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clean-code-python
Table of Contents
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")
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()
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:
import hashlib
def create_micro_brewery(name: str = "Hipster Brew Co."):
slug = hashlib.sha1(name.encode()).hexdigest()
# etc.
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)
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
)
)
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, 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
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:
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()
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 dataclasses import dataclass
@dataclass
class Person:
name: str
@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"]
Classes
Single Responsibility Principle (SRP)
Robert C. Martin writes:
A class should have only one reason to change.
"Reasons to change" are, in essence, the responsibilities managed by a class or function.
In the following example, we create an HTML element that represents a comment with the version of the document:
Bad
from importlib import metadata
class VersionCommentElement:
"""An element that renders an HTML comment with the program's version number
"""
def get_version(self) -> str:
"""Get the package version"""
return metadata.version("pip")
def render(self) -> None:
print(f'<!-- Version: {self.get_version()} -->')
VersionCommentElement().render()
This class has two responsibilities:
- Retrieve the version number of the Python package
- Render itself as an HTML element
Any change to one or the other carries the risk of impacting the other.
We can rewrite the class and decouple these responsibilities:
Good
from importlib import metadata
def get_version(pkg_name: str) -> str:
"""Retrieve the version of a given package"""
return metadata.version(pkg_name)
class VersionCommentElement:
"""An element that renders an HTML comment with the program's version number
"""
def __init__(self, version: str):
self.version = version
def render(self) -> None:
print(f'<!-- Version: {self.version} -->')
VersionCommentElement(get_version("pip")).render()
The result is that the class only needs to take care of rendering itself. It
receives the version text during instantiation and this text is generated by
calling a separate function, get_version(). Changing the class has no impact
on the other, and vice-versa, as long as the contract between them does not
change, i.e. the function provides a string and the class __init__ method
accepts a string.
As an added bonus, the get_version() is now reusable elsewhere.
Open/Closed Principle (OCP)
“Incorporate new features by extending the system, not by making modifications (to it)”, Uncle Bob.
Objects should be open for extension, but closed to modification. It should be possible to augment the functionality provided by an object (for example, a class) without changing its internal contracts. An object can enable this when it is designed to be extended cleanly.
In the following example, we try to implement a simple web framework that
handles HTTP requests and returns responses. The View class has a single
method .get() that will be called when the HTTP server will receive a GET
request from a client.
View is intentionally simple and returns text/plain responses. We would
also like to return HTML responses based on a template file, so we subclass it
using the TemplateView class.
Bad
from dataclasses import dataclass
@dataclass
class Response:
"""An HTTP response"""
status: int
content_type: str
body: str
class View:
"""A simple view that returns plain text responses"""
def get(self, request) -> Response:
"""Handle a GET request and return a message in the response"""
return Response(
status=200,
content_type='text/plain',
body="Welcome to my web site"
)
class TemplateView(View):
"""A view that returns HTML responses based on a template file."""
def get(self, request) -> Response:
"""Handle a GET request and return an HTML document in the response"""
with open("index.html") as fd:
return Response(
status=200,
content_type='text/html',
body=fd.read()
)
The TemplateView class has modified the internal behaviour of its parent
class in order to enable the more advanced functionality. In doing so, it now
relies on the View to not change the implementation of the .get()
method, which now needs to be frozen in time. We cannot introduce, for example,
some additional checks in all our View-derived classes because the behaviour
is overridden in at least one subtype and we will need to update it.
Let's redesign our classes to fix this problem and let the View class be
extended (not modified) cleanly:
Good
from dataclasses import dataclass
@dataclass
class Response:
"""An HTTP response"""
status: int
content_type: str
body: str
class View:
"""A simple view that returns plain text responses"""
content_type = "text/plain"
def render_body(self) -> str:
"""Render the message body of the response"""
return "Welcome to my web site"
def get(self, request) -> Response:
"""Handle a GET request and return a message in the response"""
return Response(
status=200,
content_type=self.content_type,
body=self.render_body()
)
class TemplateView(View):
"""A view that returns HTML responses based on a template file."""
content_type = "text/html"
template_file = "index.html"
def render_body(self) -> str:
"""Render the message body as HTML"""
with open(self.template_file) as fd:
return fd.read()
Note that we did need to override the render_body() in order to change the
source of the body, but this method has a single, well defined responsibility
that invites subtypes to override it. It is designed to be extended by its
subtypes.
