Why is Python Growing so Quickly? | Future Trends

Python has various applications ranging from Python for web development to data science to system administration and DevOps.

The fastest-growing Python-related tag is Pandas, responsible for academic research, data science, and machine learning.

Python is evenly growing across all industries, predominantly in manufacturing, software, government, and universities.

The fastest-growing application of Python is visible in data science, Machine Learning, and academic research.

Many organizations have adopted these technologies for business development and growth, making Python their programming language of choice.

The different frameworks of Python support the various applications. For example, Django and Flask are used for web andapplication development with Python.

On the other hand, NumPy and Pandas are used for data science packages.

Checkout 10 real-world Python Use Cases and Applications

Why is Python Growing so Quickly?
Python development services are booming in the digital landscape, majorly because of their versatility, robust features, and simple syntax.

1. Vast Library and Framework Support
Dedicated Python developers have access to libraries with many tried and tested modules. This allows them to be able to develop software faster and in a cost-effective manner.

As a result, the cost of development is also effectively reduced.

Some popular libraries that support Python are:

Tensorflow: Released by Google, TensorFlow allows developers to keep Deep Learning functionalities, including image detection, face recognition, and others.
Pandas: It is involved with high-performance data structure management and will enable developers to use analysis models.
Scikit Learn: It is used for data mining, Machine Learning, and data analysis in Python web application development.
Keras: It is used as a Machine Learning guidance for Python app development.
Numpy: It provides support in scientific and mathematical computing to developers.
Some popular frameworks that Python supports are:

Django: It is an efficient and secure framework for complex application development. It allows developers to build customizable apps with the benefits of Python.
Pyramid: As the name suggests, this framework allows developers to scale their applications as the project grows.
Tornado: It is a non-blocking network library that scales up to thousands of open connections and makes it the best-suited alternative for long polling.
Flask: This is a microframework that allows developers to build small and flexible applications with reduced effort and time.
2. Easy to decipher and learn
Python contributes to building superior quality applications in less time than any other software in the market. The simple reason behind it is the easily understandable language and clean syntax that Python offers.

It allows developers to detect and debug the codes written by other developers and data engineers as it has a close resemblance to English.

Time saved is money saved with Python software development. It also reduces the time to launch applications into the market.

Several applications, including scientific computations, AI, data science, and Machine Learning, use Python because of its lean learning curve.

3. Open Source Environment
Python has a robust environment that is most suitable for enterprise development.

All latest versions of Python are affiliated with the Open Source Initiative, which ensures all benefits of Python to its developers without a subscription fee.

Its open-source environment includes a wide range of frameworks and libraries like Django, Pyramid, Pandas, Numpy, etc.

Python development companies can benefit from the range of free tools that the software ensures.

They can also work on Graphic User Interfaces, web development, app development, image analysis, game development, data science, and AI, etc.

Read more: A Definitive Python Development Guide Pros and Cons of Python

Growth Patterns in Python App Development Growth by Visitor tags
The growth patterns in Python app development companies can also be classified by user category. As per the Stack Overflow trends, the following is the influence of each type on Python’s growth:

Data Scientists: The most visited tags by data scientists were Pandas, NumPy, or Matplotib.
Web developer: The most visited tags among Python web developers were JavaScript, Django, HTML.
System administrators/DevOps Engineers: The top visited tags were Linux, Bash, or Windows.
Growth by Industry
Another way to study the growth in Python language is by checking the industries and companies it is used by.

Taking into consideration the two countries for Python development as the United States and United Kingdoms, the following are the growth patterns:

More than 20% of the academic industry traffic is redirected towards Python development services. The reason for this is the teaching of Python as a leading programming language in colleges and universities.
Python is widespread and rapidly growing in the government sector, majorly for Python migration services and data security.
Government, manufacturing, and technology – all three industries have around 10-12% of traffic dedicated to Python app development.
With the latest digital transformation wave, healthcare, finance, insurance, and consulting industries have seen a spike in Python development services.
Python benefits in Healthcare: Checkout How it helps

Python Development: Future Trends 2021
With Python, we are talking about one of the most demanded programming languages in the world. The year of pandemic crisis, 2020, observed around 50% growth in Python web development across industries.

We can vouch that Python is the language for now and for the years to come. The prospective growth domains for Python are as follows:

Big Data Trends:
Being the most adaptable and chosen language among developers and an open-source framework, many sectors will benefit from its real-time facts analysis.
Speech processing, data synthesis, and machine learning are other attributes that promise the growth of Python in the future of Big Data.
Game Development:
The vivid network of libraries and built-ins/plug-ins make Python an easily understandable concept among game developers and programmers.
It is used as the ideal language to create fictional scenarios, characters, and plotlines on top of the unique C/C++ graphic and gaming engines.
It’s foreseen that Python will have more leverage in the gaming sector in 2021.
Machine Learning:
The flexible and highly versatile nature of Python app development makes it the most suitable language forMachine Learning projects. The same features also enable it to cater to the dynamic needs of Artificial Intelligence projects.
Python-based networks of libraries like SciKit, NumPy, and TensorFlow help build data processing pipelines, implement machine learning algorithms and visualization to enhance the development lifecycle.
Business Applications:
Business/Enterprise Applications demand high scalability and quality, which Python best delivers. Many domains like ERP, e-commerce, recruiting, and more have an upward growth curve for Python software development.
Python also offers libraries like Tryton, which provide a space for successful application development.
Embedded Applications:
In the current scenario, C remains the leader for embedded computing, but Python is rapidly growing. It is predicted that Python will take over this domain of embedded development.
Python is equipped to program cameras, microcontrollers, face recognition, etc., to build state-of-the-art solutions.
On top of this, Python’s extensive community and frameworks will make it easy for the developers to shift from C to Python shortly.

