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Python

A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
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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.
Python is a tool in the Languages category of a tech stack.
Python is an open source tool with 28.1K GitHub stars and 12.7K GitHub forks. Here’s a link to Python's open source repository on GitHub

Who uses Python?

Companies
6028 companies reportedly use Python in their tech stacks, including Uber, Netflix, and Spotify.

Developers
22950 developers on StackShare have stated that they use Python.

Python Integrations

Django, Flask, PyCharm, CircleCI, and LiteSpeed are some of the popular tools that integrate with Python. Here's a list of all 266 tools that integrate with Python.

Why developers like Python?

Here’s a list of reasons why companies and developers use Python
Python Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Python in their tech stack.

Nick Parsons
Nick Parsons
Director of Developer Marketing at Stream · | 33 upvotes · 270.7K views
atStreamStream
Stream
Stream
Go
Go
JavaScript
JavaScript
ES6
ES6
Node.js
Node.js
Babel
Babel
Yarn
Yarn
Python
Python
#FrameworksFullStack
#Languages

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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Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 24 upvotes · 347.2K views
atFundsCornerFundsCorner
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MongoDB Stitch
MongoDB Stitch
Node.js
Node.js
Amazon SQS
Amazon SQS
Python
Python
SQLAlchemy
SQLAlchemy
AWS Lambda
AWS Lambda
Zappa
Zappa

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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Dmitry Mukhin
Dmitry Mukhin
at Uploadcare · | 22 upvotes · 242.8K views
atUploadcareUploadcare
Django
Django
Python
Python
React
React
Ember.js
Ember.js
Preact
Preact
PostCSS
PostCSS

Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.

Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.

For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d go with Ember.js.

However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.

All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.

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Google Cloud IoT Core
Google Cloud IoT Core
Terraform
Terraform
Python
Python
Google Cloud Deployment Manager
Google Cloud Deployment Manager
Google Cloud Build
Google Cloud Build
Google Cloud Run
Google Cloud Run
Google Cloud Bigtable
Google Cloud Bigtable
Google BigQuery
Google BigQuery
Google Cloud Storage
Google Cloud Storage
Google Compute Engine
Google Compute Engine
GitHub
GitHub

Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

Check Out My Architecture: CLICK ME

Check out the GitHub repo attached

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Dmitry Mukhin
Dmitry Mukhin
at Uploadcare · | 20 upvotes · 65.1K views
atUploadcareUploadcare
Tornado
Tornado
Python
Python
Amazon EC2
Amazon EC2
AWS Elastic Load Balancing (ELB)
AWS Elastic Load Balancing (ELB)

The 350M API requests we handle daily include many processing tasks such as image enhancements, resizing, filtering, face recognition, and GIF to video conversions.

Tornado is the one we currently use and aiohttp is the one we intend to implement in production in the near future. Both tools support handling huge amounts of requests but aiohttp is preferable as it uses asyncio which is Python-native. Since Python is in the heart of our service, we initially used PIL followed by Pillow. We kind of still do. When we figured resizing was the most taxing processing operation, Alex, our engineer, created the fork named Pillow-SIMD and implemented a good number of optimizations into it to make it 15 times faster than ImageMagick

Thanks to the optimizations, Uploadcare now needs six times fewer servers to process images. Here, by servers I also mean separate Amazon EC2 instances handling processing and the first layer of caching. The processing instances are also paired with AWS Elastic Load Balancing (ELB) which helps ingest files to the CDN.

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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.

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Python Alternatives & Comparisons

What are some alternatives to Python?
Java
Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!
R
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
JavaScript
JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
Scala
Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.
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.
See all alternatives

Python's Followers
23202 developers follow Python to keep up with related blogs and decisions.
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