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In this article we are going to compare as well as contrast two major technologies, that is NodeJS and Python. Let’s us jump right into the basics:

1) What is NodeJS and What is Python?

Before we start discussing the basics of these technologies and platforms, we need to clarify what exactly these are that we are comparing.

Python is a computer programming language. Node JS is not. The language used for NodeJS is JavaScript. Node Js is a runtime environment for JavaScript.

The main key difference between Python and NodeJS means that when you do coding inside of NodeJS you are using the same basic language (JavaScript) for both the frontend and the backend.

Lets us deep dive into the breakdown of these technologies.

2) The advantages provided by Python to developers is as follows

Python is very friendly to junior developers and inexperienced developers.

Python is a very beginner-friendly language. This is why it is much likely to be the most popular introductory language in US universities. Major mistakes made by junior developers in some frameworks are easily forgivable, and they are less of a hindrance moving forward in development.

On the other hand, inexperienced developers tend to get slowed down by making mistakes in development. These mistakes are common to people who do not fully understand some of the more advanced concepts and the inner workings of JavaScript.
This phenomenon can be easily attributed to the way JavaScript has advanced over the years. Concepts such as object-oriented programming have been receiving meaningful traction only after plenty of much less elegant code snippets and tutorials have been spread online.

Most of the Python frameworks do not require a very high skill level from the developer’s point of view.
A very good example of this is the Django framework, that has a mature ecosystem and allows you to write quality code swiftly.

Python is more versatile as well as universal
Python can be used for desktop app development, and all the credit goes to electronJS, the main use case that is favorable to node js is the web. The applications of Python have known to go far beyond the web.

Python is the main candidate to become the next level programming language for data science. This is a great asset to system administrators because it allows them to write small one-off scripts and on top of that it provides large system admin tools such as Ansible

A good example that has used Python is Dropbox, one of the most common desktop apps written in Python.

Python is simpler as well as easy to use, better documented and better covered.
This is proven to give Python an edge over NodeJS, even after both technologies are admittedly fast to write in.
NodeJS is a JavaScript implementation, this is a language with a long history and a predominantly a front end tool. Therefore solutions that involve, NodeJS can be easily found online. But these may be inadequate for backend use and they rely on inaccessible interfaces

This use-case cannot be overstated, because each JavaScript framework or runtime environment tends to have a slight deviation on how it was implemented using JavaScript.

This situation has been improved over the years. But it is still a long way off from Python where CPython being a single implementation that is used in a vast majority of Python projects.

3) The advantages of Node JS are as follows

Node JS developers are more flexible

Since we have the same language that is used for both the front end as well as the back end, you need to be a JavaScript developer to use NodeJS. This functionality can come handy when you most needed it. The fact remains that when you have a solid understanding of the project and have the right people to build it, you cannot go wrong with JavaScript and NodeJS.

The development environment of NodeJS is less opinionated

Numerous NodeJS packages are simple, single-purpose libraries and micro frameworks that force the developers to make conscious decisions on what to use them for and when.

This makes NodeJS much more demanding. It requires a high level of advancement on the developer’s front than what it takes to generate code in Python on top of Django using built-in solutions for ORM, cache abstraction, and so on.

With NodeJS you can use JavaScript for coding any functionality

NodeJS allows you to code both the backend as well as frontend using JavaScript. This implies that you do not need to know different technologies and that shows that your developers do not need to learn a different implementation of the same programming paradigms.

At the stage of assembling your development team, you do not know how many, Python or JavaScript developers you will be required, your needs will also differ in sprints, depending on your goals.

Using the same language for the entire project removes the risks from the equation. You can also share a part of the code with the front and the back ends. This is a huge benefit to not having to do the same thing twice.

The NodeJS community is very large and JavaScript is one of the most dynamic and fastest-growing programming languages out there

JavaScript has been growing at an exponential rate. There are more than 500 packages for JavaScript being introduced every day. Python has no longer the advantage of numerous libraries that it once in time used to have. JavaScript has caught up with NodeJS in this aspect.

Read More: PHP vs Python

4) The problem with NodeJS and JavaScript

JavaScript has been developing super fast now. But this was not the case earlier.
In the beginning, the language was constructed haphazardly, and it has been struggling with issues caused with its older versions as well as the compatibility issues that bring with it.

JavaScripts recent rise in popularity has also brought another major downside to it.
So many developers have turned to the language in such a short period of time that it has been pretty difficult to keep up with the latest updates in the languages

The swift and rapidly grown happening to the language also brings another problem with it. This problem is that there is no proper and detailed documentation for these libraries that have been coming up for Node JS. This results in the poor quality of programs for Node JS programs.

Sadly this is nothing new for JavaScript as it has been very typical. If history has taught us anything it is that Python is much more reliable.

5) Main aspects to consider

Here are 3 main aspects that shed light on the different aspect of the story, this will help you to make an important decision:

Trending Technologies:

Python has been a major player in Machine Learning applications in the field of data science and it provides tested libraries for ML
It is far easier to find ML experts who are well versed in Python than JavaScript
MicroPython-a lighter and smaller version of Python can be run with much less resources and less power thus making it a perfect fit for IoT devices.

