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Machine Learning with IoT: Exploring Future Possibilities

Posted on May 6, 2020 by probytesnextjs

The fusion of machine learning with IoT signifies the start of a new technological era. Machine learning has brought about a sea change in many work processes by automating repetitive processes. In the same way, IoT has brought in significant changes in many processes by enabling machines to communicate with one another.

15 Top Trending Web Development Techniques to Watch Out In 2020-banner

Now, imagine the scope for growth and development when the two self-developing technologies are brought together. Though IoT and ML are being used across horizontals and vectors, data scientists believe that we are still in the nascent stage and there’s a whole new world of opportunities that we need to explore.

What is IoT?

Internet of Technologies or IoT can be described as a network of machines that can be programmed to work in synchrony. The devices are also programmed to interact with the external and internal environment and react accordingly. The number of devices that can be connected in an IoT network can be anywhere from 2 or 3 to hundreds.

The entire network is built based on sensor technology. IoT finds applications in smart homes, automated cars, precision farming, connected grids, smart retail, and lots more. Market research by Grand View Research shows that the market size of IoT has been gradually growing from 2012 in sectors such as healthcare, consumer electronics, manufacturing, transportation, retail, and others.

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The largest utilizer of IoT technology is the consumer electronics sector, which is projected to go up by nearly 100 percent by 2022.

What is Machine Learning?

The term “Machine Learning” was coined by its inventor Arthur Samuel who described it as a part of artificial intelligence that deals with machines that are able to self-learn and don’t need expressive coding. Machine learning is used to automate work processes, engage visitors to a website, and work on repetitive tasks.

The neural network is a part of machine learning. It is a relatively new domain and a lot of experiments are being done to build neural networks that replicate the functions of the human nervous system. We are still in the beginning stages of research and application in this field and have a long way to go.

Market research by Market Research Future predicts the proposed growth of the ML applications in the market. In 2017, the value of ML applications was less than 2,000 USD million. It is predicted that the value of this market will increase to almost 15,000 USD million by 2024. The top applications of ML will be in hardware, followed by software applications and service applications.

IoT and ML – A Symbiotic Relationship

An IoT network contains embedded technologies that are connected using both wired and wireless communications. One of the most important components for the development of an IoT network is data. The system should be able to access raw data from different sources and then processes it to gauge the internal and external parameters.

This where the contribution of Data Science and its subset Machine Learning becomes important. For an IoT network to work efficiently, it requires the right information, which can be collated using data science techniques and models such as neural networks, clustering methods, classification methods.

We need to explore and experiment with different ML algorithms to find precise high-volume data that can be used to gain insights for informed decision making and efficient automation.

IoT with Machine Learning – What are the future possibilities?

There are so many possibilities that we can expect to see in the future by bridging ML and IoT. Let’s take a look at a few possibilities here:

Self-driving cars

The concept of autonomous or self-driving cars has been around for quite sometime now. In fact, the first semi-autonomous car was developed by Tsukuba Mechanical Engineering Laboratory in Japan in 1977. Since then, a lot of research and development have gone into building such cars.

Now, automated cars are being developed with sonar, radar, odometry, GPS, lidar and other sensors for better navigation and vehicle control. A*STAR’s Institute for Infocomm Research (I2R) in Singapore was the first research institute to get approval for road testing of an autonomous vehicle.

As of now, the development of the connected car is still in the laboratory stage. Whether it will become a reality is something we need to wait and watch.

Robotic Vacuum Cleaners

Compactly designed, these robotic vacuum cleaners can navigate their way around your home and vacuum it for you. They can easily reach out to those hard corners and crevices and remove dust. Originally, these robotic cleaners were designed to be operated using a remote control.

However, now they are being designed with ML algorithms to self-operate. The advantage of using robotic cleaners is that they need no supervision. The downside is that this automated appliance takes a long time to clean and also comes with a huge price tag.

Smart HVAC

The heating, ventilation and air conditioning (HVAC) system consist of all the appliances that are used to heat, cool and ventilate the space. A smart thermostat automatically adjusts the temperature of the air conditioner or heater based on the external conditions.

The system sends your connected device a notification when it’s time to replace the air filter. It also identifies possible problems in the system function and updates you regarding that.

Risk Management

IoT and ML not just find application in product development, but also in service management. The two technologies can be used in tandem to identify possible causes of system or hardware failure in advance and prevent them. It will help avert conditions that could cause operational problems.

Smart Energy Management

Another of the sectors where IoT and ML can be applied together is in smart energy management. These technologies can be effectively used to optimize energy consumption, which can be done by building smart grids.

Sensors can be used in the electric grid to divert electricity based on consumption. Data can be used to monitor areas of high consumption and regions of low energy consumption and accordingly divert the electricity. Sensors can also be used to detect energy leaks and take preventive action.

Smart Cities

One of the most ambitious futuristic applications of the fusion of the two technologies is the development of Smart cities. These futuristic cities will have smart energy, smart traffic management, smart mobility, smart transportation, and smart agriculture.

ML algorithms such as Classification, Clustering, and anomaly detection can be used to smartly monitor and regulate traffic. Linear regression is the ML algorithm that can be used for real-time prediction of market and economics. Principal component analysis and canonical correlation analysis can be used for monitoring public places.

Final Thoughts

Data analytic techniques and tools can be used to effectively analyse data and gain insightful information. This information can be applied by ML algorithms to build self-learning machines, which can be used to build an integrated Internet of Things network.

There are numerous opportunities for research and development in the field of IoT and machine learning. The future is in Machine Learning and IoT.

What is a Web Framework?

Posted on November 15, 2019 by probytesnextjs

A web framework ties up the user interface, database, server, browser and middleware system and streamlines the process flow. In other words, this software framework is essential for the development of web applications, web services and dynamic as well as static websites.

A web framework consists of template frameworks, libraries with accessible data and session management.

How does a Web Framework work?

Type a website’s name into a browser say, for example www.probytes.com, and the page opens. However, in the backend of the browser, numerous processes happen in just a few seconds.

Step 1

The web server sends the HTML of the website to the web browser. (Note that the machine where this application is placed is also called web server). Even if the application is built using CSS or JSON, they are converted to HTML and the message is sent.

Step 2

In the next step, the web browser that received the HTML message “downloads” the website from web servers also known as application servers.

This process happens through the universal HTTP protocol, which is based on request-response model. The browser “requests” the data from the web application and the web application “responds” by sending the data.

