See the comparison table in https://www.interviewbit.com/blog/python-vs-r/ : Python Vs. R
https://www.interviewbit.com/python-interview-questions/
https://www.interviewbit.com/blog/java-vs-python/
In summary, Python is for data-intensive work or academic reasons. JavaScript is great for building apps.
C++ is commonly employed in large-scale projects and low-level hardware programming. It is also well-suited for building extensive games.
Python is for AIML s/w development and web apps.
2.Python is for quick learning & rapid development of small programs.
3. Google favoring Python and Netflix relying on C++,
Python Vs. R
a language that’s good for research work with analyses and statistics: Python and R are PLs frequently used in data science, predictive analytics and data visualization, and both have large communities of users.
R gets its name from its developers, Ross Ihaka and Robert Gentleman, 2 statisticians at the University of Auckland developed and released the language in the early 1990s to carry out and display complex statistical analyses. Original target group= those with extensive knowledge of statistics and programming. R is based on the programming language S and is a free implementation.
R can be compiled and runs on UNIX platforms, Linux, Windows. R is for developing statistics software and performing deep data analysis. R has numerous libraries. R can also be used for graphic displays of data. R is Open source.
R Developers have already created around 20,000 packages based on R, which can often provide tailor-made solutions in specialized subject areas.
Python is significantly more well known than R .
R PL was developed in 1991 by Guido van Rossum. Goal: providing the simplest code possible.
Python became a popular tool for writing scripts for audio recording and web development.
Python programming skills are needed for a wide variety of projects, from web applications to embedded systems (such as microcomputers in home appliances) to enterprise software.
Python’s is easier for beginners to learn and use than many of the alternatives.
Python is unique: Python is one of the most versatile programming languages as it supports multiple programming paradigms. For example, you can use it for object-oriented programming, aspect-oriented or functional programming. From the very beginning, Guido van Rossum’s goal: to help programmers write programs with easily readable source code.
Unlike C, Python doesn’t require curly braces because the block structure is automatically delimited by the indentations used for structuring.
If a line contains only a single statement, no semicolon is required at the end of the line.
Python doesn’t use variable declarations.
Programs written in Python are usually much more streamlined than applications coded in traditional languages (such as Java), but are just as effective.
Python provides an extensive standard library.
The programming language is available on various platforms thanks to a highly portable interpreter.
Ongoing development (in cooperation with the large Python community) is ensured.
Why did Google choose R instead of Python?
If you are interested in data visualization and statistics R is the best choice, but if you are looking to work with large datasets and machine learning python is the way to go.
Python: Pros & Cons
Pros of Python
Versatility: Python is a versatile language in every sense. It can be used in a number of areas and thus makes it possible to take a holistic approach to projects. It’s also platform independent, meaning it can be used on a number of systems. And it has numerous interfaces with other programs, languages and databases.
Open source: Like R, Python is also open source and freely available. Continued development of Python is coordinated by the Python Software Foundation, but every user can adapt the language for their own projects.
Scope: Python users have developed a wide variety of packages. There are over 300,000 solutions available for download. That makes working on most projects significantly easier.
Learning curve: Python is one of the simplest programming languages out there. Despite its impressive scope, it can be learned and used in a relatively short amount of time. The code is also relatively clear, which makes it easier to work in teams and implement small projects on your own.
Community: Python has a large community that’s constantly creating documentation and libraries. It’s known for being helpful and supportive, so if you have questions or problems you’re likely to find someone to help you.
Pros of Python
Performance: As a dynamic language, Python could certainly be faster. That’s especially true when it comes to large data sets, leading many programmers to look for alternatives in that case.
Errors: Python isn’t a particularly error-prone language but if you’ve made a mistake in the code, you won’t find out until runtime. Regular and extensive testing are therefore very important when working with Python.
Visualization: Python is also lacking when it comes to visualizing statistical values and results. There are only a few tools that can deliver truly satisfying results.
Mobile devices: Python isn’t optimal for use on mobile devices. While there are a few solutions for this, most app developers opt for an alternative language with native compatibility for Android and iOS.
