Code Quality check

example.py

x = a + 1   # a is not yet defined
 
a = x + 1
 
class A:
    def __init__(self, rarely_used_parameter=False):
        if rarely_used_parameter:
            self.m += 1  # the member 'm' is not yet initialised, but if the testing happens to never set the 'rarely_used_parameter' to True, this error may go undetected
        self.m = 0
    def inc(self):
        self.m += self.n  # A does not have a member named 'n'.

import re as regex
 
matches = set()
 
def is_digits(input_string):
    regex = '(0|1|2|3|4|5|6|7|8|9)*'  # the 'regex' variable defined here 'shadows' or hides the name of the module re
    match = regex.fullmatch(regex, input_string) # this should thow an error, but if the 'regex' variable happened to thave the fullmatch method, this could slip in undetected 
    if match is not None:  
        matches = matches.union({match})  # this does NOT update the global variable
        return True
    return False 
def process_matches(matches): # the parameter name shadows the global variable
    for m in matches:
        m = get_some_value()  # this redefines the loop variable
        ...

def f():
  print(1)
 
def g():
  x = 2
  def f():  # this shadows the global function 'f'
     print(x)
  f()
 
def f():  # this is an error
  print(3)
 
class A:
    def g(self):
        print(6)
    def __init__(self):
        self.g = 5  # this attribute hides the method g
    def f(self): # this is ok, as it is a method so it will never be confused with the outer 'f'
        print(4)
    def f(self):
        print(5)  # this is an error

incorrect_imports.py

import nonexistent_module  # module does not exist, remove this or replace with correct module
from time import *  # only import what is needed. for example, 'form time import perf_counter'
form typing import Generic # unused import
from re import DFA # no object called 'DFA' exists in re
from numpy import * # if you use many different things from a module, consider importing the module (qualified import): 'import numpy' or 'import numpy as np'
import pandas as pandas # useless renaming using 'as'
import cyclic
 
...

cyclic.py

import incorrect_imports # this will cause a cyclic import. python knows how to handle this most of the time, but it WILL cause some errors in certain situations

PI = 3.1416  # constants are a good use of global variables
T = typeVar('T') # typeVars will almost always need to be global
 
def GenericClass(Generic[T]):
    pass

global_stack = []
 
def exported_function(param):
   global global_stack
   global_stack = []
   fill_global_stack_with_possible_solutions(param)
   for sol in global_stack:
      check_solution(sol, param)
# if exported_function is called by 2 or more concurrent threads, they may interfere with each other, and there is a strong possibility that it will be called like that

inport time # this import is not used
 
def f():
    x = 1  # this value is never used
    1+2  # this statement has no effect
    y = 1
    y = y # this statement is redundant
    return True
    y += 1  # this code is unreachable
 
def g(z): # z is not used
    if 1 < 2:  # two errors: constant comparison (the condition always evaluates to True, so it can be safely replaced with True), and constant if condition (the 'then' branch is always executed, so the if can be removed)
       # the following if can be easily simplified to 'return f()' or 'return bool(f())' if the result is not guaranteed to be a bool but is interpreted as such
       if f():
           return True
       else:
           return False
 
h = lambda x: x + 1 # use a more easily readable function def
 
t = (lambda x: x * 2)(4) # do not define a lambda just to immediatly call it, use the expression 4 * 2 directly (or, in this case, just write 8)
 
l = [1,2,3]
 
if not not 1 in l: # double not is redundant
    print(not 1 < 2) # replace not less than with >=
 
s = {x for x in l}  # do not use comprehensions, just use the built-in set() constructor
 
for i in range(len(l)):
    print(l[i]) # do not iterate over range, then index the list, just iterate over the list (use enumerate if you also need the index)
 
def reurns_nothing():
    print("imagine this changed some state")
    return  # this return is not needed
 
while True:
    if some_condition():  # use 'while some_condition():' instead
        break
    print("do something")
 
def my_min(ls):  # do not do this, python already has min, max, any, all, sum implemented (along with many others)
   m = ls[0]
   for x in ls[1:]:
       if x < m:
          m = x
   return x