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Some base points:

• 
  Python method calls are "expensive" due to its interpreted nature. In theory, if your code is simple enough, breaking down Python code has negative impact besides readability and reuse (which is a big gain for developers, not so much for users).


• The single responsibility principle (SRP) keeps code readable, is easier to test and maintain.


• The project has a special kind of background where we want readable code, tests, and time performance.

For instance, code like this which invokes several methods (x4) is slower than the following one which is just one.

from operator import add

class Vector:
 def __init__(self,list_of_3):
 self.coordinates = list_of_3

 def move(self,movement):
 self.coordinates = list( map(add, self.coordinates, movement))
 return self.coordinates

 def revert(self):
 self.coordinates = self.coordinates[::-1]
 return self.coordinates

 def get_coordinates(self):
 return self.coordinates

## Operation with one vector
vec3 = Vector([1,2,3])
vec3.move([1,1,1])
vec3.revert()
vec3.get_coordinates()

In comparison to this:

from operator import add

def move_and_revert_and_return(vector,movement):
 return list( map(add, vector, movement) )[::-1]

move_and_revert_and_return([1,2,3],[1,1,1])

If I am to parallelize something such as that, it is pretty objective I lose performance. Mind that is just an example; my project has several mini routines with math such as that - While it is much easier to work with, our profilers are disliking it.

How and where do we embrace the SRP without compromising performance in Python, as its inherent implementation directly impacts it?

Are there workarounds, like some sort of pre-processor that puts things in-line for release?

Or is Python simply poor at handling code breakdown altogether?

is Python simply poor at handling code breakdown altogether?

Unfortunately yes, Python is slow and there are many anecdotes about people drastically increasing performance by inlining functions and making their code ugly.

There is a work around, Cython, which is a compiled version of Python and much faster.

Many potential performance concerns are not really a problem in practice. The issue you raise may be one of them. In the vernacular, we call worrying about those problems without proof that they are actual problems premature optimization.

If you are writing a front-end for a web service, your performance is not going to be significantly affected by function calls, because the cost of sending data over a network far exceeds the time it takes to make a method call.

If you are writing a tight loop that refreshes a video screen sixty times a second, then it might matter. But at that point, I claim you have larger problems if you're trying to use Python to do that, a job for which Python is probably not well-suited.

As always, the way you find out is to measure. Run a performance profiler or some timers over your code. See if it's a real problem in practice.

The Single Responsibility Principle is not a law or mandate; it is a guideline or principle. Software design is always about trade-offs; there are no absolutes. It is not uncommon to trade off readability and/or maintainability for speed, so you may have to sacrifice SRP on the altar of performance. But don't make that tradeoff unless you know you have a performance problem.