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# clean-code-python
[](https://travis-ci.com/zedr/clean-code-python)
[](https://www.python.org/download/releases/3.8.3/)
## Table of Contents
1. [Introduction](#introduction)
2. [Variables](#variables)
3. [Functions](#functions)
4. [Objects and Data Structures](#objects-and-data-structures)
5. [Classes](#classes)
1. [S: Single Responsibility Principle (SRP)](#single-responsibility-principle-srp)
2. [O: Open/Closed Principle (OCP)](#openclosed-principle-ocp)
3. [L: Liskov Substitution Principle (LSP)](#liskov-substitution-principle-lsp)
4. [I: Interface Segregation Principle (ISP)](#interface-segregation-principle-isp)
5. [D: Dependency Inversion Principle (DIP)](#dependency-inversion-principle-dip)
6. [Don"t repeat yourself (DRY)](#dont-repeat-yourself-dry)
## Introduction
Software engineering principles, from Robert C. Martin"s book
[*Clean Code*](https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882),
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](https://github.com/ryanmcdermott/clean-code-javascript)
Targets Python3.7+
## **Variables**
### Use meaningful and pronounceable variable names
**Bad:**
```python
import datetime
ymdstr = datetime.date.today().strftime("%y-%m-%d")
```
**Good**:
```python
import datetime
current_date: str = datetime.date.today().strftime("%y-%m-%d")
```
**[⬆ back to top](#table-of-contents)**
### Use the same vocabulary for the same type of variable
**Bad:**
Here we use three different names for the same underlying entity:
```python
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:
```python
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:
```python
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()
```
**[⬆ back to top](#table-of-contents)**
### 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:**
```python
import time
# What is the number 86400 for again?
time.sleep(86400)
```
**Good**:
```python
import time
# Declare them in the global namespace for the module.
SECONDS_IN_A_DAY = 60 * 60 * 24
time.sleep(SECONDS_IN_A_DAY)
```
**[⬆ back to top](#table-of-contents)**
### Use explanatory variables
**Bad:**
```python
import re
address = "One Infinite Loop, Cupertino 95014"
city_zip_code_regex = r"^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$"
matches = re.match(city_zip_code_regex, address)
if matches:
print(f"{matches[1]}: {matches[2]}")
```
**Not bad**:
It"s better, but we are still heavily dependent on regex.
```python
import re
address = "One Infinite Loop, Cupertino 95014"
city_zip_code_regex = r"^[^,\\]+[,\\\s]+(.+?)\s*(\d{5})?$"
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.
```python
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']}")
```
**[⬆ back to top](#table-of-contents)**
### Avoid Mental Mapping
Don’t force the reader of your code to translate what the variable means.
Explicit is better than implicit.
**Bad:**
```python
seq = ("Austin", "New York", "San Francisco")
for item in seq:
#do_stuff()
#do_some_other_stuff()
# Wait, what's `item` again?
print(item)
```
**Good**:
```python
locations = ("Austin", "New York", "San Francisco")
for location in locations:
#do_stuff()
#do_some_other_stuff()
# ...
print(location)
```
**[⬆ back to top](#table-of-contents)**
### Don"t add unneeded context
If your class/object name tells you something, don"t repeat that in your
variable name.
**Bad:**
```python
class Car:
car_make: str
car_model: str
car_color: str
```
**Good**:
```python
class Car:
make: str
model: str
color: str
```
**[⬆ back to top](#table-of-contents)**
### Use default arguments instead of short circuiting or conditionals
**Tricky**
Why write:
```python
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**:
```python
from typing import Text
import hashlib
def create_micro_brewery(name: Text = "Hipster Brew Co."):
slug = hashlib.sha1(name.encode()).hexdigest()
# etc.
```
**[⬆ back to top](#table-of-contents)**
## **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:**
```python
def create_menu(title, body, button_text, cancellable):
pass
```
**Java-esque**:
```python
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**
```python
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**
```python
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**
```python
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**
```python
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
)
)
```
**[⬆ back to top](#table-of-contents)**
### 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:**
```python
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**:
```python
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**
```python
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)
```
**[⬆ back to top](#table-of-contents)**
### Function names should say what they do
**Bad:**
```python
class Email:
def handle(self) -> None:
pass
message = Email()
# What is this supposed to do again?
message.handle()
```
**Good:**
```python
class Email:
def send(self) -> None:
"""Send this message"""
message = Email()
message.send()
```
**[⬆ back to top](#table-of-contents)**
### 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:**
```python
# 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:**
```python
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
```
**[⬆ back to top](#table-of-contents)**
### 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:**
```python
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:**
```python
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()
```
**[⬆ back to top](#table-of-contents)**
### 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:**
```python
# 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:**
```python
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**
```python
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"]
```
**[⬆ back to top](#table-of-contents)**
## **Objects and Data Structures**
*Coming soon*
**[⬆ back to top](#table-of-contents)**
## **Classes**
### **Single Responsibility Principle (SRP)**
### **Open/Closed Principle (OCP)**
### **Liskov Substitution Principle (LSP)**
### **Interface Segregation Principle (ISP)**
### **Dependency Inversion Principle (DIP)**
*Coming soon*
**[⬆ back to top](#table-of-contents)**
## **Don"t repeat yourself (DRY)**
*Coming soon*
**[⬆ back to top](#table-of-contents)**