Alternatives to Anaconda logo

Alternatives to Anaconda

Python, PyCharm, pip, Jupyter, and NumPy are the most popular alternatives and competitors to Anaconda.
380
412
+ 1
0

What is Anaconda and what are its top alternatives?

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.
Anaconda is a tool in the Data Science Tools category of a tech stack.

Top Alternatives to Anaconda

  • 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. ...

  • 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! ...

  • pip
    pip

    It is the package installer for Python. You can use pip to install packages from the Python Package Index and other indexes. ...

  • Jupyter
    Jupyter

    The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...

  • NumPy
    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • Pandas
    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • SciPy
    SciPy

    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. ...

  • Dataform
    Dataform

    Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure. ...

Anaconda alternatives & related posts

Python logo

Python

173K
144.2K
6.6K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
173K
144.2K
+ 1
6.6K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 937
    Readable code
  • 830
    Beautiful code
  • 774
    Rapid development
  • 677
    Large community
  • 422
    Open source
  • 381
    Elegant
  • 273
    Great community
  • 266
    Object oriented
  • 211
    Dynamic typing
  • 73
    Great standard library
  • 54
    Very fast
  • 51
    Functional programming
  • 39
    Easy to learn
  • 39
    Scientific computing
  • 32
    Great documentation
  • 25
    Productivity
  • 25
    Matlab alternative
  • 24
    Easy to read
  • 20
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Free
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 12
    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
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Great for tooling
  • 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
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 4
    Readability counts
  • 3
    Lists, tuples, dictionaries
  • 3
    CG industry needs
  • 3
    Now is better than never
  • 3
    Multiple Inheritence
  • 3
    Great for analytics
  • 3
    Complex is better than complicated
  • 3
    Plotting
  • 3
    Beautiful is better than ugly
  • 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
    Flexible and easy
  • 1
    Batteries included
  • 1
    Powerful language for AI
  • 1
    Should START with this but not STICK with This
  • 1
    Good
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Better outcome
  • 1
    إسلام هشام
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 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 · | 40 upvotes · 4.8M 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 · 1.6M 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
PyCharm logo

PyCharm

21.1K
17.5K
424
The Most Intelligent Python IDE
21.1K
17.5K
+ 1
424
PROS OF PYCHARM
  • 105
    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
    Plugin architecture
  • 2
    Docker
  • 1
    Debug mode support docker
  • 1
    Perforce integration
  • 1
    Emacs keybinds
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
pip logo

pip

173
125
2
A package installer for Python
173
125
+ 1
2
PROS OF PIP
  • 2
    Best package management system for python
CONS OF PIP
    Be the first to leave a con

    related pip posts

    Jupyter logo

    Jupyter

    1.3K
    1.1K
    48
    Multi-language interactive computing environments.
    1.3K
    1.1K
    + 1
    48
    PROS OF JUPYTER
    • 17
      In-line code execution using blocks
    • 9
      In-line graphing support
    • 6
      Can be themed
    • 5
      Multiple kernel support
    • 3
      Best web-browser IDE for Python
    • 3
      Export to python code
    • 2
      LaTex Support
    • 1
      Can you provide me full list of companies Who uses Jupy
    • 1
      Multi-user with Kubernetes
    • 1
      HTML export capability
    CONS OF JUPYTER
      Be the first to leave a con

      related Jupyter 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

      I am learning Python coding and doing lots of hands on python problem. I like the feel of Jupyter notebook but I have concern will that slow my computer performance. Will PyCharm or Jupyter or Atom-IDE is good for python coding?

      See more
      NumPy logo

      NumPy

      1.4K
      672
      8
      Fundamental package for scientific computing with Python
      1.4K
      672
      + 1
      8
      PROS OF NUMPY
      • 7
        Great for data analysis
      • 1
        Faster than list
      CONS OF NUMPY
        Be the first to leave a con

        related NumPy posts

        Server side

        We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

        • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

        • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

        • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

        Client side

        • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

        • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

        • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

        Cache

        • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

        Database

        • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

        Infrastructure

        • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

        Other Tools

        • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

        • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

        See more
        Pandas logo

        Pandas

        1.4K
        1.1K
        20
        High-performance, easy-to-use data structures and data analysis tools for the Python programming language
        1.4K
        1.1K
        + 1
        20
        PROS OF PANDAS
        • 19
          Easy data frame management
        • 1
          Extensive file format compatibility
        CONS OF PANDAS
          Be the first to leave a con

          related Pandas posts

          Server side

          We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

          • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

          • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

          • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

          Client side

          • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

          • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

          • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

          Cache

          • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

          Database

          • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

          Infrastructure

          • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

          Other Tools

          • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

          • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

          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
          SciPy logo

          SciPy

          425
          143
          0
          Scientific Computing Tools for Python
          425
          143
          + 1
          0
          PROS OF SCIPY
            Be the first to leave a pro
            CONS OF SCIPY
              Be the first to leave a con

              related SciPy posts

              Dataform logo

              Dataform

              277
              36
              0
              A framework for managing SQL based data operations.
              277
              36
              + 1
              0
              PROS OF DATAFORM
                Be the first to leave a pro
                CONS OF DATAFORM
                  Be the first to leave a con

                  related Dataform posts