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What is Python, you wonder? Well, that’s a good question, especially for those who are completely new to this. You see, Python is a high-level programming language that comes packed with integrated dynamic semantics designed mainly for app and web development.

What is Python programming language

When it comes down to Rapid Application Development, we have to say that Python is best in this regard because it offers dynamic binding and dynamic typing options.

One should note that most programmers will say Python is very simple to learn. The reason for this is because it requires just a special syntax that focuses much on readability than anything else. Furthermore, it is well known that developers are capable of reading and translating code written in Python much easier than other languages.

Now, because of the easier reading and translating, the language becomes cheaper to maintain and develop. Furthermore, collaborating with others shouldn’t be a huge problem since there is no significant barrier to language and experience.

What’s interesting about Python is its support or packages and modules, and that is a feature many Python programmers have come to live by. The support for modules and packages means programmers can design their programs in a modular style. Codes can be reused across multiple projects by simply scaling them to fit accordingly.

Not only that, but it is also possible to import and export modules with relative ease, and that is always a good thing.

There are multiple benefits of using Python, but we prefer the one where the interpreter and the standard library are all available free of cost. But that’s not all; you see, Python is not exclusive to a single platform because it is available on all the major ones, which includes the likes of Windows, Mac, and Linux.

How do we use Python?

Who is more likely to use Python?

Sample codes for beginners

Hello World

Create a print dictionary

1] How do we use Python?

OK, so one of the best things about Python is the fact that it is a general-purpose programming language. Yes, we do mean that it can in almost all situations, a Jack of All Trades if you will. Additionally, we should point out that Python is an interpreted language, and that means, the code is not translated to a readable format for computers at runtime, but rather, after.

Furthermore, the language, in truth, is viewed as a “scripting language” because it was originally designed for trifling projects. But clearly, that is no longer the case, and as such, the language has become one of the most loved in the world of programming.

2] Who is more likely to use Python?

The multipurpose aspect of this language should never be underestimated. If you take a look at NASA, you might not have realized that Python is quite popular in the Space industry. It is the central commanding system that powers the International Space Station’s Robonaut 2.

In the world of Video Games, the language is quite popular as well. We understand that Activision uses it to build and test its titles. Not only that, but it is also used to track cheaters and stop them in their tracks.

Artificial Intelligence is another big piece of the puzzle where Python is growing quite rapidly. The small footprint and ease of use make it perfect for this area when compared to the likes of Java and many others.

Read: What is the R programming language?

Sample codes for beginners 1] Hello World!

According to tradition, folks who are learning to code for the first time should program Hello World before all else, so that’s what we’re going to do.

To begin, open Python, then type the following line of code, and follow up by hitting the Enter key on your keyboard:

print(‘Hello World!’)

The end result should look like the figure below.

2] Create a print dictionary

It’s now time to create a simple print dictionary:

thisdict = { "brand": "Subaru", "model": "Impreza", "year": 2023 } print(thisdict) You can easily change the values of a specific item by running this code. Note that we’ve referred to the key name: thisdict = { "brand": "Ford", "model": "Mustang", "year": 1964 } thisdict["year"] = 2023 Conclusion

If you are serious about programming, then we urge you to learn Python because its usage will increase even further in the future due to the rise of artificial intelligence and machine learning, a space where Python is already thriving quite comfortably.

Read next: What is Google Go Programming Language.

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Can I Use Python As My First Programming Language? Why?

In today’s world, everyone is upgrading their skill by learning to program. As the market is challenging and competitive as well, knowing how to code gives you an upper edge in your workplace. However, selecting the best language is also a challenge to start with. Fortunately, Python has got your back.

But can Python serve as a suitable introduction to programming? This article will go into that very issue and examine the factors that contribute to Python’s appeal to novice programmers.

Let’s have a look at the following factors before opting for a programming language such as Python −

Python has raised its popularity since its introduction in 1991. Beginners should choose this high-level, interpreted language since it is simple to read and write. Python is surrounded everywhere. Whether it is data analysis, artificial intelligence, machine learning, or web development, Python has a major role to play here. A perfect choice for giant or small applications to start with.

Python has reached a greater height because of its straightforward and simple-to-read syntax. The program’s logic may easily be understood thanks to the indentation-based code layout. As a result, without becoming mired down in complicated grammar rules, you can pick up the fundamentals of Python rather quickly. Python is more user-friendly for beginners because its grammar is frequently compared to that of the English language.

