clean-code-python SOURCE

Table of Contents

1. Introduction
2. Variables
3. Functions
4. Objects and Data Structures
5. Classes
   1. S: Single Responsibility Principle (SRP)
   2. O: Open/Closed Principle (OCP)
   3. L: Liskov Substitution Principle (LSP)
   4. I: Interface Segregation Principle (ISP)
   5. D: Dependency Inversion Principle (DIP)
6. Don’t repeat yourself (DRY)

Variables

Use meaningful and pronounceable variable names

Bad:

ymdstr = datetime.date.today().strftime("%y-%m-%d")

Good:

current_date: str = datetime.date.today().strftime("%y-%m-%d")

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Use the same vocabulary for the same type of variable

Bad: Here we use three different names for the same underlying entity:

get_user_info()
get_client_data()
get_customer_record()

Good: If the entity is the same, you should be consistent in referring to it in your functions:

get_user_info()
get_user_data()
get_user_record()

Even 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:

class User:
    info : str

    @property
    def data(self) -> dict:
        # ...

    def get_record(self) -> Union[Record, None]:
        # ...

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Use searchable names

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:

# What the heck is 86400 for?
time.sleep(86400);

Good:

# Declare them in the global namespace for the module.
SECONDS_IN_A_DAY = 60 * 60 * 24

time.sleep(SECONDS_IN_A_DAY)

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Use explanatory variables

Bad:

address = 'One Infinite Loop, Cupertino 95014'
city_zip_code_regex = r'^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$'
matches = re.match(city_zip_code_regex, address)

save_city_zip_code(matches[1], matches[2])

Not bad:

It's better, but we are still heavily dependent on regex.

address = 'One Infinite Loop, Cupertino 95014'
city_zip_code_regex = r'^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$'
matches = re.match(city_zip_code_regex, address)

city, zip_code = matches.groups()
save_city_zip_code(city, zip_code)

Good:

Decrease dependence on regex by naming subpatterns.

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)

save_city_zip_code(matches['city'], matches['zip_code'])

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Avoid Mental Mapping

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` for again?
    dispatch(item)

Good:

locations = ('Austin', 'New York', 'San Francisco')

for location in locations:
    do_stuff()
    do_some_other_stuff()
    # ...
    dispatch(location)

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Don't add unneeded context

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: str

Good:

class Car:
    make: str
    model: str
    color: str

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Use default arguments instead of short circuiting or conditionals

Tricky

Why write:

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 ist clear that you are expecting a string as the argument.

Good:

def create_micro_brewery(name: str="Hipster Brew Co."):
    slug = hashlib.sha1(name.encode()).hexdigest()
    # etc.

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Functions

Function arguments (2 or fewer ideally)

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):
    # ...

Good:

class Menu:
    def __init__(self, config: dict):
        title = config["title"]
        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):
    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!"
    )
)

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Functions should do one thing

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:

def email_clients(clients: List[Client]):
    """Filter active clients and send them an email.
    """
    for client in clients:
        if client.active:
            email(client)

Good:

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 clients:
        email(client)

Do you see an opportunity for using generators now?

Even better

def active_clients(clients: List[Client]) -> Generator[Client]:
    """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 clients:
        email(client)

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Function names should say what they do

Bad:

class Email:
    def handle(self) -> None:
        # Do something...

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()

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