First Semester in Numerical Analysis with Julia, 2020a

CHAPTER 1. INTRODUCTION 21
Using anonymous functions we can manipulate arrays easily. For example, suppose
we want to pick the elements of an array that are greater than 0. This can be done
using the function filter together with an anonymous function x->x>0.
In [44]: filter(x->x>0,[-2,3,4,5,-3,0])
Out[44]: 3-element Array{Int64,1}:
3
4
5
The function count is similar to filter, but it only computes the number of elements
of the array that satisfy the condition described by the anonymous function.
In [45]: count(x->x>0,[-2,3,4,5,-3,0])
Out[45]: 3
Types
In Julia, there are several types for integers and floating-point numbers such as Int8,
Int64, Float16, Float64, and more advanced types for Boolean variables, characters,
and strings. When we write a function, we do not have to declare the type of its
variables: Julia figures what the correct type is when the code is compiled. This is
called a dynamic type system. For example, consider the squareit function we defined
before:
In [46]: function squareit(x)
return x^2
end
Out[46]: squareit (generic function with 1 method)
The type of x is not declared in the function definition. We can call it with real or
integer inputs, and Julia will know what to do:
In [47]: squareit(5)
Out[47]: 25