Alternatives to Jupyter logo

Alternatives to Jupyter

Apache Zeppelin, PyCharm, IPython, Spyder, and Anaconda are the most popular alternatives and competitors to Jupyter.
1.2K
1K
+ 1
46

What is Jupyter and what are its top alternatives?

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.
Jupyter is a tool in the Data Science Notebooks category of a tech stack.
Jupyter is an open source tool with 8.7K GitHub stars and 3.6K GitHub forks. Here’s a link to Jupyter's open source repository on GitHub

Top Alternatives to Jupyter

  • Apache Zeppelin

    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

    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

    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

    Spyder

    It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. ...

  • Anaconda

    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

    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

    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. ...

  • Deepnote

    Deepnote

    It is a new kind of data science notebook – Jupyter-compatible with real-time collaboration and zero setup in the browser. ...

Jupyter alternatives & related posts

Apache Zeppelin logo

Apache Zeppelin

165
256
32
A web-based notebook that enables interactive data analytics
165
256
+ 1
32
PROS OF APACHE ZEPPELIN
  • 7
    In-line code execution using paragraphs
  • 5
    Cluster integration
  • 4
    Multi-User Capability
  • 4
    In-line graphing
  • 4
    Zeppelin context to exchange data between languages
  • 2
    Privacy configuration of the end users
  • 2
    Execution progress included
  • 2
    Allows to close browser and reopen for result later
  • 2
    Multi-user with kerberos
CONS OF APACHE ZEPPELIN
    Be the first to leave a con

    related Apache Zeppelin posts

    PyCharm logo

    PyCharm

    18.5K
    15K
    422
    The Most Intelligent Python IDE
    18.5K
    15K
    + 1
    422
    PROS OF PYCHARM
    • 104
      Smart auto-completion
    • 88
      Intelligent code analysis
    • 74
      Powerful refactoring
    • 57
      Virtualenv integration
    • 50
      Git integration
    • 20
      Support for Django
    • 9
      Multi-database integration
    • 7
      VIM integration
    • 4
      Vagrant integration
    • 3
      In-tool Bash and Python shell
    • 2
      Docker
    • 2
      Plugin architecture
    • 1
      Perforce integration
    • 1
      Debug mode support docker
    CONS OF PYCHARM
    • 8
      Slow startup
    • 5
      Not very flexible
    • 4
      Resource hog
    • 2
      Periodic slow menu response

    related PyCharm posts

    christy craemer

    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?

    See more

    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.

    See more
    IPython logo

    IPython

    118
    92
    4
    A command shell for interactive computing in multiple programming languages
    118
    92
    + 1
    4
    PROS OF IPYTHON
    • 1
      Interactive exploration then save to a script
    • 1
      Persistent history between sessions
    • 1
      It's magical are just that
    • 1
      Help in a keystroke
    CONS OF IPYTHON
      Be the first to leave a con

      related IPython posts

      Guillaume Simler

      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

      See more
      Spyder logo

      Spyder

      77
      118
      10
      The Scientific Python Development Environment
      77
      118
      + 1
      10
      PROS OF SPYDER
      • 5
        Variable Explorer
      • 2
        More tools for Python
      • 2
        Free with anaconda
      • 1
        Intellisense
      CONS OF SPYDER
      • 1
        Slow to fire up

      related Spyder posts

      Anaconda logo

      Anaconda

      347
      381
      0
      The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
      347
      381
      + 1
      0
      PROS OF ANACONDA
        Be the first to leave a pro
        CONS OF ANACONDA
          Be the first to leave a con

          related Anaconda posts

          Shared insights
          on
          JavaJavaAnacondaAnacondaPythonPython

          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

          See more
          Guillaume Simler

          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

          See more
          RStudio logo

          RStudio

          305
          333
          9
          Open source and enterprise-ready professional software for the R community
          305
          333
          + 1
          9
          PROS OF RSTUDIO
          • 2
            Visual editor for R Markdown documents
          • 2
            In-line code execution using blocks
          • 1
            Sophitiscated statistical packages
          • 1
            Supports Rcpp, python and SQL
          • 1
            Can be themed
          • 1
            In-line graphing support
          • 1
            Latex support
          CONS OF RSTUDIO
            Be the first to leave a con