Python Vs. R
So which language comes out ahead, Python or R? They’re both very powerful languages, so the answer has a lot to do with what you intend to do. If you’re primarily looking to create and visualize statistical models, R will be the better choice. If your project goes beyond statistics, Python will offer you far more possibilities
Python is a general-purpose PL. R is a statistical PL.
Python is more versatile and can be used for a wider range of tasks, such as web development, data manipulation, and machine learning.
What is better R or Python?
R: for statistical calculation and data visualization portions of data analysis.
Python for becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms.
Can Python do everything R can?
Python and R are both open-source PLs. While Python has a more general purpose, R is for specific tasks in statistical data analysis (for example, academic purposes). R and its packages provide you with enormous data visualization capabilities – your imagination is the only limit.
R is less popular than Python but is still widely recognized. It is not beginner friendly and has a steep learning curve as its syntax is difficult to read and requires programmers to write more lines of code even for simple operations. R is mainly used for complex data analysis in data science.
R is primarily intended to be used for statistical analyses and visualisations and is very good at this. Python has a far more comprehensive approach and is also suitable for programming software and deep learning.
R is a PL for statistical analysis or computing and graphics. R comes with a wide range of statistical techniques such as linear modelling, non-linear modelling, statistical tests, clustering, etc. One of R’s strengths is the ease at which a plot can be produced, including the mathematical notations and formulas.
Python tends to perform faster than R in many scenarios. For example, in a speed benchmark comparison, the Python code was found to be 5.8 times faster than the R alternative.
R:statistical calculation and data visualization portions of data analysis, R could be a good fit for you. Python is to become a data scientist and working with big data, artificial intelligence, and deep learning algorithms
Python is an interpreted, high-level OOPL. It comes with built-in data structures, dynamic typing(a process wherein type checks are done during the runtime), and binding(mapping of different objects with one another), which makes it a top language used for the development of applications. Python syntaxes are simple, easy to read, and easy to learn, hence learning Python is an excellent choice for beginners and experienced programmers alike.
The Python interpreter and libraries are free for distribution. Programmers love Python as it helps them increase their code efficiency. As it is an interpreted language, the debugging of the program becomes very easy. It includes libraries like Scikit, Keras, Tensorflow, Matplotlib, NumPy, Pandas, etc., that provide sophisticated functionalities. The addition of Jupyter Notebook, a web application to share the code live, makes the data science explanations smooth.
Versatility: The language is one of the most versatile ones. It is neat, uncomplicated to use, and well-structured. Python’s flexibility makes exploratory data analysis hassle-free. Python is object-oriented, but it makes a transition to functional features allowing itself into different paradigms of programming.
Open Source: Python can be downloaded easily. It has one of the most active supporting forums, and anyone can contribute to improving the libraries and their functionalities.
Libraries: Python has many libraries that are necessary to carry out major data science-related functions.
Productivity: Its integration and control capabilities enhance and save a lot of time.
Embeddable: Python codes are embeddable. Python codes can be integrated with other programming languages like C++.
Speed: Python is an interpreted language and thus is relatively slower than other programming languages.
Mobile environment: Python is not suitable for Android and iOS environments. Developers claim it to be weak language in such an environment. However, it can be used with additional efforts.
Memory consumption: Python consumes a significant amount of RAM. The process gets slower when more objects need to be accessed.
Database Access Layers: Python’s database access layers are underdeveloped in comparison to Java Database Connectivity(JDBC), and Open Database Connectivity(ODBC), making it a less used database connectivity.
Threading: Threading or the flow of multiple functions at the same time is a downside in Python due to its Global Interpreter Lock(GIL).
Open Source: R is an open-source language and is free to download and use. One can also contribute by optimizing its source code.
Platform independent: R is platform-independent and can work on all operating systems like UNIX, Windows, and Mac.
Data Wrangling: Through its packages like readr and dplyr, R has the capability of converting messy code into a structured one.
Plots and Graphs: Through ggplot and plotly, R creates attractive graphs with notations and formulas.
Package Availability: R has numerous packages dedicated to the development of machine learning, data analysis, and statistical projects.
Memory: R consumes more memory as all the objects get stored in the physical memory. Over time, as the program has more data, the process slows down.
Security: R lacks basic security that makes it practically difficult to embed in web applications.
Difficult to learn: Unlike Python, R is a complicated language and is difficult for a beginner to learn.