The request – response action is only unidirectional. Only the web browser can request for data and the web application cannot do it. The different ways in which this request message is sent are HTTP GET, HTTP POST, and HTTP Response Codes. On the web application side, it responds with the HTTP 200 response.

Components of Web Framework Architecture

Web framework architecture includes the following elements – administration tool, Client Layer, Recommender Server and Generator Server. The administrator layer or tool contains Management, API Documentation, and Administration.

The Recommender Server contains API Layer, Application layer (containing online, action, offline recommender services), and Database layer that has database actions and association rules. The Generator Server has a Data mining layer, which contains both action-based and content-based generators.

Types of Framework Architectures

1. Model-View-Controller

This push-based architecture is based on the logic of separating functions and responsibilities. The tables in the database are represented as models and the business logic is contained in controllers.

The benefits of this architecture are that it allows for code reuse and also modularizes code. This architecture enables web applications to show web pages for human users and web interface for remote applications.

Action-based web frameworks are Ruby on Rails, Symfony, Spring MVC, Stripes, Django, and Diamond.

2. Component-based Framework

This kind of framework “pulls” data from controllers or view layers. This architecture allows the web application to pull multiple controllers for a single view. Frameworks that use this architecture are Wicket, Lift, Tapestry, JavaServer Faces, and JBoss Seam.

Note: Certain frameworks are both push based and pull based. A few of them are RIFE, Struts, Play and ZK.

3. Three-tier Organization

As the name says, this architecture has three physical tiers – application, database and client. The application uses business logic to run on the server and communicate with the client, which is a web browser. The web browser operates on HTML that is generated by the web application.

4. Server-side Application

When web frameworks were first developed, they were server based. This type of framework is essential as it has the necessary app logic to ensure proper user experience and interaction on the user interface.

These frameworks control and manage the database, handle HTTP requests, and also map URLs. The popular server-side frameworks are Django, Symfony, Ruby on Rails, ASP.NET, and Express Node.

5. Client-side Application

This framework deals with the interface that the users see and navigate. Rendering, playing sound, playing video, routing input, and more functions are handled by the front-end application framework.

This dynamic framework is also known as Single Page Application. It updates the data on the same page that the user is viewing without loading new pages.

This process enhances the performance and also reduces content reloading time. The client-side frameworks are Angular.js, JavaScript, Bootstrap, React js, Semantic UI and Backbone.

6. Mobile Application Architecture

The mobile application architecture has three layers, namely presentation, data and business. The presentation or user interface is the layer that users face. All interactions and operations happen in this layer.

The business layer takes care of the workflow and manages end user behavior. The data layer manages the data functionality, configurations, communications and settings.

The mobile operating system, storage space, device screen resolution, screen size and processor capability need to be considered while developing a mobile framework application.

Types of Frameworks

Web frameworks are classified into different types based on the programming language that is used in building them.

1. Java Web Framework

Java Framework architecture includes three components – User Machine, Enterprise Server and Database. The user machine has web browser (JavaScript/HTML/CSS).

The Enterprise Server contains Web Container (Apache Tomcat and Java Server Pages) and EJB Container that contains Business Methods, Data Access Objects, and JBOSS/ Web Logic. The database is written in SQL and RDBMS.

Java framework with is layered architecture can be used in a range of versatile applications. This framework has numerous tool kits that can be used to simple applications to full-fledged systems.

2. Node.js Framework

Node.js Framework features four components – Client, Server, Business Layer, and Data Layer. The client includes a mobile browser, web browser, and application.

The Business Layer contains the Application Server and File System while the Data Layer includes Database and External System.

Node.js Framework works with both front end and backend applications. The architecture shares and reuses codes and exchanges codes for consistent performance. It is the best option to build cloud-based platforms.

3. Ruby on Rails Framework

The architecture contains a browser, web server, user interface, database and Rails Framework. Information travels both ways in the browser, web server and user interface.

From the user interface, the command is routed to the Controller and then travels to the View and Model. From View, the response goes back to the web server. This framework works on an open-source model and delivers good speed and configuration to complete tasks.

4. ASP.NET framework

This robust framework developed by Microsoft is built using C language. It is a lightweight framework that is used to build applications for both mobile and computer systems. This full stack framework has multiple libraries.

The web framework determines the performance of websites and web applications and so all the pros and cons of each framework need to be considered while building and integrating a web framework.

Our expert team of website developers has in-depth knowledge of the different kinds of web frameworks and architecture. Get in touch with us to know more about the best web framework for your application.

Most Popular Programming Languages 2020

Posted on October 30, 2019 by probytesnextjs

The language that humans use to communicate with computers is known as a programming language. Did you know that the first programming language was developed between 1943 and 1945, but then was put to use only in 1998?

This programming language was called Plankalkül and was developed by Konrad Zuse. In 1949, John Mauchly laid the foundation for a high-level programming language with his computing language called Short Code.

This language consisted of a series of mathematical expressions and could be read by humans. However, as it had to be translated to machine language, running of the codes took time.

FORTRAN (FORmula TRANslation) was the first commercially developed programming language that was launched in the year 1954.

From the early 1940s till now, computing languages have undergone drastic developments. They are both machine readable and human readable and are used to execute complex applications.

The tremendous growth in programming languages has spurred the development of digitization, automation, and robotics. These computer codes are the foundation of millions of applications and products.

Over the decades, many programming languages became large scale and many disappeared into obscurity. This blog post will walk you through the most popular programming languages in 2020.

1. Python

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With the world going gaga over data science and machine learning, Python is sure to be one of the most popular computing languages of 2020. According to a recent survey conducted by StackOverflow, Python is the most wanted programming language.

This computer language was first launched in 1991. Its popularity percentage was 23.8% in 2015 and 41.7% in 2019. It is expected that the popularity ratings will increase in 2020. According to the Popularity of Programming Language (PYPL) Index, the popularity of Python has increased by 5.2%.

This general purpose language is commonly used to write plug-ins and scripts, especially in video games. The popularity of Python is because of its numerous benefits such as:

Open source and free
Object oriented
Possibility of asynchronous coding
Supports most libraries
Syntax features make it easy for programmers to create plug-ins
Easy to learn
It is both human and machine readable
It allows for parallel programming
Can be used to create cross-platform solutions

2. JavaScript

JavaScript is one of the popular front-end computing languages used in client and server side programming. It finds application in web development and the creation of dynamic content.

This computing language is also used to create animations, design buttons, manage animations and do other front-end design and development. Based on trend analytics by Stackoverflow, JavaScript had a popularity percentage of 54.4% in 2015, which rose to 54.4% in 2019.

