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Programming Leftovers

  • Excellent Free Books to Learn Java

    Java is a general-purpose, concurrent, class-based, object-oriented, high-level programming language and computing platform first released by Sun Microsystems in 1995. It is related in some ways to C and C++, in particular with regard to its syntax, and borrows a few ideas from other languages. Java applications are compiled to bytecode that can run on any Java virtual machine (JVM) regardless of computer architecture. Java is designed to be simple enough that many programmers can quickly become proficient in the language. It’s one of the most popular programming languages especially for client-server web applications.

  • GFX-RS Portability 0.7 Released With Vulkan Events, Binding Model Improvements

    The GFX-RS high performance graphics API for the Rust programming language and based on Vulkan while mapping to Metal when on Apple systems is out with a new release. GFX-RS continues to be about being a cross-platform API for Rust that is bindless and high performance while retaining the traits of Vulkan but with back-ends as well for Direct3D 11/12, Metal, and even OpenGL 2 / GLES2.

  • Use the Requests module to directly retrieve the market data

    Hello and welcome back to our cryptocurrency project. In the previous article I had mentioned before that I want to use the cryptocompy module to create our new cryptocurrency project, however, after a closer look at the CriptoCompare API I think we have better used the original API to make the rest call instead of using the wrapper module because the original API seems to provide more returned data type than the one offered by the cryptocompy module.

  • Eli Bendersky: Faster XML stream processing in Go

    XML processing was all the rage 15 years ago; while it's less prominent these days, it's still an important task in some application domains. In this post I'm going to compare the speed of stream-processing huge XML files in Go, Python and C and finish up with a new, minimal module that uses C to accelerate this task for Go. All the code shown throughout this post is available in this Github repository the new Go module is here.

  • How to Use Binder and Python for Repoducible Research

    In this post we will learn how to create a binder so that our data analysis, for instance, can be fully reproduced by other researchers. That is, in this post we will learn how to use binder for reproducible research. In previous posts, we have learned how to carry out data analysis (e.g., ANOVA) and visualization (e.g., Raincloud plots) using Python. The code we have used have been uploaded in the forms of Jupyter Notebooks.

  • Wingware Blog: Introducing Functions and Methods with Refactoring in Wing Pro

    In this issue of Wing Tips we explain how to quickly create new functions and methods out of existing blocks of Python code, using Wing Pro's Extract Method/Function refactoring operation. This is useful whenever you have some existing code that you want to reuse in other places, or in cases where code gets out of hand and needs to be split up to make it more readable, testable, and maintainable. Wing supports extracting functions and methods for any selected code, so long as that code does not contain return or yield statements. In that case automatic extraction is not possible, since Wing cannot determine how the extracted function should be called from or interact with the original code.

  • How to Use Binder and Python for Reproducible Research

    In this post we will learn how to create a binder so that our data analysis, for instance, can be fully reproduced by other researchers. That is, in this post we will learn how to use binder for reproducible research. In previous posts, we have learned how to carry out data analysis (e.g., ANOVA) and visualization (e.g., Raincloud plots) using Python. The code we have used have been uploaded in the forms of Jupyter Notebooks. Although this is great, we also need to make sure that we share our computational environment so our code can be re-run and produce the same output. That is, to have a fully reproducible example, we need a way to capture the different versions of the Python packages we’re using.

  • NumPy arange(): How to Use np.arange()

    NumPy is the fundamental Python library for numerical computing. Its most important type is an array type called ndarray. NumPy offers a lot of array creation routines for different circumstances. arange() is one such function based on numerical ranges. It’s often referred to as np.arange() because np is a widely used abbreviation for NumPy. Creating NumPy arrays is important when you’re working with other Python libraries that rely on them, like SciPy, Pandas, Matplotlib, scikit-learn, and more. NumPy is suitable for creating and working with arrays because it offers useful routines, enables performance boosts, and allows you to write concise code.

  • Cogito, Ergo Sumana: Beautiful Soup is on Tidelift

    I've been doing a tiny bit of consulting for Tidelift for a little over a year now, mainly talking about them to open source maintainers in the Python world and vice versa. (See my October 2018 piece "Tidelift Is Paying Maintainers And, Potentially, Fixing the Economics of an Industry".) And lo, in my household, my spouse Leonard Richardson has signed up as a lifter for Beautiful Soup, his library that helps you with screen-scraping projects.

  • Chris Moffitt: Automated Report Generation with Papermill: Part 1

    This guest post that walks through a great example of using python to automate a report generating process. I think PB Python readers will enjoy learning from this real world example using python, jupyter notebooks, papermill and several other tools.

  • Cryptocurrency user interface set up

    As mentioned above, in this article we will start to create the user interface of our latest cryptocurrency project. Along the path we will also use the CryptoCompare API to retrieve data.

  • Python Snippet 2: Quick Sequence Reversal
  • 10x Evilgineers | Coder Radio 367

    Mike rekindles his youthful love affair with Emacs and we debate what makes a "10x engineer". Plus the latest Play store revolt and some of your feedback.

BlueStar Linux 5.2.1

Today we are looking at BlueStar Linux 5.2.1. This release of BlueStar is an Arch rolling distro and comes with Linux Kernel 5.2.1 and KDE Plasma 5.16.3 and uses about 700MB of ram when idling. Bluestar Linux is a beautiful Arch/KDE distro that works great out of the box and is receiving a lot of love from their very active developer. Read more Direct/video: BlueStar Linux 5.2.1 Run Through

GNU Parallel 20190722 ('Ryugu') released

GNU Parallel 20190722 ('Ryugu') has been released. It is available for download at: http://ftpmirror.gnu.org/parallel/ GNU Parallel is 10 years old next year on 2020-04-22. You are here by invited to a reception on Friday 2020-04-17. Read more

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