Things you’re probably not using in Python 3 – but should

Many people started switching their Python versions from 2 to 3 as a result of Python EOL. Unfortunately, most Python 3 I find still looks like Python 2, but with parentheses (even I am guilty of that in my code examples in previous posts – Introduction to web scraping with Python). Below, I show some examples of exciting features you can only use in Python 3 in the hopes that it will make solving your problems with Python easier.

All the examples are written in Python 3.7 and each feature contains the minimum required version of Python for that feature.

f-strings (3.6+)

It is difficult to do anything without strings in any programming language and in order to stay sane, you want to have a structured way to work with strings. Most people using Python prefer using the format method.

user = "Jane Doe"
action = "buy"

log_message = 'User {} has logged in and did an action {}.'.format(
  user,
  action
)

print(log_message)
# User Jane Doe has logged in and did an action buy.

Alongside of format, Python 3 offers a flexible way to do string interpolation via f-strings. The same code as above using f-strings looks like this:

user = "Jane Doe"
action = "buy"

log_message = f'User {user} has logged in and did an action {action}.'
print(log_message)
# User Jane Doe has logged in and did an action buy.

Pathlib (3.4+)

f-strings are amazing, but some strings like file paths have their own libraries which make their manipulation even easier. Python 3 offers pathlib as a convenient abstraction for working with file paths. If you are not sure why you should be using pathlib, try reading this excellent post – Why you should be using pathlib – by Trey Hunner.

from pathlib import Path

root = Path('post_sub_folder')
print(root)
# post_sub_folder

path = root / 'happy_user'

# Make the path absolute
print(path.resolve())
# /home/weenkus/Workspace/Projects/DataWhatNow-Codes/how_your_python3_should_look_like/post_sub_folder/happy_user

Type hinting (3.5+)

Static vs dynamic typing is a spicy topic in software engineering and almost everyone has an opinion on it. I will let the reader decide when they should write types, but I think you should at least know that Python 3 supports type hints.

def sentence_has_animal(sentence: str) -> bool:
  return "animal" in sentence

sentence_has_animal("Donald had a farm without animals")
# True

Enumerations (3.4+)

Python 3 supports an easy way to write enumerations through the Enum class. Enums are a convenient way to encapsulate lists of constants so they are not randomly located all over your code without much structure.

from enum import Enum, auto

class Monster(Enum):
    ZOMBIE = auto()
    WARRIOR = auto()
    BEAR = auto()
    
print(Monster.ZOMBIE)
# Monster.ZOMBIE

An enumeration is a set of symbolic names (members) bound to unique, constant values. Within an enumeration, the members can be compared by identity, and the enumeration itself can be iterated over.

https://docs.python.org/3/library/enum.html
for monster in Monster:
    print(monster)

# Monster.ZOMBIE
# Monster.WARRIOR
# Monster.BEAR

Built-in LRU cache (3.2+)

Caches are present in almost any horizontal slice of the software and hardware we use today. Python 3 makes using them very simple by exposing an LRU (Least Recently Used) cache as a decorator called lru_cache.

Below is a simple Fibonacci function that we know will benefit from caching because it does the same work multiple times through a recursion.

import time

def fib(number: int) -> int:
    if number == 0: return 0
    if number == 1: return 1
    
    return fib(number-1) + fib(number-2)

start = time.time()
fib(40)
print(f'Duration: {time.time() - start}s')
# Duration: 30.684099674224854s

Now we can use the lru_cache to optimize it (this optimization technique is called memoization). The execution time goes down from seconds to nanoseconds.

from functools import lru_cache

@lru_cache(maxsize=512)
def fib_memoization(number: int) -> int:
    if number == 0: return 0
    if number == 1: return 1
    
    return fib_memoization(number-1) + fib_memoization(number-2)

start = time.time()
fib_memoization(40)
print(f'Duration: {time.time() - start}s')
# Duration: 6.866455078125e-05s

Extended iterable unpacking (3.0+)

I will let the code speak here (docs).

head, *body, tail = range(5)
print(head, body, tail)
# 0 [1, 2, 3] 4

py, filename, *cmds = "python3.7 script.py -n 5 -l 15".split()
print(py)
print(filename)
print(cmds)
# python3.7
# script.py
# ['-n', '5', '-l', '15']

first, _, third, *_ = range(10)
print(first, third)
# 0 2

Data classes (3.7+)

Python 3 introduces data classes which do not have many restrictions and can be used to reduce boilerplate code because the decorator auto-generates special methods, such as __init__() and __repr()__. From the official proposal, they are described as “mutable named tuples with defaults”.

class Armor:
    
    def __init__(self, armor: float, description: str, level: int = 1):
        self.armor = armor
        self.level = level
        self.description = description
                 
    def power(self) -> float:
        return self.armor * self.level
    
armor = Armor(5.2, "Common armor.", 2)
armor.power()
# 10.4

print(armor)
# <__main__.Armor object at 0x7fc4800e2cf8>

The same implementation of Armor using data classes.

from dataclasses import dataclass

@dataclass
class Armor:
    armor: float
    description: str
    level: int = 1
    

    def power(self) -> float:
        return self.armor * self.level
    
armor = Armor(5.2, "Common armor.", 2)
armor.power()
# 10.4

print(armor)
# Armor(armor=5.2, description='Common armor.', level=2)

Implicit namespace packages (3.3+)

One way to structure Python code is in packages (folders with an __init__.py file). The example below is given by the official Python documentation.

sound/                          Top-level package
      __init__.py               Initialize the sound package
      formats/                  Subpackage for file format conversions
              __init__.py
              wavread.py
              wavwrite.py
              aiffread.py
              aiffwrite.py
              auread.py
              auwrite.py
              ...
      effects/                  Subpackage for sound effects
              __init__.py
              echo.py
              surround.py
              reverse.py
              ...
      filters/                  Subpackage for filters
              __init__.py
              equalizer.py
              vocoder.py
              karaoke.py
              ...

In Python 2, every folder above had to have an __init__.py file which turned that folder into a Python package. In Python 3, with the introduction of Implicit Namespace Packages, these files are no longer required.

sound/                          Top-level package
      __init__.py               Initialize the sound package
      formats/                  Subpackage for file format conversions
              wavread.py
              wavwrite.py
              aiffread.py
              aiffwrite.py
              auread.py
              auwrite.py
              ...
      effects/                  Subpackage for sound effects
              echo.py
              surround.py
              reverse.py
              ...
      filters/                  Subpackage for filters
              equalizer.py
              vocoder.py
              karaoke.py
              ...

EDIT: as some people have said, this is not as simple as I pointed it out in this section, from the official PEP 420 Specification__init__.py is still required for regular packages, dropping it from the folder structure will turn it into a native namespace package which comes with additional restrictions, the official docs on native namespace packages show a great example of this, as well as naming all the restrictions.

Closing note

Like almost any list on the internet, this one is not complete. I hope this post has shown you at least one Python 3 functionality you did not know existed before, and that it will help you write cleaner and more intuitive code. As always, all the code can be found on GitHub.