Splat unpacking

Operator splat ... adalah operator yang kuat di Julia yang dapat digunakan untuk melakukan unpack pada iterable. Ini adalah cara yang lebih ringkas dan bersih untuk membongkar iterable dengan cepat, yang jika tidak, Anda perlu menggunakan sebuah loop.

Pada contoh ini, gunakan operator splat untuk melakukan unpack pada sebuah range menjadi vektor, alih-alih menggunakan loop.

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

Petunjuk latihan

Gunakan operator splat untuk melakukan unpack pada range my_range menjadi sebuah vektor.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Unpack my_range using the splat operator
my_range = 0:10:50
println(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

SkillTag.level.beginnerSkillTag.label

4.7+

Mulai Kursus Gratis

Loops are one of the core concepts that underpin Julia. In this chapter, you'll learn about for loops and while loops, and how to use them to iterate over data structures that you are already familiar with. You will also cover ranges, a useful tool for generating sequences of data.

Exercise 1: For loop Exercise 2: Pertanyaan for loop Exercise 3: Melakukan loop pada sebuah vektor Exercise 4: Loop dan enumerate Exercise 5: Melakukan loop pada struktur bertingkat Exercise 6: While loop Exercise 7: Menulis while loop Exercise 8: While loop untuk iterasi Exercise 9: Rentang Exercise 10: Ranges - iterasi Exercise 11: Mendefinisikan range Exercise 12: Melakukan loop pada range Exercise 13: Splat unpacking

Latihan Saat Ini

This chapter focuses on expanding your knowledge of the data structures available in Julia. Learn how to use tuples, dictionaries, multi-dimensional arrays, and structures to store and traverse data quickly and efficiently.

Exercise 1: Tuples Exercise 2: When should you use a tuple?Exercise 3: Create, index, and slice a tuple Exercise 4: Create a NamedTuple for a person Exercise 5: Dictionaries Exercise 6: Create untyped dict, iterate Exercise 7: Create typed dict, iterate Exercise 8: Modify keys/values in dict, use get()Exercise 9: Multi-dimensional arrays Exercise 10: Create 1D and 2D arrays Exercise 11: Index and slice a 2D array Exercise 12: Array merging Exercise 13: Structs Exercise 14: Define a structure Exercise 15: Mutable and typed structs

In this chapter, you’ll extend your understanding of functions, exploring positional, keyword, and default function arguments. You will also cover code execution timing, getting a strong understanding of how to measure the time your code takes to run. This chapter will round off with a capstone on writing your own functions to solve real-world problems.

Exercise 1: Execution time and measurement Exercise 2: Built-in time macro Exercise 3: Timing a function Exercise 4: Positional and Default Arguments, Type Declarations Exercise 5: Positional arguments recap Exercise 6: Default arguments Exercise 7: Type declarations Exercise 8: Keyword Arguments Exercise 9: Keyword arguments Exercise 10: Variable number of arguments Exercise 11: Writing Your Own Function Exercise 12: Writing your own functions Exercise 13: Writing your own functions - structs

This final chapter will introduce anonymous functions and will recap one of the powerful features of Julia; multiple dispatch. You will learn how to use functions from Python and R packages within Julia and discover how to clean and modify data within dataframes.

Exercise 1: Anonymous Functions and Multiple Dispatch Exercise 2: Multiple dispatch Exercise 3: Anonymous functions Exercise 4: Filtering DataFrames Exercise 5: Importing Functions from Python and R Exercise 6: Importing Python and R Exercise 7: Python functions in Julia Exercise 8: R functions in Julia Exercise 9: Cleaning Data Exercise 10: Renaming columns Exercise 11: Missing data Exercise 12: Advanced missing data Exercise 13: Congratulations!