            related RStudio posts

            Python logo

            Python

            151.3K
            125.2K
            6.5K
            A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
            151.3K
            125.2K
            + 1
            6.5K
            PROS OF PYTHON
            • 1.1K
              Great libraries
            • 933
              Readable code
            • 824
              Beautiful code
            • 772
              Rapid development
            • 674
              Large community
            • 420
              Open source
            • 380
              Elegant
            • 270
              Great community
            • 262
              Object oriented
            • 209
              Dynamic typing
            • 71
              Great standard library
            • 53
              Very fast
            • 50
              Functional programming
            • 37
              Scientific computing
            • 36
              Easy to learn
            • 31
              Great documentation
            • 25
              Matlab alternative
            • 23
              Productivity
            • 23
              Easy to read
            • 20
              Simple is better than complex
            • 18
              It's the way I think
            • 17
              Imperative
            • 15
              Very programmer and non-programmer friendly
            • 15
              Free
            • 14
              Powerfull language
            • 14
              Powerful
            • 13
              Fast and simple
            • 12
              Scripting
            • 11
              Machine learning support
            • 9
              Explicit is better than implicit
            • 8
              Ease of development
            • 8
              Unlimited power
            • 8
              Clear and easy and powerfull
            • 7
              Import antigravity
            • 6
              Print "life is short, use python"
            • 6
              It's lean and fun to code
            • 5
              Fast coding and good for competitions
            • 5
              Flat is better than nested
            • 5
              There should be one-- and preferably only one --obvious
            • 5
              Python has great libraries for data processing
            • 5
              High Documented language
            • 5
              I love snakes
            • 5
              Although practicality beats purity
            • 5
              Great for tooling
            • 4
              Readability counts
            • 3
              Plotting
            • 3
              CG industry needs
            • 3
              Beautiful is better than ugly
            • 3
              Complex is better than complicated
            • 3
              Great for analytics
            • 3
              Multiple Inheritence
            • 3
              Now is better than never
            • 3
              Lists, tuples, dictionaries
            • 3
              Rapid Prototyping
            • 3
              Socially engaged community
            • 2
              List comprehensions
            • 2
              Web scraping
            • 2
              Many types of collections
            • 2
              Ys
            • 2
              Easy to setup and run smooth
            • 2
              Generators
            • 2
              Special cases aren't special enough to break the rules
            • 2
              If the implementation is hard to explain, it's a bad id
            • 2
              If the implementation is easy to explain, it may be a g
            • 2
              Simple and easy to learn
            • 2
              Import this
            • 2
              No cruft
            • 2
              Easy to learn and use
            • 1
              Better outcome
            • 1
              It is Very easy , simple and will you be love programmi
            • 1
              Powerful language for AI
            • 1
              Should START with this but not STICK with This
            • 1
              Flexible and easy
            • 1
              Batteries included
            • 1
              Good
            • 1
              A-to-Z
            • 1
              Only one way to do it
            • 1
              Because of Netflix
            • 1
              Pip install everything
            • 0
              Powerful
            • 0
              Pro
            CONS OF PYTHON
            • 51
              Still divided between python 2 and python 3
            • 29
              Performance impact
            • 26
              Poor syntax for anonymous functions
            • 21
              GIL
            • 19
              Package management is a mess
            • 14
              Too imperative-oriented
            • 12
              Dynamic typing
            • 12
              Hard to understand
            • 10
              Very slow
            • 8
              Not everything is expression
            • 7
              Indentations matter a lot
            • 7
              Explicit self parameter in methods
            • 6
              No anonymous functions
            • 6
              Poor DSL capabilities
            • 6
              Incredibly slow
            • 6
              Requires C functions for dynamic modules
            • 5
              The "lisp style" whitespaces
            • 5
              Fake object-oriented programming
            • 5
              Hard to obfuscate
            • 5
              Threading
            • 4
              Circular import
            • 4
              The benevolent-dictator-for-life quit
            • 4
              Official documentation is unclear.
            • 4
              Lack of Syntax Sugar leads to "the pyramid of doom"
            • 4
              Not suitable for autocomplete
            • 2
              Meta classes
            • 1
              Training wheels (forced indentation)

            related Python posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

            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

            See more
            Nick Parsons
            Director of Developer Marketing at Stream · | 35 upvotes · 1.4M views

            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

            See more
            Deepnote logo

            Deepnote

            37
            47
            9
            Zero setup collaborative data science notebook in the browser
            37
            47
            + 1
            9
            PROS OF DEEPNOTE
            • 3
              Real-time collaboration
            • 3
              GitHub / Gitlab integration
            • 3
              Fully managed notebook
            CONS OF DEEPNOTE
              Be the first to leave a con

              related Deepnote posts