For beginners starting to code, Python’s modular architecture is another benefit. Developers can save time and effort by using the language to create compact programs that can be applied to bigger projects. Complex software development is made simpler by Python’s great modularity, which enables you to build on prior work. The reuse of code is a tremendous choice for those studying the fundamentals of programming.

In addition to its simplicity and modularity, Python is common for its brilliant and lively community. This community is made up of programmers who collaborate and share knowledge, presenting beneficial sources and aid for beginners. You can discover a variety of online tutorials, forums, and documentation that makes studying and programming with Python plenty easier. Moreover, Python boasts a massive library of modules and packages that enable you to solve common coding troubles and create environment-friendly and high-quality code.

Python has turned out to be a well-known language for machine studying and facts research. Python has established itself as the go-to language for facts analysis, generally due to the fact of its sizable library and tool collection. Data evaluation libraries in the language, including Pandas, NumPy, and Matplotlib, make it simple to work with big datasets and current information visually. Complex machine-learning mannequin building is made simpler via Python’s machine-learning packages, such as TensorFlow and Scikit-learn.


Programming is a beneficial capability to have in the current digital era, and Python is a top-notch preference for freshmen who prefer to research programming. Python is a remarkable language to look up to due to the fact of its simple and simple-to-read syntax, modularity, and sizable community. It is a precious Genius to have in a variety of fields, from web development to statistics science and laptop learning, due to the fact of its popularity and adaptability. Python is absolutely a language to take into consideration if you’re thinking about gaining knowledge of code.

Top 8 Python Libraries For Natural Language Processing (Nlp) In 2023

This article was published as a part of the Data Science Blogathon.


Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the basics – is all about teaching machines how to comprehend human dialects and extract significance from the text. This is additionally why Artificial Intelligence is regularly essential for NLP projects.

So in this article, we are going to cover the top 8 Natural Language Processing(NLP) libraries and tools that could be useful for build real-world projects. So let’s start!

Table Of Contents

Natural Language Toolkit(NLTK)








Natural Language Toolkit (NLTK)

Entity Extraction

Part-of-speech tagging



Semantic reasoning


Text classification


For more information, check official documentation: Link.


SpaCy is an open-source python Natural language processing library. It is mainly designed for production usage- to build real-world projects and it helps to handle a large number of text data. This toolkit is written in python in Cython which’s why it much faster and efficient to handle a large amount of text data. Some of the features of SpaCy are shown below:

It provides multi trained transformers like BERT

It is way faster than other libraries

Provides tokenization that is motivated linguistically In more than 49 languages

Provides functionalities such as text classification, sentence segmentation, lemmatization, part-of-speech tagging, named entity recognition and many more

has 55 trained pipelines in more than 17 languages.

For more information, check official documentation: Link.


Stanford CoreNLP contains a grouping of human language innovation instruments. It means to make the use of semantic analysis tools to a piece of text simple and proficient. With CoreNLP, you can extract a wide range of text properties (like part-of-speech tagging,named-entity recognition and so forth) in a couple of lines of code.

Since CoreNLP is written in Java, it requests that Java be introduced on your device. Notwithstanding, it offers programming interfaces for some well-known programming languages, including Python. The tool consolidates various Stanford’s NLP tools like the sentiment analysis, part-of-speech (POS) tagger, bootstrapped pattern learning, parser, named entity recognizer (NER), coreference resolution system, to give some examples. Besides, CoreNLP upholds four dialects separated from English – Arabic, Chinese, German, French, and Spanish.

For more information, check official documentation: Link.


TextBlob is an open-source Natural Language Processing library in python (Python 2 and Python 3) powered by NLTK. It is the fastest NLP tool among all the libraries. It is beginners friendly. It is a must learning tool for data scientist enthusiasts who are starting their journey with python and NLP. It provides an easy interface to help beginners and has all the basic NLP functionalities such as sentiment analysis, phrase extraction, parsing and many more. Some of the features of TextBlob are shown below:

Sentiment analysis


Word and phrase frequencies

Part-of-speech tagging


Spelling correction


Classification( Decision tree. Naïve Bayes)

Noun phrase extraction

WordNet integration

For more information, check official documentation: Link.


For more information, check official documentation: Link.


This marginally lesser-realized library is one of my top choices since it offers an expansive scope of analysis and great language inclusion. On account of NumPy, it likewise works super quick. Utilizing multilingual is like spaCy – it’s proficient, clear, and fundamentally a fantastic choice for projects including a language spaCy doesn’t uphold.

