Before starting a project, most development teams meet several times to decide the best language for their software.

Often this discussion comes down to Python and Golang.

Today Elitech Systems offers you a comparison between Golang and Python, on different parameters that will help you decide as a developer which language is best for you.

What is Python?

Python is one of the fastest growing programming languages. Python favors simplicity, easy-to-read code, and uses these features to attract people who are hesitant to learn complicated programming languages.

An important feature of Python is that it is an interpreted language.

Therefore, Python does not need to be compiled into machine code before it is run.

However, code interpretation generally takes longer, which means that Python is slower than compiled languages.

Many of today’s most successful tech companies choose Python for the back-end of their website.

In this list, there are notably Instagram, Spotify or even Netflix to name a few.

What is Golang?

Golang, also known as Go, is a computer programming language developed by Google.

Its development started in 2007 at Google, and it was presented to the public in 2009.

The objective was to create a language, freely based on the syntax of the C programming language, which would eliminate “foreign waste” from languages ​​such as C ++.

As a result, Go instills many characteristics of other modern languages, such as overloading of methods as well as that of operators, pointer arithmetic and type inheritance.

Go, is a static typing language with a library that has nothing to envy to other languages, as well as performance and increased speed.

Docker, the most active open-source project in the world, is coded in Go as well as Uber and in particular Twitch, which shows that Golang is gaining more and more weight on the market.

Key Differences Between Python and Go

Below are the key differences between Python and Go:

• Python being a scripting language has to be interpreted whereas Go is faster most of the time since it does not have to consider anything at runtime.
• Python is a great language with an easy to understand syntax and hence more readable, flexible whereas Go is also in the prime league when it comes to clear syntax which holds zero unnecessary components.
• Python does not provide built-in concurrency mechanism whereas Go has built-in concurrency mechanism.
• When it comes to safety, Python is a strongly typed language which is compiled, hence adding a layer of security whereas Go is very decent since every variable must have a type associated with it. It means a developer cannot let away the details which will further lead to bugs.
• Python is less verbose than Go to achieve the same functionality.
• Python has tons of libraries as compared to Go but gradually this situation with Go is improving.
• Python still gains an upper hand when it comes syntax and thus user-friendly.
• Python is still a favorite language when it comes to solving data science problems whereas Go is more ideal for system programming.
• Python is dynamically typed language whereas Go is a statically typed language, which actually helps catch bugs at compile time which can further reduce serious bugs later in the production.
• Python is great for basic programming, using it can become complicated if one wishes to build complex systems whereas, with Go, the same task can be accomplished rapidly without going into subtleties of programming language.
• Python is more concise than Go. Dynamic typing helps but overall, one can also write very compact code with Go.

Python and Go Comparison Table

Below is the list of points, Describe the comparison Between Python and Go.

Speed

Go is definitely faster than Python. This makes sense, as it was designed specifically for high performance competition in the construction and rapid deployment of server-side scripts.

If you are programming systems, then Go might be the best choice. In addition, Python is used for just about everything in the world of software development and data science.

Over the next 20 years, as more developers and businesses implement Go, it could become a stronger competitor to Python. For now, Python still reigns supreme even if Go is faster.

Scalability

Go was specially designed for scalability the size of Google. The language makes it possible to manage several tasks and quickly increase the size of the application according to demand. It is designed to be scaled. Go is therefore very scalable over time and is a great force for him.

Python was not originally designed to be very scalable.

In terms of comparison to Go, Go is going to be the winner. Python has multiprocessing capabilities and a rich library, but Go being a little more modern wins hands down

Conclusion

It is difficult to draw conclusions in the Go vs. discussion. Python because comparing a mature language with a relatively young language does not seem fair.

Python is the leading language not only in machine learning, data analysis, but also in web development.

Golang has only been in existence for a decade, and it has not yet built a solid ecosystem or community.

An obvious advantage of Go is its speed. While Python bows to Go in terms of code execution, Go admits defeat in production speed.

