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Python Design Patterns - Anti
Sanjeev Shah
4 years ago
Anti-patterns follow a strategy in opposition to predefined design patterns. The strategy includes common approaches to common problems, which can be formalized 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 literally 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))
Maintainability
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 maintainability.
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.
To learn more about python, visit the InsideAIML page.