With the increasing application of this programming language, the demand and popularity of JavaScript is sure to rise by 2020.

The benefits of this computing language include:

Functional frameworks for efficient performance
Delivers high speed performance as it can run on the client side browser
Compatible with other programming languages
Able to create a crowded interface
Updated every year
Easy to learn

3. TypeScript

TypeScript-Most-Popular-Programming-Languages

This Microsoft-developed programming language extends the capabilities of JavaScript and also makes coding easy. TypeScript was developed to improve the attributes and performance of computing languages. It can be used in applications with high syntax and higher levels of security.

The benefits of TypeScript that make it a popular computing language are:

Features extended toolbox to create components
Object-oriented language
Can be used in the development of Microsoft Visual Studio
Fewer errors created
Easy language for beginners to learn
TypeScript codes can be compiled into JavaScript applications
Special architecture reduces the need for multiple tests
Complete description of code components reduces chances of one element being violated when another is altered

4. Ruby

This web development language was developed in 1994. Though its popularity percentage has not greatly increased over the last five years, it has been consistent at an average of around 8 to 10%. This back-end programming language is facing stiff competition from Python ad JavaScript.

This general purpose scripting language is used in the development of standard libraries, web application, system utilities and servers. The benefits of this programming language are:

Dynamic typing that allows for changes on the go without affecting the overall result
Duck typing that focuses on operations rather than class objects
Intuitive code quality
Easy to maintain
Smooth performance

5. C#

Released in 2001, C# is one of the core programming languages that has been used in numerous applications and products. Its popularity percentage was 34.1% in 2017 and is almost at the same level at 31% in 2019.

This .NET framework language developed by Microsoft is vendor specific. So, developers looking to build products for the Microsoft environment choose this programming language.

The top advantages of this computing language are:

Cross platform functionality
Easy to develop
Automatic garbage collection
Strong memory backup
Backward compatibility

6. Java

One of the top reasons for the popularity of Java programming language is its high level of stability. It is used in building mobile applications. Java was very popular when it was first released.

It had a popularity percentage of nearly 10.5% in 2015, but it has now reduced to around 8.3%. The decline in the popularity index can be contributed to the fact that Kotlin is increasingly being used for Android app development.

However, according to the TIOBE index, which is determined based on search engine results; Java is the first ranked programming language of 2019. The same trend can be expected to continue in 2020.

The top benefits of this computing language are:

Easy to read and manage
Automatic memory management
Object-oriented language
Stable language
Doesn’t need any specific hardware to run

7. PHP

Though PHP is facing a tough battle with JavaScript and Python, it has a strong following, which makes it to the list of popular languages in 2020. In WordPress sites, nearly 90% of the code is written in PHP.

Meanwhile in other websites, almost 70% of the code is written in this computing language. This programming language is used to build dynamic and interactive sites. The features of this website that make it popular are:

Large ecosystem
Huge community
High-quality debugging
Easy to get started

8. Swift

Swift-Most-Popular-Programming-Languages

This programming language was released in 2014 and has been gradually replacing Objective C, especially in building Apple products. However, it is a vendor-specific language and is only used for Apple products. So, though this programming language is popular, its scope is limited.

9. Kotlin

Kotlin-Most-Popular-Programming-Languages

Released in 2011, this Google support language is being used as an alternative to Java for android application development. It had a popularity percentage of around 4.7% in 2018, which rose to 6.4% in 2019.

Extrapolating this trend, we can assume an increase in the popularity ratings in 2020. Moreover, as this language is being promoted by Google, we can expect a surge in the popularity index in the years to come. The benefits of this computing language are:

Can be used for both front end and back end development
Secure
Object-oriented programming
Compatible with Java frameworks
Supported by Android Studio, IntelliJ and other IDEs
Easy to debug

10. Go

Golang-Most-Popular-Programming-Languages

Golang or Go is another computing language that is being promoted by Google. Its popularity index has been steadily increasing from 2017, as Google is using it as a primary language in its products.

Go has a syntax that is similar to the C language. It has other components such as structural typing, CSP-style concurrency, garbage collection, memory safety and such. It finds application in the development of static and dynamic websites, micro services, asynchronous APIs and functions.

11. Rust

First released in 2010, Rust is one of the popular languages of 2020. This multi paradigm programming language has a syntax similar to C++. It is used in embedded software, distributed services, web and command line.

As this programming language has a low overhead, it is the best fit for microcontrollers and other products that run on constricted resources. Plus, it is compatible with WebAssembly. This computing language has a minimal footprint, making it the best choice to build online distributed systems.

The other popular programming languages to watch out for in 2020 are:

C++
WebAssembly
SQL
HTML
CSS
C
R
Ruby
Scala
Dart

Python Developer Salary – Entry Level to Senior [In Us Dollars]

Posted on October 25, 2019 by probytesnextjs

In this article Python Developer’s salary has been estimated by compiling the salary trends for the last 5 years.

Software developers and computer applications allow the interference at the user end to perform certain specific tasks on their machines like desktop and computers. They also help in developing and customizing certain system that is already running through the particular device control.

Although a software developer works in an office and requires an engineering degree in computer science information technology. In a few developing companies you may require relevant job experience but some newcomers with apt talent can be recruited.

What can affect your package?

One must be familiar with various computer languages like C++, Java, PHP, XML, HTML, and JavaScript but in few companies’ knowledge of python is sufficient. Knowing all these languages may add up to the brownie point for sure and enhance your overall package.

Knowing other operating systems like Linux, Solaris, HPUX, BSD, Mac OS X, and windows can enhance your market value for sure. This not only increases the chance of better money and designation but also ensures the long stability within the organization

Knowledge of various other databases like Oracle, PL/SQL, MySQL is also widely used within the business sphere can also increase the chances of recruitment and better money.

A developer should be able to do work in team efforts. A software developing system closely works with each other and other collaterals within the organization. A developer must be able to convey the information to other colleagues in the most informal and non-technical way.

Written and verbal communication skills are also required. They must also be updated with the latest trend changes and day to day updates changes occurring in the technological industry. These changes may be software or hardware, and should be willing and acceptable to the influx of knowledge both at organization and personal reasons.

What a Python Developer Usually Does

Type a message

On an average, a python developer is expected to accomplish the following tasks within the hiring organization.

Write modify or debug the required software for clients or the organization and its application as a whole.
A developer must be able to use visual developers’ environments.
Test and make the documentation of the software for clients for the application or the organization.
Write code to create a multiple or single thread for user interface event-driven application.
Assess the server or related services.

