Which built-ins should you know about?

I estimate most Python developers will only ever need about 30 built-in functions, but which 30 depends on what you’re actually doing with Python.

We’re going to take a look at all 69 of Python’s built-in functions, in a birds eye view sort of way.

GitHub Gist: instantly share code, notes, and snippets.

The Linux fd command is an quick and efficient way to find files in the terminal. Like the find command, it comes in handy often, however, the find command can be a bit tricky for those new to Linux and especially it's command line. This guide will help you get up to speed with this very useful command line tool.

The xargs utility allows you to build and execute commands from standard input. Xargs is usually used in combination with other commands through piping. In this tutorial, we'll cover the basics of using the xargs command.

In order to successfully work with the Linux sed editor and the awk command in your shell scripts, you have to understand regular expressions or in short regex. Since there are many engines for regex, we will use the shell regex and see the bash power in working with regex.

First, we need to understand what regex is, then we will see how to use it.

Table of contents include:

What is regex, Types of regex, Define BRE Patterns, Special Characters, Anchor Characters, The dot Character, Character Classes, Negating Character Classes, Using Ranges, Special Character Classes, The Asterisk, Extended Regular Expressions, The question mark, The Plus Sign, Curly Braces, Pipe Symbol, Grouping Expressions, Practical examples, Counting Directory Files, Validating E-mail Address.

The author tried to edit data in spreadsheet programs.

This post illustrate ho to use Vim to edit tabular data, although there are a few things that will make it more pleasant. It is assumed that editing files are in tab-separated value format (TSV).

"But what about CSV files?" Just. Don't.

Do: convert your CSV to TSV and back for editing.

From Josh Mcguigan.

This is a tutorial on building your own shell using Rust, in the spirit of the build-your-own-x list. Creating a shell is a great way to understand how the shell, terminal emulator, and OS work together.

There may be complex and unknown relationships between the variables in your dataset.

It is important to discover and quantify the degree to which variables in your dataset are dependent upon each other. This knowledge can help you better prepare your data to meet the expectations of machine learning algorithms, such as linear regression, whose performance will degrade with the presence of these interdependencies.

In this tutorial, you will discover that correlation is the statistical summary of the relationship between variables and how to calculate it for different types variables and relationships.

After completing this tutorial, you will know:

• How to calculate a covariance matrix to summarize the linear relationship between two or more variables.
• How to calculate the Pearson’s correlation coefficient to summarize the linear relationship between two variables.
• How to calculate the Spearman’s correlation coefficient to summarize the monotonic relationship between two variables.

Move beyond the basics of poi with this course designed to transition you from the beginner poi world to intermediate level spinning! You'll learn the basics of behind the back poi spinning as well as important transition tools such as pendulums and CAPs. Got your weaves down and are curious where to go next? This is the course for you!

Inspired by Ollie Charles and his excellent 24 days of Hackage series, I'm going to try and introduce you to a number of Rust language features, useful libraries and cool projects built with Rust.

In fact this is a learning opportunity for me too - as much as I love Rust, I'm just diving in. If you think I'm wrong or know an interesting library you want me to write about, feel free to comment!

Examples of Python and Rust code to illustrate the concept of ownership.

Very good tutorial.

In their hearts, computers are sequential beasts. Their power comes from being able to break down the largest tasks into tiny steps that can be performed one after another. Often, though, our users need to see things occur in a single instantaneous step or see multiple tasks performed simultaneously.

A typical example, and one that every game engine must address, is rendering. When the game draws the world the users see, it does so one piece at a time — the mountains in the distance, the rolling hills, the trees, each in its turn. If the user watched the view draw incrementally like that, the illusion of a coherent world would be shattered. The scene must update smoothly and quickly, displaying a series of complete frames, each appearing instantly.

Double buffering solves this problem, but to understand how, we first need to review how a computer displays graphics.

🙀 A cat explains how HTTPS works...in a comic! 😻

A brief overview of how to use fastText to train powerful text classifiers in a python notebook. - mpuig/textclassification

Indoor movement prediction involves using wireless sensor strength data to predict the location and motion of subjects within a building. It is a challenging problem as there is no direct analytical model to translate the variable length traces of signal strength data from multiple sensors into user behavior.

You are handed data and told to develop a forecast model. What do you do? This is a common situation; far more common than most people think. Perhaps you are sent a CSV file. Perhaps you are given access to a database. Perhaps you are starting a competition. The problem can be reasonably well defined: …

TimeShift is a system restore tool for Linux. It provides functionality that is quite similar to the System Restore feature in Windows or the Time Machine tool in MacOS. TimeShift protects your system by making incremental snapshots of the file system manually or at regular automated intervals.