On the other hand, NodeJS is much more comfortable to be used in the Internet of Things (IoT)
When you compare the most popular libraries for Python and JavaScript

Speed and Performance:

One major drawback of Node JS is its inability to multitask., It cannot do many things at the same time unless you write very well code for it. Both Python and NodeJS pretend to do a lot of stuff at once, but Python applications tend to use a much simpler and traditional multiprocessing model approach instead of a more advanced asynchronous paradigm.

When written correctly, your software will send information to the Operating system that it needs to perform a certain task and move on, while the system works on that. But if you did not code the application efficiently, the app will wait for the system to complete the task and does not do anything in the meantime.

Team Composition:

The truth is that Python is well suited for some projects and JavaScript for others. Lets not that building software is a fluid process. The most important thing is team composition. If you have good python developers, then use python. Or if you have good JavaScript developers then use JavaScript.

Recommended reading: Ruby vs Python

Conclusion

At the end of the day, it depends on what features do you want to implement in your project and how you want your overall application to behave. You can and you should choose the technology that best fits your requirements and fulfills all your objects, it does not matter whether this is achieved using Python or NodeJS JavaScript for that matter.

If you’re looking to hire node JS developers or looking to hire python developers then drop us an email with your requirements. We have experienced developers who have profound knowledge of implementing web systems using node JS or python. You can share your requirements with us at enquiry@nimapinfotech.com.

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Often there are computer technologies that have matured fully to provide stability and benefits for the developers to solve computing problems. In this article, we are going to discuss as well as compare two very much mature technologies and see how do they stack side by side. These technologies are Ruby and the other one is Python. We are going to compare and contrast as well as highlight the features of these two languages.

Let’s start by listing down the similarities as well as the differences between the two languages:

Similarities between Ruby VS Python

These languages are available under the OSI and FSF approved licenses, so because of this, you do not have to pay license fees in order to use them or to distribute software written using them.

These languages are also Cross-Platform which means that it is convenient for distribution teams where people use Linux or Windows on their computers.
Ruby, as well as Python, are High-Level Scripting Languages, and the programs written using them need not be compiled. The need for a compiler is eliminated.

Both these languages are dynamically typed languages, and this means that you are free to use a variable without declaring it first.
These languages also support Object-Oriented Programming right out of the box

Differences between Ruby Vs Python

Ruby is commonly used in organizations that create web applications. Programs written using Ruby enables teams to swiftly and rapidly develop as well as focus on business processes instead of coding functions from scratch. Ruby is able to provide an excellent Model-View-Controller (MVC) structure so you are able to strictly separate your user interface, data as well as the business functions. Further down the stack, the configuration management software (Chef) is exclusively written using Ruby so if you need to create a new Chef module, it would be using Ruby.

When you compare Ruby using Python, Django is Pythons most popular MVC framework, not only this, but python is also popular beyond the domain of web applications. For example, consider the Pandas library, which is used for data munging and preparation along with other math libraries such as stats-model and numpy. Tensorflow is popular for any kind of machine learning tasks, and Matplotlib is powerful for data visualizations. There is also another library called SciPy which has resources in order to solve math functions.

Flexibility

When it comes to the Flexibility part of Ruby, it has adopted the Perl’s philosophy that there is more than one way to do it. Because of this you will find many different application methods in order to accomplish a certain task in Ruby. Depending on who is writing the code, this can lead to unnecessary complexity as well as obfuscation

On the other hand, Python uses a different approach where simplicity has more preferences and value than complexity. Hence its philosophy is that there should be only one way to do the task. So although Python code will not be the most flexible, it has a very good chance to be readable to an inexperienced programmer.

Read More:  PHP vs Python

Popularity

Over the past few years, we have witnessed the Use of Ruby slowing down and slowly declining from the list of the top ten languages, from fifth place in 2014 to the tenth place in 2018. Comparatively, Python has maintained its spot as well as it shows an improvement in its Rankings moving from the 4th position in 2014 up to the 3rd position in 2015 where it maintained its rankings till the end of 2018.

We can also check the job opportunities for these languages. Stack overflow returned twice as many results compared to Ruby. If you use LinkedIn, Python is leading by up to 20% over Ruby. Therefore if you want a skill set high in demand then you should go for Python as your preferred skill set. But if you prefer something niche with higher pay in the long run, then you should opt for Ruby.

Know More: 5 Reasons Why You Should Choose Python for Big Data

Reusable Code

When the question is about Reusable code, publicly available and ready-to-use code is an important factor when deciding on a programming language. Python is used to call them as Modules and these are available via PyPI where you can search from over 150000 modules. Comparatively Reusable code in Ruby is called Gems and there are close to 150000 gems. But the most differentiation between these is the ability to filter them. PyPI allows filtering by categories like “development status” and this can be much more straightforward when manually comparing libraries and manually evaluating the code.

