The Python Guide

Python is one of the best first languages you can learn. It reads almost like English, runs everywhere, and is widely used for data science, machine learning, automation, scripting, and backend development.

I wrote a long series of Python tutorials covering the language from the ground up. The core of Python changes very slowly, so these fundamentals are solid ground. This page organizes the best of them into a learning path.

Where to start

Read these in order to get the foundation:

• Introduction to Python explains what Python is and why it matters
• The basics of working with Python covers variables and the core syntax
• Python Data Types
• Python Operators
• Python Control Statements covers if and conditionals
• Python Loops
• Python Functions

If you’re on a Mac, Installing Python 3 on macOS gets you set up first.

Data structures

Python’s built-in collections are a big part of what makes it pleasant to use:

• Python Strings
• Python Lists are the workhorse you’ll use every day
• Python Tuples
• Python Dictionaries
• Python Sets
• Python List comprehensions for the idiomatic way to transform lists

Objects, classes, and beyond

Once the basics feel comfortable, these take you to intermediate territory:

• Python Objects
• Python Classes
• Python Modules to organize code across files
• Python Exceptions for error handling
• Python Lambda Functions
• Python Decorators is a classic “aha” topic that is worth the effort

Practical Python

The everyday tasks you’ll reach for again and again:

• Python, read the content of a file
• Python, how to write to a file
• Python, accept arguments from command line
• Python, create a network request
• The with statement in Python

Tooling

Read these early:

• Install third-party Python packages with pip
• Python Virtual Environments keep dependencies isolated per project. Learn this before your first real project, not after.
• The PEP8 Python style guide covers how Python code is expected to look

Go deeper

I collected the whole series, organized and expanded, in the free Python Handbook. You can download it as PDF or EPUB and work through it offline.

All posts on this topic are in the Python tag archive.

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