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What is Python used for? (All applications today)

What else do Google, YouTube, Instagram, Reddit and Spotify have in common besides being some of the world's most popular software services? True: they all use Python.

Python is everywhere. You can not even imagine how wide it is. Most importantly, Python is easy to learn, easy to read and easy to write. It also has a high development speed without compromising reliability or scalability.

Thanks to the high demand for Python, it is well supported and very popular.

But what exactly is Python used for? Which technology or business does Python benefit the most from? If you want to get into any of the following areas, consider whether or not you want to choose Python to develop your technology.

Python for web development

In the current market there may be a business without a website. In addition, this trend is looking for more and more impressive web applications, which include the following:

Perfect mobile and desktop versions
Asymmetric layout
Progressive web applications
Integrated animations
ML chatbots
Today, more than ever, it's important to choose the right tools when building (or possibly rebuilding) your website or web application.

Benefits of Using Python for Web Development

There are many benefits to Python that will help you get great results in web development fast:

  1. Python has a large collection of pre-built libraries for almost anything . For example, libraries for scientific computing, image processing, data processing, machine learning, deep learning, etc.

2- Python code takes less time to write due to its simple and clear syntax. Because of this, the code written in Python can build prototypes very well and quickly.

3- Python accelerates ROI of commercial projects. The reason for this is similar to the previous point: you can write and send your code faster. This is especially important for startups.

  1. Python has a built-in framework for unit testing. This helps you send bug-free code. In addition to the standard features of Python, one of its most important strengths in web development is the variety of web frameworks offered.

With a wide selection of well-supported frameworks, you can find the right starting point for any project. Python provides you with the tools to do the job with confidence:

Quick and out-of-the-box implementation solutions

Solutions that require the cooperation of many specialized micro services
A program whose performance is very important
Top Python Web Frameworks
Django: Python's most extensive web framework - at least until recently. The Django trademark is complete because it aims to provide all the tools needed to build a web application in a single package. This is a good option if your program is relatively standard, as it allows you to search through the basics and find an efficient solution faster.

Flask: Compared to Django, Flask focuses much more on minor services, which may be the reason for its new No. 1 popularity based on JetBrains. Unlike Django, which is all in one package, Flask works more like glue, allowing you to blend libraries together. Flick throws itself well into a repetitive approach to adding new features and services "once in a while."

Bottle: Bottle is another framework that prefers to ignore the fact that it overshadows the user with anything else it may need. This framework is lightweight and has no external dependencies other than the standard Python library (stdlib). Great for prototyping, as a learning tool, or for building and running simple personal web applications.

Pyramid: Pyramid perfection comes from the legacy of two previous frameworks: Pylons and repoze.bfg.Now, which are now integrated into Pyramid. Pylons was one of Python's top frameworks. The most important advantage of Pyramid over Django is that it is very easy to customize, while Django is more "thoughtful". This makes Pyramid a great choice for non-standard projects that can be more complex.

Python for Internet of Things (IoT)

Depending on your point of view, the Internet of Things can be understood differently. Because of this explanation, suppose we are talking about physical objects in an embedded system that the system connects to the Internet.

These "objects" now have their own IP address and can communicate with other "objects" remotely or locally using the network. The IoT is often involved in projects involving wireless sensor networks, data analysis, cyber physical systems, big data, and machine learning. In addition, IoT projects often involve real-time analysis and processes.

Ideally, your programming language for an IoT project should already be a powerful choice for the above areas, yet light and scalable. Python meets these criteria very well.

Advantages of using Python for IoT

  1. The popularity of Python is a significant asset. The language is supported by a large and useful community, which has led to the creation of a large collection of pre-written libraries and easier implementation and deployment of work solutions.

2- Python is portable, expandable and embedded. This makes Python independent of the system and allows it to support many of the computers on the market, regardless of architecture or operating system.

Python is great for managing and organizing complex data. This is especially useful for IoT systems, which are particularly heavy data.

4- Learning Python is easy without forcing you to get acquainted with many formatting standards and options. The most immediate result of this is faster results.

5- Python code is compact and easy to read thanks to its clean syntax. This is useful on small devices with limited memory and computing power. In addition, syntax is partly responsible for Python's growing popularity, and is further strengthening its community.

  1. Python's close connection to scientific computing has paved the way for the development of IoT. If a social scientist or biologist wants to create a program for his or her smart device in the lab, they will be happy to use their favorite language. In most cases, this language will be Python, because this technology has become scientific computing.

  2. Python is the language of choice for the Raspberry Pi. This is important because the Raspberry Pi is one of the most popular microcontrollers on the market.