Following are the features of Polyglot:

Tokenization (165 Languages)

Language detection (196 Languages)

Named Entity Recognition (40 Languages)

Part of Speech Tagging (16 Languages)

Sentiment Analysis (136 Languages)

Word Embeddings (137 Languages)

Morphological analysis (135 Languages)

Transliteration (69 Languages)

For more information, check official documentation: Link.


For more information, check official documentation: Link


So in this article, we have covered the top 8 Natural Language Processing libraries in python for machine learning in 2023. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

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What Are Some Python Game Engines?

In this article, we will learn some Python game engines

Python game engines are known as an affliction for a variety of 2D and 3D games that may be used to help those who want to make their own video games. Python, a feature-rich programming language, has been utilized in a wide range of popular video games.

Python has proven to be one of the most popular programming languages still in use in the gaming business. However, there is still a lot of uncertainty about how to meet the ultimate needs of the firm.

Why Game Engines?

You’ll see the benefits of having all the basics, such as classes, and functions, available once you start creating your game. You may get going and focus on the details of your project in this fashion. You might be asking what makes a framework different from a game engine. The game engine, which often deals with visuals, manages all of the rapid processes at a high level. The logic and components you choose to include in your game—the plot, if you will—is implemented by the framework.

The following are some of the best python game engines −







PyGame is a collection of Python Modules. This set of modules is designed small in order to assist in getting started.

The developers have also opted to build a large number of small components for the library. Because of this design, you can swap out parts of the library as your needs change.

For example, the mixer is a separate piece of software; you can switch to another piece of software as you progress in your programming.

The graphics rendering machine is also distinct(separate), and so on. In fact, you could use PyGame to prototype a game and then transfer one element at a time to more advanced software. Having said that, PyGame already has a huge number of games under its name.

Starting with PyGame is simple, but you can make it even simpler. The pgzero package is designed for instructors to demonstrate programming. The package allows you to easily write numerous aspects of your game without using boilerplate code.


The Ren’Py Visual Novel Engine, which is free to use, provides immersive storytelling/narrative. This is why it is a popular choice among video game developers. It’s designed for video game developers who wish to create life-simulation and interactive games with the ability to mix sound, words, and visuals in new and interesting ways.

The framework is intended for individuals who are prepared to fight with visual novels, and it helps with play transcription. Its fundamental functions are simple to grasp, allowing users to generate fantastic outcomes.

The elements of the game are ideal for video gamers that believe in learning from graphic novels without making any modifications.


This well-known framework is used for quick application development. Kivy features a distinct user experience, including multi-touch apps, and it is compatible with Windows, Android, Linux, OS X, and other platforms. Its most appealing aspect is its wireless capability. It is completely customizable due to the availability of 20 plugins.

Kivy can also be installed as Python modules. This is the most effective technique to use Python with any framework. Kivy is more powerful than PyGame and produces more elegant output. Kivy features excellent tutorials directly on their webpage to help you get started. One of Kyvi’s strengths is their Kv Design language. This design language simplifies the creation of menus, game plans, and graphics. This approach eliminates the need to create images for even the most basic graphics.


The Panda3D engine’s framework is unique in that it holds all of the Python bindings collectively. In the C++ programming language, it is referred to as a 3D engine (all written). As a result, it has become one of the engine’s standard methods of operation, and it is still up-to-date and complete, with everything from template assistance to silkscreen and precise duplication. The engine excels at encouraging rapid learning, rapid development, and maintaining stability.

This is not a package for learning to program or quickly creating a game. This package includes a professional tool for designing animations and games. To ensure performance, Panda3D is written in C++. The majority of the documentation, however, is aimed at Python users. While the concept behind Panda3D is for game creators to utilise Python to create their games, you can also use C++.

Panda3d is a powerful system that has its own SDK. If you are prepared to put in the time and are already familiar with Python programming, this system is significantly superior to anything you could develop. After all, Disney uses it for commercial purposes.


Pyglet is a framework with a user interface, soothing music and sounds, and the ability to load photos and pictures. This framework is compatible with Windows, Linux, and Mac OS X.

It’s a straightforward framework that greatly simplifies the delivery method, works with the game’s specifications and makes installation easier. Furthermore, the ability to use many windows at the same time with full-screen games and deployment is the best feature.

