Is Go Better Than Python For Data Mining
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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.
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Is Go Better Than Python For Data Mining
- 2. Is Go Better Than Python For Data Mining?
- 3. Table of Content ● What Is Go? ● What Is Python? ● Comparing Go and Python ● Closing Remarks
- 4. 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.
- 5. 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.
- 6. Comparing Go and Python
- 7. Closing Remarks 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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