  3. Python provides tools that simplify the IoT development process, such as webrepl. This option allows you to use your browser to execute Python code for IoT. In addition, the mqtt messaging protocol allows you to update your code or configuration.

  4. Because Python is an interpretive language, you can easily test your solution without compiling code or flashing the device. Using a C program, you have to compile the code on your PC, then upload it to your "object". Python allows you to enter directly into the interpreter about your "object", and this experiment makes different solutions easier.

  5. AWS provides the Python SDK for AWS IoT. Think of it as a cherry on top of a delicious cake right now.

What tools are available for Python in the IoT?

Raspberry Pi

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Have you ever seen an interesting IoT project on the web? If so, you've probably seen the Raspberry Pi.

Raspberry Pi:

It is small (85 mm 56 mm for Raspberry Pi 3).
Consumes very little energy.
Equipped with USB ports, HDMI port, Ethernet port and Micro SD support.
Most importantly, the software has Linux on board, which means it also uses Python, making Raspberry Pi encryption easy and portable. The Raspberry Pi is a super-versatile device that you can use to build anything: media center, operating system gaming device, time-lapse camera, robot controller, FM radio station, web server, system Security with motion capture, Twitter robot, small desktop computer. It is also one of the most popular tools for teaching programming.

The Raspberry Pi is an incredibly versatile device that you can use to make anything:

Media Center,
Gaming machine integration with the operating system,
Camera over time,
Controller robot
FM radio station,
Web server,
Security system with motion capture,
Twitter robot,
Small desktop computer.
It is also one of the most popular tools for teaching programming.

MicroPython

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When it comes to Python solutions for the IoT, it is no smaller than the MicroPython: a small microcontroller to run Python on a screen that is only a few square inches in size.

This set includes a bundle, so if you are just starting IoT with Python, you no longer need to look for more.

One of the most attractive features of MicroPython is WebREPL (Read-Evaluation-Print Loop), which is similar to the command line and is accessible through a web page. Using WebREPL, you can run Python code on an IoT device using a simple terminal in your browser without the need for a serial connection.

To sweeten this deal, you do not need to connect the board to WiFi, because it can create your own network.

Zerynth

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Zerynth is hailed as an "IoT and Industry 4.0 intermediary".

It provides developers with a complete ecosystem of tools including IDE, development tools, real-time RTOS, device manager, and mobile-friendly application for monitoring and controlling Zerynth-enabled devices.

Zerynth accelerates IoT development by allowing you to write articles in Python or a combination of C and Python.

You can use Zerynth to program the most popular 32-bit microcontrollers, connect them to the Cloud infrastructure, and run your devices with the latest firmware versions of Over-the-Air. It is also fully compact and requires only 60-80 KB of flash and 5-5 KB of RAM.

Home Assistant

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Home Assistant is an open source Python project for smart home automation. You can install it on your PC or Raspberry Pi.

Home Assistant works automatically; For example, it can control the lights in your home and measure the temperature of each room.

In addition, Home Assistant is compatible with a variety of drivers and sensors.

Python for machine learning (ML)
Machine learning is the newest field in the world of software development. Due to its seemingly limitless possibilities, it is regularly and correctly increasing in popularity. The idea that computers can actively learn instead of working according to written rules is very exciting. This offers a completely new approach to problem solving.

Python is at the forefront of machine learning. Numerous studies have clearly welcomed Python as the most popular language for machine learning and data science. But why is that? What is the secret of Python?

Advantages of using Python for ML
There are several reasons why Python is the best way to learn a car:

Python syntax is efficient and accurate;
Python has a low entry point.
Python integrates well with other programming languages.
But here's another argument for Python, which is more about machine learning than anything else: Extensive open source library support.

Top Python Libraries for Machine Learning
Python is especially famous for its many libraries, especially for data science. This is the main reason Python is considered as a machine learning solution. Here are some of the most popular Python libraries for machine learning.

scikit-learn

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Scikit-Learn is the best known Python library used for machine learning. scikit-Learn is built using SciPy and NumPy and is designed to interact with them. Open source, accessible to all and reusable in a number of areas.

This library has a variety of algorithms: classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. These algorithms include: support vector machines (SVM), random forests, gradient boosting, K-means and DBSCAN.

scikit-Learn provides options, the data mining and data analysis tools provided are simple and efficient.

TensorFlow

TensorFlow was originally developed by Google engineers and researchers to meet their needs for a system that can detect and train neural networks to find relationships and patterns. This process is designed in the same way that humans reason and learn.

The flexible, high-performance architecture of the open source library makes numerical computing easy across multiple operating systems, from desktops to server clusters to mobile devices.