These tasks can be handled on both the regular in-house basis within the company or individual as a freelancer as per the wish and requirement of the individual but most of the jobs within the market scenario are for a permanent in-house developer.

As we all know in today’s scenario when many organizations are pushing java on the back and enhancing other developmental and language skills, the craze for the python is touching new heights.

If someone is really into making the carrier option into the python software developmental industry then a clear goal should be set regarding the requirement and demands of the job.

The salary is also the crucial part of the carrier as it is the most important deciding factor on whether one wants to pursue future opportunities or not. This becomes the deciding factor in the forward movements.

A python developer is usually responsible for writing down the server type web application based on logic. The job may be demanding sometimes and also required to finish off the back-end components and the connecting applications with or without the third party involvement.

So both the front end and back working project is finished off by the python developer. Sometimes in many organization python developers also include the advanced open-source framework written over python. A developer may choose the relative choice he wants to socialize in this serves as an added benefit.

Current scenario

Although job data analysis and web development are still one of the major back-ups of python field, machine learning and artificial intelligence are also opening the gates for more python.

More and more systems are amalgamating with advancing artificial intelligence giving way to many software developmental languages like python. As per the recent results of the python developer’s survey 2019, many developments came forward which indicated how favourable the options have become for young minds.

Also, during similar surveys, it was found that python is the world’s seventh most popular programming language in the world. This is being one of the popular languages being used by the software developer. That’s why many large-scale organizations are also preferring the language to use on the professional as well as the commercial front.

Although the frequency of developers preferring this language is increasing widening up the horizon of the industry still needs a lot to be done and the salary is dependent on many things like skills of the individual, level of expertise and the location. This can be easily tracked down to some of the information like industry-driven aspects.

Python Developer Salary

The average salary can be tracked down to many credible sources present online which can give the clear cut information regarding the scope, nature of the job, requirement and the average salary that can be expected. One who wishes to take the full details can find it easily over web portals like PayScale, ZipRecruiter, and Glassdoor.

The average salary of the python developer is a lurking figure and may range up to 77,000 U.S dollars with various bonuses and profit-sharing. We have tried dive the information into the various levels of experience and expertise so that it can be understood and processed easily.

An entry-level Python developer salary may range from 35,000 U.S dollars to 40,000 thousand dollars, whereas a junior level python developer gets somewhere between 50 to 70 thousand U.S dollar every year.

On the other hand, senior level python developer can get an annual package of 103,000 to 150000 U.S dollars easily. On average, the median range is 65000 to 75000 U.S dollars per year. The median hourly rate for the python developer is 30.25 U. S dollars.

On the other hand, the average salary as reported by the Glassdoor is us dollar 96,000 annually, and when we add to this to the working location again the bracket may fluctuate. Similarly, we compare working in countries like the United States the average salary scales up to us dollar 1, 23,656.

In another website ZipRecruiter that deals with the computing-related jobs database following pattern can be observed:

The entry-level Python developer salary on an average is $78,176 a year
Junior Python developer on an average draws the salary $89,776,
The mid-level Python developer salary can reach up to us $111,896.
Senior Python developer earns $122,093 on average

This shows how experience and location may act as a turning factor. A salary is hugely dependent on the experience of the individual and a hefty amount can be drawn very easily if someone has already handled a list of responsibilities.

The average salary may boost up if a python developer knows Designing and implementing quality protocols and application implementations.

Efficiently write down the codes and come up with solutions related to data

An average salary is dependent on the combination of core skills that include core python, web framework, object-relational mapper and latitudinal architect and developing API’’s. Few additional skills may act as sugar on the cake. These may include system administration, scripting, and knowledge of C++.

Conclusion

As in the article and through the current trend in the market we can say that python has already become one of the most powerful and highest paying programming languages all around the world.

Most of the jobs related to python have been concentrated in the United States of America. So surely this is a promising field and a green signal to go ahead if anyone wishes to make big in the industry.

So, go ahead and trust your interest and calibre to get the best out of the industry as per your expectations.

7 Reasons Why Python Development is Widely Used in Enterprises

Posted on May 22, 2019 by probytesnextjs

Due to the more robust feature and versatility python has become the first choice for developers all around the world. From 2016 itself the growth rate of its use has increased to 27 percent.

As the resourses are easily available now python has become one of the most used and best programming language.

More and more enterprises are asking for a python developer to build the software. Now we have told that python is the foremost choice of the enterprises but the question arises why it is so, let us elaborate the reason for its popularity among enterprises.

1. As it is Scalable

Due to its quality of being highly versatile, handling of the different task at the different size of projects becomes easier. Also, it is capable of supporting various programming paradigms, including both functional paradigms as well as centers paradigms.

An accessible approach toward the software development and use of integrated language enable the quick code functioning and easy to make a script on a large scale.

Usually, the development of larger solution processes takes much more time which is not possible with another language program then python.

Hence lesser time means lesser costing. Even if the client decides to increase the corresponding scale time and production cost usually remains the same. So there is the scope of adoption and expansion always.

2. Python has Most of the Libraries Support

Developer who prefers python has the benefit of access to most of the libraries. This allows the programmer to use modules that are already tried and tested which in turn helps in the faster development of the respective software without decreasing the quality and compromising on the features.

Some of the popular libraries that support python

Pandas – data structure with high performance which helps the developer to use the various analysis aspects and modules.
Nimoy – this is used for mathematical and computing
Keras – used for writing networks like neural and machine learning purposes.
Tensorflow -released by Google that creates deep learning features and processes like image reorganization as well as detection.
Scikit learns -used in the mining industry, data analysis, and machine learning.

Python can be used in another framework like Django, Pyramid, Scrappy, Flask, etc.

3. Python has a Fast Prototyping Feature

Due to rapid developmental speed, python act as a good choice for projects whose viability factor is very important.

Programmer can switch smoothly between prototypes to the solution. And the best part is developer never loses any of the previous work when they move ahead or in between the prototype phase.

4. It is Easier to Decipher

One of the biggest benefits of python is that it is highly readable and somewhat similar to our diction language English.

The writing process after use of python becomes very intuitive and hence python is so loved by the developers. Also being easy to use makes it easy to read also and check the work of others.

Another quality of python id the seamless debugging process as well as code review due to its much readable nature, this will the kind the product in the market faster.

5. It has an extensive source environment

Python developers are one step ahead of others as they have access to a vast range of free tools which are easy and handy too.

