# The Type System of Julia

Every value in Julia has a type. Like other popular languages such as Python, Julia is dynamically typed. That means, we don’t need to explicitly define the type of a value when we create it. However, types are particularly important in Julia because we can use explicit type definitions to speed up calculations. Here we will get an introduction into the type system of Julia.

## Dynamic Typing

When we assign a value in Julia we don’t need to specify its type. The type is guessed based on the given value. Let’s create an integer, a float and a string.

```myint = 3
myfloat = 3.0
mystr = "3.0"
println(typeof(myint), ": ", myint)
println(typeof(myfloat), ": ", myfloat)
println(typeof(mystr), ": ", mystr)
# Int64: 3
# Float64: 3.0
# String: 3.0
```

We use `typeof()` to find out what type a value has. To create an integer, we assign a number without decimal. To create a float, we simply attach a decimal place. For a string we need quotation marks around our value. There is another way to more explicitly define the type. Our float value is a Float64 (it uses 64 bits). What if we wanted a Float32?

```myfloat = convert(Float32, 3.0)
typeof(myfloat)
# Float32
```

We use the `convert(Type, value)` function to explicitly convert `3.0` to Float32. If we want to ensure that a value is of a certain type, we can use the double colon syntax (`::`). It evaluates normally, if the value has the specified type but throws an error, if it has a different type.

```3::Int
# 3
3::Float64
# TypeError: in typeassert, expected Float64, got Int64
#
# Stacktrace:
#  [1] top-level scope at In[8]:1
```

This type assertion syntax is frequently used when defining functions. A function is a piece of code that takes input arguments and processes them to produce output values. We will learn more about functions later. Because Julia is dynamically typed, we could write our functions in a way that they work the same regardless of input types. However, defining functions in a way that is specific to the input types can be good for performance and Julia makes heavy use of that fact. For a given function, multiple methods might exist. Each method is responsible for a given set of input parameters. For example, we can inspect all the different methods of the mathematical `cos` by calling the `methods` function on it.

```methods(cos)
"""
# 12 methods for generic function cos:
cos(x::BigFloat) in Base.MPFR at mpfr.jl:744
cos(::Missing) in Base.Math at math.jl:1167
cos(a::Complex{Float16}) in Base.Math at math.jl:1115
cos(a::Float16) in Base.Math at math.jl:1114
cos(z::Complex{T}) where T in Base at complex.jl:823
cos(x::T) where T<:Union{Float32, Float64} in Base.Math at special/trig.jl:100
cos(x::Real) in Base.Math at special/trig.jl:124
cos(A::LinearAlgebra.Hermitian{#s661,S} where S<:(AbstractArray{#s662,2} where #s662<:#s661) where #s661<:Complex) in LinearAlgebra at C:\Users\Daniel\AppData\Local\Programs\Julia\Julia-1.4.2\share\julia\stdlib\v1.4\LinearAlgebra\src\symmetric.jl:907
cos(A::Union{LinearAlgebra.Hermitian{#s662,S}, LinearAlgebra.Symmetric{#s662,S}} where S where #s662<:Real) in LinearAlgebra at C:\Users\Daniel\AppData\Local\Programs\Julia\Julia-1.4.2\share\julia\stdlib\v1.4\LinearAlgebra\src\symmetric.jl:903
cos(D::LinearAlgebra.Diagonal) in LinearAlgebra at C:\Users\Daniel\AppData\Local\Programs\Julia\Julia-1.4.2\share\julia\stdlib\v1.4\LinearAlgebra\src\diagonal.jl:561
cos(A::AbstractArray{#s662,2} where #s662<:Real) in LinearAlgebra at C:\Users\Daniel\AppData\Local\Programs\Julia\Julia-1.4.2\share\julia\stdlib\v1.4\LinearAlgebra\src\dense.jl:793
cos(A::AbstractArray{#s662,2} where #s662<:Complex) in LinearAlgebra at C:\Users\Daniel\AppData\Local\Programs\Julia\Julia-1.4.2\share\julia\stdlib\v1.4\LinearAlgebra\src\dense.jl:800
"""
```

The cos function has 12 different methods. The double colon type assertion syntax checks the input type. Which method is used can depend on the type of all inputs. This is called multiple dispatch. Multiple inputs determine which method is dispatched. We will learn more about multiple dispatch in another post. Since arrays are central to scientific computing, we next look at the type system and arrays.

## Array Types

There are several different ways to define arrays. One way is to use literal numbers enclosed by square brackets. In that case, the type is inferred in the same way as above, where we used literal numbers.

```myintarr = [1, 2, 3]
typeof(myintarr)
# Array{Int64,1}
myfloatarr = [1.0, 2.0, 3.0]
typeof(myfloatarr)
# Array{Float64,1}
```

For an array, `typeof()` tells us not only the type of the array elements but also how many dimensions the array has. One important feature of the array is that all elements must have the same type. So what happens when we create an array from literals with different types? The type is determined by the most complex type.

```myintarr = [1, 2, 3]
typeof(myintarr)
# Array{Int64,1}
myfloatarr = [1, 2.0, 3]
typeof(myfloatarr)
# Array{Float64,1}
mystrarr = [1, "2.0", 3]
typeof(mystrarr)
# Array{Any,1}
```

We see that only one float value makes the entire array Float64. However, when one of the elements is a string, the type becomes `Any`, rather than `String`. That is because Julia does not convert numbers to strings. We get an error if we call `convert(String, 3)`. If the most complex type is the `Float64`, Julia tries to convert all other values to that type. This works in the case where the other values are integers, because `convert(Float64, 3)` works. If other values cannot be converted to the most complex type all values take on the `Any` type. This means that values in the array can be of different types. They could literally be anything. This defeats the purpose of an array so we generally try to avoid it. There are other array creation methods besides the literal way. We can call a variety of methods that allow us to create arrays and they usually allow us to specify the type.

```zerosarr = zeros(Int8, (2, 3))
# 2×3 Array{Int8,2}:
#  0  0  0
#  0  0  0
zerosarr = zeros((2, 3))
# 2×3 Array{Float64,2}:
#  0.0  0.0  0.0
#  0.0  0.0  0.0
```

If we don’t specify the type we want, the `zeros()` function default to Float64. There are other functions such as `ones()`, giving an array of ones, or rand(), giving an array of random numbers. The type of the output can be specified for each.

That is it for our basic introduction into types. You can find a more complete introduction to Julia types in the official documentation. In summary, we don’t need to explicitly specify the type of values but sometimes it might help to make math more efficient. We will learn in later posts more about performance and efficiency in Julia.