Basics
Shared from "Study" on Inkdown
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.
basic-ques
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.
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.
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.
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.
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.
f-strings
- f-strings are the cleanest way to insert variables into strings.
- Put
f before the string, then use {} inside it.
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.
Truthiness
- In Python, some values are treated as
False in conditions.
- Common falsy values are:
None, 0, "", [], {}, set().
- Most other values are truthy.
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.
Collections & Comprehensions
list / dict / set / tuple
list: ordered, mutable collectiondict: key-value mappingset: unordered collection of unique valuestuple: ordered, immutable collection
List comprehensions
- A compact way to create lists from loops.
- Very common and very Pythonic.
Dict comprehensions
- Similar to list comprehensions, but for dictionaries.
- Useful when transforming data into key-value form.
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.
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.
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.
Keyword-only arguments
- You can force some arguments to be passed by name using
*.
- This makes calls more explicit and avoids confusion.
Decorators
- A decorator wraps a function and changes or extends its behavior.
- Common uses: logging, authentication, caching, route handling.
dataclass
- A dataclass is a simpler way to write classes that mostly store data.
- It automatically creates methods like
__init__ and __repr__.
Dunder methods
- Dunder means “double underscore”.
- These special methods let your class behave in custom ways.
- Examples:
__init__, __str__, __len__, __getitem__.
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.
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.
Custom exceptions
- You can create your own exception types for clearer error handling.
- This makes bigger applications easier to maintain.
Imports
import lets you use code from another file or module.- This helps organize code into reusable parts.
__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:
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.
Everything is an object
- Numbers, strings, functions, classes, everything in Python is an object.
- That is why Python feels very consistent.
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.
