Python vs Scala: What are the differences?
Python: A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best; Scala: A pure-bred object-oriented language that runs on the JVM. Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
Python and Scala belong to "Languages" category of the tech stack.
"Great libraries", "Readable code" and "Beautiful code" are the key factors why developers consider Python; whereas "Static typing", "Jvm" and "Pattern-matching" are the primary reasons why Scala is favored.
Python and Scala are both open source tools. It seems that Python with 25.3K GitHub stars and 10.5K forks on GitHub has more adoption than Scala with 11.8K GitHub stars and 2.75K GitHub forks.
Uber Technologies, Spotify, and Netflix are some of the popular companies that use Python, whereas Scala is used by Twitter, 9GAG, and Asana. Python has a broader approval, being mentioned in 2826 company stacks & 3632 developers stacks; compared to Scala, which is listed in 437 company stacks and 324 developer stacks.
I'm making my university community web service with a team. (6 members myself included)
There are tons of languages, tools, etc., but I'm really new to programming, so I'd like to get some help to figure out what tools we need.
So my question is this: are there any good examples of web community services we can mimic the tools or get an insight from them?
Since you're following Python, I would recomend using Django as your main back-end language. If you know Python it would be a great experience. Django is well documented on their official website: https://www.djangoproject.com/ I would also use React for front-end as well. Also this article is worth reading, I think progressive web app is something worth learning these days: https://web.dev/progressive-web-apps/ Hope that helps :)
Hello, I am interested in learning how to program. I am a beginner, and many articles saying I should go with Python if I am new to programming. I considered Lua a long time ago, but for my career, I believe major programming languages should be better for me. I'm considering Python at this moment, but if you have other tools I should use, let me know.
My name is Brayden. I’m currently a Frontend React Developer, striving to move into Fullstack so I can expand my knowledge.
For my main backend language, I am deciding between Python, Rust, and Go. I’ve tried each of them out for about an hour and currently, I like Python and Rust the most. However, I’m not sure if I’m missing out on something!
If anyone has advice on these technologies, I’d love to hear it!
Rust is still in low demand. It's a great language but you'll have a hard time finding jobs. Go is the mix of both Rust and Python. Great language with modern features, fast, scalable, fun to write, and at the same time it has high demand (not as much as python).
Python on the other hand is a language that you can't go wrong with. Look around you and see what your job market prefers. If there isn't much difference to you personally, pick the one with more demand.
All of these are solid options, however considering your expertise currently, I would probably suggest Node.JS considering your past experience with JS. However Python offers a similar development environment to JS in my opinion, and Go is a good sort of intermediate between Rust and Node.JS and Python. It's fast, but not as fast as Rust, and offers a development experience that combines C-styled languages (like Rust), and Python-y languages... So: Rust for the fastest, Node for familiarity, Python for ease of development, and Go for a good middle ground. I have used all in personal projects... If you use Go, I suggest a easy to use web server framework like Fiber.
Rust is a challenging choice, but worth to be chosen. It has strong memory-safety and type-safety, this gives you no bother about those errors. However, static typing languages often slow our developing speed down in early stage. In that case, it's effective to write prototype in an easy language like Python, and rewrite it in a hard language. It's important not to be afraid to throw away first code you write.
The other answers are excellent, but I want to be a bit of a contrarian and say you should learn Rust. While the number of jobs for it are (relatively) low(er), it is certainly expanding and you'd be surprised at which companies do use Rust (Discord, for example, is starting to move away from Golang to Rust!).
But the main reason is that learning Rust itself will teach you a lot about systems design (/backend) because of its borrow checker. You can try out a lot of ideas and make a lot mistakes and the borrow checker will always be there guide you to a better solution (thereby teaching you in the process).
Also, I wouldn't underestimated how important managing memory (and memory safety) is. While Golang is great in some ways, it doesn't protect you from pushing memory leaks into production. And eventually you'll come upon a scenario where you'll have to make your Python code run faster and the optimizations you'd have to do won't look pretty (or be very Pythontic).
