This was because dynamic typing doesn’t give the safety net that static typing does. So you write more tests. Which is a good thing.

I'm not a fan of listing exceptions functions can throw, especially here in Python, where it's easier to ask forgiveness than permission.

This type relation is sometimes written S <: T

subtyping allows a function to be written to take an object of a certain type T, but also work correctly, if passed an object that belongs to a type S that is a subtype of T (according to the Liskov substitution principle)

chose the term ad hoc polymorphism to refer to polymorphic functions that can be applied to arguments of different types, but that behave differently depending on the type of the argument to which they are applied (also known as function overloading or operator overloading)

In computer science and logic, a dependent type is a type whose definition depends on a value. A "pair of integers" is a type. A "pair of integers where the second is greater than the first" is a dependent type because of the dependence on the value.

On the flip side, it can go further than mere types, including emulating dependent types and programming-by-contract.

almost all languages are dynamic and involve dynamic typing, even languages like Ocaml and Haskell. Every time your code interprets data and makes choices based on that, you have dynamic typing. The simple fact is that there's no hard and fast distinction between type information and data: constraints on data, such as the format of a stream of text, are type constraints which are beyond the static type system to check, so the checks are done dynamically at run time by your code, and that's dynamic typing!