Software engineering principles, from Robert C. Martin"s book Clean Code, adapted for Python. This is not a style guide. It"s a guide to producing readable, reusable, and refactorable software in Python.
Not every principle herein has to be strictly followed, and even fewer will be universally agreed upon. These are guidelines and nothing more, but they are ones codified over many years of collective experience by the authors of Clean Code.
Inspired from clean-code-javascript
Targets Python3.7+
Bad:
import datetime
ymdstr = datetime.date.today().strftime("%y-%m-%d")Good:
import datetime
current_date: str = datetime.date.today().strftime("%y-%m-%d")Bad: Here we use three different names for the same underlying entity:
def get_user_info(): pass
def get_client_data(): pass
def get_customer_record(): passGood: If the entity is the same, you should be consistent in referring to it in your functions:
def get_user_info(): pass
def get_user_data(): pass
def get_user_record(): passEven better Python is (also) an object oriented programming language. If it makes sense, package the functions together with the concrete implementation of the entity in your code, as instance attributes, property methods, or methods:
from typing import Union, Dict, Text
class Record:
pass
class User:
info : str
@property
def data(self) -> Dict[Text, Text]:
return {}
def get_record(self) -> Union[Record, None]:
return Record()
We will read more code than we will ever write. It"s important that the code we do write is readable and searchable. By not naming variables that end up being meaningful for understanding our program, we hurt our readers. Make your names searchable.
Bad:
import time
# What is the number 86400 for again?
time.sleep(86400)Good:
import time
# Declare them in the global namespace for the module.
SECONDS_IN_A_DAY = 60 * 60 * 24
time.sleep(SECONDS_IN_A_DAY)Bad:
import re
address = "One Infinite Loop, Cupertino 95014"
city_zip_code_regex = r"^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$"
matches = re.match(city_zip_code_regex, address)
if matches:
print(f"{matches[1]}: {matches[2]}")Not bad:
It"s better, but we are still heavily dependent on regex.
import re
address = "One Infinite Loop, Cupertino 95014"
city_zip_code_regex = r"^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$"
matches = re.match(city_zip_code_regex, address)
if matches:
city, zip_code = matches.groups()
print(f"{city}: {zip_code}")Good:
Decrease dependence on regex by naming subpatterns.
import re
address = "One Infinite Loop, Cupertino 95014"
city_zip_code_regex = r"^[^,\\]+[,\\\s]+(?P<city>.+?)\s*(?P<zip_code>\d{5})?$"
matches = re.match(city_zip_code_regex, address)
if matches:
print(f"{matches['city']}, {matches['zip_code']}")Don’t force the reader of your code to translate what the variable means. Explicit is better than implicit.
Bad:
seq = ("Austin", "New York", "San Francisco")
for item in seq:
#do_stuff()
#do_some_other_stuff()
# Wait, what's `item` again?
print(item)Good:
locations = ("Austin", "New York", "San Francisco")
for location in locations:
#do_stuff()
#do_some_other_stuff()
# ...
print(location)If your class/object name tells you something, don"t repeat that in your variable name.
Bad:
class Car:
car_make: str
car_model: str
car_color: strGood:
class Car:
make: str
model: str
color: strTricky
Why write:
import hashlib
def create_micro_brewery(name):
name = "Hipster Brew Co." if name is None else name
slug = hashlib.sha1(name.encode()).hexdigest()
# etc.... when you can specify a default argument instead? This also makes it clear that you are expecting a string as the argument.
Good:
from typing import Text
import hashlib
def create_micro_brewery(name: Text = "Hipster Brew Co."):
slug = hashlib.sha1(name.encode()).hexdigest()
# etc.Limiting the amount of function parameters is incredibly important because it makes testing your function easier. Having more than three leads to a combinatorial explosion where you have to test tons of different cases with each separate argument.
Zero arguments is the ideal case. One or two arguments is ok, and three should be avoided. Anything more than that should be consolidated. Usually, if you have more than two arguments then your function is trying to do too much. In cases where it"s not, most of the time a higher-level object will suffice as an argument.