On January 9, 2020, the World Health Organization (WHO) officially launched the alert based on information provided by the Chinese authorities. What was a series of unknown pneumonia cases initially observed in Wuhan province is now identified as a new type of coronavirus whose exponential spread is turning into a pandemic. The question, since, is often repeated: could the alert have been given earlier?

And for the Canadian company BlueDot, the answer is yes; it claims to have been able to alert its customers to the spread of the coronavirus as of December 31. Its sources: press articles, Internet and aerial data.

AI to raise the alarm

By the end of December 2019, several players were indeed detecting worrying signs of contamination. For the many specialists according to whom the alert could have been given earlier, we could rely on medical data identified by many state bodies and data collected on social networks. This big data, coupled with machine learning , would make it possible to “follow” the evolution of certain diseases such as the flu… and, today, the Covid-19.

These “forecasts”, obtained thanks to the massive data processing by AI, thus offer a better visibility of the evolution, in particular geographical, of the epidemics and their propagation, by using the network. But this use of AI, before the crisis, is not the only possible one.

Speed ​​up clinical trials with AI… and save lives with machines?

Artificial intelligence can also be used to speed up clinical trials, simulate chemical compositions of new drug leads, or even test patients’ lung x-rays to detect Covid-19 more quickly. 20 seconds and 96% accuracy: this is the time required and the precision of the algorithm developed by the Alibaba group to detect, from a chest scanner, the contamination or not of a patient with Covid-19.

On the side of “machines”, we can speak of artificial respirators as “robots to keep alive”. Faced with the scale of the crisis, the number of respirators became insufficient, but industrialists, roboticists, etc. organize themselves to help in the production of spare parts, valves and masks.

Several decontamination robots are also already in operation on the streets and in hospitals in China. Equipped with UV-C lamps, they diffuse decontaminating products. In France, robots are gradually being deployed, such as the decontaminating robot from the La Rochelle company Shark Robotics .

A robot to do your shopping and assist isolated people

Let us not forget the fleets of robot-deliverers who, by moving without any contaminating physical contact, become a precious help for populations in crisis situations. Small or large, these robots allow human deliverers to be safe like the others …

Others rather help to keep in touch with our elders, in nursing homes or confined to their homes. Indeed, many tele-presence robots, often singled out as simple “gadgets”, become the only possible means of contact, without danger, with the outside world. Some of these robots are, for example, currently deployed in France in many nursing homes .

Finally, and in line with the issue of deliveries, automated warehouses operating without human beings drastically reduce the problems of contamination by goods. We have been able to see that in the event of a pandemic, most of the automated sites are able to provide valuable logistical assistance to feed the population confined to their homes.

From prevention… to surveillance by AI?

Initial tests of drones equipped with cameras and speakers were carried out within the police force in France, and more precisely in Nice. The objective is to allow the police to disseminate information and control exits during containment by avoiding contact with the officers.

In China, helmets equipped with thermal cameras have been provided to local police to enable them to detect pedestrians with a temperature above normal. But we are touching here on a more controversial aspect of the use of new technologies, since it touches on the delicate question of surveillance in the name of prevention. Let us mention nevertheless, to perhaps come back to it in more detail another time, geolocation “apps” such as Alipay , developed in China and which assigns a QR-Code according to your risk of contamination. If your test is positive for Covid-19, all the people with whom you have been in contact in a given period are warned and also become “at risk” and their QR-Code changes color.

A technology thus making it possible to isolate people “at risk”, but whose possible drifts worry – we are already talking about enhanced surveillance. It could indeed be deployed in Europe with the aim of “softening” the containment measures and better managing the peaks to come.

Virtual reality… and training!

But all is not subject to controversy, far from it! To finish with another domain, the situation has shown all the usefulness and creativity of distance training , or tele-training, to continue one’s professional training … or take advantage of it to improve or discover an area during confinement. It should be noted, however, that some training can hardly be done on a two-dimensional screen; fortunately virtual, mixed or augmented reality comes to the rescue of learners!

Already used to learn several dangerous trades and technical gestures, virtual reality can simulate all kinds of situations and facilitate learning, for example, the use of a fire extinguisher. This opens up a whole new field of possibilities, doesn’t it?

