6a. Overview and Goals - Julia Book

Home
Chapters

Generic expressions are represented by <something> (e.g., <function> or <operator>). This is just notation, and the symbols < and > should not be misconstrued as Julia's syntax.

Action	Keyboard Shortcut
Previous Section	`Ctrl + 🠘`
Next Section	`Ctrl + 🠚`
List of Sections	`Ctrl + z`
List of Subsections	`Ctrl + x`
Close Any Popped Up Window (like this one)	`Esc`
Open All Codes and Outputs in a Post	`Alt + 🠛`
Close All Codes and Outputs in a Post	`Alt + 🠙`

When benchmarking, the equivalence of time measures is as follows.

Unit	Acronym	Measure in Seconds
Seconds	s	1
Milliseconds	ms	10^-3
Microseconds	μs	10^-6
Nanoseconds	ns	10^-9

Links
Dark Mode

Personal

Website

The previous chapter equipped us with techniques for indexing and modifying vectors, expanding our toolkit for working with data collections. This section builds on this knowledge to achieve several goals.

Firstly, we'll introduce additional types for collections, including dictionaries and named tuples. Building on our now grasp of tuples and vectors, we're now well-positioned to appreciate the unique features of these alternatives and understand when they're more suitable.

Secondly, we'll expand on tools for streamlining code, which will become indispensable in your daily use of Julia. These tools will make your coding experience smoother, by reducing boilerplate code and improving syntax readability. One notable example is the use of pipes.

Thirdly, we'll introduce several standard functions for manipulating vectors, enabling you to perform operations such as removing duplicates and sorting elements.

To conclude the chapter, we'll put into practice all the tools we've covered. This will be done through a hypothetical scenario involving a YouTuber's earnings. This hands-on approach will demonstrate how to apply the tools learned, helping you bridge the gap between theory and practice. Furthermore, it'll lay the foundation for more advanced data analysis tools: by mastering the application of fundamentals such as vector indexing, you'll be well-equipped to seamlessly transition to typical data-analysis tools (e.g., the DataFrames package).

this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible this is invisible

NOTATION

PAGE LAYOUT

LINKS TO SECTIONS

KEYBOARD SHORTCUTS

TIME MEASUREMENT

Dark Mode

PART I: INTRODUCTION TO JULIA

INTRODUCTION

CORE CONCEPTS

USING JULIA