Bad:
def create_menu(title, body, button_text, cancellable):
passJava-esque:
class Menu:
def __init__(self, config: dict):
self.title = config["title"]
self.body = config["body"]
# ...
menu = Menu(
{
"title": "My Menu",
"body": "Something about my menu",
"button_text": "OK",
"cancellable": False
}
)Also good
from typing import Text
class MenuConfig:
"""A configuration for the Menu.
Attributes:
title: The title of the Menu.
body: The body of the Menu.
button_text: The text for the button label.
cancellable: Can it be cancelled?
"""
title: Text
body: Text
button_text: Text
cancellable: bool = False
def create_menu(config: MenuConfig) -> None:
title = config.title
body = config.body
# ...
config = MenuConfig()
config.title = "My delicious menu"
config.body = "A description of the various items on the menu"
config.button_text = "Order now!"
# The instance attribute overrides the default class attribute.
config.cancellable = True
create_menu(config)Fancy
from typing import NamedTuple
class MenuConfig(NamedTuple):
"""A configuration for the Menu.
Attributes:
title: The title of the Menu.
body: The body of the Menu.
button_text: The text for the button label.
cancellable: Can it be cancelled?
"""
title: str
body: str
button_text: str
cancellable: bool = False
def create_menu(config: MenuConfig):
title, body, button_text, cancellable = config
# ...
create_menu(
MenuConfig(
title="My delicious menu",
body="A description of the various items on the menu",
button_text="Order now!"
)
)Even fancier
from typing import Text
from dataclasses import astuple, dataclass
@dataclass
class MenuConfig:
"""A configuration for the Menu.
Attributes:
title: The title of the Menu.
body: The body of the Menu.
button_text: The text for the button label.
cancellable: Can it be cancelled?
"""
title: Text
body: Text
button_text: Text
cancellable: bool = False
def create_menu(config: MenuConfig):
title, body, button_text, cancellable = astuple(config)
# ...
create_menu(
MenuConfig(
title="My delicious menu",
body="A description of the various items on the menu",
button_text="Order now!"
)
)Even fancier, Python3.8+ only
from typing import TypedDict, Text
class MenuConfig(TypedDict):
"""A configuration for the Menu.
Attributes:
title: The title of the Menu.
body: The body of the Menu.
button_text: The text for the button label.
cancellable: Can it be cancelled?
"""
title: Text
body: Text
button_text: Text
cancellable: bool
def create_menu(config: MenuConfig):
title = config["title"]
# ...
create_menu(
# You need to supply all the parameters
MenuConfig(
title="My delicious menu",
body="A description of the various items on the menu",
button_text="Order now!",
cancellable=True
)
)This is by far the most important rule in software engineering. When functions do more than one thing, they are harder to compose, test, and reason about. When you can isolate a function to just one action, they can be refactored easily and your code will read much cleaner. If you take nothing else away from this guide other than this, you"ll be ahead of many developers.
Bad:
from typing import List
class Client:
active: bool
def email(client: Client) -> None:
pass
def email_clients(clients: List[Client]) -> None:
"""Filter active clients and send them an email.
"""
for client in clients:
if client.active:
email(client)Good:
from typing import List
class Client:
active: bool
def email(client: Client) -> None:
pass
def get_active_clients(clients: List[Client]) -> List[Client]:
"""Filter active clients.
"""
return [client for client in clients if client.active]
def email_clients(clients: List[Client]) -> None:
"""Send an email to a given list of clients.
"""
for client in get_active_clients(clients):
email(client)Do you see an opportunity for using generators now?
Even better
from typing import Generator, Iterator
class Client:
active: bool
def email(client: Client):
pass
def active_clients(clients: Iterator[Client]) -> Generator[Client, None, None]:
"""Only active clients"""
return (client for client in clients if client.active)
def email_client(clients: Iterator[Client]) -> None:
"""Send an email to a given list of clients.
"""
for client in active_clients(clients):
email(client)Bad:
class Email:
def handle(self) -> None:
pass
message = Email()
# What is this supposed to do again?
message.handle()Good:
class Email:
def send(self) -> None:
"""Send this message"""
message = Email()
message.send()When you have more than one level of abstraction, your function is usually doing too much. Splitting up functions leads to reusability and easier testing.
