Is Go Better Than Python For Data Mining?
Let's compare Go and Python to see how they fit different applications of data mining and what it is that has people divided over which one is better.
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Go and Python both are popular data mining programming languages. They both have their own pros and cons. Yet, somehow, there is always this question about which one of these languages is better. So let’s compare them and see how they fit different applications of data mining and what it is that has people divided over which one is better.
What Is Go?
Developed in 2007, Go was introduced by Google as a functional, simplified alternative to the more complicated C++. Go was designed from the outset for concurrency via multi-core processors, making it well suited for networking and infrastructure environments. An open-source programming language, Go was created with improvements over Python, Java, etc., with in-built memory safety, garbage collection, and CSP-style concurrency.
The language is very popular among data scientists who need to develop programs for large-scale infrastructure. Go is also used in DevOps and site reliability automation, and it’s not uncommon for developers to use Go for robotics and gaming software as well. All this makes Go a better base for Cloud-enabled APIs and on the server-side of things. And because Go has concurrent functions like goroutines and channels that let the rest of the program compute while they run, it is great for efficient dependency management.
Further, Go is a statically typed language, which means you need to declare your variable data types in advance before you apply them. When a language is statically typed, it doesn't compile unless all variable types are defined as expected. This is why when you write in Go, conversions, and compatibility are much easier and you don’t face run-type errors.
What Is Python?
Python is a procedural language that is easy to learn and is great if you’re a beginner and want to get a good grasp of coding concepts.
Python has been around longer than Go, having been developed in 1991 by Guido van Rossum. It has a versatile range of syntax, sprawling libraries, and numerous frameworks. And because it’s been around so long, it has seen multiple versions of itself in the form of Python 2 and Python 3. The migration of Python 2 to Python 3 was a messy one, introducing many backward compatibility issues. But any new project today should be done in Python 3 as almost all 3rd party libraries have now been migrated to Python 3.
Where Python has really established itself is in the realm of machine learning. Specialized libraries and Deep Learning frameworks like Pandas, TensorFlow, Scikit-Learn, and PyTorch have emerged to become the de facto tool for ML researchers.
Comparing Go and Python
Most data scientists will tell you that Go is great but if Python was a 100% perfect language, they’d never choose Go over it for anything. There are a number of reasons for this. Python is simpler, perfect for beginners, has a huge ecosystem of 3rd party libraries, and tons of community support.
And yet, when you need speed, you turn to Go.
If you’re working with websites, Python is great. But if you need a program where concurrency is crucial to improving throughput, Go is the language of choice.
A quick chart gives you an overview:
Is speed everything you’re looking for? Go is fast but you need to consider other criteria before you decide which language is better for you. Let's examine a few.
1. In terms of emotion mining, when we’re analyzing sentiment in data, we are using the ML program in real-world, practical business settings. You need a language that allows easy refinement of data, string manipulation, and matrices. Python allows this with ease unlike Go, which doesn't offer much flexibility.
2. Go is memory efficient. And that’s a big plus. If you need to use complex logic and numerous objects in memory as you work across large-scale network servers and even larger distributed systems, Go offers you an advantage.
3. Go offers concurrency. It can handle several heterogeneous tasks simultaneously, which adds to its speed and efficiency. This is not possible with Python.
Apart from all these, I made several observations when I migrated a large part of our code at Repustate from Python to Go. You can read them here.
Why Did I Choose Go?
Historically Go is not really apt for data mining and munging. When you need to parse a .csv file with heterogeneous data, which is often the case in social media listening or voice of the customer data analysis, it can be a challenge. It also doesn't have a REPL environment like Python has, which is necessary for exploratory data analysis that can expedite data munging. And yet, we decided to shift a big chunk of our code to Go.
I realized that despite being different from Python, Go still functions as first-class objects, and that’s a thumbs up for functional programming. Do you want to scale up to mammoth projects? No problem. Additionally, goroutines and channels ensure that you have finer control over memory allocation. Our API processes thousands of documents, and once we migrated to Go, we noticed that it was using a fraction of the memory than it was when the program was in Python. All this and yet you get the performance boost of static typing. Not to mention, Go is built for Cloud-based environments.
In the end, there is no definitive answer. Both languages are brilliant in the environments they are used for and are here to stay. If you’re looking at developing ML models for network security and fraud detection, you’d probably do well not to use Python but Java. But if it’s sentiment analysis, then Python is your language. Yet again, if you’re a veteran coder, and speed and scale are your concerns, Go provides you with these and many other advantages.
Look at your requirements, know your priorities, and experiment with both languages. If you’re already familiar with Python, you won’t find Go difficult. And even though Python has much larger community support, Go is getting there. It already has several libraries and modules that are very helpful to those who are new. Added to this, AWS, Azure, and Google, of course, offer excellent support as well.
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