Pyglet appears tiny and constrained at first glance, but there is complexity hidden beneath the simplicity. This software supports OpenGL and has no dependencies; however, you can increase media capabilities by calling ffmpeg. Pyglet also supports dual-monitor configurations. Third-party libraries are available to extend the system. You should be aware of these libraries because of the functionality they provide. You could also wish to utilize glooey to create a consistent user interface for your application. Use PyShaders for rendering and Ratcave for 3D scene handling. If you start with pyglet3d, you should know that cocos2d also uses pyglet. This tool creates a comprehensive framework for game apps and other graphical user interface-based applications.


In this article, we learned some of the most well-known Python game engines and why they are useful for game development.

Aiml – A Language For Chatbots

This article was published as a part of the Data Science Blogathon.

Chatbots are intelligent digital assistants which may address customer’s basic and predictable queries. They offer numerous services via chatting and perform basic customer service operations. Chatbots work 24/7 and hence they provide assistance when offices are closed on holidays.

There are a variety of synonyms for chatbot, including “talkbot,” “bot,” “IM bot,” “interactive agent” or “artificial conversation entity.“

Most organizations have already started implementing chatbots on their sites especially on banking, airlines, and e-commerce websites.

Some more samples of chatbot technology are virtual assistants like Amazon’s Alexa, Google Assistant, and Messaging apps, like WeChat and Facebook messenger.

So, welcome to the planet of Artificial Intelligence!! Today, we are visiting to explore how straightforward chatbots are often created easily using the AIML library.

Types of Chatbots – Broadly, there are two kinds of chatbots:

Rule-based Chatbots

The rule-based chatbots work supported pre-written keywords that they understand. They use regular expressions or other types of string analysis. If the user has asked an issue without employing a single keyword, the chatbot might not realize it.

AI-Based Chatbots

than pre-prepared answers, the chatbot responds with adequate suggestions on the subject. additionally, the chatbot can find new ways to reply by learning from the previous communications.

What is  AIML?

AIML stands for computing language. it’s an XML dialect for creating tongue software agents. 

AIML contains a collection of rules which define the conversational capabilities of the chatbot. it’s used with a linguistic communication Understanding (NLU) processor which takes AIML rules to investigate and reply to the text queries asked via the chatbot. The more rules we add in AIML – the more intelligent the chatbot is.

AIML based chatbots come under the rule-based chatbots category, however, some level of self-learning feature is feasible.​​​​​​​

AIML is that the language to make a brain for chatbots.

NLU in chatbots process AIML and their chat behavior is controlled through AIML rules.

One chatbot application can have multiple sets of AIML and might behave differently.​​​​​​​

The below flow diagram shows how AIML based chatbot can work with a range of input, which essentially represents the texts with identical meaning.

The three texts “take call”, “accept call”, and “join call” end up in the identical action of “accept call”.

Also, as a limitation of the AIML based chatbot, if no input pattern is satisfied, the boat will simply reply with default the generic statement “could not understand the phrase”.

However, given rich AIML rules, it’s possible to form a chatbot that will handle a broad range of queries. It makes it the most effective suited domain-specific businesses, like banking where chatbot should handle generic queries associated with the banking domain.

AIML History

AIML was originally developed by Dr. Richard Wallace and a worldwide free software community between 1995 and 2002. It formed the idea for what was initially a highly extended Eliza called “A.L.I.C.E..” (“Artificial Linguistic Internet Computer Entity”), which won numerous awards.

An AIML interpreter AliceBot program is on the market under GNU GPL, which might be the accustomed test and develop AIML based intelligent bot.

The latest version is AIML 2.0 which has powerful NLP processing.

AIML Fundamentals

AIML describes a category of information objects called AIML objects and partially describes the behavior of computer programs that process them.

AIML objects are made of units called topics and categories, which contain either parsed or unparsed data. Parsed data is created of characters, several of which form character data, and a few of which form AIML elements.

AIML elements encapsulate the stimulus-response knowledge contained within the document. Character data within these elements is usually parsed by an AIML interpreter and sometimes left unparsed for later processing by a Responder.

Below is the coding structure of the A :

   ………    ……… <!–     Let’s say we have one     Question: Do you know who (any person name) is?      The AIML would be      <!–


The basic unit of information in AIML is named a category. Each category consists of an input question, an output answer, and an optional context.

The question, or stimulus, is termed the pattern. The answer, or response, is named the template. the 2 primary varieties of optional context are called “that” and “topic.”

