makes data refining, string manipulation, and matrices simple. Unlike Go, which has limited flexibility, Python makes this possible with simplicity.
2. Go is memory-friendly. That’s a huge plus. If you need to work with complicated logic and a big number of objects in memory across large-scale network servers and even larger distributed systems, Go can help.
3. Concurrency is a feature of Go. It can tackle a variety of activities at the same time, increasing its speed and efficiency. Python does not allow for this.
Why Did I Choose Go?
Go has never been particularly well suited to data mining and munging. It can be difficult to read a.csv file containing heterogeneous data, which is common in social media listening and voice of the customer data analysis. It also lacks a REPL environment, which is required for exploratory data analysis and can speed up data munging in Python. Despite this, we opted to move a significant portion of our code to Go.
Despite its differences from Python, I discovered that Go still treats first-class objects as first-class objects, which is a win for functional programming. Do you want to take your project to the next level? It’s no problem. Furthermore, goroutines and channels provide you more control over memory allocation. Our API handles thousands of pages, and we discovered that once we switched to Go, it used a quarter of the RAM it did when it was written in Python. Despite all of this, static typing provides a performance advantage. Furthermore, Go is designed to work in cloud-based systems.
There is no definitive solution in the end. Both languages are fantastic in the contexts in which they are used, and they are here to stay. If you’re building machine learning models for network security and fraud detection, you’d be better off using Java rather than Python. Python, on the other hand, is the language to use for sentiment analysis. If you’re a seasoned programmer who is concerned about speed and scale, Go offers these and many more benefits.
Examine your requirements, choose your priorities, and try out both languages. You won’t have any trouble learning Go if you’re already familiar with Python. Even though Python has a far larger community, Go is gaining traction. It already contains a number of libraries and modules that are extremely useful to newcomers. In addition, AWS, Azure, and Google, of course, provide fantastic assistance.