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Python vs R: What are the differences?
Developers describe Python as "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. On the other hand, R is detailed as "A language and environment for statistical computing and graphics". R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
Python and R belong to "Languages" category of the tech stack.
"Great libraries" is the top reason why over 1022 developers like Python, while over 58 developers mention "Data analysis " as the leading cause for choosing R.
Python is an open source tool with 25.3K GitHub stars and 10.5K GitHub forks. Here's a link to Python's open source repository on GitHub.
According to the StackShare community, Python has a broader approval, being mentioned in 2826 company stacks & 3632 developers stacks; compared to R, which is listed in 128 company stacks and 97 developer stacks.
I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.
Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.
Thanks in advance.
Hi Caue, I don't think any language is dead in 2022, and we still see a lot of Cobol and Fortran out there, so Ruby is not going to die for sure. However, based on the market, you'll be better off learning Goland and Python. For example, for data science, machine learning, and similar areas, Python is the default language while backend API, services, and other general purpose Goland is becoming the preferred.
I hope this helps.
I feel most productive using go. It has all the features I need and doesn't throw road blocks in your way as you learn. Rust is the most difficult to learn as borrow checking and other features can puzzle a newcomer for days. Python is a logical next step as it has a huge following, many great libraries, and one can find a gig using python in a heartbeat. Ruby isn't awful, it's just not that popular as the others.
Another reason to use python is that it is not compiled. You can muck around in the interpreter until you figure things out. OTOH, that makes it less performant. You really need to think about your use cases, your interest in lower-lever versus high-level coding, and so on.
Then, I have learned and worked with Golang. I use it where I think I would need a slightly better performance than in Python. Plus, relatively small and self-contained executable is a great thing to have. If you plan to write distributed systems, extend Kubernetes or do similar things I think Golang is a great choice. It's also simple and straightforward, especially when you want to do effective multithreading. Although I don't like that Golang is more low-level than Python. Sometimes I feel like I need to implement myself too much things.
Now, about Rust. It's my second try to learn Rust. First time I decided to learn Golang as I understood it in 30mins or so while I was struggling to compile/do anything meaningful there for quite a bit. So I personally don't think Rust is super easy. I have got back to learning Rust as it's going to fill one of gaps in my problem solving toolkit - let me write low-level system programs (e.g. linux kernel modules). I don't want to learn "obsolete" C/C++ (my reasons are similar to why Google has recently introduced Carbon - a replacement for C/C++ codebases). If you are not going to tight your life with system-like programming, Rust may be an overkill for you.
Finally, I have never coded in Ruby, so are not going to comment it.
Because it opens endless possibilities you can do anything and everything you want to. from ai to app development to web development.
I'm almost same position as you. 8 years same company with c#. I tried both Python and Golang. I like working with Golang. Check this litte go doc. After reading this document and following its examples, I decided to work with "go" https://www.openmymind.net/assets/go/go.pdf
Since you are very experienced, picking up a language will not take you more than a week. Rust is a very new language. Many startups are still experimenting with it. Golang is very popular nowadays. You can see a lot of golang jobs in the market. The best part is, compiled code is single binary and has a minimal footprint. Rails is a compelling framework; believe me, many websites like Shopify, GitHub, GitLab, etc., are powered by the rails framework. You can also leverage the power of metaprogramming in Ruby. Python is memory and CPU intensive. It is not as performant as the other three. If you want to go into Data Science, Python is the language. Good luck, buddy. Feel free to connect with me: https://twitter.com/avirajkhare00
Either Python or Golang, for all the enlightened reasons already mentionned in all advices/comments :) Enjoy!
it is highly recommended to take a look at that survey
I am planning to implement a ETL test system for checking data quality and business use cases. I am confused on what stack to use. Any advice on the below will be very helpful.
- Any existing frameworks and its source code for help
- Any other stack apart from the mentioned stack (that might be suitable)
- Any ideas for features are welcomed.
- The usage of multiple BE stacks.