They also work in GUI interference, web development and data science and computer vision. Python in itself is an ecosystem of wide echo of tools that can impact the business craze of many enterprises; moreover, this becomes essential for high coding quality.

So this makes python almost error-free, so this, in turn, protects enterprises from the impact of immediate failures.

6. It accelerates the code reviews in a better way

When an enterprise work on the particular project then the speed of completion plays very important role. If the desired speed of work and development is not maintained then it will be a huge blow to the working practices of enterprises.

So it becomes important to maintain the quality of coding. As python uses syntax and tools developer can easily check for bugs and code formation working become more convenient which ultimately pacify and speeds up the development process.

7. Python is convenient to use

Enterprises software development and its solution have many layers of complexity in it when python is used for the technology to develop the required software it becomes much more easy and comfortable.

With the dynamic nature of python new versions are being launched after every month so there is always a scope of improvement always along with requirement having fixes and new features.

This constant development allows having high structural grounds with a time-intensive approach that helps to finish the complex projects on time.

Why a python is an ideal option

Python is versatile in nature hence it enables the developer to handle the challenges the software development which can create problem in the future.
Python allows high quality of tool and additional libraries also.
It can be created in a very short span of time.
It has a shorter product development cycle.
Python has code light nature which helps to have deep depth knowledge in no time

How big companies are utilizing some of the qualities of python

Python has become favorite foer many enterprises like Instagram and facebook. tThey are using it to channelize their huge data and related processes.
Due to its modular approach, Spotify is using it for the analytic purposes at the backend.
As python is much more dynamic and portable in nature even Netflix is using it for software development.
With extensible tools in c and c++ it is been used by industrial light to power CGI.
Google is currently using python for its server languages.
As python is most versatile in nature it can be used for web applications, cloud computing, data science and many more, still thinking why companies are betting upon python.

Conclusion

With increasing credibility python now has an extreme fan following with vast community of its supporters.

Many professionals work seamlessly to bring out every minute enhancement and features forward so that it can work to its core functionality in the ever-growing competitive enterprise’s field.

Python enables the optimal integration capability which proves to be the best choice for developers nowadays.

Detect Language Using Python

Posted on November 20, 2018 by probytesnextjs

Python, one of the most powerful and popular coding language can be put to many use. One of such include language detection.

What is Language Detection?

Are you able to tell which language are the following sentences written in:

“sonrakine bir göz at”

No? Try this one

“iyi, eğer bir selamı hakedebilirsen”

Hard right?

Well, finding a documents source language is an important step for numerous cross-language tools.

Hence, being the reason for the implementation of a Language Identification algorithm.

Language Detection comes in the category of NLP (Natural Language Processing), which involves the study regarding how computers can decode a code and value from human language.

It deals with the problem of determining which natural language given content is in.

Need For Language Identification

Natural Language models are usually specific to a discrete language. If one is not sure of the receiving document’s language, it becomes very hard to provide anyone with a good experience.

Wouldn’t it be great, if a technical support chatbot is made capable to determine a speaker’s language and could reply with documentation in the same language?

Or a sentiment analysis tool that automatically detects the sentiment of any human language?

Well, Language Identification is designed to do exactly the same.

Language Identification is an important objective in the text mining process. Successful study of the extracted text with natural language processing or machine learning training demands a good language identification algorithm.

If it is unsuccessful in identifying the language, this misconception will invalidate the processes ahead.

NLP algorithms have to be modified for different corpus and in accordance with the grammar of different languages.

Certain NLP software is best suited for certain languages, such as NLTK and FreeLing.

NLTK is the most favored natural language processing package for English under Python, but FreeLing is best for Spanish.

The effectiveness of language processing depends on various factors.

A superior quality model for text analysis includes the following:

1. Text Extraction:

Text can be extracted by web data extraction, importing it in a particular format, grabbing it from a database, or obtain it through an API.

2. Text Identification:

it is a process of separating interesting text from another format that adds noise to the analysis

3. NLP:

is a set of algorithms that support in the processing of different languages.

4. Machine Learning:

is an essential step for objectives such as collaborative, sentiment analysis and clustering.

Software Options

There are a lot of languages recognizing software available online. NLTLK use Crúbadán, whereas Gate includes TexCat.

We prefer using Google Language API because it is very precise even for just one word. It consists of an accuracy measure in return.

Language detection with Python

langdetect is one derived directly from Google language detection. From the home page of the Python library, you can get access to the project page, this seems to be different from the code on which the R library CLDR is based. And in fact, the Python library seems to be well organized and maintained.

It claims it can detect 55 languages and upon a simple call to the function “detect” will return the two letter iso code of the language detect while a call to detect Lang will return a vector of probabilities strings. The vector contains a single item.

In Python, there are certain options which it offers, and the alternative library that found here is langid.

This claims to be a standalone library capable of detecting 97 languages and you can use the langid to classify (“your text”) to get the most likely language and its “score”.

Better the score, more probable the language. Let us see a bit of code these two libraries quickly:

Combination of Python and NLTK for Language Detection

Most of us are involved in search engines and Social networks to show data in certain languages, for example, Spanish and English.

To accomplish this process, an indexed text has to be examined well which would give intended result and store it together.

Read also : How is Python Being Used At Facebook?

There are quite a number of way to achieve that, the easiest way to go through is the stop words based approach.

“Stopword” is used in Natural Language Processing to mention words which should be filtered from the text before any kind of processing takes place.

Your Way through Stop words

Now, We have a text to detect the language. The basic step will be to Tokenize the given text to a list of “words” and “tokens”- using an approach depending as per our requirement.

Following is the example of detecting language with the help of Python and NLTK

As you can see, the text has been tokenized and processed further to get the intended results. The outcome of the above programme is given below:

The Result shows clearly that the text inserted in the programming, containing different languages were identified by the Python coding method. There were total four of them and all were detected successfully.

Why is Python So Popular in Machine Learning?

Posted on November 10, 2018 by probytesnextjs

Python and Machine learning, both name has been heard conjointly countless times if you are a programmer or an internet savvy.

Why do you this happens often?

Is it just because of the hype Python language is getting nowadays? Or Python is the most efficient language for machine learning?

What Makes Python a Good Choice for Machine Learning?

Python is eminent for its compact, readable code, and is relatively unrivaled with regards to simplicity and convenience, especially for new engineers.

This has a few points of interest for machine learning and deep learning.

Machine Learning depends on multi-stage work processes and greatly complex calculations.