TensorFlow is used by companies like Uber, Dropbox, eBay, Snapchat or Coca Cola.

nilearn

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Nilearn is a high-level Python library for easy and fast statistical learning of neural imaging data. This library learns from scikit-learn due to its advanced machine learning techniques, such as pattern recognition or multivariate statistics. Its applications include prediction modeling and connection analysis.

The engineering of domain-specific features has the highest nilearn value for machine learning professionals. This means shaping neural imaging data into a matrix of features suitable for statistical learning or other methods.

mlpy

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Mlpy is a high-performance Python library for predictive modeling built on top of SciPy, NumPy, and GNU Scientific Libraries. It is multimedia and open source. mlpy offers a wide range of pioneering approaches to provide solutions to supervised and unsupervised problems.

Finding a reasonable compromise between efficiency, modularity, reproducibility, maintainability and usability is the main goal of mlpy.

Python for supervised learning
Supervised machine learning is one of the applications of artificial intelligence. In supervised learning, an algorithm learns from a tagged data set whose output is already known. The two main methods in this group are classification and regression.

Classification is used to classify data into arbitrary and discrete classes and to predict discrete values, which can help assess validity or aid in medical diagnosis.

Regression is used in matters involving continuous numbers, including demand and financial forecasting, as well as property price estimation. The predicted result here is a numerical value estimate.

Classification and regression problems thanks to a large number of Python libraries, including:

scikit-Learn (backup vector machines, linear and quadratic discriminant analysis, nearest neighbor algorithms, simple Bayesian classifier, decision tree, ensemble methods, etc.);
TensorFlow;
Keras;
PyTorch;
Caffe2 (deep learning);
XGBoost;
CatBoost;
LightGBM (tilt booster).
Python for reinforcement learning
In machine learning without supervision, this algorithm relies on its ability to solve problems after accessing unlabeled datasets without training instructions and known results.

Clustering and matrix factorization are two common methods of machine learning. Both methods are often used in customer and referrer classification systems, based on the similarity between object properties, both methods are used to group elements.

Some of the most popular libraries used in clustering and recommendation system engines are:

Surprise (Neighbor-centric methods, SVD, PMF, SVD ++, NMF)
LightFM (Description of Combined Hidden Representation with Matrix Factoring)
Spotlight (uses PyTorch to build suggested models)
Python for reinforcement learning
Reinforcement learning algorithms learn to modify their behavior to make the right decisions after receiving feedback. They have been tested in self-paced solutions, including video games and traffic light control systems.

Problems with reinforcement learning are often specific and finding solutions to them can be quite challenging. These Python libraries can help you:

Keras-RL (Deep Boost Learning for Cross)
TensorForce (TensorFlow Library for Applied Reinforcement Learning)
(Coach (NAF, DQN, DFP and netlifyothers)
Python for Fintech
While Python may not be a new technology, its growing popularity among hedge funds and the investment banking industry is a recent development. But the fact that Python is the fastest language in finance should come as no surprise.

If your company wants to enter the world of fintech, you need a programming language that has high performance, easy scalability and maturity. The technical stack you choose must also have ready-made solutions and many libraries to get back to it. This allows Python and fintech to have a good relationship.

Advantages of using Python for fintech
Hedge Funds and the investment banking industry have long decided that Python is an ideal choice for fintech because it meets many of their very specific needs:

Creating risk management and trading platforms;
Solve the rate problem a bit;
Adjust information, adaptation and data analysis using the abundance of Python libraries.
Why choose Python for your fintech software product?
Fintech belongs to Python for a variety of reasons:

Clean syntax: Python code is very easy to understand because it looks like real English. This allows developers to learn it quickly and master it in a short amount of time.
Fast for the market: Python is a dynamic language and progresses faster than static languages ​​like Java. When writing in Python, you need less code, which in turn allows for faster deployment.
Useful Libraries: Python serves a wide range of purposes with a wide range of libraries. Many of these are great for fintech and finance.
Do you need an algorithmic trading library? Try pyalgotrade. A library for scientific and technical calculations? There is SciPy. What about the little economy? Check quantecon.py. Any questions you have are answered in Python.

The last word
Python programs are numerous and have many benefits. Great for many other things like web development, IoT, machine learning, startups and fintech. We have discussed in detail why Python is suitable for all of these purposes. However, a quick recap says:

Due to its readability and ease of use, Python allows you to optimize your development resources by writing faster code.
Python has a clear and simple syntax that allows you to easily browse your code.
Python gives you ready-made and tested frameworks and libraries instead of building everything you need from scratch;
Python offers extensive support for a variety of tutorials and guides as well as a strong and thriving community of enthusiasts.
Python is used by tech giants like Google, YouTube or Reddit, so if they trusted Python, there's no reason why you shouldn't trust it.

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