Python Masterclass

Install Python on macOS by downloading the latest Python from the official Python website, running the installer, and verifying with python3 --version in the terminal.

update packages with apt on Ubuntu, install python3 and pip, set up a virtual environment for clean development, and verify installation with python3 --version.

Learn how to use Python's print function to display strings, numbers, booleans, and lists, with default spacing and optional parameters like sep for customized output.

Learn how escape characters modify strings in Python print() outputs, using backslash sequences for new lines, tabs, quotes, and backslashes; combine them to format paths and text.

Explore how Python comments explain code purpose and logic, aiding debugging and future maintenance, with single line hash comments and multi-line triple quotes, and guidelines for meaningful, concise inline documentation.

Use None to indicate the absence of a value in Python; as a separate non-type, it serves as a placeholder for uninitialized variables, default parameters, and functions with no return.

Explore arithmetic, comparison, and logical operators in Python through a hands-on session, using Idle to cover floor division, modulus, exponentiation, and assignment and membership operators.

Discover how Python data types tell the interpreter how to handle and manipulate data, from numeric types to strings, lists, dictionaries, and sets, with memory considerations and operations.

Explore common string methods in the Python masterclass, including len, lower, upper, strip, replace, split, startswith, endswith, find, count, join, capitalize, and indexing, with practical examples for daily use.

Explore advanced Python string methods such as rfind, rindex, title, swapcase, isalpha, isdigit, and isalnum, demonstrating how to locate substrings, validate content, and format text in Colab notebooks.

Discover Python's numerical types: integers with virtually unlimited size, IEEE 754 floats with potential rounding, and complex numbers for scientific calculations. Learn conversions between int, float, and complex.

Explore how lists in Python store an ordered, mutable collection of items, support multiple data types, and use indexing, slicing, and methods like append, insert, remove, and pop.

Explore tuples, a built-in Python data structure, and learn their immutability, ordering, and ability to store heterogeneous data, with operations like indexing, concatenation, slicing, and unpacking.

Explore how Python dictionaries store key-value data, access values with brackets or get, handle missing keys safely with defaults, and merge dictionaries for consolidated data.

Master Python sets as unordered, unique collections that are mutable, enabling add, remove, update, and the in operator, along with union, intersection, and difference for fast membership checks.

Explore how Python sets ensure uniqueness by converting a word list to a set and counting unique words with the len function, illustrating a concise, practical approach to text analysis.

Explore how virtual environments isolate Python projects to prevent library conflicts and ensure reproducible setups. Learn to create, activate, install libraries, and manage environments with venv for clean, project-specific workflows.

Compare Python 2 and Python 3 to learn differences in print as a function, integer division, and Unicode support, guiding migration.

Explore how Python uses statements and blocks defined by colons and indentation, not braces, to structure code. Understand scope, readability, and the role of global and local variables within blocks.

Map cases efficiently with Python dictionaries by using get for safe lookups, default values, and nested mappings, replacing long conditionals with direct lookups and functions or lambdas.

Explore for loops with break, continue, and pass to control flow. Practice patterns for finding targets, skipping multiples, and building results like vowel counts and even-number lists.

Explore practical while loops through eight interactive examples: input prompts with exit, counting with skips, placeholder logic, stop when zero, countdown simulation, login attempts, summing until negative, and input validation.

Discover how the is and == operators differ in Python, comparing memory references versus values, with examples of integers, strings, and lists to prevent bugs.

Learn how the walrus operator, introduced in Python 3.8, assigns values within expressions to reduce redundancy, improving readability in loops, conditionals, and list comprehensions.

Learn how global and nonlocal keywords control variable scope in Python. See when to use global for module-wide access and nonlocal for modifying variables in enclosing functions.

Use the return statement to send values from a function back to the caller, enabling modular, flexible code and early exits. See how tuples, print, and none differ.

Explore higher-order functions by passing discount strategies as arguments to a single apply discount function. Implement 10%, flat -20, and no discount to demonstrate dynamic cart pricing.

