Python is a high-level, interpreted programming language. Python is quickly ascending to the forefront of the most popular programming languages in the world. Python is a general-purpose programming language, so it can be used for many things. Python is used for web development, AI, machine learning, operating systems, mobile application development, and video games.
Python is used in every industry and scientific field which you can imagine as follows:
Python offers pre-built libraries and frameworks such as Pyramid, Django, and Flask; web applications can be developed rapidly. Python is a go-to for complex web development projects, as its flexibility makes it possible to create sophisticated web utilities with relative ease.
Python Frameworks provides unparalleled security, scalability, and convenience as compared to building websites from scratch.Giant websites and platforms like YouTube and Google rely heavily on Python for critical infrastructure. This has further enhanced its reputation as a solid component in the web developer’s toolkit.
Interactive games can be built with Python; it provides functionality and libraries for game development. Some popular games like EVE Online and Pirates of the Caribbean were created using Python. There are libraries and frameworks such as Pygame, Pyglet, and PyOgre to help you create your own games in Python.By using the
Pygame module, you can control the logic and graphics of your games without worrying about the backend complexities required for working with video and audio.
Entire development platforms with every tool you need including editors, animation, render engines, export functionality, etc. Python is sort of stuck in the middle ground in this regard. Yeah, Pygame is okay. But that’s just it, and okay isn’t the best.Most people who are looking to create a serious career in the gaming industry will choose C# or C++ instead, using Python for hobby projects and understanding the concepts of coding.
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.Python is used for machine learning via specific machine learning libraries and frameworks including scikit-learn and TensorFlow.Machine Learning is simply making a computer perform a task without explicitly programming it. In today’s world every system that does well has a machine learning algorithm at its heart.
Take for example Google Search engine, Amazon Product recommendations, LinkedIn, Facebook etc, all these systems have machine learning algorithms embedded in their systems in one form or the other. They are efficiently utilising data collected from various channels which helps them get a bigger picture of what they are doing and what they should do.
Now let’s understand why would anyone want to use only Python in designing any Machine Learning project. Machine learning, in layman terms, is to use the data to make a machine make intelligent decisions. For example — You can build a spam detection algorithm where the rules can be learned from the data or an anomaly detection of rare events by looking at previous data or arranging your email based on tags you had assigned by learning on email history and so on.
Python can be easily integrated with other programming languages. The applications built using Python are easily scalable and future-oriented. All the above-mentioned features of Python makes it important for the data scientists. This has made Python the first choice of Data Scientists.Python is an object-oriented, open-source, adaptable and simple to learn programming language. It has a rich arrangement of libraries and tools that makes the assignments simple for Data scientists.
As per a survey by industry analyst O’Reilly, about 40% of data scientists use Python in their day-to-day work for computing, analyzing and reporting of data. Big organizations such as NASA, Google, and CERN also use Python for almost every purpose of programming.
In many scenarios, Python is the programming language of choice for the daily tasks that data scientists tackle, and is one of the top data science tools used across industries. For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often.
Python offers an interactive environment in which to explore procedural, functional and object oriented approaches to problem solving. Its high level data structures and clear syntax make it an ideal first language, while the large number of existing libraries make it suitable to tackle almost any programming tasks.Python is so widely used as a teaching tool because:
It is easy to learn: its syntax is simple and it can be learned quickly. Students start diving into more advanced aspects of computer science much more quickly than with other programming languages.
It is powerful: it is used in real-world applications, so students immediately start acquiring valuable skills for their careers.
It is versatile: it supports various programming paradigms including imperative programming, functional programming, procedural programming, and object-oriented programming.
Python's syntax is simple and straightforward, so students can start learning computational thinking and problem-solving skills much more quickly, which is usually the main goal of introductory computer science courses.
Python is the language with the greatest potential to be used across the breadth of biology. It is increasingly utilized by folks spanning from traditional bioinformatics to climate modelers.
Python and its scientific packages emerged as a promising platform for researchers in neuroscience and psychology, including PsychoPy for running experiments (Peirce, 2007, 2009), pandas1 and statsmodels2 for data analysis, PyMVPA (Hanke et al., 2009) and scikit-learn (Pedregosa et al., 2011) for machine learning data analyses, and NeuroDebian (Halchenko and Hanke, 2012) as an overarching platform providing an easy deployment of these tools. Together, these tools are increasingly opening possibilities for development, sharing and building upon experimental and analysis code.
Computer vision is the automated extraction of information from images. Information can mean anything from 3D models, camera position, object detection and recognition to grouping and searching image content.When programming computer vision we need representations of vectors and ma-trices and operations on them. This is handled by Python’s NumPy module where both vectors and matrices are represented by the array type.