Bad:
# type: ignore
def parse_better_js_alternative(code: str) -> None:
regexes = [
# ...
]
statements = code.split('\n')
tokens = []
for regex in regexes:
for statement in statements:
pass
ast = []
for token in tokens:
pass
for node in ast:
passGood:
from typing import Tuple, List, Text, Dict
REGEXES: Tuple = (
# ...
)
def parse_better_js_alternative(code: Text) -> None:
tokens: List = tokenize(code)
syntax_tree: List = parse(tokens)
for node in syntax_tree:
pass
def tokenize(code: Text) -> List:
statements = code.split()
tokens: List[Dict] = []
for regex in REGEXES:
for statement in statements:
pass
return tokens
def parse(tokens: List) -> List:
syntax_tree: List[Dict] = []
for token in tokens:
pass
return syntax_treeFlags tell your user that this function does more than one thing. Functions should do one thing. Split your functions if they are following different code paths based on a boolean.
Bad:
from typing import Text
from tempfile import gettempdir
from pathlib import Path
def create_file(name: Text, temp: bool) -> None:
if temp:
(Path(gettempdir()) / name).touch()
else:
Path(name).touch()Good:
from typing import Text
from tempfile import gettempdir
from pathlib import Path
def create_file(name: Text) -> None:
Path(name).touch()
def create_temp_file(name: Text) -> None:
(Path(gettempdir()) / name).touch()A function produces a side effect if it does anything other than take a value in and return another value or values. For example, a side effect could be writing to a file, modifying some global variable, or accidentally wiring all your money to a stranger.
Now, you do need to have side effects in a program on occasion - for example, like in the previous example, you might need to write to a file. In these cases, you should centralize and indicate where you are incorporating side effects. Don"t have several functions and classes that write to a particular file - rather, have one (and only one) service that does it.
The main point is to avoid common pitfalls like sharing state between objects without any structure, using mutable data types that can be written to by anything, or using an instance of a class, and not centralizing where your side effects occur. If you can do this, you will be happier than the vast majority of other programmers.
Bad:
# type: ignore
# This is a module-level name.
# It"s good practice to define these as immutable values, such as a string.
# However...
fullname = "Ryan McDermott"
def split_into_first_and_last_name() -> None:
# The use of the global keyword here is changing the meaning of the
# the following line. This function is now mutating the module-level
# state and introducing a side-effect!
global fullname
fullname = fullname.split()
split_into_first_and_last_name()
# MyPy will spot the problem, complaining about 'Incompatible types in
# assignment: (expression has type "List[str]", variable has type "str")'
print(fullname) # ["Ryan", "McDermott"]
# OK. It worked the first time, but what will happen if we call the
# function again?Good:
from typing import List, AnyStr
def split_into_first_and_last_name(name: AnyStr) -> List[AnyStr]:
return name.split()
fullname = "Ryan McDermott"
name, surname = split_into_first_and_last_name(fullname)
print(name, surname) # => Ryan McDermottAlso good
from typing import Text
from dataclasses import dataclass
@dataclass
class Person:
name: Text
@property
def name_as_first_and_last(self) -> list:
return self.name.split()
# The reason why we create instances of classes is to manage state!
person = Person("Ryan McDermott")
print(person.name) # => "Ryan McDermott"
print(person.name_as_first_and_last) # => ["Ryan", "McDermott"]Coming soon
We create a class called Burglar, which has a method called steal. This method breaks the SRP because it doesn't just steal. It also puts on and removes the invisibility cloak, which might lead to all sorts of issues for the burglar.
class Burglar:
def __init__(self):
self._artifacts = []
def steal(self, artifact: str):
print("Putting on the invisibility cloak.")
print("Taking the artifact.")
self._artifacts.append(artifact)
print("Removing the invisibility cloak.")
bilbo = Burglar()
bilbo.steal("Arkenstone")A better way would be to create separate methods that can be called when appropriate.
class Burglar:
def __init__(self):
self._artifacts = []
def steal(self, artifact: str):
print("Taking the artifact.")
self._artifacts.append(artifact)
def cloak(self):
print("Putting on the invisibility cloak.")
def remove_cloak(self):
print("Removing the invisibility cloak.")
bilbo = Burglar()
bilbo.cloak()
bilbo.steal("Arkenstone")
bilbo.remove_cloak() Now Bilbo can put on the cloak, walk in, steal the Arkenstone, walk out, so he won't be seen by Smaug and remove the cloak.
