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.
Adapted from clean-code-javascript
Targets Python3.7+
Bad:
import datetime
ymdstr = datetime.date.today().strftime("%y-%m-%d")Additionally, there's no need to add the type of the variable (str) to its name.
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
class Record:
pass
class User:
info: str
@property
def data(self) -> Dict[str, str]:
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:
import hashlib
def create_micro_brewery(name: str = "Hipster Brew Co."):
slug = hashlib.sha1(name.encode()).hexdigest()
# etc.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)A large amount of parameters is usually the sign that a function is doing too much (has more than one responsibility). Try to decompose it into smaller functions having a reduced set of parameters, ideally less than three.
If the function has a single responsibility, consider if you can bundle some or all parameters into a specialized object that will be passed as an argument to the function. These parameters might be attributes of a single entity that you can represent with a dedicated data structure. You may also be able to reuse this entity elsewhere in your program. The reason why this is a better arrangement is than having multiple parameters is that we may be able to move some computations, done with those parameters inside the function, into methods belonging to the new object, therefore reducing the complexity of the function.
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
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: str
body: str
button_text: str
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<
5276
/span>="Order now!"
)
)Even fancier
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: str
body: str
button_text: str
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
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: str
body: str
button_text: str
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
)
)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, Dict
REGEXES: Tuple = (
# ...
)
def parse_better_js_alternative(code: str) -> None:
tokens: List = tokenize(code)
syntax_tree: List = parse(tokens)
for node in syntax_tree:
pass
def tokenize(code: str) -> 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 tempfile import gettempdir
from pathlib import Path
def create_file(name: str, temp: bool) -> None:
if temp:
(Path(gettempdir()) / name).touch()
else:
Path(name).touch()Good:
from tempfile import gettempdir
from pathlib import Path
def create_file(name: str) -> None:
Path(name).touch()
def create_temp_file(name: str) -> 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?