The AIML pattern language is easy, consisting only of words, spaces, and wildcard symbols sort of a and *. The words may include letters and numerals, but no other characters. The pattern language is case invariant. Words are separated by one space, and also the wildcard characters function like words.

AIML supports mainly two forms of wildcards * and ^ symbols.

The * symbol is in a position to capture one (1) or more words within the user input

The ^ symbol is additionally a wildcard, however, it can capture 0 or more words.


There are two other wildcards, _, and #. These can override even exact matches and employed in a posh scenario.

You can have quite one wildcard per pattern. you’ll be able to echo multiple wildcards in your pattern by using, where x corresponds to the indicator (position within the sentence) of the wildcard. Not including the index assumes the primary wildcard.

For example :

<!– OUTPUT User: My name is Gyan and I am 30 years old. Bot: Hi Gyan, I am also 30 years old!

AIML Predicates

Predicate values in AIML are similar to local variables specific to one client. The most popular predicates are client profile information like name, gender, marital status, age, predicates, but it can be used to store any string.

<!– e.g. User: Hi Bot: Howdy. User: Hi Bot: Good Day. User: Hi Bot: Hello! <!– Human: I am Gyan Robot: Hello Gyan! Human: Good Night


AIML can be used to solve many real-life applications. The creation of Chatbot is one such application that is now widely used in the market. Also, AIML acts as the core of Machine Learning which is integration to Natural Language Processing.

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Top 10 Software Programming Languages

Check out these top 10 software programming languages to hone your technical skills

Software programming languages are the backbone of modern technology, enabling developers to create innovative systems and applications. Choosing the right one for a project can be daunting with a vast array of programming languages. This article highlights the top 10 software programming languages, showcasing their popularity, versatility, and relevance in today’s development landscape.

1. Python

Python has become one of the most widely used programming languages. Known for its simplicity and readability, Python allows developers to write clean and concise code. It offers a vast collection of libraries and frameworks, making it versatile for various domains such as web development, data analysis, artificial intelligence, and machine learning.

2. Java

Java remains a robust and mature programming language used extensively in enterprise-level applications. Its “write once, run anywhere” principle allows it to run on different platforms, making it highly portable. Java is widely adopted for Android app development and has a large ecosystem of libraries and frameworks.

3. JavaScript

JavaScript powers the interactivity of the web, making it an essential language for front-end development. It enables developers to create dynamic and responsive user interfaces. JavaScript frameworks like React and Angular have gained significant popularity, facilitating the development of complex web applications.

4. C#

C# (C sharp) is the go-to language for developing Microsoft’s .NET framework applications. With its elegant syntax and strong typing, C# is widely used for building Windows desktop applications, web services, and game development using Unity. Its integration with Microsoft technologies and tools makes it a preferred choice for developers.

5. Swift:

Swift is Apple’s programming language for developing iOS, macOS, watchOS, and tvOS applications. It offers a modern, safe, and expressive syntax, making it easier for developers to write reliable and efficient code. Swift’s growing popularity is driven by its seamless integration with Apple’s frameworks and its ability to coexist with Objective-C.

6. Go:

Go, also known as Golang, has gained significant traction for its simplicity, performance, and scalability. Developed by Google, Go focuses on simplicity and ease of use, making it an excellent choice for building scalable web servers, network tools, and distributed systems. Its built-in support for concurrency and a robust standard library makes it a reliable language for cloud-based applications.

7. Rust:

Rust is a system programming language designed for safety, concurrency, and performance. It empowers developers to write low-level code without sacrificing memory safety and offers strict compile-time guarantees. Rust’s adoption has surged due to its ability to write safe and efficient systems-level software, making it an ideal choice for building operating systems, web servers, and embedded systems.

8. Kotlin:

Kotlin is a modern programming language that has gained popularity for Android app development. Developed by JetBrains, Kotlin combines the best features of Java with modern language concepts. Its concise syntax, null safety, and interoperability with Java have made it a preferred language for building Android applications.

9. TypeScript:

TypeScript is a statically-typed superset of JavaScript that brings static typing and additional features to JavaScript development. It helps developers catch errors during development and improves code maintainability. TypeScript is widely adopted in large-scale JavaScript applications and is supported by popular frameworks like Angular and React.

10. PHP:

PHP (Hypertext Preprocessor) is a server-side scripting language most commonly used for web development. Despite facing criticism, PHP continues to power a significant portion of the web, thanks to its ease of use, extensive community support, and rich ecosystem of frameworks like Laravel and Symfony.


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