Example taken from:
https://codingwithjohan.com/blog/python/solid-single-responsibility-principle/
Software entities (classes, function, module) open for extension, but not for modification (or closed for modification)
The following example violated the OCP principle:
class Discount:
"""Demo customer discount class"""
def __init__(self, customer, price):
self.customer = customer
self.price = price def give_discount(self):
"""A discount method"""
if self.customer == 'normal':
return self.price * 0.2
elif self.customer == 'vip':
return self.price * 0.4This example is failed to pass the Open and Close Principle(OCP). Assume, we have a super VIP customer and we want to give a discount of 0.8 percentage. What would we do in this case? Maybe we will solve the problem this way.
.......
def give_discount(self):
"""A discount method"""
if self.customer == 'normal':
return self.price * 0.2
elif self.customer == 'vip':
return self.price * 0.4
elif self.customer == 'supvip':
return self.price * 0.8But this solution violates the OCP. Because we can’t modify the give_discount method. Only we can extend the method.
Solution:
class Discount:
"""Demo customer discount class"""
def __init__(self, customer, price):
self.customer = customer
self.price = price
def get_discount(self):
"""A discount method"""
return self.price * 0.2
class VIPDiscount(Discount):
"""Demo VIP customer discount class"""
def get_discount(self):
"""A discount method"""
return super().get_discount() * 2
class SuperVIPDiscount(VIPDiscount):
"""Demo super vip customer discount class"""
def get_discount(self):
"""A discount method"""
return super().get_discount() * 2Example from: https://medium.com/@vubon.roy/solid-principles-with-python-examples-10e1f3d91259
Let φ(x) be a property provable about objects x of type T. Then φ(y) should be true for objects y of type S where S is a subtype of T. More formally, this is the original definition (LISKOV 01) of Liskov’s substitution principle: if S is a subtype of T, then objects of type T may be replaced by objects of type S, without breaking the program.
Liskov Substitution Principle was introduced by Barbara Liskov in her conference keynote “Data Abstraction” in 1987.
Example of Violation of LSP
class Vehicle:
"""A demo Vehicle class"""
def __init__(self, name: str, speed: float):
self.name = name
self.speed = speed
def get_name(self) -> str:
"""Get vehicle name"""
return f"The vehicle name {self.name}"
def get_speed(self) -> str:
"""Get vehicle speed"""
return f"The vehicle speed {self.speed}"
def engine(self):
"""A vehicle engine"""
pass
def start_engine(self):
"""A vehicle engine start"""
self.engine()
class Car(Vehicle):
"""A demo Car Vehicle class"""
def start_engine(self):
pass
class Bicycle(Vehicle):
"""A demo Bicycle Vehicle class"""
def start_engine(self):
passIn Bicycle class violates the LSP. Cause in the Vehicle class has an engine method. But naturally, a bicycle has no engine. So we could not start any engine. Refactor the code and make a solution for this problem.
Solution:
class Vehicle:
"""A demo Vehicle class"""
def __init__(self, name: str, speed: float):
self.name = name
self.speed = speed
def get_name(self) -> str:
"""Get vehicle name"""
return f"The vehicle name {self.name}"
def get_speed(self) -> str:
"""Get vehicle speed"""
return f"The vehicle speed {self.speed}"
class VehicleWithoutEngine(Vehicle):
"""A demo Vehicle without engine class"""
def start_moving(self):
"""Moving"""
raise NotImplemented
class VehicleWithEngine(Vehicle):
"""A demo Vehicle engine class"""
def engine(self):
"""A vehicle engine"""
pass
def start_engine(self):
"""A vehicle engine start"""
self.engine()
class Car(VehicleWithEngine):
"""A demo Car Vehicle class"""
def start_engine(self):
pass
class Bicycle(VehicleWithoutEngine):
"""A demo Bicycle Vehicle class"""
def start_moving(self):
passActually, LSP is a concept that applies to all kinds of polymorphism. Only if you don’t use polymorphism of all you don’t need to care about the LSP.