Hello Folks, my first time here, and for requesting advice. I am trying to create some automation from my cloud stack on AWS to something more cloud native. I have containerised the services, however, I am stuck at DB, my Data warehouse, and messaging. Would love some recommendations on how can I automate this for some future work too.
I recommend cloud-init for base setup of machines and configuring them.. Its simple (YAML file) and is industry standard. Even works on bare metal as well as cloud.
Hey everyone, I have a matrix chart drawn in HTML5/CSS 3 dominantly using CSS grid. I would like to add interactive features and am unsure about the best tool. My programming knowledge is limited to 2 semesters of Java in college, so I'd have to learn the language as I go. I am open to anything, but the selected languages would be useful in future projects.
Here are the features I am attempting to add to the site linked as my blog:
Assign over 120 attributes each to over 400 elements (probably in a DB)
Procedurally position elements in a matrix chart based on user-inputted filters (filtering and searching)
Procedurally position matrix elements based on attributes weighted by user-input
Change style of elements based on user input (highlighting)
Allow saving matrix chart states to be revisited or shared
Provide a user-friendly interface for users to submit the above input
Build several columns or matrices that are separate but related and seamless to the viewer
I know Java but it need 4x time more code and code is not clear (too much forced use of @decorators) - too complex and takes more memory :)
Remember if you code in Python it is easy to code in Java but if you code in Java you must understand that Python is much more flexible and powerful - also easier to learn.
- Should I forget Python and move on?
- What's the point of me learning Python if it's not useful for web development?
You should not ditch or forget Python because of what you hear or because of one particular project. It's probably going to stay relevant and useful for the coming 20 years. If you're a programmer, you should however be prepared to use several tools, and programming languages are just part of the toolbox (like HTML or CSS, but also your IDE, powershell, linux commands, etc.) It's not for nothing that this site is called "stackshare".
Python is great for data science but it's not very performant and eats up loads of resources. I recommend that you give Go a go. It's easy to learn and very fast!
Python is definitely not useless, It has a ton of usecases, with a huge community behind it, but not that performant and consumes lots of resources, I don't think you should abandon it, and PWA is kind a in its early stage, so I doubt that there will be any language better than js for developing it any time soon, so I guess there are no alternatives, but I guess you will like js/ts if you spend a little more time playing with it, and the same goes for wasm it is also in its early stage, and i guess web assembly and rust will be used a lot for that, and lets say you have built a frontend web app , now with the help of python + django or flask you can write server code, and learn a little bit about databases, then bravo you are a full stack dev.
Actually, I'll add, C++ and C# as well.
Well, I'm into Computer Science since 1996, so I understand a bit of everything plus a lot of different OSs, I study 10 hours per day every day. However back in the 90s we didn't have books or universities about programming, all were passed through if you knew somebody in that profession. Which I did and in that time, he showed me .NET and MySQL, and that offered a lot of jobs also Java. Today you have a lot of options but I'm already discarding new languages as I believe they will jot succeed.
My always dream was to create game, and software. I don't understand all programming concepts and I'm studying all languages at the same time, so I'm heavy loaded. But that keeps me more aware.
I made a choice: use Python for everything but if you want performance, apps, security, compatibility, Multiplatform. What should I choose? The real question here is: which language should I go 100% and that language will teach me all I need about programming BUT without getting lost in that language forever (I discard any Assembly possibility) and one that has full documentation, support and libraries.
In my experience: I found a lot of info for python and java. But hardly I have ever found anything for C lang, C++ and, what about C# (it's only for Windows, is it easy, I saw a lot of documentation). Thanks!!
I would go with Python, it is fast to code, readable and very powerful without giving you too much to think about (e.g. memory management). If you're looking for speed, Cython is a fairly good way to get there, since Python is a C-based language it can be compiled to C using Cython and will get you a very significant boost in speed! You can also make use of C libraries if you prefer. The only downside to Cython over Python is that it is compiled and not interpreted, which can make debugging a pain (but you might find yourself doing most of the debugging in Python before switching to Cython). C languages are a bit of a pain to read up on (API, libraries etc.), but Stack Overflow has you covered in most cases!