Explore function factories in Python by creating a greeting factory that returns occasion-specific inner functions for birthday, wedding, or generic greetings, demonstrating modular, dynamic behavior.

Discover how functions become first-class citizens by passing them as arguments to other functions, enabling dynamic behavior, and applying the strategy pattern with operations like add or multiply in Python.

Explore how the __init__ constructor initializes objects in Python by setting attributes, using self, and handling default and parameterized inputs with practical car and dog examples.

Create a bank account class with a shared bank name, initialize holder and balance, and implement deposit, withdraw, and display methods to run simple transactions on two accounts.

Discover how Python uses multiple inheritance to inherit from several parent classes, initialize attributes like name and job title in a manager, and manage method resolution order.

Polymorphism lets a single interface represent different types, enabling the same makesound method to behave differently on dog, cat, or generic animal, with Python using method overriding.

Explore has-a relationships in object-oriented programming and distinguish composition from aggregation, illustrating strong ownership with a car and engine and the independent existence of players within a team.

Master Python error handling by recognizing syntax, runtime, logical errors, and exceptions, and apply try and except blocks and debugging techniques to prevent program crashes and tracebacks.

Explore built-in Python exceptions and common runtime errors, including value error, type error, index error, key error, zero division error, and file not found error; learn catching multiple exceptions.

Raise exceptions manually in Python to control flow and enforce validity. Use built-in or custom exceptions to validate input and prevent invalid operations, like negative age or insufficient funds.

Explore Python's built-in logging module, configure log levels (debug, info, warning, error, critical), enable file logging with rotation, separate logs by module, and protect sensitive data in production.

Explore imperative and functional thinking by summing a list: imperative uses a loop and mutable sum, while functional uses built-in functions with immutability and a focus on what to transform.

Learn pure functions that always produce the same output for the same input and have no side effects, emphasizing immutability in Python with strings and tuples.

Explore higher-order functions in Python that take or return functions, enabling modular, reusable code. Learn how map, filter, and reduce compose behavior and produce flexible, concise solutions.

Build higher-order Python functions to perform numeric operations like doubling, squaring, and adding a constant. Combine functions with compose to create chained operations and test with main, including lambda usage.

Harness the functools module to write cleaner, more efficient Python code with higher order functions like reduce, partial, and lru_cache. Apply these tools to functional programming tasks and improve performance.

Explore how decorators wrap and extend function behavior without altering original code, enabling logging and timing while supporting dry, open-closed, and single responsibility principles.

Explore how to implement a rate limiting decorator in Python, using a deque-based timestamp queue to cap calls within a time window and prevent API overuse.

Explore how file i/o enables reading, writing, and appending data in Python using open, and manage text, CSV, and JSON formats for persistent configuration, logs, and data processing.

Open text files in Python using read mode to retrieve content with read, readline, and readlines. Close files using the with statement and handle file not found and IO errors.

Master Python file modes including read, write, append, binary, and read/write, and learn when to create or overwrite files, handle existence checks, and track cursor position.

Explore absolute and relative file paths, and use the Python os module to join paths, check existence, navigate directories, and manage basic permissions.

Master Pandas to manipulate and analyze data using Series and DataFrame structures, read and write CSV files, and apply filtering and transformations.

Enhance the order processing system with a category field and read, add, edit, and delete operations, plus generate insights and updated csv and json reports.

Learn to handle Python file I/O errors with try and except blocks, catching file not found and permission errors, use with for automatic closing, and implement custom messages and logging.

Load JSON data into a pandas dataframe, filter employees older than 30, add a seniority column, and save the transformed data back to a modified JSON file.

Learn to work with binary files in Python using the struct module to pack and unpack integers, and to manage memory by reading large text files line by line.

Explore the shelve module to persist Python objects in a file-based dictionary, enabling storage, retrieval, and management of lists, tuples, and custom objects between runs.

Learn how a Python module is a file containing code, including functions, classes, variables, runnable statements, and how to create and import a .py module with aliases to organize code.

Create and use custom modules and packages with init.py to power a financial calculator via a cli, importing tax, currency conversion, loan interest, and json exchange rates, and saving results.