Type declarations

Another control that we can exercise over our function parameters is the type of these parameters. We can specify a data type for each variable, just as we did in the previous chapter with tuples. If the data type of the argument passed into the function does not match the data type specified when the function was defined, then an error is thrown.

This is another way to control our functions and keep them safe from unwanted exceptions. In the current version of our my_profit function, we could currently pass a string into each argument, but we would get an error when the return statement tries to subtract a string from another string. We can safeguard against this by defining the data type of each parameter.

In the below example, set the data type for each parameter in the function.

Note that if we try to pass a data type that does not match what we have defined, we will get the error:

MethodError: no method matching my_profit(::Int64, ::Int64)

Este exercício faz parte do curso

Intermediate Julia

Instruções do exercício

Set the data type for each parameter, where current_price and previous_price should be Float64 values, and fees should only be an Int64 value.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Define my_profit with three type-restricted arguments
function my_profit(previous_price____, current_price____, fees____=2)
	return current_price - previous_price - fees
end

# Call my_profit, passing in values of the correct data type
my_profit(100.00, 105.00)

Editar e executar o código

Este exercício faz parte do curso

Intermediate Julia

InicianteNível de habilidade

4.9+

Iniciar curso de graça

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

Exercício atual

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!