R=Slow Runtime: R is a slow processing language. R Vs. MATLAB and Python: R takes more time to give an output.
Data Handling: Data handling in R is tedious as it requires all the data to be in one place. R is not ideal for Big Data. However, it does have an integration that makes handling slightly easier.
Java Vs. Python: Java is a compiled and statically typed language, whereas Python is an interpreted and dynamically typed language. This is why learning Java is more difficult than learning Python. This one point has led to Java being easier to debug and faster at runtime than Python, and Python being more intuitive and easier to understand than Java.
Python is currently the most widely used multi-purpose, high-level PL, which allows programming in Object-Oriented and Procedural paradigms. Python programs are generally smaller than other programming languages like Java. Programmers have to type relatively less and the indentation requirement of the language, makes them readable all the time
Django is a back-end server side web framework.
Django is free, open source and written in Python.
Django makes it easier to build web pages using Python.
https://about.gitlab.com/topics/devops/ DevOps is a combination of s/w development(dev) and operations (ops). It is defined as a S.E. methodology which aims to integrate the work of development teams and operations teams by facilitating a culture of collaboration and shared responsibility.
Can I learn NodeJS without knowing JavaScript?
Learning Node. js requires solid knowledge of JavaScript, HTML, and CSS, making it moderately difficult for those not already versed in these languag
Is Node faster than Python?
Between Nodejs vs Python performance, Node allows you to code outside the web browser using TCP sockets, making it more resource-efficient. Node. js development enables caching of fetched data which is unlikely to change. This contributes to a higher speed than Python web development.
Can I learn NodeJS in one day?
Average learning time for Node. js depends on the learner's JavaScript experience, with skilled coders needing about a week of study. A prerequisite for learning Node. js is having a substantial knowledge of JavaScript.
Who owns NodeJS?
Node. js is a server-based open source development platform, created by Ryan Dahl in 2009. This platform uses the V8 virtual machine
Is NodeJS a backend?
Node. js is a runtime environment built on the V8 JavaScript engine, and is primarily used for creating both server-side and dynamic web applications. The technology isn't just another backend development tool; it also allows performing frontend-related tasks.
Does Google use NodeJS?
Google Cloud Endpoints uses Node. js to provide a client library for Google Cloud Platform services.
Is NodeJS is an API?
The Express framework, and other similar frameworks, let you build web applications using Node. js. That's not an API but a proper web application with HTML and JavaScript and CSS and everything.
Is NodeJS a frontend language?
This is not the case; Node. js can be used on the frontend as well as the backend. The event-driven, non-blocking nature of Node. js frameworks is one of the reasons it is a popular choice for developers designing a flexible and scalable backend.
Who invented NodeJS?: Ryan Dahl
Ryan Dahl (born 1981) is an American software engineer who is best known for creating the Node. js JavaScript runtime as well as the Deno JavaScript/TypeScript runtime.
Is NodeJS is a server?
Node. js is a powerful runtime environment for executing JavaScript code outside of a web browser. It brings the JavaScript language to the server-side, enabling developers to build scalable, high-performance, and event-driven applications.
What are the backend languages?
Here are some of the most popular and widely used backend languages:
JavaScript., Python, Java, Ruby, PHP, C#, Perl
Which is better Django or NodeJS?
js and Django are both great frameworks for building web applications. Node. js is perfect for real-time applications, while Django is ideal for building secure and maintainable websites.
Node.js is a runtime environment that allows JavaScript to be run on the server-side. It is fast, lightweight and easy to learn. Node.js uses an event-driven, non-blocking I/O model that makes it highly scalable and efficient. Node.js is perfect for building real-time applications, such as chats and games.
2. Performance: Both Node.js and Django are fast, can handle a large amount of traffic. Node.js is known for its scalability and the ability to handle a large # of connections concurrently.
3.Complexity: Django is more complex than Node.js. DJango requires more setup to start developing apps. Node.js is easier to learn and lightweight.
4.Ecosystem: Node.js has a larger, more extensive ecosystem of modules available through NPM. i.e. Developers can easily find pre-built functionality to add to their apps.
5.Community: Node.js has a larger community of developers, which means that there are more resources available for developers who need help or want to learn more about the framework.
6.
