Alternatives to Anaconda logo

Alternatives to Anaconda

Python, PyCharm, pip, Jupyter, and Pandas are the most popular alternatives and competitors to Anaconda.
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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. ...

  • 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. ...

  • 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. ...

  • 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. ...

  • PySpark

    PySpark

    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data. ...

Anaconda alternatives & related posts

Python logo

Python

131.7K
105.8K
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A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
131.7K
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PROS OF PYTHON
  • 1.1K
    Great libraries
  • 928
    Readable code
  • 817
    Beautiful code
  • 768
    Rapid development
  • 671
    Large community
  • 418
    Open source
  • 379
    Elegant
  • 268
    Great community
  • 261
    Object oriented
  • 209
    Dynamic typing
  • 70
    Great standard library
  • 52
    Very fast
  • 48
    Functional programming
  • 35
    Scientific computing
  • 33
    Easy to learn
  • 30
    Great documentation
  • 25
    Matlab alternative
  • 23
    Productivity
  • 22
    Easy to read
  • 19
    Simple is better than complex
  • 17
    It's the way I think
  • 17
    Imperative
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerful
  • 14
    Free
  • 13
    Fast and simple
  • 13
    Powerfull language
  • 12
    Scripting
  • 9
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Unlimited power
  • 8
    Ease of development
  • 7
    Import antigravity
  • 7
    Clear and easy and powerfull
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 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
    CG industry needs
  • 3
    Beautiful is better than ugly
  • 3
    Multiple Inheritence
  • 3
    Complex is better than complicated
  • 3
    Great for analytics
  • 3
    Socially engaged community
  • 3
    Rapid Prototyping
  • 3
    Lists, tuples, dictionaries
  • 3
    Plotting
  • 2
    Generators
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 2
    List comprehensions
  • 2
    Special cases aren't special enough to break the rules
  • 2
    Now is better than never
  • 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
  • 1
    Many types of collections
  • 1
    Better outcome
  • 1
    Batteries included
  • 1
    Ys
  • 1
    Good
  • 1
    Pip install everything
  • 1
    Easy to setup and run smooth
  • 1
    Because of Netflix
  • 1
    Flexible and easy
  • 1
    Web scraping
  • 1
    Should START with this but not STICK with This
  • 1
    Powerful language for AI
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Only one way to do it
  • 1
    A-to-Z
  • 0
    Pro
  • 0
    Powerful
CONS OF PYTHON
  • 50
    Still divided between python 2 and python 3
  • 27
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 19
    Package management is a mess
  • 19
    GIL
  • 13
    Too imperative-oriented
  • 12
    Hard to understand
  • 11
    Dynamic typing
  • 9
    Very slow
  • 8
    Not everything is expression
  • 7
    Explicit self parameter in methods
  • 7
    Indentations matter a lot
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 6
    Requires C functions for dynamic modules
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Hard to obfuscate
  • 4
    Fake object-oriented programming
  • 4
    Incredibly slow
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 3
    Official documentation is unclear.
  • 3
    Circular import
  • 3
    Not suitable for autocomplete
  • 1
    Training wheels (forced indentation)
  • 1
    Meta classes

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 38 upvotes · 3.7M 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

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Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.3M 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

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PyCharm logo

PyCharm

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13K
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The Most Intelligent Python IDE
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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
    Docker
  • 2
    Plugin architecture
  • 1
    Debug mode support docker
  • 1
    Perforce integration
CONS OF PYCHARM
  • 7
    Slow startup
  • 4
    Not very flexible
  • 3
    Resource hog
  • 1
    Periodic slow menu response

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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?

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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.

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pip logo

pip

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1
A package installer for Python
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PROS OF PIP
  • 1
    Best package management system for python
CONS OF PIP
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    Jupyter logo

    Jupyter

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    Multi-language interactive computing environments.
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    PROS OF JUPYTER
    • 14
      In-line code execution using blocks
    • 8
      In-line graphing support
    • 5
      Multiple kernel support
    • 5
      Can be themed
    • 3
      Best web-browser IDE for Python
    • 3
      Export to python code
    • 2
      LaTex Support
    • 1
      Multi-user with Kubernetes
    • 1
      HTML export capability
    CONS OF JUPYTER
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      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
      Pandas logo

      Pandas

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      High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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      19
      PROS OF PANDAS
      • 18
        Easy data frame management
      • 1
        Extensive file format compatibility
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        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
        NumPy logo

        NumPy

        807
        584
        6
        Fundamental package for scientific computing with Python
        807
        584
        + 1
        6
        PROS OF NUMPY
        • 6
          Great for data analysis
        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
          SciPy logo

          SciPy

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          112
          0
          Scientific Computing Tools for Python
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          0
          PROS OF SCIPY
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            CONS OF SCIPY
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              PySpark logo

              PySpark

              141
              159
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              The Python API for Spark
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              PROS OF PYSPARK
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