Python Training in Chandigarh

Characteristics of Python:

The following are some of the most essential aspects of Python programming:

• It has more data types and simpler syntax than any other programming language.
• It is a scripting language that is platform-independent and has full access to operating system APIs.
• It provides more run-time flexibility than other programming languages.
• It provides Perl and Awk's basic text manipulation capabilities.
• In Python, a module may contain one or more classes and free functions.
• Python libraries are cross-platform compatible with Linux, Macintosh, and Windows.
• Python may be converted to byte-code in order to construct big applications.
• Python enables both functional and structured programming, as well as object-oriented programming (OOP).
• It has an interactive mode that allows you to interact with it. Testing and debugging of code snippets .
• In Python, because there is no compilation phase, editing, debugging, and testing are all quick.

Python Features:

Python has several helpful features that distinguish it from other programming languages and make it popular and valuable. It allows for object-oriented programming, procedural programming, and dynamic memory allocation. We've outlined a few key characteristics below.

Simple to Understand and Apply:- Python is simpler to learn than other programming languages. Its syntax is simple and similar to that of the English language. The semicolon and curly-bracket are not used; the indentation defines the code block. It is the programming language of choice for newcomers.

Expressive Language:- Python can handle complicated tasks with just a few lines of code. As an example, to run the hello world programme, simply type print ("Hello World"). It will just need one line to execute, whereas Java or C will require many lines.

Interpreted Language:- Python is an interpreted language, which means that each line of the Python programme is run one at a time. The benefit of being an interpreted language is that it makes debugging simple and portable.

cross-platform Language:- Python can operate on a variety of platforms, including Windows, Linux, UNIX, and Macintosh. As a result, we may state that Python is a portable language. It lets programmers to create applications for several rival platforms by creating a single programme.

Open Source and Free:- Python is open source and free to use. It may be downloaded for free from its official website, www.python.org. It has a big global community that is committed to creating new Python modules and functions. Anyone can help the Python community. The term "open-source" refers to the fact that "anyone can obtain its source code for free."

Object-Oriented Programming Language:- Python enables object-oriented programming, and the notions of classes and objects emerge. It allows for inheritance, polymorphism, and encapsulation, among other things. The object-oriented procedure assists programmers in writing reusable code and developing applications with fewer code.

Extensible:- It indicates that other languages, such as C/C++, may be used to compile the code, which can then be utilised in our Python programmes. It transforms the programme to byte code, which may be used on any platform.

Extensive Standard Library:- It offers a wide selection of libraries for diverse disciplines such as machine learning, web development, and scripting. Machine learning libraries include TensorFlow, Pandas, Numpy, Keras, and Pytorch, among others. The most popular Python web development frameworks are Django, Flask, and Pyramids.

GUI Programming Support:- When creating a desktop programme, a graphical user interface is employed. The libraries used for creating the web application include PyQT5, Tkinter, and Kivy.

Integrated:- It is simple to integrate with languages such as C, C++, and JAVA, among others. Python, like C, C++, and Java, executes code line by line. It makes debugging the code much easier.

Embeddable:- Other programming languages' code can be used in the Python source code. Python source code may also be used in other computer languages. It is capable of incorporating different languages into our code.

Dynamic Memory Allocation:- In Python, we don't need to define the variable's data type. When we assign a value to a variable, the variable's memory is automatically allocated at run time.

Python market trends:

Many websites nowadays are built with the Python programming language. Indeed, the major aggregators have successfully created robust apps employing this Python. Python is unquestionably the programming language of the future. The information technology industry has experienced tremendous expansion, particularly in the previous 8-10 years. New programming languages and trends enter the world of innovation on a regular basis. One constant has remained constant in the ever-changing world of invention. Indeed, as you may have guessed, Python is the most well-known programming language.

Mobile Apps and Web Development:- Yes, web development is still alive and well in 2020! Who would have guessed? If you ask me, not only are there many more years of online development ahead of us, but the border between web and mobile apps is becoming increasingly blurred. To be sure, Python may not play a leading role here, but there is an advantage: you can project manage things more easily, moving team members around, because other ends of the ecosystem you're working with are likely to be developed in Python as well.

Data Science:- Python, like AI, has unequivocally established its position in the data science area alongside players like as R and MATLAB. To be honest, while these other languages were not intended to be general-purpose tools, they did outperform Python in terms of performance and features. That is no longer the true, since Python has gone a long way since then, and there is almost no given work that you cannot accomplish as well — if not more effectively — in Python as you would on these other platforms. Python is still a general-purpose language, which means it can do a lot more for you.

Finance and Cryptocurrencies:- I won't delve into whether Bitcoin and other cryptocurrencies are an economic bubble (they are! ), because it would spark an unending argument.But one thing is certain: *the applications of blockchain technology extend beyond cryptocurrencies and ICOs.)If you want to get into finance, you may apply your expertise to all financial markets, including cryptocurrency.

Cloud development:- All of the integrations you can think of, including mobile, Internet of Things (IoT), various APIs, and even managing and deploying Infrastructure as Code (IaC), all point to the cloud.As a Python programmer, this implies more chances to create microservices using the serverless execution architecture.

AI:- Artificial intelligence is everywhere these days (I defy you to identify a process that hasn't been enhanced by the introduction of AI), and it's a broad field of research in which Python excels.