Python can be linked with C++ both language are similar in many places (using same libraries or concepts to build libraries) - except memory and static types. C++ is more assembler and have different syntax (need 3x-4x coding more).
If you do engineering it is perfect stack - Java is to slow in coding (4x more code) and little faster than Python - whatever it is hard to mix Java/C++ what is easy Python/C++.
In the most program you do not need super performance but if you need C++ is the best and have rich Object Language much richer than Java and more poor than Python. Python is true object language - everything is object.
Whatever sometimes more important is framework than language for specific use.
All programming languages are cross platform except Java, but even that's not that bad. Performance: C(++), Go, Rust, Java, Ada, OCaml, Haskell, C# Apps: JS, TS, ReScript, Go, C(++), Java, Haskell, C#, Dart Security: Java, Go, Rust, COBOL, C(++), C# Compatibility: Java(due to it's VM), C(++), Go, C# Libraries: Java, Go, C(++), C# Documentation: Java, C(++) (since they are mature) What do you mean without getting lost in the language? I'd not advocate for C(or C++), considering it's hard to understand the memory, and it's for those into programming theory. You are looking for all you need. Go for Java, it has a library for everything, it has a reasonable learning curve, and pretty much you are going to encounter it everywhere- it's like a programming black hole you can't escape.
I want to create a mobile-first e-commerce platform app. I think Dart and Flutter is a way for me to build cross-platform apps from a single codebase but I might be wrong so what do you guys think?
I also don't know what to do about the back-end. I mean managing the database of products and users. handing orders and invoices. I think Firebase can be an answer to my problems but how far I can go with firebase and its user authentication and database tools? Just firebase is enough for all my back-end needs?
What suits my needs, a relational database or a non-relational database?
Do I need to learn another programming language for handling back-end, like Python or Go?
I would appreciate your opinion. Thanks
Hi, I have 3 years with Flutter and I can see that Flutter with Firebase will be a good choice for you, Just start with Firebase, it's a little bit expensive when you have a lot of users, but there you will have some money to build your own API using any other language, and here I recommend Elixir or Python.
And about what you need to learn: - Dart - Flutter - State management for Flutter - Firebase
Then you can publish your app finally, and I wish you a happy published app :)
Hello, I am still a student and would like to ask a question. Currently, I am developing in mobile development with Flutter in the frontend and Python in the backend part. Right now I have to make a choice about developing a mobile app or developing a backend to progress more professionally. My questions are as follows:
1) If I prefer the mobile application area, will I only work with the Ui/Ux developer with the front-end and code the designs in Swift Kotlin languages, am I responsible for the back-end software?
2) I have a product that generates new ideas so I like to control the development and work there because the backend is the brain, but are they independent from each other in the backend mobile application? Is the mobile app developer responsible for the backend software?
3) I don't like graphic design because I don't like it if it's not perfect and I get stressed. Am I responsible for the graphic design in the mobile app?
4) Is a mobile app developer also a backend developer?
I know these are very simple questions, but they are very important to me. Thanks for your answers.
Hi Hüseyin! 1-2) In my experience If you are a Mobile Applications Developer you will have the following responsabilities: - Develop (not designing) both functionality and screens of the app you are working - Consume (not develop) third party or self company owned APIs or Backend services - Distribution tasks. - Mantainance tasks. Now, there will always be companies wishing you know the whole thing (ui/ux, backend, frontend, mobile, cd/ci, data science, etc.). And of course it will be helpful for you to know a little bit of the stuff around mobile development, but it's not very common since it's not part of the responsabilities of a mobile app dev.
3) No, you are not responsable for the designs of your application, that's why companies have Product designers, ux designers, ui designers for preparing the screens, logos, color palettes, etc for products. As a developer your job is to see and examine the designs and take them from Figma, InVision, Zeplin, etc to the Code editor.
4) This is the thing, if you are working as a Mobile Developer you might know about Mobile development, not backend, not frontend, not ui ux. BUT if you know a little about backend that might be helpful although backend should not be your responsability.