OpenCV is the most popular library for computer vision. Originally written in C/C++, it also provides bindings for Python. OpenCV is a C++ library with modules that cover many areas of computer vision. Be-sides C++ (and C) there is growing support for Python as a simpler scripting language through a Python interface on top of the C++ code base. The Python interface is still under development and not all parts of OpenCV are exposed and many functions are undocumented.
Python has played its amazing role in the medical field. It's really a wonder for us to know how technology is being combined with medical knowledge to provide accurate and efficient diagnoses and treatments to patients.Some examples of the use of Python in medicine and pharmacology are here:
These broad applications include thousands of examples around the world.
According to the official Python Success Stories, one of the world's leading pharmaceutical companies, AstraZeneca, used Python to improve their existing computational models to make them more robust, extensible, and maintainable. AstraZeneca is one of the world's leading pharmaceutical companies. With over 54,000 employees world-wide, it provides innovative, effective medicines designed to fight cancer, provide pain control, heal infection, and fight diseases of the cardiovascular, central nervous, gastrointestinal, and respiratory systems.
Researchers used Python based models to simulate the chemical structure of molecules and their effect in the body. This helped scientists identify potential molecules for new drugs and start testing them more quickly in the laboratory.
The use of Python is also growing among the scientific community, and Astronomy is one of this.The power of Python consists of being an extremely versatile high-level language, easy to program that combines both traditional programming and data reduction and analysis tools. Python also has applications in Astronomy and Astrophysics.There are three main Python packages used in the scientific area:
This package contains various classes, utilities, and a packaging framework intended to provide commonly-used astronomy tools.Astropy is part of a larger project called The Astropy Project, which is "is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages.
This package is described as "the community-developed, free and open-source solar data analysis environment for Python." It builds upon the capabilities of Python packages such as NumPy, SciPy, Matplotlib, and Pandas.
This package is "a package for Python, targeted at the space sciences, that aims to make basic data analysis, modeling and visualization easier."
Python is a good choice if you want to become a robotics developer (i.e. program robots), especially if you want to program your robots with ROS. In robotics and mechatronics education tools for programming, simulation, visualization and control are common practice to clarify abstract theory, to support and validate designs and to perform experiments and process their results. In our education the Python programming language together with its rich ecosystem is playing a central and powerful role.There are various Python libraries used to program robots as follows:
Top 10 Python Libraries You must know For Robotics:
Python has become the language of choice for data science and artificial intelligence ..two technology trends essential for global businesses to stay competitive today.It’s used across a wide variety of applications from web development to task automation to data analysis.
Due to its readability and versatility, Python is used by businesses of all sizes. Startups may optimize a small engineering team’s workflow by utilizing Python’s efficient syntax and leveraging its many package libraries. Large enterprises may turn to Python to process giant datasets using machine learning algorithms. Python can be a powerful tool to analyze data generated by businesses and to forecast future trends. Python’s simplicity and reliability have long been its biggest draws, while the success enjoyed by the many digital products built with the language demonstrate that it’s the right choice for a variety of different applications.
Python is also useful for Earth Scientists. The Python package climate-indices contains Python implementations of various climate index algorithms which provide a geographical and temporal picture of the severity of precipitation and temperature anomalies useful for climate monitoring and research. Python library MetPy is an Open Source project aimed at providing a Pythonic library for meteorological data analysis that meshes well with the rest of the scientific Python ecosystem. The project heavily leverages the work already done by the Numpy, Scipy, and Matplotlib projects, and adds on top functionality specific to meteorology: plotting (e.g. Skew-Ts), calculations, and reading files
A graphical user interface (GUI) is an application that has buttons, windows, and lots of other widgets that the user can use to interact with your application. A good example would be a web browser. It has buttons, tabs, and a main window where all the content loads. Python can be used to create graphical user interfaces. There are many graphical user interface (GUI) toolkits that you can use with the Python programming language. The big three frameworks are Tkinter, wxPython, and PyQt. Each of these toolkits will work with Windows, macOS, and Linux, with PyQt having the additional capability of working on mobile.
Python's popularity with autonomous vehicle engineers is largely due to the various available resource libraries such as AI, Deep learning and Data Visualization.Building an autonomous vehicle is complex.Whether an engineer is retrofitting an existing vehicle for autonomy or building an autonomous vehicle from the ground up, there’s a lot of work to be done. Not only do they need to program over 250 million lines of code on the hardware in the vehicle, they also need to navigate all the programs, tools, and platforms to develop, deploy, optimize, and manage the autonomous vehicle software and hardware.