when writing complex algorithms the best approach is usually to write pseudocode, just literally write comments that explain what you are going to do in that particular block of code but not actually code it at all. that lets you build the mental model of how it's all going to work, but without any of the overhead of actually writing code. then write the main algorithm but leave some functions as stubs, just name them and have an idea of what they're going to have to do. then finally you go through and write the low-level details.
using python is both unnecessary and counterproductive. variable names don't take hardly any time at all to type, the logical design is what's actually challenging, and python isn't going to help you understand what needs to be written faster, especially compared to just writing pseudocode.
using python because it's faster for design iteration is a lot like a coder who uses a dvorak keyboard because it lets you type faster. The actual difference between two programmers of equal skill using two different languages is, by and large, nil, because the actual act of typing a program is barely any of the time involved at all.
but on the other hand it does make a massive difference in the long term in code maintainability. having to read someone else's code, especially bad code from someone else, and figure out what goes in, what happens with every variable assignment, and what returns back? Python doesn't give you any hints there, everything is established by programmer convention particular to your specific codebase, it's a mental load on top of the actual programming itself. And sure there's IDEs to help carry some of that burden, but IDEs also make developing statically typed languages a snap, and IDEs for statically typed languages can be even more powerful with their refactoring tools because they can make more assertions about what is going on in the code they're working on.
there's a reason that basically any large codebase that starts out as dynamically typed realizes their mistake and hacks in static types after the fact. Facebook was written in PHP, then realized they were screwed and created a typed dialect of PHP called "hacklang" that they gradually rewrote their codebase into. Microsoft realized their Javascript was turning into a mess and wrote TypeScript to get types into their javascript. Same thing, it's a superset that lets them run their existing codebase while they port but port their existing code and write new code with types as they go.
https://en.wikipedia.org/wiki/Gradual_typing
Once you get a non-trivial codebase, the benefits are just too big to ignore. It's fine when you wrote all the code yourself, and you know how it works and it's all up to your personal quality standards, but once you have to start working with other people's code the regularity imposed by a statically typed language is just too beneficial to ignore. And that's what Microsoft and Facebook and others have all found out in practice. Small, highly skilled teams, sure you can do whatever. Big corporate teams with coders of varying skill? Types help.
Now, what do you do if you're not Facebook or Microsoft and can't afford to implement a whole new programming language/toolchain/etc to fix a bad choice of language? Make better choices up front.
dynamic typing is the dvorak of programming languages. it sounds super cool, and everyone hates verbose java 6 code, but it's just not a productivity boost in practice, in fact in the long term it's the opposite. Best case it improves on something that's not a bottleneck, worst case it actually reduces productivity in a large codebase, and that's the experience from Facebook, Microsoft, and other big companies with good engineering teams.
but the dynamic vs static typing is an age-old slapfight and nobody's mind is going to change.