What is Jupyter and what are its top alternatives?
Top Alternatives to Jupyter
- Apache Zeppelin
A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more. ...
- PyCharm
PyCharm’s smart code editor provides first-class support for Python, JavaScript, CoffeeScript, TypeScript, CSS, popular template languages and more. Take advantage of language-aware code completion, error detection, and on-the-fly code fixes! ...
- IPython
It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. ...
- Spyder
It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. ...
- Anaconda
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...
- RStudio
An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages. ...
- Python
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. ...
- Postman
It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...
Jupyter alternatives & related posts
- In-line code execution using paragraphs7
- Cluster integration5
- Multi-User Capability4
- In-line graphing4
- Zeppelin context to exchange data between languages4
- Privacy configuration of the end users2
- Execution progress included2
- Multi-user with kerberos2
- Allows to close browser and reopen for result later2
related Apache Zeppelin posts
- Smart auto-completion112
- Intelligent code analysis93
- Powerful refactoring77
- Virtualenv integration60
- Git integration54
- Support for Django22
- Multi-database integration11
- VIM integration7
- Vagrant integration4
- In-tool Bash and Python shell3
- Plugin architecture2
- Docker2
- Django Implemented1
- Debug mode support docker1
- Emacs keybinds1
- Perforce integration1
- Slow startup10
- Not very flexible7
- Resource hog6
- Periodic slow menu response3
- Pricey for full features1
related PyCharm posts
UPDATE: Thanks for the great response. I am going to start with VSCode based on the open source and free version that will allow me to grow into other languages, but not cost me a license ..yet.
I have been working with software development for 12 years, but I am just beginning my journey to learn to code. I am starting with Python following the suggestion of some of my coworkers. They are split between Eclipse and IntelliJ IDEA for IDEs that they use and PyCharm is new to me. Which IDE would you suggest for a beginner that will allow expansion to Java, JavaScript, and eventually AngularJS and possibly mobile applications?
I am a QA heading to a new company where they all generally use Visual Studio Code, my experience is with IntelliJ IDEA and PyCharm. The language they use is JavaScript and so I will be writing my test framework in javaScript so the devs can more easily write tests without context switching.
My 2 questions: Does VS Code have Cucumber Plugins allowing me to write behave tests? And more importantly, does VS Code have the same refactoring tools that IntelliJ IDEA has? I love that I have easy access to a range of tools that allow me to refactor and simplify my code, making code writing really easy.
- Interactive exploration then save to a script1
- Persistent history between sessions1
- It's magical are just that1
- Help in a keystroke1
related IPython posts
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
- Variable Explorer6
- More tools for Python2
- Free with anaconda2
- Intellisense1
- Slow to fire up1
related Spyder posts
Anaconda
related Anaconda posts
Which one of these should I install? I am a beginner and starting to learn to code. I have Anaconda, Visual Studio Code ( vscode recommended me to install Git) and I am learning Python, JavaScript, and MySQL for educational purposes. Also if you have any other pro-tips or advice for me please share.
