Python Design Patterns - Anti

Anti-patterns follow a strategy in opposition to predefined design patterns. The strategy includes common approaches to common problems, which can be formapzed and can be generally considered as a good development practice. Usually, anti-patterns are opposite and undesirable. Anti- patterns are certain patterns used in software development, which are considered as bad programming practices.

Important features of anti-patterns

Let us now see a few important features of anti-patterns.

Correctness

These patterns pterally break your code and make you do wrong things. Following is a simple illustration of this −

class Rectangle(object):
def __init__(self, width, height):
self._width = width
self._height = height
r = Rectangle(5, 6)
# direct access of protected member
print("Width: {:d}".format(r._width))

Maintainabipty

A program is said to be maintainable if it is easy to understand and modify as per the requirement. Importing module can be considered as an example of maintainabipty.

import math
x = math.ceil(y)
# or
import multiprocessing as mp
pool = mp.pool(8)

Example of anti-pattern

Following example helps in the demonstration of anti-patterns −

#Bad
def filter_for_foo(l):
   r = [e for e in l if e.find("foo") != -1]
   if not check_some_critical_condition(r):
      return None
   return r

res = filter_for_foo(["bar","foo","faz"])

if res is not None:
   #continue processing
   pass

#Good
def filter_for_foo(l):
   r = [e for e in l if e.find("foo") != -1]
   if not check_some_critical_condition(r):
      raise SomeException("critical condition unmet!")
   return r

try:
   res = filter_for_foo(["bar","foo","faz"])
   #continue processing

except SomeException:
   i = 0
while i < 10:
   do_something()
   #we forget to increment i

Explanation

The example includes the demonstration of good and bad standards for creating a function in Python.