And Rust is freakin fast! If you have Rust, you wouldn't need any other language for the backend (or any other systems level code). Check this blog post: https://blog.discord.com/why-discord-is-switching-from-go-to-rust-a190bbca2b1f?gi=dd8bc5d669d. Discord found that even after spending months optimizing Golang code it still wasn't fast enough. But unoptimized, first-draft Rust code was (is) faster by an order of magnitude!
Python has become the most popular language for machine learning right now since almost all machine learning tools provide service for this language, and it is really to use since it has many build-in objects like Hashtable. In C, you need to implement everything by yourself.
C++ is one of the most popular programming languages in graphics. It has many fancy libraries like eigen to help us process matrix. I have many previous projects about graphics based on C++ and this time, we also need to deal with graphics since we need to analyze movements of the human body. C++ has much more advantages than Java. C++ uses only compiler, whereas Java uses compiler and interpreter in both. C++ supports both operator overloading and method overloading whereas Java only supports method overloading. C++ supports manual object management with the help of new and delete keywords whereas Java has built-in automatic garbage collection.
Go is a way faster than both Python and PHP, which is pretty understandable, but we were amazed at how good we adapted to use it. Go was a blessing for a team , since strict typing is making it very easy to develop and control everything inside team, so the quality was really good. We made huge leap forward in dev speed because of it.
I am working in the domain of big data and machine learning. I am helping companies with bringing their machine learning models to the production. In many projects there is a tendency to port Python, PySpark code to Scala and Scala Spark.
This yields to longer time to market and a lot of mistakes due to necessity to understand and re-write the code. Also many libraries/apis that data scientists/machine learning practitioners use are not available in jvm ecosystem.
Simply, refactoring (if necessary) and organising the code of the data scientists by following best practices of software development is less error prone and faster comparing to re-write in Scala.
Pipeline orchestration tools such as Luigi/Airflow is python native and fits well to this picture.
I have heard some arguments against Python such as, it is slow, or it is hard to maintain due to its dynamically typed language. However cost/benefit of time consumed porting python code to java/scala alone would be enough as a counter-argument. ML pipelines rarerly contains a lot of code (if that is not the case, such as complex domain and significant amount of code, then scala would be a better fit).
In terms of performance, I did not see any issues with Python. It is not the fastest runtime around but ML applications are rarely time-critical (majority of them is batch based).
I still prefer Scala for developing APIs and for applications where the domain contains complex logic.
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Scala is the God of languages. A legend. The Mount Rushmore of hybrid OO/functional languages is Scala's face four times over.
Ok, honestly, we love Scala. We love(d) Java (and it's parents C and C++), and we love(d) all the languages that borrowed cough stole cough from Java over the years such as Groovy, Clojure, and C#.
It may not be perfect (it totally is, but since programming languages don't have egos of their own, we don't want to paint it too bright), but it is awesome. It runs on the JVM, you can utilize Spring, it works great for data processing (which is sorta kinda the thing we do here, folks), and it just makes sense at all levels.
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
Nearly our entire server codebase is written in Scala (if you haven't heard of it, it's a programming language that is basically what you would get if Java + ML had a baby). This has worked out super well. It enables us to write concise easy to deal with code that is typechecked at compile time. It's also been a big help with recruiting.
To me, this is by far the best programming language. Why? Because it’s the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..
Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.
been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.
worked with scala for around 2 years. really enjoyed the language and getting back into the world of functional. unfortunately the community is heavily fragmented and the language itself broken and inconsistent. that with the various factions involved made it a put of for long term investment.
Scala, Akka and Spray (which became Akka-Http) provided the building blocks for the menu service.
Akka's actors and finite-state machine were a natural way to model a USSD menu (a series of stateful interactions between a subscriber and the USSD gateway).
Replaces entirely the Java Language to build a much more expressive and powerful code on the backend, while leveraging at the same time the Java Platform Tools and Frameworks, is a mixture of old and mature with new and sexy.