Hence, a developer needs to stress less over the complexities of coding, the more they can center on discovering answers for issues and accomplishing the objectives of the task.

Python’s simple syntax structure implies that it is additionally quicker in development than several other programming languages, and enables the programmers to rapidly test algorithms without implementing them.

Furthermore, an effortlessly readable code is important when machine learning ventures change hands between development groups or for collaborative coding.

This is especially valid if a venture contains a lot of third-party elements or custom business rationale.

Easy of Learning

Python is so natural to learn, without a doubt. Some of you may have studied other programming languages, for example, C language. The pointer issue in C is difficult. Python is intended to be not at all difficult to learn and easy to apply. There’s no implicit variable type, no pointer. All the more significantly, it is object oriented!

Utilizing Python is like chatting with the PC. What’s more, Python is similar to our natural dialect, writing computer programs is simply like having a communication with the PC.

Since it is simple to learn, numerous individuals without Computer Science base are also able to utilize Python these days.

So, when a huge number of people are using this language it ought to become mainstream.

Now, let’s comprehend the reason why everybody needs to utilize just the Python language in planning any Machine Learning project

Machine learning, in simple terms, is to utilize the information to make a machine settle on a wise choice by building smart algorithms.

A critical undertaking of a Machine learning developers in their work life is to extricate, process, interpret, clean, organize and after that comprehend the data to create smart algorithms.

Now and then the notions of Calculus, Linear Algebra are complex to the point, that they require the greatest degree of endeavor.

A speedy usage and simplicity of Python causes the Machine Learning developers to approve an idea instantly.

A Whole Lot of Libraries

One of the viewpoints that address Python as a popular selection, all in all, is its plenitude of frameworks and libraries that promote coding and spare development time.

Machine learning is incredibly very much provisioned for.

To take care of the most machine learning issues which don’t even need deep learning, the appropriate solution is easy.

You simply need to install a couple of python libraries like NumPy, pandas, and scikit-learn. These tools are free and intended to function admirably together.

Python has a proper ecosystem. It has numerous packages available that cover a wide range of tasks. For instance, you have matplotlib for the graphs, you have NumPy for scientific calculation, you have pandas for data processing.

And those libraries are not anywhere near to difficulty of usage.Furthermore, in case that you have a huge classification issue that runs too gradually on a solitary CPU utilizing scikit-learn, you can utilize the xgboost library to operate it on various CPUs.

The purpose behind why SciPy, NumPy, scikit-learn, and numerous different libraries were made, in any case, is on the grounds that Python has a few features that make it exceptionally engaging for scientific computing.

Python has an easy and steady syntax which makes programming more convenient for individuals who are not engineers. Other important libraries are:

Keras

It makes controlling these complicated programming significantly simpler than utilizing them specifically. It’s exceptionally easy to understand, putting user experience as the first preference.

They deal with this by applying simple APIs and phenomenal feedback on lapses.

It’s additionally modular, implying that distinctive models – cost functions, neural layers, etc. can be secured together with fewer limitations.

This makes it accessible to extend as well, since it’s easy to include new models and interface them with the current ones.

NLTK

NLTK is an unquestionable requirement when working with Natural Language Processing (NLP). It accompanies a heap of datasets and other lexical assets along with libraries for running with content, for functions, for example, tokenization, classification, tagging, stemming, parsing and that’s just the beginning.

TensorFlow

The Google Brain group developed TensorFlow for using in machine learning apps internally. This was open sourced in 2015. Many suppose this library to be used for both research and production environments, supporting enormous GPUs clusters for training. It holds sufficient computational graph visualizations, a more natural API, and greater flexibility.

PyTorch

A Python implementation of Torch open sourced by Facebook is called PyTorch. The PyTorch is extremely useful for debugging because one of the significant differences between PyTorch and TensorFlow/Theano is that the later use symbolic computation while the former doesn’t.

It’s the code stack and collection of different open source repositories which is generated by individuals (sometimes yet in the process) to consistently enhance the current strategies.

The best thing about utilizing such libraries is that they have zero expectation to learn and adapt.

When you have a fundamental comprehension of Python, you can simply actualize it.

They are freely available to use under GNU license. Simply import the package and employ.

Final thoughts….

When Python was initially formed, a main objective of the language was to be simple to write and read both.

The code is read notably more frequently than it is written, particularly in environments where it switches cards from one team to another.

When you’re acquiring a machine learning application from another engineer, primarily one that makes control of various third-party elements or has a lot of custom business concept, it serves to have it written in a language that appends less additional cognitive cost as feasible.

High-grade Python code will have that characteristic, even higher than the other languages.

Machine Learning includes extending the application with useful data from gigantic stores of registers, statistics, and data.

These pieces of information are normally unsorted and are hard to associate with any significant exactness.

Machine learning can make associations between unique datasets yet needs genuine computational fallacy and power.

Python satisfies this need by being a universally useful programming language. Python has the toolset to implement a diversity of dominant functions.

It’s no surprise why data scientists gripped Python. Eventually, as machine learning gets more popular the need for more Python-skilled practitioners is also set to grow.

How is Python Being Used At Facebook?

Posted on November 8, 2018 by probytesnextjs

Python is becoming a very popular programming language and many industries are embracing and developing new applications using this language. Google, YouTube, Netflix, and many others are the core examples of popular companies who are using Python.

But do you know that Facebook, the social media king is using Python for various developmental purposes?

Use of Python at Facebook

Not all the coding of Facebook is written in Python, but still, millions of lines of code are written in Python.

Learning about the fact that production engineers at Facebook commonly use Python programming language may surprise Python expert.

Python is the third most favored programming language that is currently being used at Facebook after PHP and C++.

Usually, production engineers use Python to maintain thousands of libraries and binaries Facebook’s infrastructure.

Read also : How To Make A Website Like Facebook?

In order to achieve the purpose of hardware imaging, infrastructure management, a binary distribution, and operational automation, a number of Production engineers write code in Python services and frameworks.

Over 21% percent of overall codebase infrastructure is covered by Python at Facebook while rest is covered by Java, C, and C++ with 9%, 15%, and 55% respectively.
Maintaining thousands of libraries and binaries distribution is done with Python.
Use of Python’s framework Py3 increased by 5% as of May 2016.
From 2016 to till date, an average of 5000 codes is modified by more than 1000 committers on

Python in Production Engineering at Facebook

Facebook’s infrastructure team extensively uses Python which can be easily found everywhere in production engineering.

