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Python roadmap

Q: What is a generator?

A function that uses yield to produce values one at a time, saving memory for large or infinite sequences.

Q: Explain list vs dict lookup performance.

Checking x in list scans every item (O(n)); x in dict/set uses hashing (O(1) on average).

Learn Python from zero to writing clean, tested, professional code. Python is the main language for AI, data science, automation and many backends.

Thời gian: ⏱ 6–8 weeks
Level: Beginner
Steps: 6 steps
Progress: 0/6 done

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The roadmap

Click a step to open the full guide.

Python cheat sheet

The essentials on one screen. Bookmark it for revision.

Syntax

f-string	`f"Total: {total:.2f}"`
List comprehension	`[x for x in xs if x]`
Dict comprehension	`{k: v for k, v in pairs}`
Unpacking	`a, b = b, a`
Safe dict get	`d.get("key", default)`
Enumerate	`cho năm i, x in enumerate(xs):`
Zip	`cho năm a, b in zip(xs, ys):`
Lambda sort	`sorted(xs, key=lambda x: x["price"])`

Commands

New venv	`python -m venv .venv`
Activate (Windows)	`.venv\Scripts\activate`
Activate (Mac/Linux)	`source .venv/bin/activate`
Install	`pip install httpx`
Run tests	`pytest -q`
Lint + format	`ruff check . && ruff format .`

Improve performance

Use the right data structure: dicts/sets for lookups instead of searching lists.
Use built-ins (sum, sorted, max) and comprehensions; they run in fast C code.
Use async or threads for many network calls; multiprocessing for CPU-heavy work.
Profile before optimising: python -m cProfile script.py shows where time goes.
Use generators for large data so you don't load everything into memory.
Cache repeated expensive calls with functools.lru_cache.

Security checklist

Never hard-code secrets; use environment variables.
Never use eval()/exec() on user input.
Use parameterised SQL queries, never string formatting.
Pin and audit dependencies (pip-audit); install only trusted packages.
Validate all external input (Pydantic) and set timeouts on network calls.

Most important interview questions

Click a question to see a strong answer. Each step also has its own questions.

1What are Python's main data types?

int, float, bool, str, list, tuple, dict, set and None; plus bytes for binary data.

2a generator là gì?

A function that uses yield to produce values one at a time, saving memory for large or infinite sequences.

3Explain list vs dict lookup performance.

Checking x in list scans every item (O(n)); x in dict/set uses hashing (O(1) on average).

4the difference between deep and shallow copy là gì?

A shallow copy copies the outer container but shares nested objects; a deep copy (copy.deepcopy) copies everything recursively.

5How does Python manage memory?

Reference counting plus a cyclic garbage collector; objects are freed when no references remain.

6What are decorators used for in real projects?

Adding cross-cutting behaviour such as routes in web frameworks, caching, retries, logging, timing and access checks.

Related lộ trình

Python Developer

6 steps · 4–5 months · Beginner

Build backends, automation and data tools with Python.

View roadmap →

FastAPI

6 steps · 3–4 weeks · Intermediate

Build fast, documented Python APIs.

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AI Engineer

6 steps · 4–6 months · Intermediate

Build products on top of large language models.

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Data Analyst

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Turn data into answers and dashboards.

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Python · Step 1 of 6 · 1 week

Basics: values, control flow, functions

Write your first real programs.

Start by writing small programs that do something useful: a tip calculator, a password checker, a unit converter. Functions are the most important idea: name a piece of logic and reuse it.

What you'll learn

Numbers, strings and booleans
if / elif / else
cho năm and while loops
Functions and return values
f-strings
Reading errors

How a Python program runs

Your .py file
Python interpreter
Bytecode
Runs line by line
Output / errors

Key concepts

Variable: A name that points to a value. Python figures out the type for you.
Indentation: Spaces that define blocks in Python (use 4 spaces).
Function: Reusable, named logic with inputs (parameters) and an output (return).

Step by step

1.1
Install and run
Install Python 3.12+, open a terminal, run python --version, then write hello.py.
1.2
Values and variables
Numbers, strings, booleans; name = "Asha"; print with f-strings.
1.3
Decisions and loops
if/elif/else, for over lists and range(), while with a stop condition.
1.4
Functions
Wrap logic in def, take parameters, return results; add type hints.

def grade(score: int) -> str:
    if score >= 90:
        return "A"
    elif score >= 75:
        return "B"
    return "C"

for s in [95, 80, 60]:
    print(f"{s} -> {grade(s)}")

Notes

Indentation is part of Python's syntax.
Use f-strings for formatting: f"Hi {name}".

Tips & tricks

Type every example yourself instead of copying.
Read the last line of an error first.

Interview questions

the difference between `==` and `is` là gì?

== compares values (are they equal?). is compares identity (are they the same object?). Use is only for None, True, False.

Is Python compiled or interpreted?

Both: source is compiled to bytecode, which the Python virtual machine interprets. In practice it behaves like an interpreted language.

The quick way to study this

Do one interactive beginner course in a week, then build three tiny programs.

Python · Step 2 of 6 · 1 week

Data structures

Lists, dictionaries, sets and tuples.