I hope this makes sense to you. Cheers!
As a mobile developer, I'm usually a member of a larger team and it's usually another person's responsibility to develop the backend/API, and another person's to do the UX/design. Very very few teams use cross-platform tools like Flutter or React Native, because tools like those tend to make mediocre apps that scale poorly and are impossible to debug, so make sure to get familiar with Swift/iOS or Kotlin/Android (or both).
Hi! I think most of your questions led to these answers:
Mobile software developers don't responsible for the back-end part, or even graphic design. Of course, the back-end part should be done by a back-end developer. The graphic design, I'd say that if you work on a start-up, you might be the one who does since there isn't much manpower there, but in the larger company, they would have a designer especially in UI/UX. You'll have a mockup for the application that you need to follow. As a developer, you're expected to code, not design.
I've said that the responsibility isn't yours, but of course, you'll have an advantage over others if you know UI/UX, or back-end as well. That would help you a lot to be a good mobile developer.
Generally speaking, what are the most important things you expect a junior developer to know and be able to do from day 1 in your respective tech stack? Firm grasp of OOP? SQL? MVC? ORM? Algorithms and Datastructures? Understanding CRUD & the request response cycle? Database design? framework familiarity? Postman? Deployment? TDD? Git? Language-specific knowledge? Other things?
Start with building a solid understanding of computer science fundamentals. Understand the basics of building blocks - memory, processing, storage, networking. Understand what CPU bound, memory bound, I/O bound, network bound processes are. Understand the cost of accessing data from Memory vs. Disk vs Network. Understand how multiple CPU threads help in optimizing the performance of a single machine.
Build expertise on a programming language. You may pick any language of your choice. I would recommend starting with Java / Python. Make sure you know one language really well. Build a strong understanding of Data Structures and Algorithms. You should be able to develop an intuition on when to use what. You may practice DS and Algorithm problems, using the language of your choice, on a competitive coding platform (e.g. Leetcode) or by building your own App!
Next, get familiar with basic cloud computing and distributed system concepts. Here is a good resource for that - https://www.youtube.com/watch?v=p7NkTUyEE1o&ab_channel=JeffreyRichter If you understand the computer science fundamentals well, you will be able to apply those concepts here as well.
Hope it helps!
Ability to read code and willingness to try to reason flow of operations and information. Tools and technologies change, one doesn't need to have them in toolbelt from day one. All things you name are relevant in some contexts, so it's not bad to understand them.
For me, it is less of a specific technology you know (although I would prefer you have some knowledge of some of my team stack). It is more the way you get into a problem, the eagerness to learn more, the true sincerity to say "I don't know", the open mind to find solutions in different ways and the "Yes we can" mentality no matter how hard it is.
Just learn to learn. Learn to search and develop your logical thinking, that's all you need. No books, no deep study of how computers work, just logic and willingness to learn
Most employers don't expect from you to know how to implement CI/CD or any other funcy stuff. As junior developer you should focus on building a good toolset of good software practices & principles. Your soft skills are important as well. Learn about soft skills. Be eager to learn, be humble and show you talent and your creativity through your work. If you want to become a good developer ( at first) and a star engineer (at a later stage) then computer programming (coding) is your number one priority . Coding is like painting. Putting aside your talent, you have to practice a lot and improve your outcome each time. As junior developer you can learn how to write good code by studying existing code found in public git repositories (i e , github). As junior developer you should study some good software principles (i.e., DRY, KISS, YAGNI) and always recall them each time you write software code. As junior developer you should learn about coding standards and conventions. You will have to follow to your company's coding conventions (soon or later) as well as you will realize that you have to write code cosistent to the existing code base. At the end of the day, code consistency matters a lot. You have to improve your code day by day. If you manage to follow some good software practices you will find out that you will need an ORM to work with your database. Then you will realize that you need the X web framework to build your REST API etc. To sum up, you will start building a toolset with a single programming language and some good software practices & principles and then you will put new tools in it day-by-day.