Example from: https://medium.com/@vubon.roy/solid-principles-with-python-examples-10e1f3d91259
Actually, This principle suggests that “A client should not be forced to implement an interface that it does not use”
Example of Violation of ISP:
class Shape:
"""A demo shape class"""
def draw_circle(self):
"""Draw a circle"""
raise NotImplemented
def draw_square(self):
""" Draw a square"""
raise NotImplemented
class Circle(Shape):
"""A demo circle class"""
def draw_circle(self):
"""Draw a circle"""
pass
def draw_square(self):
""" Draw a square"""
passIn the above example, we need to call an unnecessary method in the Circle class. Hence the example violated the Interface Segregation Principle. Solution:
class Shape:
"""A demo shape class"""
def draw(self):
"""Draw a shape"""
raise NotImplemented
class Circle(Shape):
"""A demo circle class"""
def draw(self):
"""Draw a circle"""
pass
class Square(Shape):
"""A demo square class"""
def draw(self):
"""Draw a square"""
passAnother example:
class BankAccount:
"""A demo Bank Account class"""
def __init__(self, balance: float, account: str):
self.account = {f"{account}": balance} def balance(self, account: str):
"""Get current balance"""
raise NotImplementedclass Deposit(BankAccount):
"""A demo circle class"""
def balance(self, account: str):
"""Get current balance"""
return self.account.get(account) def deposit(self, amount: float, account: str):
"""Deposit a new amount"""
current = self.balance(account)
new_amount = current + amount
self.account.update({account: new_amount})Example from: https://medium.com/@vubon.roy/solid-principles-with-python-examples-10e1f3d91259
This principle suggests that below two points. a. High-level modules should not depend on low-level modules. Both should depend on abstractions. b. Abstractions should not depend on details. Details should depend on abstractions.
Example of Violation of DIP:
class BackendDeveloper:
"""This is a low-level module"""
@staticmethod
def python():
print("Writing Python code")class FrontendDeveloper:
"""This is a low-level module"""
@staticmethod
def javascript():
print("Writing JavaScript code")class Project:
"""This is a high-level module"""
def __init__(self):
self.backend = BackendDeveloper()
self.frontend = FrontendDeveloper() def develop(self):
self.backend.python()
self.frontend.javascript()
return "Develop codebase"project = Project()
print(project.develop())Another example:
class NewsPerson:
"""This is a high-level module"""
@staticmethod
def publish(news: str) -> None:
"""
:param news:
:return:
"""
print(NewsPaper().publish(news=news))class NewsPaper:
"""This is a low-level module"""
@staticmethod
def publish(news: str) -> None:
"""
:param news:
:return:
"""
print(f"{news} Hello newspaper")person = NewsPerson()
print(person.publish("News Paper"))The project class is a high-level module and backend & frontend are the low-level modules. In this example, we found that the high-level module depends on the low-level module. Hence this example are violated the Dependency Inversion Principle. Let’s solve the problem according to the definition of DIP.
Solution:
class BackendDeveloper:
"""This is a low-level module"""
def develop(self):
self.__python_code()
@staticmethod
def __python_code():
print("Writing Python code")
class FrontendDeveloper:
"""This is a low-level module"""
def develop(self):
self.__javascript()
@staticmethod
def __javascript():
print("Writing JavaScript code")
class Developers:
"""An Abstract module"""
def __init__(self):
self.backend = BackendDeveloper()
self.frontend = FrontendDeveloper()
def develop(self):
self.backend.develop()
self.frontend.develop()
class Project:
"""This is a high-level module"""
def __init__(self):
self.__developers = Developers()
def develops(self):
return self.__developers.develop()
project = Project()
print(project.develops())Second example:
class NewsPerson:
"""This is a high-level module"""
@staticmethod
def publish(news: str, publisher=None) -> None:
print(publisher.publish(news=news))
class NewsPaper:
"""This is a low-level module"""
@staticmethod
def publish(news: str) -> None:
print("{} news paper".format(news))
class Facebook:
"""This is a low-level module"""
@staticmethod
def publish(news: str) -> None:
print(f"{news} - share this post on {news}")
person = NewsPerson()
person.publish("hello", NewsPaper())
person.publish("facebook", Facebook())Coming soon
For more information, please check the websites: