Gestione avanzata dei dati mancanti

Nel precedente esercizio hai visto come identificare quanti valori mancanti ci sono in ciascuna colonna di un DataFrame e come eliminare le righe che contengono valori mancanti. Ma cosa succede se ci sono molte righe con valori mancanti? E se non vuoi semplicemente iniziare a cancellare righe dai dati? Qui entra in gioco il concetto di sostituzione: puoi rimpiazzare i valori mancanti con qualcos'altro.

In questo esercizio lavorerai con lo stesso DataFrame sales_df dell'esercizio precedente, ma invece di eliminare i valori mancanti, sostituirai i mancanti in ogni colonna con la media di tutti i valori non mancanti. Scriverai una funzione che potrà poi essere applicata a qualsiasi colonna di un DataFrame.

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# Define a function replace_missing that takes one argument, the name of the column we want to modify
____ replace_missing(____)
end

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Questo esercizio fa parte del corso

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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: 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: 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: Funzioni anonime e multiple dispatch Exercise 2: Multiple dispatch Exercise 3: Funzioni anonime Exercise 4: Filtrare i DataFrame Exercise 5: Importare funzioni da Python e R Exercise 6: Importare Python e R Exercise 7: Funzioni Python in Julia Exercise 8: Funzioni R in Julia Exercise 9: Pulizia dei dati Exercise 10: Rinominare le colonne Exercise 11: Dati mancanti Exercise 12: Gestione avanzata dei dati mancanti

Esercizio in corso

Exercise 13: Congratulazioni!