When the case is about packaging and automatic dependency resolution, both these languages offer a painless installation of dependencies in the development environment. An advantage on the python’s side is virtualenv enables you to isolate different versions of the Python interpreter. Ruby also has a similar feature through rbenv or rvm, but this can be a little cumbersome to use.

Learning Curve

Python is much easier and user-friendly as well as quicker to learn for beginners who are new to programming. The syntax is the easiest to understand and is much more readable to the average beginner. But beyond that, you need to make decisions on what framework you should use to supplement the bare-bones Python.

Ruby can take much more time to get used to but it has inbuilt features like scaffolding and Active Record in order to accelerate your development. As soon as you know about them you will be able to build a CRUD application using API access in a matter of minutes.

Testing and Troubleshooting

Testing the code in the early phases of development is important and has to be done during the code development stages only. That is why Test Driven Development (TDD) is one strategy that you can use to your advantage. Even though PyTest and unit test have been present, there has not been a single framework for Behavior Driven Development (BDD) in Python. RSpec plays this role in Ruby and behaves seem to show some results but it does not look mature as RSpec.

Recommended Reading: Java Vs Python

Conclusion:

Ruby and Python are the two most mature language frameworks that have provided many benefits for web development and made the overall development effort a whole lot easier and flexible. If you’re looking to hire Ruby Developers or to hire python developers then contact us at enquiry@nimapinfotech.com with your requirements. Our representatives will get back to you within 24 hrs.

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What is NLP?

NLP stands for natural language processing. It is the field of computer science and artificial intelligence that deals with the communication between a human and a machine. NLP helps machines to interpret the human language and extract the intent, context, and meaning of the message and come up with proper response based on analysis that is similar to a human being. Over a period of training, it can be used to personalize and enhance the user experience.

What is chatbot?

It is a program built using artificial intelligence that responds to the user using text or sound. Chatbots are generally used in customer support, seat reservation, product ordering and making appointments. The famous example of chatbots is Alexa, Siri, Google assistant. Chatbots provide 24×7 assistance to the user. Today almost every company has a chatbot to engage with its users. Find out more about chatbots – read what is a chatbot.

How NLP works?

The first step in NLP is tokenization. Tokenization mean splitting sentences into words. We can just split the words where space occurs. For the example purposes, consider the following sentence “Delhi is the capital of India”. After tokenization, we get a set of words {“Delhi”, “is”, “the”, “capital”, “of”, “India”}

The next step is identifying the parts of speech of each token. For example, “Delhi” and “India” are proper nouns. Thus, the knowledge gained from this step is that Delhi and India are proper nouns so the sentence must be talking about them.

Next step is to identify the stop words. Stop word is nothing but a list of words that frequently occurs in a sentence. So NLP filters out these stop words. In the given above example “is”, “the”, “of” is primarily the stop words. Stop words can be identified by just by checking a hard-coded list of the available stop words.

The following step includes dependency parsing where the NLP determines how all the words are related to each other. Next step is NER (Named Entity Recognition) which detects and label these nouns with the real-world concepts that they represent. For example “Delhi” and “India” represents the location.

These above steps are just a short summary of how NPL extracts information from messages and uses it for further analysis.

You may also want to know more: Artificial Intelligence

Why NLP is required in the chatbot?

The main reason why chatbots are developed is to provide human-like assistance to the user and help him to achieve his/her goal. NLP helps the chatbot to understand the user’s message and respond appropriately. For example, if you send a message “Hi”, it is the NLP that lets the chatbot understand, that the message is a standard greeting. Thus using AI functionality the chatbot will respond with a return greeting.

Many chatbots are of little or no value without NLP. Only with NLP the chatbots understand messages and respond appropriately. The system is powerful enough to make or break a chatbot’s success as well as make it popular. With NLP chatbots can connect to queries and identify entities as well as the sentiments to generate own intelligent responses. Without NLP a chatbot cannot meaningfully understand the context of the message. The message is just a text input for a chatbot. It’s the natural language processing that helps to extract the context and meaning of the message.

NLP empowers bots with personality. It enhances the functions of chatbots for all businesses. It elevates their scripted and sequential conversations of chatbots to that of humans. Assessing, analyzing and communicating skills of NLP based chatbots makes this possible.

Read More: Creating Chatbot with Deep Learning

NLP understands and enables the needs of the customer with better clarity as well as the accuracy. It gains importance in the business world in order to provide better user experience across applications like chatbots, speech recognition, social media listening & texting, voice-enabled applications and devices. NLP with its translational and other features are the need of the hour. NLP based chatbots improve customer experience. You can fulfil the Expectations of today’s millennial business people around the globe with NLP’s business intelligence.

Want to build your own business app with cool development hacks? We at Nimap Infotech have experienced Python developers to take your business one step closer to your goals. Hire Python developers to experience the best full-stack python web development services. Contact us to offer you the most optimized solutions for web and mobile technologies.