Hi! I'm currently studying Flutter for mobile apps, but I also have a demand to automate some tasks on the web and create backends' for my apps, so thinking about which one of those could be better? Considering the performance and how easy it's to learn and create stuff? (I'm already familiar with .NET stack but want something more "simple" to write)
Definitely Python. Lots of libraries, dead simple syntax. Lots of code examples and reference projects. Elixir is pure functional and takes time to grasp the concepts. Go is great, with simple syntax and performant runtime, but more strict as it is statically typed. For quick coding, nothing beats Python. As you come from .net I’d consider similar approach and be considering Java with SpringBoot as it makes Java faster and much more fun to code web servers
Elixir really has a good performance for the web (and in general). Its framework Phoenix for the web is a great tool, easy to install and to use, with features for websockets (and Pub/Sub) or LiveView to write reactive and real time app with only HTML (and Elixir) so basically everything is in one place
It can take some time to learn a few things in Elixir but I really think it's worth it, and it's very easy to go distributed and concurrent with Elixir. Also it's easier to code quickly with some features like the pattern matching or some operators like the pipe or the capture one
And in the case you need it you can still connect and interface Python and Elixir pretty quickly, and now Elixir has a lot of different frameworks : web, embedded or even neural networks now
Never went far with Go but I have some trouble with its syntax, I find it a bit messy
I don't have a lot of experience with the web with Python but I don't have a good experience with the little I did
Judging your previous experience we will benefit from Golang in terms of portability and speed. If you want to go simplier use Python. If it's only scripts use Python.
Hey Vitor, You can use Node and Express JS to create a backend for your app. You can create REST APIS to connect your front end with the backend. It is a very simple and scalable solution for building backend web apps.
We have chosen a mix of Java and Python for building an open source data observability tool. The application can work as a standalone command line tool with a rich shell interface (using even command completion). The Java ecosystem is more mature when it comes to connectivity to various databases using JDBC. Also picocli with jline3 let us make a very dynamic shell interface with command completion. The definitions of data quality checks that should be executed are defined in YAML files, backed by a YAML (in fact JSON) schema files. Our YAML files can be edited in Visual Studio Code (and other code editors) with support of the code completion. It is possible because all the data model is defined as pure Java classes for which we are generating a YAML/JSON schema. There is still place for Python because it is very popular in the database space. We are simply starting a Python interpreter in the background (from a Java code). Python is used to evaluate validation rules (defined as Python functions) and render SQL queries from Jinja2 templates.
A developer and project manager from our team X says the following about our use of Rails at i22:
"We use Rails to build stable and flexible backend systems. Rails is extremely good for managing data structures and quickly setting up new systems. It is the perfect base for most use cases."
I asked the same Team X member why the team prefers to work with Ruby on Rails, rather than Python and Django:
"Because Python is a scripting language and from my point of view not suitable for building stable web services. Python is for me rather good for scripts and fast small tools. Not for stable business applications. And if I want it fast I prefer Go."
As we're developing a critical piece of software, type safety is very important to minimize the errors we have. While Python supports type hints nowadays, Go makes it much more easy to work with and allows us to be confident in the software we ship.
Take look at our code in our github
Ever since the introduction of the PWA, I felt forced to learn JS, React, and Angular. I encountered WASM, which compiles Go/Rust to JS. I decided to give go a shot and made a simple weather PWA that tells the weather of various Japanese cities. It was 40x faster than Transcrypt and 0.9x faster than regular JS. Go is even simpler than Python when coming to tools like list comprehension and Pandas.
Coming from a C/C++ background, I picked up PHP 20 years ago. Today, the language is still in constant evolution while still having a stable base. It powers all of my backend project. It is fast to prototype and get started, and is supported almost everywhere.
Python and Node.js do not provide anything that PHP cannot already offer, so there is no point for me to switch to those language. Mature framework like Laravel provides real ease and speed of development to kick-start any new web project, be it a simple API or a robust ERP running on server-less architecture. There are libraries available for machine learning, crypto, web3 and pretty much anything you can think of.
