Basics

Python Basics (Engineer Cheat Sheet)

Environment & Tooling

pyenv + venv

• pyenv is a Python version manager, similar to nvm for Node.js.
• It helps you install and switch between multiple Python versions.
• venv creates an isolated environment for one project, so packages from one project do not affect another.

Example:

pip + requirements.txt

• pip is Python’s package installer.
• requirements.txt stores the list of dependencies for a project.
• This makes it easy for others, or your future self, to install the same packages.

Example:

Poetry (pyproject.toml)

• Poetry is a modern dependency and project manager for Python.
• It handles dependencies, virtual environments, and package configuration in one place.
• pyproject.toml is the main config file Poetry uses.

Example:

Python REPL + ipython

• REPL means Read-Eval-Print Loop.
• It is a quick interactive shell where you can test Python code.
• ipython is a better REPL with autocomplete, command history, and nicer output.

Example:

Syntax — What’s Different from TS/JS

Indentation, no braces

• Python uses indentation to define code blocks.
• It does not use {} like JavaScript or TypeScript.
• A colon : starts a new block.

Example:

Type hints

• Type hints let you describe what type a variable or function argument should be.
• They make code easier to understand and improve editor support.
• They are not strictly enforced by Python at runtime by default.

Example:

f-strings

• f-strings are the cleanest way to insert variables into strings.
• Put f before the string, then use {} inside it.

Example:

None vs null / undefined

• Python has None to represent “no value”.
• There is no separate null or undefined.
• The correct check is is None, not == None.

Example:

Truthiness

• In Python, some values are treated as False in conditions.
• Common falsy values are: None, 0, "", [], {}, set().
• Most other values are truthy.

Example:

Walrus operator :=

• The walrus operator assigns a value and uses it in the same expression.
• It is useful when you want to avoid doing the same work twice.

Example:

Collections & Comprehensions

list / dict / set / tuple

• list: ordered, mutable collection
• dict: key-value mapping
• set: unordered collection of unique values
• tuple: ordered, immutable collection

Example:

List comprehensions

• A compact way to create lists from loops.
• Very common and very Pythonic.

Example:

With condition:

Dict comprehensions

• Similar to list comprehensions, but for dictionaries.
• Useful when transforming data into key-value form.

Example:

Unpacking *args and **kwargs

• *args collects extra positional arguments into a tuple.
• **kwargs collects extra keyword arguments into a dictionary.
• Useful when you do not know how many arguments will be passed.

Example:

Generators (yield)

• A generator returns values one at a time instead of all at once.
• This makes it memory-efficient.
• It is useful for large data, streams, and lazy processing.

Example:

Functions & OOP

Default args, keyword args

• Default arguments give a parameter a fallback value.
• Keyword arguments let you pass values by name.
• This makes function calls clearer and more flexible.

Example:

Keyword-only arguments

• You can force some arguments to be passed by name using *.
• This makes calls more explicit and avoids confusion.

Example:

Decorators

• A decorator wraps a function and changes or extends its behavior.
• Common uses: logging, authentication, caching, route handling.

Example:

dataclass

• A dataclass is a simpler way to write classes that mostly store data.
• It automatically creates methods like __init__ and __repr__.

Example:

Dunder methods

• Dunder means “double underscore”.
• These special methods let your class behave in custom ways.
• Examples: __init__, __str__, __len__, __getitem__.

Example:

Context managers (with)

• A context manager handles setup and cleanup automatically.
• It is commonly used for files, database connections, and locks.
• Even if an error happens, cleanup still runs.

Example:

Error Handling & Modules

try / except / finally

• try contains code that may fail.
• except handles the error.
• finally always runs, whether there was an error or not.

Example:

Custom exceptions

• You can create your own exception types for clearer error handling.
• This makes bigger applications easier to maintain.

Example:

Imports

• import lets you use code from another file or module.
• This helps organize code into reusable parts.

Example:

__init__.py

• __init__.py tells Python that a folder should behave like a package.
• It can also run package setup code or expose selected imports.

Example folder structure:

Import example:

Mental Model

Python is interpreted

• Python code is executed by the Python interpreter.
• You do not usually compile it manually like C or C++.

Python is dynamically typed

• You do not need to declare variable types beforehand.
• A variable can point to different types of values over time.

Example:

Everything is an object

• Numbers, strings, functions, classes, everything in Python is an object.
• That is why Python feels very consistent.

Example:

Clean code matters a lot in Python

• Python is built around readability.
• Simple, clear code is preferred over clever but confusing code.

What to Learn Next

async / await

• Used for asynchronous programming.
• Important for APIs, web servers, and I/O-heavy systems.

FastAPI

• A modern Python web framework.
• Great for building APIs quickly.

Pydantic

• Used for validation and structured data handling.
• Common in FastAPI apps.

File handling and streams

• Important for working with files, uploads, logs, and data pipelines.

Concurrency

• Learn the difference between threads, processes, and async tasks.

Packaging and deployment

• Important for shipping real Python applications.