The main focus of the infrastructure team is to deliver and maintain Python client libraries for the interface and binary communication protocol that enables other teams who want to interact with them by offering reliable interfaces.

With the help of these interfaces, the amount of code that is written, tested and maintained by production engineers is reduced, enabling them to move ahead at a faster pace in integrating services to Facebook’s infrastructure.

It also helps to scale up the reliability and efficiency of the entire infrastructure. Python helps in maintaining and managing the infrastructure of Facebook.

Facebook’s production engineer own various software, written in Python that enables them to manage the infrastructure and covers the life cycle of their hardware.

Services Driven by Python at Facebook

Currently, Python is responsible to drive various services at Facebook which are listed below:

Network Switch Setup and Imaging: Python helps the service to form a successful communication bridge among network devices.
Whitebox Switch CLIs: Python enables full-scale network deployment that enables the client to manage switches from a CLI interface.
Service Turn-up: Python provides core service such as DNS, Chef etc turn-up with the support of a pluggable system known as Kobald.
Hardware Faults: It also enables auto-remediation of service failures and faults in service in hardware.
Scheduling Maintenance Work: Using Dapper it also helps in automating and scheduling maintenance work.
Testing and Repair management: Using Cyborg Python helps in burn-in testing, server imaging, and repair management
Server Check: Also, it helps in detecting fault and diagnosis with the help of Machinechecker. Machinechecker is CLI utility software that checks the health of Facebook’s servers.

Platform Services

Scaling up of the Facebook’s infrastructure has also scaled up the services. Services which were uniform once are now broken into different components.

This division has given rise to a wide variety of general-purpose services for Python.

Read also : How To Make a Social Media Website?

The infrastructure management tools that are used by production engineering are built on various platforms. Hence, these are listed below:

Job Engine: A framework which is used to scale up job scheduling and execution that can be extended by any team.
fbpkg: It is a blob distribution service that allows the transfer of large files and software packages including binary of facebook.com.
FBTFTP: It is an open source framework to build high-performance TFTP servers. Currently, they use high-performance IPv6 TFTP.
Osmosis: It is an execution tool used by teams for office or data center development and kernel and OS updates.

Service Configuration Management at Facebook

Facebook’s uses Chef (automated IT infrastructure tool) to manage their host-level configuration while to manage their service-level configuration they use Configerator (an authored project of Facebook).

Production engineers use Python to write code which is executed to produce configuration objects.

These objects are later stored in JSON format that can be easily consumed by any other service.

Also, Validators are used to ensure whether these configuration objects are defined correctly or not which is also written in Python.

Python can also be used as a configuration language for container deployment system for Facebook, Tupperware.

Improvement in Operational Efficiency

The building on top of existing libraries and system has helped many teams in improving their general or own operations at Facebook.

Using Python, MYSQL infrastructure team has developed a service known as MySQL Pool Scanner that keeps their database up to date.

This tool automatically manages and maintain databases in the way same as a database administrator.

Another tool which they have built on top of job engine using Python is Orchestrator that allows their widely distributed binaries to roll out safely with safety checks.

Deployment of Python 3 in Facebook Codebase

Currently, Facebook codebase uses Python 2 and Python 3 for their various libraries and models (such as Gevent or Twisted).

All the projects on Facebook mainly use Python 3 unless there is a need of using Python 2.

In production engineering, over 5% of Python services are running in Python 3. FBOSS CLI, Facebook Python Ads API, PYAIB, FBTFTP are the Python 3 compatible projects.

Facebook uses Real-Time Framework for Python

It has become important for a social network side to have real-time updates that make it simple for the people to share with friends.

News feed needs to be updated in real time to make stream relevant and engaging.

To deal with this issue, Facebook acquired Friend Feed’s real-time web framework known as Tornado that helped them to build a site which is updated in real-time.

Tornado is a Python web framework originally developed by the FriendFeed team to handle thousands of connections simultaneously.

Thus, it becomes easier for Facebook to enable real-time services. Initially, it was designed to handle a large amount of web traffic with speed.

Tornado is a similar Python web framework to Django, Google’s web app or web.py that also provides other features like security, localization, user authentication or cookie handling.

With the help of the Python web framework, it has become possible for Facebook to ensure that user’s news feed is updating regularly in real-time.

Python For Finance, Why not R?

Posted on October 26, 2018 by probytesnextjs

Many finance companies nowadays depend on Big Data and Data Science. But which programming language do they use to make use of this cutting edge tech?

If you’ve already considered advancing your career in these fields, you too will surely come across this question.

Most common programming languages that are used in the financial data analysis are R and Python.

But how would you know which one is better? Thus, we’ll look into both of these programming languages and learn their characteristics in detail.

Why Learn Python?

Python is a popular programming language which can be used in all types of the field including data science.

Learning Python can be very beneficial for data scientist and even if they aren’t from the technical background they can easily grasp Python’s basics and use it as per their requirement.

Features of Python

Python is open-source language and is also available for free to programmers. From the companies perspective, it can be a great option to save cost.
Programmers who are already familiar with object-oriented programming languages like Java, C++ or Ruby can easily grasp Python. For programmers, it is easy to learn and easy to code.
Since finance involves complex data sets and functions, Python uses less coding and intense debugging feature with which data scientists can easily step into the analysis
Python has the potential to perform and develop high-quality application and services related to web or desktop.
Python involves short syntaxes which make it clearer and easier for data scientists to understand and write.
Many companies use Python for development of data-centric applications and scientific computation which is associated with the financial
Python offers packages and frameworks like Pandas which is very useful for data analysis purpose. Furthermore, RPy2 package offers almost the same functionalities as R with high performance.
Python also uses packages such as Matplotlib and IPython for plotting graphs and visualization which helps in finance’s statistics and analytics.

Why Learn R?

When data scientists need to do data analysis, learning R programming language will be very beneficial for them as there is no other programming language which provides essential functions that can be used in the statistical computation.

It provides data scientists with well-designed plots including mathematical functions wherever needed.

Features of R

R is an open-source programming language that means it is freely available. A data scientist can easily install it, upgrade it and use it as per their need.
R supports compatibility with different platforms and can easily run across every operating system. Furthermore, it will also let you import data sheets from other tools such as Microsoft Excel.
R is a scripting language which allows you to use it intensively for resource simulations on high-performance computers. Also, it helps in handling big and complex data sets.
R allows you to perform specialized statistical functions with its new statistical development packages. It also offers over 2000 libraries which covers every statistical functional area of finance.
R language consists of graph-plot packages which will let you perform plotting effortlessly. Furthermore, it also supports different tools like LaTeX which helps in exporting graphical and statistical output from R to word documents.
R has a rich and large community which can help you to overcome any difficult analytic task. Pre-written packages and online support from these communities will make your data analysis job easier.
As finance requires more complex and deep functioning of statistical computation, R offers packages that can make statistics easy for data scientists. It can also be used for research purpose in data analysis.
If you are from a technical background and has experience in a programming language, it becomes even easier to grasp advanced concepts in R once you have learned the basics.

