Iterare su strutture annidate

Hai visto come puoi iterare su un vettore per restituire i valori contenuti al suo interno. Un passo oltre è iterare su un vettore di vettori, restituendo in ordine i valori corrispondenti. Questo è chiamato una struttura "annidata", dove un vettore è dentro un altro vettore. Nella pratica troverai spesso strutture annidate, quindi capire come iterare su queste strutture ed estrarre i dati è una competenza importante.

In questo esempio, abbiamo modificato il precedente vettore stock_tickers per includere il prezzo corrente di ciascun ticker e lo abbiamo rinominato stocks. Nota la struttura del vettore annidato qui sotto.

4-element Vector{Vector{Any}}:
 ["AAPL", 151]
 ["AMZN", 94]
 ["GOOG", 97]
 ["MSFT", 241]

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Istruzioni dell'esercizio

Itera sul vettore annidato stocks per restituire, per ogni elemento, il ticker e il relativo prezzo.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Loop over stocks, printing the ticker and the price
for (ticker, price) in ____
    println("The price of 1 ____ share is ____.")
end

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Julia intermedio

InicianteNível de habilidade

4.7+

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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: Cicli for Exercise 2: Domanda sui for loop Exercise 3: Cicla su un vettore Exercise 4: Cicli ed enumerate Exercise 5: Iterare su strutture annidate

Esercizio in corso

Exercise 6: While loop Exercise 7: Scrivere un ciclo while Exercise 8: Cicli while per l'iterazione Exercise 9: Intervalli Exercise 10: Range - iterazione Exercise 11: Definire i range Exercise 12: Iterare sui range 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: 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!