clean-code-python

Table of Contents

1. Introduction
2. Variables
3. Functions
4. 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)
5. Don't repeat yourself (DRY)
6. Translations

Introduction

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+

Variables

Use meaningful and pronounceable variable names

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")

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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:

def get_user_info(): pass


def get_client_data(): pass


def get_customer_record(): pass

Good: 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(): pass

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:

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()

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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:

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)

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Use explanatory variables

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']}")

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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` again?
    print(item)

Good:

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

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

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.

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Functions

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:

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)

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Function arguments (2 or fewer ideally)

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):
    pass

Java-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="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
    )
)

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Function names should say what they do

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()

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Functions should only be one level of abstraction

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:
        pass

Good:

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_tree

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Don't use flags as function parameters

Flags 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:

```python from tempfile import gettemp