Another good way to use the strengths of both object inheritance and object composition is to use Mixins .
Mixins are bare-bones classes that are meant to be used exclusively with other related classes. They are "mixed-in" with the target class using multiple inheritance, in order to change the target's behaviour.
A few rules:
- Mixins should always inherit from
object - Mixins always come before the target class,
e.g.
class Foo(MixinA, MixinB, TargetClass): ...
Also good
from dataclasses import dataclass, field
from typing import Protocol
@dataclass
class Response:
"""An HTTP response"""
status: int
content_type: str
body: str
headers: dict = field(default_factory=dict)
class View:
"""A simple view that returns plain text responses"""
content_type = "text/plain"
def render_body(self) -> str:
"""Render the message body of the response"""
return "Welcome to my web site"
def get(self, request) -> Response:
"""Handle a GET request and return a message in the response"""
return Response(
status=200,
content_type=self.content_type,
body=self.render_body()
)
class TemplateRenderMixin:
"""A mixin class for views that render HTML documents using a template file
Not to be used by itself!
"""
template_file: str = ""
def render_body(self) -> str:
"""Render the message body as HTML"""
if not self.template_file:
raise ValueError("The path to a template file must be given.")
with open(self.template_file) as fd:
return fd.read()
class ContentLengthMixin:
"""A mixin class for views that injects a Content-Length header in the
response
Not to be used by itself!
"""
def get(self, request) -> Response:
"""Introspect and amend the response to inject the new header"""
response = super().get(request) # type: ignore
response.headers['Content-Length'] = len(response.body)
return response
class TemplateView(TemplateRenderMixin, ContentLengthMixin, View):
"""A view that returns HTML responses based on a template file."""
content_type = "text/html"
template_file = "index.html"
As you can see, Mixins make object composition easier by packaging together related functionality into a highly reusable class with a single responsibility, allowing clean decoupling. Class extension is achieved by " mixing-in" the additional classes.
The popular Django project makes heavy use of Mixins to compose its class-based views.
FIXME: re-enable typechecking for the line above once it's clear how to use
typing.Protocol to make the type checker work with Mixins.
Liskov Substitution Principle (LSP)
“Functions that use pointers or references to base classes must be able to use objects of derived classes without knowing it”, Uncle Bob.
This principle is named after Barbara Liskov, who collaborated with fellow computer scientist Jeannette Wing on the seminal paper *"A behavioral notion of subtyping" (1994). A core tenet of the paper is that "a subtype (must) preserve the behaviour of the supertype methods and also all invariant and history properties of its supertype".
In essence, a function accepting a supertype should also accept all its subtypes with no modification.
Can you spot the problem with the following code?
Bad
from dataclasses import dataclass
@dataclass
class Response:
"""An HTTP response"""
status: int
content_type: str
body: str
class View:
"""A simple view that returns plain text responses"""
content_type = "text/plain"
def render_body(self) -> str:
"""Render the message body of the response"""
return "Welcome to my web site"
def get(self, request) -> Response:
"""Handle a GET request and return a message in the response"""
return Response(
status=200,
content_type=self.content_type,
body=self.render_body()
)
class TemplateView(View):
"""A view that returns HTML responses based on a template file."""
content_type = "text/html"
def get(self, request, template_file: str) -> Response: # type: ignore
"""Render the message body as HTML"""
with open(template_file) as fd:
return Response(
status=200,
content_type=self.content_type,
body=fd.read()
)
def render(view: View, request) -> Response:
"""Render a View"""
return view.get(request)
The expectation is that render() function will be able to work with View
and its subtype TemplateView, but the latter has broken compatibility by
modifying the signature of the .get() method. The function will raise
a TypeError
exception when used with TemplateView.
