Buat dict bertipe, iterasi

Kita telah membuat sebuah dictionary untuk menyimpan data saham, tetapi kita bisa saja memasukkan nilai apa pun sebagai nilainya. Misalnya, untuk key price, kita bisa memasukkan apa pun, seperti string atau nilai boolean. Ini tidak masuk akal, jadi kita dapat menggunakan dictionary bertipe untuk membatasi tipe data yang diizinkan bagi key dan value.

Untuk mendefinisikan dictionary bertipe, kita terlebih dahulu menentukan tipe data untuk key diikuti tipe untuk value, dipisahkan oleh koma. Keduanya diapit oleh kurung kurawal dan diletakkan setelah kata kunci Dict.

my_typed_dictionary = Dict{key_datatype, value_datatype}(key => value)

Latihan ini adalah bagian dari kursus

Julia Tingkat Menengah

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create a typed dictionary with keys as String and values as Float64
apple_stock_data = Dict____("price" => 131.86, "prev_close" => 131.99, "volume" => 55000000)

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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 loops Exercise 2: For loops question Exercise 3: Loop over a vector Exercise 4: Loops and enumerate Exercise 5: Looping over nested structures Exercise 6: While loops Exercise 7: Writing a while loop Exercise 8: While loops for iteration Exercise 9: Ranges Exercise 10: Ranges - iteration Exercise 11: Defining ranges Exercise 12: Looping over ranges Exercise 13: Splat unpacking

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: Tuple Exercise 2: Kapan Anda harus menggunakan tuple?Exercise 3: Buat, indeks, dan iris sebuah tuple Exercise 4: Buat NamedTuple untuk seorang orang Exercise 5: Dictionary Exercise 6: Buat dict tanpa tipe, iterasi Exercise 7: Buat dict bertipe, iterasi

Latihan Saat Ini

Exercise 8: Ubah key/nilai dalam dict, gunakan get()Exercise 9: Array multidimensi Exercise 10: Buat array 1D dan 2D Exercise 11: Indeks dan iris array 2D Exercise 12: Penggabungan array Exercise 13: Struct Exercise 14: Mendefinisikan sebuah struktur Exercise 15: Struct yang dapat diubah (mutable) dan bertipe

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!