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JSON vs Python: What are the differences?

Developers describe JSON as "A lightweight data-interchange format". JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language. 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.

JSON and Python can be primarily classified as "Languages" tools.

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 2830 company stacks & 3640 developers stacks; compared to JSON, which is listed in 20 company stacks and 104 developer stacks.

- No public GitHub repository available -

What is JSON?

JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language.

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 JSON?
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      What are some alternatives to JSON and Python?
      YAML
      A human-readable data-serialization language. It is commonly used for configuration files, but could be used in many applications where data is being stored or transmitted.
      Protobuf
      Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler.
      Avro
      It is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
      MongoDB
      MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
      OData
      It is an ISO/IEC approved, OASIS standard that defines a set of best practices for building and consuming RESTful APIs. It helps you focus on your business logic while building RESTful APIs without having to worry about the various approaches to define request and response headers, status codes, HTTP methods, URL conventions, media types, payload formats, query options, etc.
      See all alternatives
      Decisions about JSON and Python
      StackShare Editors
      StackShare Editors
      Kubernetes
      Kubernetes
      Go
      Go
      Python
      Python

      Following its migration from vanilla instances with autoscaling groups to Kubernetes, Postmates began facing challenges while “migrating workloads that needed to scale up very quickly.”

      The built-in Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization. But the challenges for Postmates is that there’s no way to configure the scale velocity of one particular cluster with an HPA.

      For Postmates, which runs at least three different types of applications with distinct performance and scaling characteristics, this proved problematic.

      To overcome these challenges, the team created and open sourced the Configurable Horizontal Pod Autoscaler, which allows for fine-grained tuning on a per-HPA object basis. The result is that “you can configure critical services to scale down very slowly, while every other service could be configured to scale down instantly to reduce costs.”

      See more
      Hampton Catlin
      Hampton Catlin
      VP of Engineering at Rent The Runway · | 6 upvotes · 7.4K views
      atRent the RunwayRent the Runway
      Java
      Java
      Python
      Python
      Ruby
      Ruby

      At our company, and I've noticed a lot of other ones... application developers and dev-ops people tend to use Ruby and our statisticians and data scientists love Python . Like most companies, our stack is kind of split that way. Ruby is used as glue in most of our production systems ( Java being the main backend language), and then all of our data scientists and their various pipelines tend towards Python

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      Ajit Parthan
      Ajit Parthan
      CTO at Shaw Academy · | 3 upvotes · 5.2K views
      atShaw AcademyShaw Academy
      Python
      Python
      PHP
      PHP
      #Etl

      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

      etl @{etlasaservice}|topic:1323|

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      SVN (Subversion)
      SVN (Subversion)
      Git
      Git
      JSON
      JSON
      XML
      XML
      Python
      Python
      PHP
      PHP
      Java
      Java
      Swift
      Swift
      JavaScript
      JavaScript
      Linux
      Linux
      GitHub
      GitHub
      Visual Studio Code
      Visual Studio Code

      I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.

      • It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.

      • Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (.rpm and .deb packages)

      • LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.

      • Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like .ini, .properties, XML and JSON files.

      • Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.

      • Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.

      • Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)

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      Markdown
      Markdown
      Docker
      Docker
      JSON
      JSON
      TypeScript
      TypeScript
      Atom
      Atom
      Visual Studio Code
      Visual Studio Code
      Angular 2
      Angular 2
      #Sass
      #HTML
      #Java
      #Typescript

      More than year ago I was looking for the best editor of Angular 2 application and I've tried Visual Studio Code and Atom. Atom had performance issues that put me off completely to use it again. Visual Studio Code became my main editor #Typescript files (and partly editor of #Java files). I'm happy with Visual Studio Code and I've never look back on Atom. There wasn't any reason to try Atom again, because Visual Studio Code fulfills my requirements very well. I use it for editing of TypeScript, #HTML, #Sass, JSON, Docker and Markdown.

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      Ajit Parthan
      Ajit Parthan
      CTO at Shaw Academy · | 1 upvotes · 4K views
      atShaw AcademyShaw Academy
      Python
      Python
      PHP
      PHP

      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 · 208.6K views
      atStitch FixStitch Fix
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Docker
      Docker
      PyTorch
      PyTorch
      R
      R
      Python
      Python
      Presto
      Presto
      Apache Spark
      Apache Spark
      Amazon S3
      Amazon S3
      PostgreSQL
      PostgreSQL
      Kafka
      Kafka
      #Data
      #DataStack
      #DataScience
      #ML
      #Etl
      #AWS

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

      Django or NodeJS? Hi, I’m 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’t 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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      Git
      Git
      Docker
      Docker
      NATS
      NATS
      JavaScript
      JavaScript
      TypeScript
      TypeScript
      PostgreSQL
      PostgreSQL
      Python
      Python
      Go
      Go

      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.