If we want the render() function to work with any subtype of View, we must
pay attention not to break its public-facing protocol. But how do we know what
constitutes it for a given class? Type hinters like mypy will raise an error
when it detects mistakes like this:
error: Signature of "get" incompatible with supertype "View"
<string>:36: note: Superclass:
<string>:36: note: def get(self, request: Any) -> Response
<string>:36: note: Subclass:
<string>:36: note: def get(self, request: Any, template_file: str) -> Response
Interface Segregation Principle (ISP)
“Keep interfaces small so that users don’t end up depending on things they don’t need.”, Uncle Bob.
Several well known object oriented programming languages, like Java and Go, have a concept called interfaces. An interface defines the public methods and properties of an object without implementing them. They are useful when we don't want to couple the signature of a function to a concrete object; we'd rather say "I don't care what object you give me, as long as it has certain methods and attributes I expect to make use of".
Python does not have interfaces. We have Abstract Base Classes instead, which are a little different, but can serve the same purpose.
Good
from abc import ABCMeta, abstractmethod
# Define the Abstract Class for a generic Greeter object
class Greeter(metaclass=ABCMeta):
"""An object that can perform a greeting action."""
@staticmethod
@abstractmethod
def greet(name: str) -> None:
"""Display a greeting for the user with the given name"""
class FriendlyActor(Greeter):
"""An actor that greets the user with a friendly salutation"""
@staticmethod
def greet(name: str) -> None:
"""Greet a person by name"""
print(f"Hello {name}!")
def welcome_user(user_name: str, actor: Greeter):
"""Welcome a user with a given name using the provided actor"""
actor.greet(user_name)
welcome_user("Barbara", FriendlyActor())
Now imagine the following scenario: we have a certain number of PDF documents that we author and want to serve to our web site visitors. We are using a Python web framework and we might be tempted to design a class to manage these documents, so we go ahead and design a comprehensive abstract base class for our document.
Error
import abc
class Persistable(metaclass=abc.ABCMeta):
"""Serialize a file to data and back"""
@property
@abc.abstractmethod
def data(self) -> bytes:
"""The raw data of the file"""
@classmethod
@abc.abstractmethod
def load(cls, name: str):
"""Load the file from disk"""
@abc.abstractmethod
def save(self) -> None:
"""Save the file to disk"""
# We just want to serve the documents, so our concrete PDF document
# implementation just needs to implement the `.load()` method and have
# a public attribute named `data`.
class PDFDocument(Persistable):
"""A PDF document"""
@property
def data(self) -> bytes:
"""The raw bytes of the PDF document"""
... # Code goes here - omitted for brevity
@classmethod
def load(cls, name: str):
"""Load the file from the local filesystem"""
... # Code goes here - omitted for brevity
def view(request):
"""A web view that handles a GET request for a document"""
requested_name = request.qs['name'] # We want to validate this!
return PDFDocument.load(requested_name).data
But we can't! If we don't implement the .save() method, an exception will be
raised:
Can't instantiate abstract class PDFDocument with abstract method save.
That's annoying. We don't really need to implement .save() here. We could
implement a dummy method that does nothing or raises NotImplementedError, but
that's useless code that we will need to maintain.
At the same time, if we remove .save() from the abstract class now we will
need to add it back when we will later implement a way for users to submit
their documents, bringing us back to the same situation as before.
The problem is that we have written an interface that has features we don't need right now as we are not using them.
The solution is to decompose the interface into smaller and composable interfaces that segregate each feature.