Facts to Know about Robotic Process Automation

RPA stands for robotic process automation, which is an automation technology that uses robotics and/or artificial intelligence (AI). RPA is regarded as the future of automation testing, with a distinct methodology and numerous advantages. It differs from the conventional automation method, which is carried out with the help of a test automation tool such as QARA Enterprise, Katalon Studio and Ranorex.

These tools are AI-powered, allowing them to intelligently analyse the user’s behaviour on the applications. RPA has a wide range of features and is widely regarded as the future of automation.

Robotic process automation (RPA) is a form of business process automation technology that uses software robots (bots) or artificial intelligence to automate business processes.

A software developer creates a set of actions to automate a task and connects to the back-end framework using internal application programming interfaces (APIs) or a dedicated scripting language in conventional workflow automation tools. RPA systems, on the other hand, create an action list by watching a user perform a task in the application’s graphical user interface (GUI), and then automate those tasks directly in the GUI. This will lower the barrier to using automation in goods that don’t have APIs for this reason otherwise.

RPA tools are somewhat similar to graphical user interface testing tools in terms of technology. These tools often automate interactions with the GUI, often by repeating a series of user-delivered demonstration behaviour. RPA tools vary from similar systems in that they enable data to be managed through various applications, such as receiving an invoice via email, extracting the data, and typing it into a bookkeeping system.

10 Facts about RPA

The most intriguing aspect of RPA is that, unlike traditional automation technology, it needs little effort from the QA team. Traditional automation technology includes writing test steps and automating them with the aid of a scripting language or API. RPA is smart enough to automate test case execution simply by watching QA practitioners execute the measures on a standard user interface (GUI).
RPA can be comparable to the GUI research that QA practitioners use today, but it is much more sophisticated and advanced technology with far greater capabilities.
RPA technology is one of the most easily implemented new technologies in 2020. However, when it comes to RPA implementation, scalability is one factor that needs to be addressed.
It’s a technology that uses “modelling” robots to model or mimic the test steps that QA professionals perform. It will be fascinating to see what possibilities RPA has in store for the future.
It all comes down to teaching the machine to be able to learn the flow of work while also familiarising it with the various rules that must be followed for effective automation.
RPA allows computers to log into websites and apps in the same way as humans do, and also send and receive emails without the need for human interaction.
Within the next 2 years, a staggering 72% of organizations will be implementing RPA in one form or the other, according to Gartner.
In 2018, RPA was named the fastest-growing Enterprise software technology in the market, and it continues to soar to new heights.
Experts predict that RPA will dominate the digital domain by the end of 2020.
Migration to the Cloud and, at the same time, semantic computing will be the foundations of RPA implementation.
So, those are some important robotic process automation (RPA) facts to be aware of. It’s a smart technology with a lot of potential, and we’re confident that RPA can open up several more doors in the future that we can’t even imagine right now.

Why So Many Types of Steel?

Essentially, steel is an alloy of iron and carbon with some other metals mixed in. Depending on the type of steel, aluminium, chromium, nickel, cobalt, tungsten and molybdenum may be present in small quantities. Steel is hard, ductile, malleable and solid. Numerous things that you use every day are made at least partially from some type of steel, including kitchen knives, pots and pans, automobile frames, tools, furniture, bridges and buildings.

High carbon steel contains a higher percentage of carbon than other types of steel. High carbon steel is often used to make cutting tools such as knives, axes, scissors and swords. It is also used in woodworking tools. The high percentage of carbon makes this type of steel both harder and more brittle than some other types of steel. High carbon steel tools can be sharpened easily but they are not hard enough to cut other hard materials such as metal or stone.

Mild steelis so-called because it contains only a low percentage of carbon. Mild steel is very versatile and is used in the manufacture of a wide variety of items, including sheet metal roofs, automobile frames, construction panels and large storage containers. Mild steel has also replaced wrought iron as the metal used in rail road tracks.

Medium carbon steel is in between high carbon steel and mild steel in terms of carbon content. Medium carbon steel has more hardness than mild steel and more tensile strength than high carbon steel, making it suitable for use in springs, tool frames and automotive components.

Stainless steel is especially corrosion resistant due to the 11 per cent chromium and small amount of nickel present in the alloy. Steel is more rust resistant than iron anyway, but stainless is the most resistant, not only to rust, but also to scratches and other types of corrosion. Stainless steel is a very common material for jewellery, watches, surgical equipment, cooking utensils and kitchen appliances.

High speed steel includes chromium, cobalt, tungsten or molybdenum in the alloy. High speed steel is especially tough and can be used to cut other metals and types of steel. Drills, power saws and other tools are made from high speed steel. The exact toughness and uses of high speed steel depend on the particular metal and percentage in the alloy.

Cobalt steel is very similar to high speed steel and contains a high amount of cobalt. Cobalt steel is not quite as hard as high speed steel, but it is often used for drilling. Cobalt steel drill bits have a slight brown tint.

Nickel chromium steel has the unique property of being shock resistant. Combined with its strength, this attribute makes nickel chromium steel ideal for use in armour plating.

Aluminium steel is smooth and fairly strong. Steel furniture is typically made from aluminium steel.

Chromium steel, like stainless steel is very corrosion resistant. Automobile and airplane components are often manufactured using chromium steel.