Python vs. Markdown vs. Groovy

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.

The backbone language here, connecting our games to our data stores and implementing all server-side logic.

Pathwright's backend is entirely written in Python.

Most back-end made with Python + Whiskey, combined with some node.js.

I use Python for various tools as well as XBMC scripts.

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Server-side code.

yryr

Python scripts gather, parse and analyse build and timing data before sending it to Keen.io for storage

Our code base is 100% Python

일반적으로 모씨를 개발하며 가장 자주 사용하는 스크립트 언어입니다. 쉽고, 빠르고, 강력하고, 월등한 생산성을 가집니다. 저희는 아직 python 2.7 을 사용하고 있습니다.

Python drives the web back end, and is the interface to the command line tools mtr, tcptraceroute and others that provide the functionality.

Best language for rapid prototyping, emphasis on the RAPID!

Putting the RAPID in Rapidprototyping!

Most of our backend code is in Python. We use TypeScript for frontend code, and we use Ruby for our build system (Guard).

Our API and back end app are built on Python.

Kudobuzz crawlers and sentence classification are built on python and mongoDB.

Job-posts data mining, dictionary builder, unsupervised learning processes (mainly NLTK processes: clustering - LDA/KMeans/HDP, pos tagging, paragraph detection)

Windows server scripting.

system administration and analytics

Scanning engine uses python, python-Cryptography, and pyopenssl libraries.

For small tasks deemed necessary by Drupal.

Python is our multi-use goto scripting language. It's used for our mobile api and is deployed at scale as well as a utility in backends.

We use Python in our ETL processes, custom reporting, and as glue to manage service and deployment scripts.

Integrate with data providers. ETL functionality. Postgres interactions. Generate static json data assets.

Python is used to roll up reports from data generated from processes I have running.

Samples for SelectPdf Html To Pdf Conversion in Python.

Just about everywhere on back-end as glue.

HTTP Parser

Python is the language of choice for the match collection pipeline.

Programming language

Python is (together with C++) the main language of GatewayAPI.com, we use it for the bulk of our APIs.

She is the one, I use to create core algorithm for the tool

Python is used to power Django

Python 3.5+ is used to run our Tornado application as it support Async/Await coroutines.

Our machine learning team use Python to prototype and develop new tools.

Some of our machine learning algorithms are developed and implemented using Python. These algorithms help us recommend content and surface insights about content.

Our core website and many internal utilities are written in Python.

this is my first weapone with django

첫 언어의 시작은 java였지만, 패스트캠퍼스에서 python으로 수업을 진행하면서 python의 매력에 푹 빠졌습니다. 지금은 저의 신경과 같은 언어입니다.

하지만 얼마든지 이 언어를 다른 언어로 대체할 준비가 되어있습니다.

For most of our backend glue and tooling.

Crawling.

We use Python for Websocket servers as well as other utilities throughout the company.

Made a contact list application for maintain contain list.

Python helps us automate the tedious and has the gold standard Natural Language Processing library.

All automated and manual system admin tools, utilising Paramiko.

All analytics scripts and tools are written in Python

I am using python to write the scripts that should run at the Future Gateway. The regression models that does the machine learning training and prediction for the application is written in python.

basic

many of our apps are written in Python for speed and reliability.

웹파싱을 하기 위해 배운 언어 인데, 생각 보다 재미 있는 언어인것 같다. 인터프리터 언어라 바로바로 돌아가는 점이 참 좋고, 최신 트렌드도 잘 반영되어 있는데, 이상하게 잘 안쓰게 된 언어중 하나다. 만약 Node.js 를 배우지 않았다면, 열심히 파이썬을 쓰고 있었을 지도 모르겠다.

Ambar crawlers and pipeline are written in Python

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.

Here & there. All on lambda. Anything guaranteed cold spin-up; otherwise bit slow (compared to node/jvm) when warm.

Time series data processing

Web Develpment

Almost all our Lambdas are written in Python

Django Framework로 인해 접하였지만, 그외 개발에 필요한 스크립트를 제작하는데도 많이 사용하고 있습니다.

Powers Django

Use it daily for everything. Hands-down best language I've used so far.

I use python for all my data analysis and Machine/Deep Learning projects.

I have been using Python for over 10 years at this point. I have enough expertise using it that this choice was a no-brainer.