Most real programs are about organising data. Dictionaries map keys to values and are used everywhere, including JSON from APIs. Comprehensions build lists and dicts in one readable line.

What you'll learn

Lists and slicing
Dictionaries
Sets
Tuples and unpacking
Comprehensions
Sorting with keys

Which data structure?

list

Ordered
Duplicates OK
Find by position

dict

Key → value
Fast lookup by key
JSON-like data

set

Unique items
Fast 'is it in?'
Remove duplicates

Key concepts

Mutable vs immutable: Lists/dicts/sets can change; strings/tuples/numbers can't.
Big-O of lookups: Dict and set lookups are O(1) on average; searching a list is O(n).
Comprehension: A compact way to build a list, dict or set from another iterable.

Step by step

2.1
Lists
Ordered and changeable: append, slicing a[1:3], sorted().
2.2
Dictionaries
Key → value lookups: d.get(key, default), .items() in loops.
2.3
Sets and tuples
Sets for unique items and fast membership; tuples for fixed records.
2.4
Comprehensions
[x*2 for x in nums if x > 0] builds lists in one readable line.

orders = [{"item": "tea", "qty": 2}, {"item": "coffee", "qty": 1}, {"item": "tea", "qty": 3}]

totals: dict[str, int] = {}
for o in orders:
    totals[o["item"]] = totals.get(o["item"], 0) + o["qty"]

print(totals)  # {'tea': 5, 'coffee': 1}
print(sorted(totals, key=totals.get, reverse=True))

Notes

Dictionary lookups are fast; use them instead of searching lists.
Sets remove duplicates instantly.

Tips & tricks

Practise turning loops into comprehensions, but keep them readable.

Interview questions

List vs tuple?

Lists are mutable and used for collections that change. Tuples are immutable, slightly faster, hashable (can be dict keys) and used for fixed records.

How do you remove duplicates from a list while keeping order?

list(dict.fromkeys(items)): dict keys are unique and keep insertion order.

The quick way to study this

Solve 20 small problems that use each structure.

Python · Step 4 of 6 · 1 week

Classes, errors and type hints

Write structured, safer code.

Classes group data with behaviour. Dataclasses remove boilerplate for simple data holders. Type hints document your code and let tools catch mistakes.

What you'll learn

Classes and methods
Dataclasses
Exceptions and try/except
Custom exceptions
Type hints
Pydantic models

Error handling flow

try: risky code
Error raised?
except: handle it
else: success path
finally: clean up

Key concepts

Class vs object: A class is the blueprint; an object is one built from it.
Inheritance: A class reusing and extending another class.
Exception: An error signal you can catch and handle.

Step by step

4.1
Classes
Bundle data and behaviour; __init__ sets attributes; methods take self.
4.2
Dataclasses
@dataclass writes __init__, __repr__ and comparisons for you.
4.3
Exceptions
try/except specific errors; raise your own; finally cho năm clean-up.
4.4
Type hints and Pydantic
Hints document intent; Pydantic validates data from outside.

from dataclasses import dataclass

@dataclass
class Product:
    name: str
    price: float

    def with_gst(self, rate: float = 0.18) -> float:
        return round(self.price * (1 + rate), 2)

print(Product("Fan", 2499).with_gst())

Notes

Catch specific exceptions, not bare except:.

Tips & tricks

Use Pydantic when data comes from outside (APIs, users, AI models).

Interview questions

What are `*args` and `**kwargs`?

*args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict.

a decorator là gì?

A function that takes a function and returns a new one, adding behaviour (logging, caching, routes) without changing the original code. Used with @name.

The quick way to study this

Refactor one script into a small class with type hints.

Python · Step 5 of 6 · 1 week

APIs, async and automation

Talk to the internet and automate tasks.

Calling APIs is the core of modern Python work, including AI. Async lets one program wait on many network calls at once.

What you'll learn

HTTP requests with httpx or requests
Working with JSON APIs
async/await basics
Environment variables
Scheduling scripts

Sync vs async API calls

Sync (one by one)

Call A, wait
Call B, wait
Call C, wait
Total ≈ A + B + C

Async (together)

Start A, B, C
Wait for all
Total ≈ slowest one

Key concepts

Async I/O: Doing other work while waiting cho năm network or disk.
Timeout: A maximum wait time so your program never hangs forever.
Retry with backoff: Try again after increasing delays when a request fails temporarily.

Step by step

5.1
HTTP requests
httpx.get(url, timeout=10), check status, read .json().
5.2
Async
async def, await, and asyncio.gather() to run calls together.
5.3
Secrets
Read keys from environment variables or a .env file (not committed).
5.4
Automate
Schedule scripts with cron / Task Scheduler or a job runner.

import httpx

resp = httpx.get("https://api.github.com/repos/python/cpython", timeout=10)
resp.raise_for_status()
data = resp.json()
print(data["stargazers_count"])

Notes

Keep API keys in environment variables, never in code.

Tips & tricks

Add timeouts and retries to every network call.

Interview questions

the GIL and does it matter là gì?

The Global Interpreter Lock lets only one thread run Python bytecode at a time. It limits CPU-bound threading (use multiprocessing), but not I/O-bound work, where threads or async work well.

The quick way to study this

Write a script that calls two APIs and combines the results.