Python Course

Welcome to the Python Academy on TechLearningPRO — a production-grade journey from your first print('hello') to building FastAPI services, async pipelines, and ML systems used a…

Python is a high-level, interpreted, dynamically typed language created by Guido van Rossum in 1991.

Install Python 3.11+ from python.org, or via pyenv (recommended for multi-version) or your OS package manager.

Your first Python program is one line: print('Hello, World!').

Python uses indentation, not braces, to define code blocks.

In Python, variables are names bound to objects, not boxes that hold values.

Python's built-in types cover most needs: int, float, bool, str, list, tuple, dict, set, None.

Python has the usual arithmetic, comparison, logical, bitwise, and assignment operators — plus identity (is) and membership (in) operators that beginners often confuse with equa…

Python conditionals use if, elif, else with colons and indented blocks.

Python's for loop iterates over any iterable (list, dict, string, file, generator).

Functions are first-class objects in Python — pass them as arguments, return them, store them in collections.

Lambdas are anonymous one-expression functions.

Python resolves names using LEGB: Local → Enclosing → Global → Built-in.

Recursion — a function that calls itself — is natural for tree/graph traversal, divide-and-conquer, and parsing.

Comprehensions let you build lists, dicts, sets in one line — faster and more readable than equivalent loops.

Generators produce values lazily using yield — memory-efficient for streams, large files, and infinite sequences.

Lists are mutable, ordered sequences — Python's workhorse collection.

Tuples are immutable ordered sequences — perfect for fixed records, dict keys, and function return values.

Sets are unordered collections of unique, hashable items — O(1) membership, union, intersection.

Dicts are hash maps — O(1) get/set/delete.

Python strings are immutable Unicode sequences.

Slicing with [start:stop:step] works on any sequence: list, tuple, string.

Python uses Timsort — O(n log n) worst case, O(n) on partially sorted data.

collections ships production-grade containers: Counter, defaultdict, deque, OrderedDict, namedtuple.

Python classes use class + __init__.

Python supports multiple inheritance with C3-linearized MRO (Method Resolution Order).

Dunder methods (__xxx__) hook into Python's protocols: __len__ for len(), __iter__ for loops, __eq__ for ==, etc.

Python doesn't enforce private attributes — convention uses _underscore.

A module is a .py file.

A production Python project uses src/ layout + pyproject.toml — the modern standard since PEP 621.

Virtual environments isolate per-project dependencies — non-negotiable for any real Python work.

pip installs from PyPI.

Open files with the with statement — it auto-closes the handle even on exceptions.

Use try / except / else / finally.

A decorator is a function that takes a function and returns a new function.

Context managers (the with protocol) guarantee setup/teardown.

Any object with __iter__ + __next__ is an iterator.

Python is dynamically typed but supports gradual typing via type hints (PEP 484).

async/await enables single-thread concurrency for I/O-bound code — perfect for web servers, scrapers, and pipelines doing many network calls.

Python's GIL (Global Interpreter Lock) means threads can't run Python bytecode in parallel — use multiprocessing or concurrent.futures for CPU-bound work.

pytest is the de-facto Python test framework — minimal boilerplate, powerful fixtures, parametrization, plugin ecosystem.

Use the stdlib logging module — never print() in production.

FastAPI is the modern Python web framework: async, type-driven, auto-generates OpenAPI docs.

Production async systems need more than await: you need bounded queues, backpressure, graceful cancellation, and timeouts to stay stable under load.

For production data layers, Python teams commonly use SQLAlchemy for modeling, querying, migrations, and transaction boundaries.

Secure Python services by default: validate input, avoid insecure deserialization, protect secrets, and keep dependencies patched.

Python dominates data science with NumPy, Pandas, Matplotlib, scikit-learn — and PyTorch / TensorFlow for deep learning.

NumPy gives you fast, typed n-dimensional arrays.

Advanced Python interviews and real projects require system design fluency: choose the right concurrency model, cache strategy, and failure handling.

Ship Python code as a wheel on PyPI using pyproject.toml + build + twine (or modern uv publish, hatch publish).

Profile first, optimize second.

Senior Python interviews mix language depth (GIL, mutability, decorators), data structures, and system design.