Julia 中 Python 的 ast.literal_eval() 等价物是什么?

There is no equivalent, although you could potentially write one fairly easily by parsing code and then recursively ensuring that you only have certain syntactic forms in the resulting expression before evaluating it. However, unlike Python where a lot of basic types and their syntax and behavior are built in and unchangeable, Julia's "built in" types are just user-defined types that happen to be defined before the system starts up. Let's explore what happens, for example, when you use vector literal syntax:

julia> :([1,2,3]) |> dump
Expr
  head: Symbol vect
  args: Array{Any}((3,))
    1: Int64 1
    2: Int64 2
    3: Int64 3
  typ: Any

julia> f() = [1,2,3]
f (generic function with 2 methods)

julia> @code_lowered f()
CodeInfo(:(begin
        nothing
        return (Base.vect)(1, 2, 3)
    end))

julia> methods(Base.vect)
# 3 methods for generic function "vect":
vect() in Base at array.jl:63
vect(X::T...) where T in Base at array.jl:64
vect(X...) in Base at array.jl:67

所以 [1,2,3] 只是一种句法形式，它被降低为对 Base.vect 函数的调用，即 Base.vect(1,2,3).现在，我们将来可能会密封"一些函数，这样就不能添加任何子方法或以任何方式覆盖它们的行为，但目前正在修改 Base.vect 的行为某些参数集是完全可能的:

julia> function Base.vect(a::Int, b::Int, c::Int)
           warn("SURPRISE!")
           return invoke(Base.vect, Tuple{Any,Any,Any}, a, b, c)
       end

julia> [1,2,3]
WARNING: SURPRISE!
3-element Array{Int64,1}:
 1
 2
 3

julia> Dict(:foo => 1, :bar => 2, :baz => 42)
Dict{Symbol,Int64} with 3 entries:
  :baz => 42
  :bar => 2
  :foo => 1

julia> dump(:(a => b))
Expr
  head: Symbol call
  args: Array{Any}((3,))
    1: Symbol =>
    2: Symbol a
    3: Symbol b
  typ: Any

julia> :foo => 1.23
:foo=>1.23

julia> =>
Pair

julia> Pair(:foo, 1.23)
:foo=>1.23

julia> :(18446744073709551616)
:(@int128_str "18446744073709551616")

julia> macro int128_str(s)
           warn("BIG SUPRISE!")
           9999999999999999
       end    

julia> 18446744073709551616
WARNING: BIG SUPRISE!
9999999999999999