We chose Rust for our web API because the Warp crate makes it easy to compose high-performance and asynchronous APIs. Rust allows us to achieve high development velocity because it provides zero-cost abstractions and enforces strict type and memory-safety checks with high quality and actionable error messages.
Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.
Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.
Python: The top language in machine learning area because of the various open-source libraries. Our company will rely on open-source libraries for development as well.
Amazon EC2: Training machine learning model needs to be running on independent 3rd party computing resources. AWS EC2 can provide a variety of virtual computing resources based on what users need.
ExpressJS: Everyone in the team has used expressJS for development. It can create server-side web applications faster and smarter.
Amazon RDS: relational database service and free to use
Postman: Tool for the team to test API endpoint.
Circle CI: is lightweight and open. Therefore for faster deployment jobs, one can execute their codes on CircleCI as it deploys on scalable and robust cloud servers.
Docker: Easily pack, ship, and run any application as a lightweight, portable, self-sufficient container, which can run virtually anywhere
Github+Git: Julian is from Github so no other choice for us 😎
Slack: Everyone likes it and it's free
Pros of Python
- Great libraries1.2K
- Readable code948
- Beautiful code835
- Rapid development780
- Large community682
- Open source426
- Great community278
- Object oriented268
- Dynamic typing214
- Great standard library75
- Very fast56
- Functional programming51
- Scientific computing43
- Easy to learn43
- Great documentation33
- Matlab alternative26
- Easy to read25
- Simple is better than complex21
- It's the way I think18
- Very programmer and non-programmer friendly15
- Machine learning support14
- Powerfull language14
- Fast and simple13
- Explicit is better than implicit9
- Clear and easy and powerfull8
- Ease of development8
- Unlimited power8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- Python has great libraries for data processing5
- Fast coding and good for competitions5
- There should be one-- and preferably only one --obvious5
- High Documented language5
- I love snakes5
- Although practicality beats purity5
- Flat is better than nested5
- Great for tooling5
- Readability counts4
- Rapid Prototyping4
- Web scraping3
- Multiple Inheritence3
- Complex is better than complicated3
- Beautiful is better than ugly3
- Now is better than never3
- Lists, tuples, dictionaries3
- Socially engaged community3
- Great for analytics3
- CG industry needs3
- Simple and easy to learn2
- Import this2
- No cruft2
- Easy to learn and use2
- List comprehensions2
- Pip install everything2
- Special cases aren't special enough to break the rules2
- If the implementation is hard to explain, it's a bad id2
- If the implementation is easy to explain, it may be a g2
- Easy to setup and run smooth2
- Many types of collections2
- Flexible and easy1
- Powerful language for AI1
- It is Very easy , simple and will you be love programmi1
- Batteries included1
- Can understand easily who are new to programming1
- Should START with this but not STICK with This1
- Only one way to do it1
- Because of Netflix1
- Better outcome1
- Good for hacking1
Pros of R Language
- Data analysis82
- Graphics and data visualization61
- Great community44
- Flexible statistical analysis toolkit37
- Access to powerful, cutting-edge analytics26
- Easy packages setup26
- R Studio IDE12
- Shiny apps7
- Shiny interactive plots6
- Automated data reports5
- Preferred Medium5
- Cutting-edge machine learning straight from researchers4
- Machine Learning2
- Graphical visualization2
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Cons of Python
- Still divided between python 2 and python 351
- Performance impact28
- Poor syntax for anonymous functions26
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow10
- Not everything is expression8
- Explicit self parameter in methods7
- Indentations matter a lot7
- Poor DSL capabilities6
- Incredibly slow6
- No anonymous functions6
- Requires C functions for dynamic modules6
- Hard to obfuscate5
- Fake object-oriented programming5
- The "lisp style" whitespaces5
- Official documentation is unclear.4
- Circular import4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- The benevolent-dictator-for-life quit4
- Meta classes2
- Training wheels (forced indentation)1
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0
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