All of our tasks and things to do are sorted out using Trello. Trello contains everything we're working on, what we plan on working on and guides on how to work on stuff.

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)

Backend language

Build some data pipelines.

90% of Vaultize server and client is written in Python.

For everything that is important.

We use Python for CBIR system related projects.

Our go to scripting language. If we need it done right now, we do it with python.

To poll the messages from the queue and import songs into DynamoDB from a local csv.

Great for our ETL stack in Matillion and for various scripts we use as part of our deployment

Spark Machine Learning, Scikit-learn, Scrapy, and Keras.

Ansible Orchestration and Network function Script.

We leverage Python for tools like awscli and cfn_flip so they are able to be leveraged during CI tests.

simulated and real data producer.

Python is the programming language (obviously).

Fully implementation of the backend representation of objects in pure python

Python is used as the language for our internal Quest-API for the RPG-Server, but also used for out bot-testing-script to benchmark our Minecraft-servers against heavy workload.

Great support for machine learning/AI programming with numpy scipy and serving the results on the web, used in both our backend forecasting and servers

Easy to handle data

I'm a beginner and like Python. Still learning from tutorials.

for django

Server Side Automation

both functional and oop, django, script,

The majority of our backend code is written in Python. Our Serverless API is made up of python lambdas.

Easy to develop/prototype with, helpful community, vast support and libraries. Perfect for the beginner, perfect for the pro.

Our website and API built using python and Django framework.

Because python looks so pretty. And I love snakes.

as api back-end

In process of Learning Technics

exports user profiles from MongoDB and stores it as Excel

Good client for PostgreSQL available and preferred programming language

It is language, in that the service is implemented

Not yet integrated want to add it to the we server

Main language of the application

Implementation for Backend and Worker Processes

Python 3.6 everywhere

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!

Machine Learning(ML) POCs and for the applications built upon ML algorithms.

Prediction, modelling, and simulation for Solar predictions

All documentation and blog posts are written in Markdown

We use Markdown primarily for documentation and other forms of writing.

We’re using Markdown parsers for Reviews, Blog Posts, and Stack Description (the text you’re reading right now). We’re using Bootstrap Markdown for content input (except for reviews) and RedCarpet for rendering in certain places.

Users can format their contributions with Markdown. Great way to post tutorials with code.

Authoring of content

News articles consumed in markdown and injected into React components using Showdown with an XSS filter.

Define the project pages.

By now, almost all my text content is created in (some dialect of) markdown. It's easy to process and extend in javascript.

Using StackEdit to edit markdown files for blog roll and about sections. MD files are stored in Google Drive and pushed to GH pages through StackEdit.

For when you need to write.

Project documentation.

Documentation and tutorials about the app

Markdown is how I format all my posts. Uses the "smart" and "footnotes" options.

Update with Parsedown

Markdown represents a highly portable and lightweight text formatting. I had converted all of my Wordpress posts to Markdown prior to migrating over to Jekyll and eventually to Hugo. The fact that many generators support Markdown means that my content remains portable regardless of the platform/engine I use.

We use this internally, but there is also Markdown CMS plugn-in for Agile Toolkit.

We use NimbusKit/markdown as a convenience wrapper around NSAttributedString

저는 markdown 덕후입니다

Almost everywhere except Python documentation.

wiki notes, internal how-to guides, technical docs, change-logs, release notes

All specifications

Writing README.md's is very important to us. Also a lot of our content is maintained in Markdown (using contentful.com). We are also cool with org-mode.

We use Markdown to write posts for our blog, and also document our processes.

Huge fan of markdown

Mainly for github Readme files, and occasionally for other documentation as desired.

간략한 문법을 통해 깔끔한 문서가 나오는것에 매료되어있습니다.

Write Blog Post @ Github Pages, Documentation like API Blueprint

Use it on a daily basis to create beautifully formatted documents / pdfs.

Because it is almost an effortless markup language without ever having to write an HTML tag. Of course, you'll want to use it in environments that make it look pretty (GitHub, etc.)

Code documentation.

Markdown is awesome and becoming more well-known. It is also easier than using some proprietary markup language.

Yes, we have a README

Standard for editing readme markdown files.

For notes and documentation

For READMEs in code respository

As long as you don't abuse def and Closure

NPL solutions and statistical machine learning.