Python vs Swift: What are the differences?
What is 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.
What is Swift? An innovative new programming language for Cocoa and Cocoa Touch. Writing code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift is ready for your next iOS and OS X project — or for addition into your current app — because Swift code works side-by-side with Objective-C.
Python and Swift can be primarily classified as "Languages" tools.
"Great libraries", "Readable code" and "Beautiful code" are the key factors why developers consider Python; whereas "Ios", "Elegant" and "Not Objective-C" are the primary reasons why Swift is favored.
Python and Swift are both open source tools. It seems that Swift with 48.2K GitHub stars and 7.71K forks on GitHub has more adoption than Python with 25K GitHub stars and 10.3K GitHub forks.
According to the StackShare community, Python has a broader approval, being mentioned in 2789 company stacks & 3500 developers stacks; compared to Swift, which is listed in 979 company stacks and 526 developer stacks.
What is Python?
What is Swift?
Want advice about which of these to choose?Ask the StackShare community!
Multiple systems means there is a requirement to cart data across them.
Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.
But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.
Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
I'm #Fullstack here and work with Vue.js, React and Node.js in some projects but also C# for other clients. Also started learning Python. And all this with just one tool!: #Vscode I have used Atom and Sublime Text in the past and they are very good too, but for me now is just vscode. I think the combination of vscode with the free available extensions that the community is creating makes a powerful tool and that's why vscode became the most popular IDE for software development. You can match it to your own needs in a couple of minutes. Did I mention you can style it your way? Amazing tool!
The performance of Swift is almost the same as that of C++, which is considered the fastest in algorithm calculation arithmetics. Apple had this idea in mind and worked to improve the speed of Swift. For example, Swift 2.0 has beaten C++ in several computation algorithms, such as Mandelbrot algorithm. Objective-C is slower because it contains C API legacy.
Swift is faster than Objective-C, because it removed the limitations of C language and has been improved with the help of advanced technologies that were unavailable when C was developed. As mentioned by Apple, Swift was originally designed to operate faster.
Despite the fact that languages are different, they both integrate, and work with Cocoa and Cocoa Touch APIs, for all Apple platforms. Therefore, a regular app-user would not recognize the difference in operating speed between Objective-C vs Swift. Speed also depends on a programmer’s level and capabilities, since a slow app can be written in Swift as well.
Its performance approaches the one of C++ which is considered the fastest algorithm calculation arithmetics. And Apple strives to improve the speed of Swift. Learn more here https://mlsdev.com/blog/51-7-advantages-of-using-swift-over-objective-c
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!
iPhone app, a new born language, it may good but the IDE, xcode is bad compare with Visual Studio. It just like a baby. playground can only use without connect to other library...you can not do a simply refactor of renaming a variable. You can go to definition and find reference, but you can not go to implementation....I should write them on xcode not here basically it is not the fault of swift, but it tightly to it, unless you want to use a notepad to write it.
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.
Most of our newer apps are written completely in swift, with our older ones and some special cases using a mix of Swift and Objective-C, but with Swift 2, the language is pretty much a must-use. "guard" is <3.
Flutter is coded with Swift v.2.3 and can be run with Xcode v.8.2.1. To launch in Xcode 9.3, the code needs to be migrated to Swift 4.1
Most of the app code was gradually rewritten in Swift for better performance and code maintenance.