Frameworks of python

Django:- Django is maybe the most well-known Python framework. It is an open-source framework that adheres to the model-view-controller architectural paradigm (MVC). It is named after Django Reinhardt, a French composer and guitarist widely regarded as one of the greatest guitarists of all time. Adrian von Holovaty and Simon Willison, two developers at the Lawrence Journal-World in Lawrence, Kansas, invented Django in 2003 to build Web programmes for the newspaper. Django comes with its own templating engine, as well as OOTB support for the widely used Jinja2 engine. It also makes use of a regex-based URL dispatcher, which allows for complex mappings with very simple URLs. One of Django's advantages has been that it can be installed as a single package. Other options necessitate developers locating numerous components just to get started. Furthermore, Django has always had excellent documentation, which has historically been a problem for open-source projects. It is a solid, well-integrated framework with a plethora of user-contributed plug-ins and add-ons. Also, the project's community appears to be much more organised, as evidenced by its extensive documentation and tutorials.

TurboGears:- TurboGears is a framework built on the foundations of several well-known Python projects, including SQLAlchemy, WebOb, Repoze, and Genshi. In some ways, TurboGears takes the approach of glueing together pre-existing open platforms. It employs the MVC architecture, as does Django. It recently added a "minimal mode," which allows it to function as a micro-framework. Kevin Dangoor created TurboGears in 2005. In September of that year, he released it as an open-source project. The project's developers shifted to support Python 3 and away from the Pylons code base with which they had previously worked.

Bottle:- Bottle is a WSGI Web framework, similar to Flask. It is distributed as a single file and has no dependencies outside of the Python Standard Library. It was created in 2009 by Marcel Hellkamp and includes basic tools such as templating, routing, and a WSGI abstraction layer. This small, powerful framework is ideal for programmers seeking flexibility and basic functionality when developing simple applications and websites or developing a Web API.

Flask:- Flask is a Python micro-framework that is built on Jinja2 and Werkzeug. It, like other frameworks, is licenced under the Berkeley Software Distribution (BSD), which is a free software licence with limited limitations. Flask is a very new framework that first appeared in 2010. Flask's conceptual goal is to not impose standards on the programmer, enabling you to use your own DB ORM, templating engine, session middleware, or other components for your project.

Tools for python

Python is a general-purpose programming language that has a large presence in data science. However, perspectives differ from person to person, but here are some resources to help you get started, or if you've already begun, this may help you.

Scikit-Learn :- Scikit-Learn is an open-source data science and machine learning platform. It is widely used for data mining and data analysis by Developers, ML Engineers, and Data Scientists. One of Scikit-most Learn's impressive characteristics is its incredible speed in completing various benchmarks on toy datasets. This tool's main features include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. It has a consistent and user-friendly API, as well as grid and random searches.

Keras:- Keras is a Python-based open-source high-level neural network framework. It is ideal for machine learning and deep learning. Keras is founded on four fundamental principles: usability, modularity, ease of extension, and compatibility with Python. It makes it easy to represent neural networks in the simplest way possible. Keras can run on top of popular neural network frameworks such as TensorFlow, CNTK, and Theano because it is written in Python.

Python Tools for Automation Testing

Selenium:- Selenium is without a doubt one of the greatest Python development tools available. It is a web application automation framework that is free source. Selenium allows you to build test scripts in a variety of computer languages, including Java, C#, Python, PHP, Perl, Ruby, and .Net.

Robots Framework:- Another open-source generic test automation framework developed for acceptance testing and acceptance test-driven development is Robot Framework (ATTD). It is keyword-driven and employs tabular test data syntax. Robot Framework combines a variety of frameworks to meet a variety of test automation needs.

Beautiful Soup:- Beautiful Soup is a Python module that allows you to extract data from HTML and XML files. You may use it in conjunction with your favourite parser to navigate, search, and change a parse tree using various Pythonic idioms. The programme, which is used for tasks such as screen scraping, can automatically convert receiving documents to Unicode and sending documents to UTF-8. It is a fantastic tool that may save you many hours of effort.

LXML:- LXML is a Python-based utility for the C libraries libxml2 and libxslt. It is a feature-rich and user-friendly library for processing XML and HTML in Python. It provides secure and simple access to the libxml2 and libxslt libraries via the ElementTree API.

Advantages of python:

Beginner friendly:- Even a complete beginner may begin programming using Python.

Multiple programming paradigms:- We can use Python as both a functional and an object-oriented programming language.

Large community:- Having a large community helps any language improve.

ocean of modules and libraries:- Python includes a large number of modules and third-party libraries.

open-source language:- Each statement's output is instantly seen in the interpreter.

dynamic programming Language:- In Python, we don't need to define the variable type. It automatically determines the kind of variable with the provided value.

Job roles and responsibilities

Python developer:- This is one of the most straightforward professions you may hope to obtain after learning this expertise. The statistics shown in the previous section clearly show that there will always be open Python developer roles to fill.

Data Analyst:- This is a fantastic opportunity for a data analyst. It is ideal for individuals who enjoy dealing with large volumes of data and finding meaning in it. This is yet another highly common employment role. Many organisations are searching for employees who can deal with enormous amounts of data that they have access to. These firms are searching for Python experts since Pandas, SciPy, and other Python libraries are quite useful in completing this work.

Product managers:- Product managers play an essential role in assisting firms in understanding the market and why creating one product is preferable to building another. They investigate the market for new features linked to a specific product or category, and use data to argue for the development of specific goods. Data is a critical component of their work. This is why, in today's market, most firms are searching for product managers that are fluent in Python.

Machine learning engineer:- If you didn't already know, job listings for this profession have risen by more than 330 percent in the previous few years. You will be given precedence over other candidates if you are proficient in Python. A machine learning engineer creates and trains machines, programmes, and other computer-based systems to make predictions based on their gained information. Python is the best programming language for machine learning because of its ability to work with data automation and algorithms.

Pay Scale:

The average yearly pay for a software engineer is 502,609. The average yearly pay for a web developer is 307,800 . The average yearly pay for a data scientist is 708,012. The average yearly pay for a DevOps Engineer is 658,143. The average yearly pay for a Machine Learning Engineer is 671,548.