Good
import abc
class DataCarrier(metaclass=abc.ABCMeta):
"""Carries a data payload"""
@property
def data(self):
...
class Loadable(DataCarrier):
"""Can load data from storage by name"""
@classmethod
@abc.abstractmethod
def load(cls, name: str):
...
class Saveable(DataCarrier):
"""Can save data to storage"""
@abc.abstractmethod
def save(self) -> None:
...
class PDFDocument(Loadable):
"""A PDF document"""
@property
def data(self) -> bytes:
"""The raw bytes of the PDF document"""
... # Code goes here - omitted for brevity
@classmethod
def load(cls, name: str) -> None:
"""Load the file from the local filesystem"""
... # Code goes here - omitted for brevity
def view(request):
"""A web view that handles a GET request for a document"""
requested_name = request.qs['name'] # We want to validate this!
return PDFDocument.load(requested_name).data
Dependency Inversion Principle (DIP)
“Depend upon abstractions, not concrete details”, Uncle Bob.
Imagine we wanted to write a web view that returns an HTTP response that streams rows of a CSV file we create on the fly. We want to use the CSV writer that is provided by the standard library.
Bad
import csv
from io import StringIO
class StreamingHttpResponse:
"""A streaming HTTP response"""
... # implementation code goes here
def some_view(request):
rows = (
['First row', 'Foo', 'Bar', 'Baz'],
['Second row', 'A', 'B', 'C', '"Testing"', "Here's a quote"]
)
# Define a generator to stream data directly to the client
def stream():
buffer_ = StringIO()
writer = csv.writer(buffer_, delimiter=';', quotechar='"')
for row in rows:
writer.writerow(row)
buffer_.seek(0)
data = buffer_.read()
buffer_.seek(0)
buffer_.truncate()
yield data
# Create the streaming response object with the appropriate CSV header.
response = StreamingHttpResponse(stream(), content_type='text/csv')
response[
'Content-Disposition'] = 'attachment; filename="somefilename.csv"'
return response
Our first implementation works around the CSV's writer interface by
manipulating a StringIO object (which is file-like) and performing several
low level operations in order to farm out the rows from the writer. It's a lot
of work and not very elegant.
A better way is to leverage the fact that the writer just needs an object with
a .write() method to do our bidding. Why not pass it a dummy object that
immediately returns the newly assembled row, so that
the StreamingHttpResponse
class can immediate stream it back to the client?
Good
import csv
class Echo:
"""An object that implements just the write method of the file-like
interface.
"""
def write(self, value):
"""Write the value by returning it, instead of storing in a buffer."""
return value
def some_streaming_csv_view(request):
"""A view that streams a large CSV file."""
rows = (
['First row', 'Foo', 'Bar', 'Baz'],
['Second row', 'A', 'B', 'C', '"Testing"', "Here's a quote"]
)
writer = csv.writer(Echo(), delimiter=';', quotechar='"')
return StreamingHttpResponse(
(writer.writerow(row) for row in rows),
content_type="text/csv",
headers={
'Content-Disposition': 'attachment; filename="somefilename.csv"'},
)
Much better, and it works like a charm! The reason it's superior to the
previous implementation should be obvious: less code (and more performant) to
achieve the same result. We decided to leverage the fact that the writer class
depends on the .write() abstraction of the object it receives, without caring
about the low level, concrete details of what the method actually does.
This example was taken from a submission made to the Django documentation by this author.
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, Dict
from dataclasses import dataclass
@dataclass
class Developer:
def __init__(self, experience: float, github_link: str) -> None:
self._experience = experience
self._github_link = github_link
@property
def experience(self) -> float:
return self._experience
@property
def github_link(self) -> str:
return self._github_link
@dataclass
class Manager:
def __init__(self, experience: float, github_link: str) -> None:
self._experience = experience
self._github_link = github_link
@property
def experience(self) -> float:
return self._experience
@property
def github_link(self) -> str:
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, Dict
from dataclasses import dataclass
@dataclass
class Employee:
def __init__(self, experience: float, github_link: str) -> None:
self._experience = experience
self._github_link = github_link
@property
def experience(self) -> float:
return self._experience
@property
def github_link(self) -> str:
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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