The number of IoT developers continues to grow and a market growth rate of 10% per year is expected until 2030. Within the IoT industry, several programming languages ​​are popular. This is particularly the case for 6 of them: Java, C, Python JavaScript, Swift and Php.

Java

Java is a language that has multiple benefits for IoT development . This is mainly used for the development of set-top boxes, which is one of the first areas of non-desktop computing.

The advantages of Java are well known: developers can debug their code from their desktop and then move it to any chip with a Java Virtual Machine. The code can therefore work not only on servers and smartphones, but also on smaller machines.

Java SE Embedded allows this code to be sent to very small machines and offers more possibilities than did Java ME. Developers can use the latest features of the Java 8 platform to send their code to very small embedded cards.

Several open source projects like Pi4J and BlueJ show that the embedded version of Java works very well, including on chips that seem limited.

VS

Despite a sometimes indigestible syntax and a million possibilities to make mistakes, the C language is still the first choice of many low level developers, especially for those who already have some hardware experience. The language makes it possible to carry out very numerous tests by modifying such or such part of the code to obtain the best possible performance from a weak device. Each bit can be reversed, and each value in the stack is available. It is however preferable not to make too many errors, because the C language does not offer many safety nets.

For many CTOs, C remains a preferred language for resource-constrained devices , such as a basic task scheduler also coded in C.

More advanced or larger devices also use the C language, but Java is increasingly preferred in this type of case.

On the smartphone side, part of the programming is still delivered in Objective C, but Swift is on the way to exceeding its popularity.

Python

Python has been gaining momentum for two years and is at the top of the general classification of programming languages ​​2018 of the IEE. For small devices with enough memory and computing power, developers are free to choose the language that will make their life easier, and it turns out that Python is becoming more and more of a frequent choice.

Both easy to learn and supported by a large community , Python is particularly practical for IoT development. Its syntax is clear and simple, and attracts more and more developers. Python is the ideal language for the most popular microcontrollers on the market, Raspberry Pi. Much of the documentation is written in Python, and many schools use the platform to teach computer programming. If the project is relatively simple and does not require great power, it is quite possible to develop effective tools from the same libraries as those used in school.

Some versions are also designed for smaller devices, such as MicroPython, a small microcontroller optimized to run Python in bare metal and offers a low-level operating system to control all kinds of electronic projects.

JavaScript

Surprisingly enough, JavaScript is quite popular for IoT application development. Most developers use Node.Js and focus their work on servers and gateways or hubs collecting and storing information . Smaller hubs and sensors running on Linux can usually run on Node.JS.

But even if most of the Node.JS code works on larger machines, several efforts are made to allow it to work on smaller devices. From small microcontrollers like Tessel or Espruino can execute JavaScript, which allows some developers to make IoT without having to learn a new language.

Swift

Swift is becoming more and more popular and is gradually overtaking Objective C in the development of iOS and macOS applications. Given the preponderance of these devices on the market, it makes sense that this language is often part of the IoT stack. To interconnect an object with an iPhone or iPad, the application will probably be coded in Swift.

Apple intends to make its iOS devices the center of the connected home of the future, so that the company has created numerous booksellers and an infrastructure considerably facilitating the handling of development. Apple’s HomeKit platform provides support for integrating data flows from a network of compatible objects. It is therefore possible to focus on the details and delegate the supervision of the integration to HomeKit.

PHP

If we understand that the popularity of Php resists the weight of years on the web, we can wonder about the reasons for its popularity for IoT development. Php is however an ideal server language for managing microservices. But the language also shows an important interest at low level. Several Raspberry Pi developers are considering setting up a complete LAMP stack with Apache, MySQL and Php running on Linux, and are paradigm shifting by transforming the lowest level of the Internet into a full-fledged web server.

When a Raspberry Pi has enough cycles, setting up a LAMP stack on the chip facilitates development. All the code developed on the server side over the past 20 years can then be sent to a small sensor. Not very orthodox, but visibly effective, so why deprive yourself of it?