Choosing The Appropriate Programming Language For Finance

Use in Data Science

Python: Python is generally used in data science when there are few web apps which need to be integrated with data analysis or there is few statistics code which needs to be embodied with the application.

R: R is mostly used for statistical and analytical tasks and needs a standalone server to perform data analysis. When we look from data analysis perspective, clearly R is the winner but now many users are switching from R to Python as it provides versatile functionalities in data science and big data fields.

Also Read : 7 Kick-ass Games Built Using Python Language

Data Handling Capabilities

Python: Python did have issues in the past for data handling but this issue has been improved significantly. To make Python usable for data analysis packages like NumPy and Pandas are required.

R: For basic data analysis, R can be used without installing packages. For huge data sets, packages like data.table or dplyr are required in R.

Popularity

Python: Python has a significant amount of popularity as compared to R. As Python has more opportunities in career, many programmers recommend Python as general purpose language. Developers to Data scientists Python can be used for a variety of applications.

R: R is mostly used by data scientists as it is used only for data analysis. But compared to Python, it has been outraced. As finance involves the calculation and analysis of data R would be best for you.

Market Usability

Python: Python is being used in almost all industries for data science, machine learning, and developing. Companies such as Facebook, Google and YouTube are leveraging Python.

R: R is being used in data analysis and currently has over 2 million users. It has become a popular tool for data science and is a right tool to use against complex statistics of finance.

Community

Python: If you want to seek help for data analysis you will always find a huge amount of queries and solutions from Python’s community.

R: Finding queries and solution related to data analysis and statistics issues will be found easily in R’s community.

Also Read : How to Build a Web Application Using Python

Conclusion

Choosing a better one might be tough as both provide the same features for data analytics.

So if you are a beginner and new to the field of data science, the learning phase would be the same if you chose either one of them.

If you are from statistical or analytical background and wants to stick with field of data analysis right option for you to choose would be R but if you are considering yourself to expand your knowledge from data analysis to other, Python would be best suited for you but choosing an appropriate language totally depends on you and your goal.

Python Structure For Your Project: How to do it?

Posted on October 11, 2018 by probytesnextjs

Creating a program is not only about writing code. If the program is well structured, definitely your program will function effectively.

‘Structure’ means the decisions that you will make to achieve the objective of the program easier.

Structuring the program means logic and code should be clear and concise, files should be properly organized in the file system.

All the functions in the system should be organized in a way that they smoothly interact with each other and perform a specific function for which they were designed for.

Common queries that circling around the internet world regarding the subjects are like this,

Determining which functions you need to add in different modules?

How will you process the data flow in the program?

What functions do you need to group together?

We’ll help you to plan your structure properly and achieve your targeted goal. Later we’ll also discuss how you can build extensible code.

What Should Your Program Include?

Following a structured plan is a lot easier task that eliminates any type of confusion among developers.

Also, there is a set of guideline which can be followed by developers to increase their efficiency in developing program structure.

Structure of the modules existing in a program is listed below, which will help you to prepare and constructs structured controlled code.

Python Statements

Python statements are basic units in the program which are executed by the Python interpreter.

Usually, the Python interpreter executes these statements sequentially one after another as it processes further.

Thus, it is important to structure them properly to reduce any type of errors in the execution. In Read-Eval-Print-Loop (REPL) session, statements in the program are executed continuously in a loop until the interpreter becomes terminated.

Since the statements have to be executed multiple times. It needs to be in a proper structure to minimize the error.

Generally, Python programs are well organized and occupy only one line per statement. It means Python statement occupies individual line with newline character which deliberately marks the end of the statement and end of the line.

Most of the statements in the Python program follow the same structure which is listed below.

Line Continuation in Statements

Sometimes a single statement in Python program can be very lengthy. For example:

Or there is another example of nested list:

You may have noticed that these types of statement are not at all easy to fit it into your window and forces it to render the code with horizontal scroll bars.

This can be a little irritating when these types of lengthy codes exist in a script file.

Sometimes, even editors can wrap lengthy statements to be visible which can increase the visibility, but it may not increase the readability. For instance:

Long statements in a Python program are always considered to be a poor practice.

In Python guide by Python Software Foundation stated that Python statements should not exceed more than 79 characters.

Statements will always grow as long as the complexity of the code grew up.

To organize these lengthy statements you need to break these syntaxes into different parts to increase the readability.

You need to remember that you can’t just break syntaxes wherever and whenever you want. The Python interpreter might assume the newline character has terminated the statements and raises an exception error if it doesn’t find the statements logically correct. There can be two ways to simplify and break these statements without terminating the syntax. These are

1. Implicit Line Continuation:

For line continuation, this technique is simple and straightforward. In this technique, the statements can be broken into different parts without raising an error. Usually, statements contain opening brackets (‘[’), curly braces (‘{’) and parentheses (‘(’) that are assumed to be incomplete until the closing brackets (‘]’), curly braces (‘}’) and parentheses (‘)’) are encountered. In between these, Python statements can be implicitly continued. For instance:

2. Explicit Line Continuation:

In most of the statements where the implicit line continuation technique is not applicable, you can use an explicit line continuation technique for the continuation of statements. Usually, if you use newline character (ENTER), it will indicate the end of line and interpreter may raise the Syntax Error exception. To avoid this issue you can easily introduce backslash (‘\’) character and the newline character as the last character. The interpreter will effectively be continued from the next line in continuation. For instance:

It is essential to use explicit line continuation only when implicit line continuation is not applicable.

Using Multiple Statements in a Single Line

Organizing multiple statements in single line can be easily done if they are separated by a semicolon (‘;’). For instance:

Using Comments Properly

Structuring your code sometimes need to add comment properly to indicate specific functions or its information.

To add a comment you need to use (‘#’) hash character. The Python interpreter will deliberately ignore everything which hash character includes. For instance:

Hash character inside any string is treated as a character and does not indicate a comment.

If every character inside the hash character is ignored, then what purpose does it mainly serve? Comments can be used to explain the detail of your code, for instance: