Anaconda聽vs聽Python

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Anaconda vs Python: What are the differences?

Developers describe Anaconda as "The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders". 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. On the other hand, Python is detailed 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.

Anaconda belongs to "Data Science Tools" category of the tech stack, while Python can be primarily classified under "Languages".

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 2831 company stacks & 3641 developers stacks; compared to Anaconda, which is listed in 4 company stacks and 5 developer stacks.

- No public GitHub repository available -

What is 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.

What is 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.
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Why do developers choose Anaconda?
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      What are some alternatives to Anaconda and Python?
      PyCharm
      PyCharm鈥檚 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!
      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
      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
      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
      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.
      See all alternatives
      Decisions about Anaconda and Python
      Ajit Parthan
      Ajit Parthan
      CTO at Shaw Academy | 1 upvotes 4.8K views
      atShaw AcademyShaw Academy
      PHP
      PHP
      Python
      Python

      Multiple systems means there is a requirement to cart data across them.

      Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

      But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

      Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box

      See more
      Eric Colson
      Eric Colson
      Chief Algorithms Officer at Stitch Fix | 19 upvotes 894.8K views
      atStitch FixStitch Fix
      Kafka
      Kafka
      PostgreSQL
      PostgreSQL
      Amazon S3
      Amazon S3
      Apache Spark
      Apache Spark
      Presto
      Presto
      Python
      Python
      R Language
      R Language
      PyTorch
      PyTorch
      Docker
      Docker
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      #AWS
      #Etl
      #ML
      #DataScience
      #DataStack
      #Data

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

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      Node.js
      Node.js
      JavaScript
      JavaScript
      Django
      Django
      Python
      Python

      Django or NodeJS? Hi, I鈥檓 thinking about which software I should use for my web-app. What about Node.js or Django for the back-end? I want to create an online preparation course for the final school exams in my country. At the beginning for maths. The course should contain tutorials and a lot of exercises of different types. E.g. multiple choice, user text/number input and drawing tasks. The exercises should change (different levels) with the learning progress. Wrong questions should asked again with different numbers. I also want a score system and statistics. So far, I have got only limited web development skills. (some HTML, CSS, Bootstrap and Wordpress). I don鈥檛 know JavaScript or Python.

      Possible pros for Python / Django: - easy syntax, easier to learn for me as a beginner - fast development, earlier release - libraries for mathematical and scientific computation

      Possible pros for JavaScript / Node.js: - great performance, better choice for real time applications: user should get the answer for a question quickly

      Which software would you use in my case? Are my arguments for Python/NodeJS right? Which kind of database would you use?

      Thank you for your answer!

      Node.js JavaScript Django Python

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      Go
      Go
      Python
      Python
      PostgreSQL
      PostgreSQL
      TypeScript
      TypeScript
      JavaScript
      JavaScript
      NATS
      NATS
      Docker
      Docker
      Git
      Git

      Go is a high performance language with simple syntax / semantics. Although it is not as expressive as some other languages, it's still a great language for backend development.

      Python is expressive and battery-included, and pre-installed in most linux distros, making it a great language for scripting.

      PostgreSQL: Rock-solid RDBMS with NoSQL support.

      TypeScript saves you from all nonsense semantics of JavaScript , LOL.

      NATS: fast message queue and easy to deploy / maintain.

      Docker makes deployment painless.

      Git essential tool for collaboration and source management.

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      Omar Melendrez
      Omar Melendrez
      Front-end developer | 4 upvotes 4.4K views
      Vue.js
      Vue.js
      React
      React
      Node.js
      Node.js
      C#
      C#
      Python
      Python
      #Fullstack
      #Vscode

      I'm #Fullstack here and work with Vue.js, React and Node.js in some projects but also C# for other clients. Also started learning Python. And all this with just one tool!: #Vscode I have used Atom and Sublime Text in the past and they are very good too, but for me now is just vscode. I think the combination of vscode with the free available extensions that the community is creating makes a powerful tool and that's why vscode became the most popular IDE for software development. You can match it to your own needs in a couple of minutes. Did I mention you can style it your way? Amazing tool!

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      Tom Klein
      Tom Klein
      CEO at Gentlent | 4 upvotes 46K views
      atGentlentGentlent
      JavaScript
      JavaScript
      Node.js
      Node.js
      PHP
      PHP
      HTML5
      HTML5
      Sass
      Sass
      nginx
      nginx
      React
      React
      PostgreSQL
      PostgreSQL
      Ubuntu
      Ubuntu
      ES6
      ES6
      TypeScript
      TypeScript
      Google Compute Engine
      Google Compute Engine
      Socket.IO
      Socket.IO
      Electron
      Electron
      Python
      Python

      Our most used programming languages are JavaScript / Node.js for it's lightweight and fast use, PHP because everyone knows it, HTML5 because you can't live without it and Sass to write great CSS. Occasionally, we use nginx as a web server and proxy, React for our UX, PostgreSQL as fast relational database, Ubuntu as server OS, ES6 and TypeScript for Node, Google Compute Engine for our infrastructure, and Socket.IO and Electron for specific use cases. We also use Python for some of our backends.

      See more
      Praveen Mooli
      Praveen Mooli
      Engineering Manager at Taylor and Francis | 12 upvotes 697.3K views
      MongoDB Atlas
      MongoDB Atlas
      Java
      Java
      Spring Boot
      Spring Boot
      Node.js
      Node.js
      ExpressJS
      ExpressJS
      Python
      Python
      Flask
      Flask
      Amazon Kinesis
      Amazon Kinesis
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon SNS
      Amazon SNS
      Amazon SQS
      Amazon SQS
      AWS Lambda
      AWS Lambda
      Angular 2
      Angular 2
      RxJS
      RxJS
      GitHub
      GitHub
      Travis CI
      Travis CI
      Terraform
      Terraform
      Docker
      Docker
      Serverless
      Serverless
      Amazon RDS
      Amazon RDS
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon S3
      Amazon S3
      #Backend
      #Microservices
      #Eventsourcingframework
      #Webapps
      #Devops
      #Data

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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      Adam Bavosa
      Adam Bavosa
      Python
      Python
      JavaScript
      JavaScript
      asyncio
      asyncio
      PubNub
      PubNub

      I love Python and JavaScript . You can do the same JavaScript async operations in Python by using asyncio. This is particularly useful when you need to do socket programming in Python. With streaming sockets, data can be sent or received at any time. In case your Python program is in the middle of executing some code, other threads can handle the new socket data. Libraries like asyncio implement multiple threads, so your Python program can work in an asynchronous fashion. PubNub makes bi-directional data streaming between devices even easier.

      See more
      Helio Junior
      Helio Junior
      Python
      Python
      JavaScript
      JavaScript
      CSS 3
      CSS 3
      #DataScience
      #UXdesign
      #NodeJS
      #Electron

      Python is a excellent tool for #DataScience , but up to now is very poor in #uxdesign . To do some design I'm using JavaScript and #nodejs , #electron stack. The possibility of use CSS 3 to draw interfaces is very awesome and fast. Unfortunatelly Python don't have (yet) a good way to make a #UXdesign .

      See more
      Guillaume Simler
      Guillaume Simler
      at Velchanos.io | 4 upvotes 142.3K views
      Jupyter
      Jupyter
      Anaconda
      Anaconda
      Pandas
      Pandas
      IPython
      IPython

      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

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      Ruby
      Ruby
      Rails
      Rails
      React
      React
      Redux
      Redux
      Create React App
      Create React App
      Jest
      Jest
      react-testing-library
      react-testing-library
      RSpec
      RSpec
      PostgreSQL
      PostgreSQL
      MongoDB
      MongoDB
      Redis
      Redis
      React Native
      React Native
      Next.js
      Next.js
      Python
      Python
      Bit
      Bit
      JavaScript
      JavaScript

      I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

      We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

      Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis 聽for cache and other time sensitive operations.

      We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

      Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

      See more
      Node.js
      Node.js
      Java
      Java
      Spring Boot
      Spring Boot
      Python
      Python
      Flask
      Flask
      Django
      Django

      Since you said that your middleware will be accessing DB and expose API, you can go with Node.js. It will make your development fast and easy. Suppose in future you will add some business logic you can choose Java with Spring Boot or Python with Flask / Django. NOTE: Language or framework doesn't matter. Choose based on your programming knowledge.

      See more
      Jakub Olan
      Jakub Olan
      DevOps Engineer | 17 upvotes 21.7K views
      ataraclxaraclx
      Java
      Java
      Python
      Python
      C++
      C++
      Node.js
      Node.js
      Rust
      Rust
      Kotlin
      Kotlin
      Go
      Go

      In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.

      See more
      Shivam Bhargava
      Shivam Bhargava
      AVP - Business at VAYUZ Technologies Pvt. Ltd. | 9 upvotes 6.6K views
      Swift
      Swift
      Kotlin
      Kotlin
      React
      React
      Python
      Python
      Rails
      Rails
      Java
      Java
      Node.js
      Node.js
      #Neobank

      Hi Community! Trust everyone is keeping safe. I am exploring the idea of building a #Neobank (App) with end-to-end banking capabilities. In the process of exploring this space, I have come across multiple Apps (N26, Revolut, Monese, etc) and explored their stacks in detail. The confusion remains to be the Backend Tech to be used?

      What would you go with considering all of the languages such as Node.js Java Rails Python are suggested by some person or the other. As a general trend, I have noticed the usage of Node with React on the front or Node with a combination of Kotlin and Swift. Please suggest what would be the right approach!

      See more
      Saurav Pandit
      Saurav Pandit
      Application Devloper at Bny Mellon | 5 upvotes 3.8K views
      JSON Web Token
      JSON Web Token
      OpenAPI
      OpenAPI
      Java
      Java
      AngularJS
      AngularJS
      Oracle
      Oracle
      Python
      Python

      I have just started learning Python 3 week back. I want to create REST api using python. The api will be use to save form data in Oracle database. The front end is using AngularJS 8 with Angular Material. In python there are so many framework for developing REST ** I am looking for some suggestions which REST framework to choose? ** Here are some feature I am looking for * Easy integration and unit testing like in Angular we just run command. * Code packageing, like in Java maven project we can build and package. I am looking for something which I can push in artifactory and deploy whole code as package. *Support for swagger/ OpenAPI * Support for JSON Web Token * Support for testcase coverage report Framework can have feature included or can be available by extension.

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      Interest over time
      Reviews of Anaconda and Python
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      How developers use Anaconda and Python
      Avatar of Exchange rates API
      Exchange rates API uses PythonPython

      Beautiful is better than ugly.

      Explicit is better than implicit.

      Simple is better than complex.

      Complex is better than complicated.

      Flat is better than nested.

      Sparse is better than dense.

      Readability counts.

      Special cases aren't special enough to break the rules.

      Although practicality beats purity.

      Errors should never pass silently.

      Unless explicitly silenced.

      In the face of ambiguity, refuse the temptation to guess.

      There should be one-- and preferably only one --obvious way to do it.

      Although that way may not be obvious at first unless you're Dutch.

      Now is better than never.

      Although never is often better than right now.

      If the implementation is hard to explain, it's a bad idea.

      If the implementation is easy to explain, it may be a good idea.

      Namespaces are one honking great idea -- let's do more of those!

      Avatar of Web Dreams
      Web Dreams uses PythonPython

      To me, this is by far the best programming language. Why? Because it鈥檚 the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..

      Avatar of ttandon
      ttandon uses PythonPython

      Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.

      Avatar of papaver
      papaver uses PythonPython

      been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.

      Avatar of Blood Bot
      Blood Bot uses PythonPython

      Large swaths of resources built for python to achieve natural language processing. (We are in the process of deprecating the services written in python and porting them over to Javascript and node)

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