All Resources

Showing the most recent resources. The latest one is from 2020-02-18.
  • A guide to comprehensions, generators and useful functions and classes
    Article  Added by Hellebore // // 4 days ago
  • Let’s explore two great Python libraries — itertools and more_itertools and see how to leverage them for data processing…
    Article  Added by MartinHeinz // // 7 days ago
  • Use TensorFlow and Machine Learning to classify Twilio texts into two categories: "loves me" and "loves me not".
    Tutorial  Added by lizziepika // // 9 days ago
  • Sometimes your data file is so large you can’t load it into memory at all, even with compression. So how do you process it quickly?

    By loading and then processing the data in chunks, you can load only part of the file into memory at any given time. And that means you can process files that don’t fit in memory.

    Learn how you can do this with Pandas.
    Tutorial  Added by itamarst // // 10 days ago
  • On my Mac, I use the menu bar countless times per day. In this post we will go through the process of creating a custom macOS menu bar app using Python. As an example, we will create a 🍅 pomodoro app, which you can use to boost your productivity and manage your time from the convenience of your menu bar. It serves as a starting point for customization to suit your individual needs – you can even use this code as a boilerplate to create a radically different application!
    Tutorial  Added by visini // // 13 days ago
  • In this article, we are going to deploy a Flask application to AWS Elastic Beanstalk via a GitHub and Travis CI deployment pipeline. Our goal: After pushing changes to your code to GitHub, Travis CI should pull our code, perform all tests, and if they pass, deploy the application to AWS Elastic Beanstalk.
    Tutorial  Added by visini // // 13 days ago
  • Tutorial how to create a simple application that tracking the spread of Coronavirus around the world
    Tutorial  Added by geomatics99 // // 16 days ago
  • A simple GitHub action for formatting, linting, testing, and building a Python application
    Article  Added by Hellebore // // 18 days ago
  • When you’re choosing a base image for your Docker image, Alpine Linux is often recommended. Using Alpine, you’re told, will make your images smaller and speed up your builds. And if you’re using Go that’s reasonable advice. For Python, Alpine is a bad idea.
    Article  Added by itamarst // // 24 days ago
  • Best practices for tracking down and resolving software issues.
    Article  Added by Hellebore // // 25 days ago
  • Learn Python, a powerful language used by sites like YouTube and Dropbox. Learn the fundamentals of programming to build web apps and manipulate data.
    Tutorial  Added by TutorialandExample // // about 1 month ago
  • Get your Python app deployed in Docker in no time!
    Tutorial  Added by Hellebore // // about 1 month ago
  • How does a Django site know where to send requests? You have to tell it! In this next article in the Understand Django series, we look at URLs and how to let your users get to the right place.
    Article  Added by mblayman // // about 1 month ago
  • Some practical advice on refactoring your code, with examples in Python.
    Article  Added by Hellebore // // about 1 month ago
  • In this tutorial, we will learn how to run a Selenium test script using Python Programming language.
    Tutorial  Added by ninja // // about 1 month ago
  • The Conda packaging tool implements environments, that enable different applications to have different libraries installed. So when you’re building a Docker image for a Conda-based application, you’ll need to activate a Conda environment—a surprisingly tricky task.

    Read this article to learn how the easy way to do it.
    Tool  Added by itamarst // // about 1 month ago
  • Starting any project from scratch can be a daunting task… But not if you have this ultimate Python project blueprint!
    Article  Added by MartinHeinz // // about 1 month ago
  • Django helps you build websites in Python. How does it work? In this series, we’ll explore Django from top to bottom to show you how to build the website you’ve wanted. We’ll start from the beginning with the browser.
    Article  Added by mblayman // // about 2 months ago
  • A regularly updated series of tutorials covering building GUI applications with Python and Qt5. From simple concepts through to fully-functional and distributable applications on the desktop.
    Tutorial  Added by mfitzp // // about 2 months ago
  • When you're processing large amounts of data in memory, copying wastes limited RAM, but mutating data in-place leads to bugs. There's a third solution, that gives safety while reducing memory usage: interior mutability.
    Tutorial  Added by itamarst // // about 2 months ago
  • After struggling with Python test mocking, I've wrote a helper library to save me time in the future.
    Tool  Added by pksol // // about 2 months ago
  • Python keeps getting better. The 3.9 alpha2 introduces some non-trival changes!
    Article  Added by Abdur-rahmaanJ // // 2 months ago
  • Article  Added by tonyxrandall // // 2 months ago
  • If you’re running into memory issues because your NumPy arrays are too large, one of the basic approaches to reducing memory usage is compression. By changing how you represent your data, you can reduce memory usage and shrink your array’s footprint—often without changing the bulk of your code.
    Tutorial  Added by itamarst // // 2 months ago
  • A hand-picked selection of AWS services which you should know about when deploying Django apps to AWS. Each entry has a link to the AWS service page, and a brief description why they might be relevant.
    Article  Added by github-vsupalov // // 2 months ago
  • Thoughts on using API Schemas for property-based testing
    Article  Added by Stranger6667 // // 2 months ago
  • If you want to process a large amount data with Pandas, there are various techniques you can use to reduce memory usage without changing your data. But what if that isn’t enough? What if you still need to reduce memory usage?

    Another technique you can try is lossy compression: drop some of your data in a way that doesn’t impact your final results too much. If parts of your data don’t impact your analysis, no need to waste memory keeping extraneous details around.
    Tutorial  Added by itamarst // // 3 months ago
  • This python Rest API tutorial help to Access SalesForce Rest API.The SalesForce API is use to access resources from across the micro services. The SalesForce REST API uses the same underlying data model and standard objects as those in SOAP API.
    Article  Added by techlover // // 3 months ago
  • Python generator gives us a lazy iterator. Let see how it can improve your code...
    Article  Added by chanhqh // // 3 months ago
  • Sending email is a very common task in any system. Let see how to do it in Python.
    Tutorial  Added by chanhqh // // 3 months ago