How To Write Clean Code In Python
clean-code-python
Table of Contents
- Introduction
- Variables
- Functions
- Objects and Data Structures
- Classes
- S: Single Responsibility Principle (SRP)
- O: Open/Closed Principle (OCP)
- L: Liskov Substitution Principle (LSP)
- I: Interface Segregation Principle (ISP)
- D: Dependency Inversion Principle (DIP)
- Don't repeat yourself (DRY)
- Translation
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.
Inspired 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")
Good:
import datetime current_date: str = datetime.date.today().strftime("%y-%m-%d")
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, 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()
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)
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']} ")
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)
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
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:
from typing import Text import hashlib def create_micro_brewery(name: Text = "Hipster Brew Co."): slug = hashlib.sha1(name.encode()).hexdigest() # etc.
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): 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
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 ) )
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)
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()
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, 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_tree
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 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()
Avoid side effects
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 McDermott
Also 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"]
Objects and Data Structures
Coming soon
Classes
Single Responsibility Principle (SRP)
Open/Closed Principle (OCP)
Liskov Substitution Principle (LSP)
Interface Segregation Principle (ISP)
Dependency Inversion Principle (DIP)
Coming soon
Don't repeat yourself (DRY)
Try to observe the DRY principle.
Do your absolute best to avoid duplicate code. Duplicate code is bad because it means that there's more than one place to alter something if you need to change some logic.
Imagine if you run a restaurant and you keep track of your inventory: all your tomatoes, onions, garlic, spices, etc. If you have multiple lists that you keep this on, then all have to be updated when you serve a dish with tomatoes in them. If you only have one list, there's only one place to update!
Often you have duplicate code because you have two or more slightly different things, that share a lot in common, but their differences force you to have two or more separate functions that do much of the same things. Removing duplicate code means creating an abstraction that can handle this set of different things with just one function/module/class.
Getting the abstraction right is critical. Bad abstractions can be worse than duplicate code, so be careful! Having said this, if you can make a good abstraction, do it! Don't repeat yourself, otherwise you'll find yourself updating multiple places any time you want to change one thing.
Bad:
from typing import List, Text, Dict from dataclasses import dataclass @dataclass class Developer: def __init__(self, experience: float, github_link: Text) -> None: self._experience = experience self._github_link = github_link @property def experience(self) -> float: return self._experience @property def github_link(self) -> Text: return self._github_link @dataclass class Manager: def __init__(self, experience: float, github_link: Text) -> None: self._experience = experience self._github_link = github_link @property def experience(self) -> float: return self._experience @property def github_link(self) -> Text: return self._github_link def get_developer_list(developers: List[Developer]) -> List[Dict]: developers_list = [] for developer in developers: developers_list.append({ 'experience' : developer.experience, 'github_link' : developer.github_link }) return developers_list def get_manager_list(managers: List[Manager]) -> List[Dict]: managers_list = [] for manager in managers: managers_list.append({ 'experience' : manager.experience, 'github_link' : manager.github_link }) return managers_list ## create list objects of developers company_developers = [ Developer(experience = 2.5, github_link = 'https://github.com/1'), Developer(experience = 1.5, github_link = 'https://github.com/2') ] company_developers_list = get_developer_list(developers = company_developers) ## create list objects of managers company_managers = [ Manager(experience = 4.5, github_link = 'https://github.com/3'), Manager(experience = 5.7, github_link = 'https://github.com/4') ] company_managers_list = get_manager_list(managers = company_managers)
Good:
from typing import List, Text, Dict from dataclasses import dataclass @dataclass class Employee: def __init__(self, experience: float, github_link: Text) -> None: self._experience = experience self._github_link = github_link @property def experience(self) -> float: return self._experience @property def github_link(self) -> Text: return self._github_link def get_employee_list(employees: List[Employee]) -> List[Dict]: employees_list = [] for employee in employees: employees_list.append({ 'experience' : employee.experience, 'github_link' : employee.github_link }) return employees_list ## create list objects of developers company_developers = [ Employee(experience = 2.5, github_link = 'https://github.com/1'), Employee(experience = 1.5, github_link = 'https://github.com/2') ] company_developers_list = get_employee_list(employees = company_developers) ## create list objects of managers company_managers = [ Employee(experience = 4.5, github_link = 'https://github.com/3'), Employee(experience = 5.7, github_link = 'https://github.com/4') ] company_managers_list = get_employee_list(employees = company_managers)
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Brazilian Portuguese: fredsonchaves07/clean-code-python
How To Write Clean Code In Python
Source: https://github.com/zedr/clean-code-python
Posted by: boydbegrold.blogspot.com
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