Python and other dynamic languages don't use static types and that may give them a (very) small advantage in productivity as well, but only for very small programs... as program size increases, in my experience, statically typed languages take the lead in productivity... as OP is about writing general software, not just small scripts, I really have a hard time agreeing that Python is either the best tool for the job, or the most productive tool at all.
#include <string>
#include <cctype>
#include <algorithm>
#include <iostream>
int main()
{
std::string s("hello");
std::transform(s.begin(), s.end(), s.begin(),
[](unsigned char c) { return std::toupper(c); });
std::cout << s;
}
print('hello'.upper())In fact it's really mostly name space management. Python doesn't require you to pull in "upper" because it starts with a relatively big name space. It doesn't require you declare the main program because you're relying on the fact that Python executes a file on loading it (and that reliance is arguably bad Python style). All of the "std::" stuff is a name space management choice.
... and the rest has nothing to do with static typing either. The way the code is indented on multiple lines is a stylistic convention. The transform primitive requiring you to specify bounds is a library choice, related to the language's apparent lack of a universal way to map over a container. I'm not sure why you need the lambda. The rewrite in place is memory model stuff inherited from C.
In Haskell, which is fully compiled and is about the most rigid static typed language you could ever imagine:
import Data.Char(toUpper)
main = putStrLn (toUpper <$> "hello")
std::string ToUpper(const std::string& str) {
std::string as_lower(str);
absl::c_transform(str, as_lower, std::toupper);
return as_lower;
}
int main() {
std::cout << ToUpper("hello");
return 0;
}
As a counter point to this - Python has a large number of C-based libraries with excellent bindings. To take Numpy has an example, you can the static-types and speed of Numpy to do all your maths, whilst Python acts as the manager. Using Numpy "feels" like normal Python, you don't really feel like you're working at native-level, but you get all the advantages of native speeds and memory usage.
And to your other point, I've worked on large C++-based projects which were a complete mess, especially when you had to make any changes to existing code. I'm not sure the language has as much an affect here as just using the correct design principles.
I consider myself as someone who knows "far too much about C++", but recently when I had to spider some webpages and read some values out of them to populate a database, I did that in Python because it's a handful of lines due to high quality libraries that all work nicely together. I honestly wouldn't know how to do that in a short amount of time in C++.