Notes From "Powerful Python"

The parts of Aaron Maxwell's Powerful Python newsletter that I don't want to forget:


Emergent Abstractions

Get used to expecting and letting abstractions emerge from projects. If you find yourself repeatedly solving similar problems in similar ways, what can you do that will simplify the code and the implementation1?

Is it a couple of convenience methods on some helper class?

The code below gives you three ways of instantiating the twitter API client within the same class:

  1. A generic "normal" way
  2. A specialized way that looks for certain environment variables
  3. A specialized way that looks for a configuration file
import os
import twitter #

class ApiClient:
    def __init__(self, consumer_key, consumer_secret,
                 access_token_key, access_token_secret):
        self.api = twitter.Api(

    def from_environ(cls):
        return cls(

    def from_config_file(cls, path):
        with open(path) as config_file:
            # ...
            return cls(...)

    # ...

Practioner, Engineer, Scientist

  1. Practioner - You can use a thing (a framework, a tool)
  2. Engineer - You can use a thing and if you needed to, you could recreate it
  3. Scientist - You can create frameworks and paradigms that have never existed before

Aim for the engineer level.

Sentinel Values

Instead of setting your sentinel value to something that is not quite impossible, like None or "None" set it to object()

This is better because it creates a unique instance of the object class and there can be no ambiguity about where it came from.

  • A sentinel value is a value you can set a variable to.
  • It's special because it differs from all other legal or possible values that the variable could have.
  • It's used as a signal or as a canary that something (bad or unexpected) has happened.

Levels of Python Code

  1. Syntax - understand what indentation is important, when you need parenthesis, colons, etc

  2. Idioms - the building blocks of a program. "Paragraphs" of code that follow common patterns, like for loops, __init__() methods (boilerplate) or context managers.

  3. Design Patterns - Less well defined that Idioms, but more useful. More info.

    • Creational Patterns, like Factories
    • Structural Patterns, like Adapters or Proxies
    • Behavioural Patterns, like Visitor or Strategy

    These tend to be the same across different languages.

  4. Architectural - the largest structures in your software system. The language itself doesn't make a lot of difference, an application would have the same architecture whether it is written in Python or Java. The interface between different components would be different, but the "organs" of the body would essentially be the same.

Read PEPs

A Python Enhancement Proposal is a document that's written to propose a new feature of Python.

It fully details the proposed feature, the arguments for and against it, and lots of sample code.

If the PEP is accepted into a future version of Python, the PEP becomes the authoritative document for that feature and how to use it.

PEPs tend to be written by the best programmers in the world, so hang out with them.


  1. Abstraction is a principal of OOP,