      See more
      Omar Melendrez
      Omar Melendrez
      Front-end developer · | 3 upvotes · 4.2K views
      Python
      Python
      C#
      C#
      Node.js
      Node.js
      React
      React
      Vue.js
      Vue.js
      #Vscode
      #Fullstack

      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 · 24.7K views
      atGentlentGentlent
      Python
      Python
      Electron
      Electron
      Socket.IO
      Socket.IO
      Google Compute Engine
      Google Compute Engine
      TypeScript
      TypeScript
      ES6
      ES6
      Ubuntu
      Ubuntu
      PostgreSQL
      PostgreSQL
      React
      React
      nginx
      nginx
      Sass
      Sass
      HTML5
      HTML5
      PHP
      PHP
      Node.js
      Node.js
      JavaScript
      JavaScript

      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.

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      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis · | 11 upvotes · 94.8K views
      MongoDB Atlas
      MongoDB Atlas
      Amazon S3
      Amazon S3
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Serverless
      Serverless
      Docker
      Docker
      Terraform
      Terraform
      Travis CI
      Travis CI
      GitHub
      GitHub
      RxJS
      RxJS
      Angular 2
      Angular 2
      AWS Lambda
      AWS Lambda
      Amazon SQS
      Amazon SQS
      Amazon SNS
      Amazon SNS
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon Kinesis
      Amazon Kinesis
      Flask
      Flask
      Python
      Python
      ExpressJS
      ExpressJS
      Node.js
      Node.js
      Spring Boot
      Spring Boot
      Java
      Java
      #Data
      #Devops
      #Webapps
      #Eventsourcingframework
      #Microservices
      #Backend

      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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      PubNub
      PubNub
      asyncio
      asyncio
      JavaScript
      JavaScript
      Python
      Python

      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.

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      Ali Soueidan
      Ali Soueidan
      Creative Web Developer at Ali Soueidan · | 16 upvotes · 48.3K views
      npm
      npm
      Babel
      Babel
      PHP
      PHP
      Adobe Illustrator
      Adobe Illustrator
      Asana
      Asana
      ES6
      ES6
      GitHub
      GitHub
      Git
      Git
      JSON
      JSON
      Sass
      Sass
      Pug
      Pug
      JavaScript
      JavaScript
      vuex
      vuex
      Vue.js
      Vue.js

      Application and Data: Since my personal website ( https://alisoueidan.com ) is a SPA I've chosen to use Vue.js, as a framework to create it. After a short skeptical phase I immediately felt in love with the single file component concept! I also used vuex for state management, which makes working with several components, which are communicating with each other even more fun and convenient to use. Of course, using Vue requires using JavaScript as well, since it is the basis of it.

      For markup and style, I used Pug and Sass, since they’re the perfect match to me. I love the clean and strict syntax of both of them and even more that their structure is almost similar. Also, both of them come with an expanded functionality such as mixins, loops and so on related to their “siblings” (HTML and CSS). Both of them require nesting and prevent untidy code, which can be a huge advantage when working in teams. I used JSON to store data (since the data quantity on my website is moderate) – JSON works also good in combo with Pug, using for loops, based on the JSON Objects for example.

      To send my contact form I used PHP, since sending emails using PHP is still relatively convenient, simple and easy done.

      DevOps: Of course, I used Git to do my version management (which I even do in smaller projects like my website just have an additional backup of my code). On top of that I used GitHub since it now supports private repository for free accounts (which I am using for my own). I use Babel to use ES6 functionality such as arrow functions and so on, and still don’t losing cross browser compatibility.

      Side note: I used npm for package management. 🎉

      *Business Tools: * I use Asana to organize my project. This is a big advantage to me, even if I work alone, since “private” projects can get interrupted for some time. By using Asana I still know (even after month of not touching a project) what I’ve done, on which task I was at last working on and what still is to do. Working in Teams (for enterprise I’d take on Jira instead) of course Asana is a Tool which I really love to use as well. All the graphics on my website are SVG which I have created with Adobe Illustrator and adjusted within the SVG code or by using JavaScript or CSS (SASS).

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

      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 .

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      Interest over time
      Reviews of JSON and Python
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      How developers use JSON 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’s 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)

      How much does JSON cost?
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