Yours thankfully, Darkhiem
I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud
RStudio
- Visual editor for R Markdown documents3
- In-line code execution using blocks2
- Can be themed1
- In-line graphing support1
- Latex support1
- Sophitiscated statistical packages1
- Supports Rcpp, python and SQL1
related RStudio posts
Python
- Great libraries1.2K
- Readable code962
- Beautiful code847
- Rapid development788
- Large community690
- Open source438
- Elegant393
- Great community282
- Object oriented272
- Dynamic typing220
- Great standard library77
- Very fast60
- Functional programming55
- Easy to learn49
- Scientific computing45
- Great documentation35
- Productivity29
- Easy to read28
- Matlab alternative28
- Simple is better than complex24
- It's the way I think20
- Imperative19
- Free18
- Very programmer and non-programmer friendly18
- Powerfull language17
- Machine learning support17
- Fast and simple16
- Scripting14
- Explicit is better than implicit12
- Ease of development11
- Clear and easy and powerfull10
- Unlimited power9
- It's lean and fun to code8
- Import antigravity8
- Print "life is short, use python"7
- Python has great libraries for data processing7
- Although practicality beats purity6
- Now is better than never6
- Great for tooling6
- Readability counts6
- Rapid Prototyping6
- I love snakes6
- Flat is better than nested6
- Fast coding and good for competitions6
- There should be one-- and preferably only one --obvious6
- High Documented language6
- Great for analytics5
- Lists, tuples, dictionaries5
- Easy to learn and use4
- Simple and easy to learn4
- Easy to setup and run smooth4
- Web scraping4
- CG industry needs4
- Socially engaged community4
- Complex is better than complicated4
- Multiple Inheritence4
- Beautiful is better than ugly4
- Plotting4
- Many types of collections3
- Flexible and easy3
- It is Very easy , simple and will you be love programmi3
- If the implementation is hard to explain, it's a bad id3
- Special cases aren't special enough to break the rules3
- Pip install everything3
- List comprehensions3
- No cruft3
- Generators3
- Import this3
- If the implementation is easy to explain, it may be a g3
- Can understand easily who are new to programming2
- Batteries included2
- Securit2
- Good for hacking2
- Better outcome2
- Only one way to do it2
- Because of Netflix2
- A-to-Z2
- Should START with this but not STICK with This2
- Powerful language for AI2
- Automation friendly1
- Sexy af1
- Slow1
- Procedural programming1
- Ni0
- Powerful0
- Keep it simple0
- Still divided between python 2 and python 353
- Performance impact28
- Poor syntax for anonymous functions26
- GIL22
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow12
- Indentations matter a lot8
- Not everything is expression8
- Incredibly slow7
- Explicit self parameter in methods7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Fake object-oriented programming5
- Threading5
- The "lisp style" whitespaces5
- Official documentation is unclear.5
- Hard to obfuscate5
- Circular import5
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- The benevolent-dictator-for-life quit4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Easy to use490
- Great tool369
- Makes developing rest api's easy peasy276
- Easy setup, looks good156
- The best api workflow out there144
- It's the best53
- History feature53
- Adds real value to my workflow44
- Great interface that magically predicts your needs43
- The best in class app35
- Can save and share script12
- Fully featured without looking cluttered10
- Collections8
- Option to run scrips8
- Global/Environment Variables8
- Shareable Collections7
- Dead simple and useful. Excellent7
- Dark theme easy on the eyes7
- Awesome customer support6
- Great integration with newman6
- Documentation5
- Simple5
- The test script is useful5
- Saves responses4
- This has simplified my testing significantly4
- Makes testing API's as easy as 1,2,34
- Easy as pie4
- API-network3
- I'd recommend it to everyone who works with apis3
- Mocking API calls with predefined response3
- Now supports GraphQL2
- Postman Runner CI Integration2
- Easy to setup, test and provides test storage2
- Continuous integration using newman2
- Pre-request Script and Test attributes are invaluable2
- Runner2
- Graph2
- <a href="http://fixbit.com/">useful tool</a>1
- Stores credentials in HTTP10
- Bloated features and UI9
- Cumbersome to switch authentication tokens8
- Poor GraphQL support7
- Expensive5
- Not free after 5 users3
- Can't prompt for per-request variables3
- Import swagger1
- Support websocket1
- Import curl1
related Postman posts
We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.
Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username
, password
and workspace_name
so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.
Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.
This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.
Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct
Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.
Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.
Our whole Node.js backend stack consists of the following tools:
- Lerna as a tool for multi package and multi repository management
- npm as package manager
- NestJS as Node.js framework
- TypeScript as programming language
- ExpressJS as web server
- Swagger UI for visualizing and interacting with the API’s resources
- Postman as a tool for API development
- TypeORM as object relational mapping layer
- JSON Web Token for access token management
The main reason we have chosen Node.js over PHP is related to the following artifacts:
- Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
- Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
- A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
- Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.