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.

Download python from python.org and run the installer, making sure to add Python to path, then verify the installation by checking the version in the command prompt.

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

Explore Python development options, from interactive shells and lightweight editors to full IDEs and Jupyter notebooks, and compare Anaconda, Miniconda, Google Colab, and online platforms.

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 variables store data, enable dynamic and flexible code, and use naming, assignment, data types like integers, floats, strings, and booleans, plus scope considerations.

Explore how Python variables store values, infer types, and support reassignment and mutability. Follow naming rules, use descriptive snake_case, beware keywords and case sensitivity, to write clear, reliable code.

Explore Python string formatting in print statements by using f-strings, the format function, and percentage formatting, embedding variables like name and age into dynamic text with placeholders.

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.

Discover how Python treats single and double quotes as interchangeable string literals, choosing quotes to avoid escaping and to handle inner quotes, and explore triple quotes for multi-line strings.

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.

Explore Python's reserved words, or keywords, that define control flow and operations. Learn why these words cannot be used as names and how the interpreter warns if misused, ensuring code.

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 dynamic typing in Python, where variables hold values of different data types at runtime without explicit declarations, offering flexibility while risking runtime errors and possible performance overhead.

Master strong typing in Python by understanding explicit type conversions and implicit conversions, and see how strict type rules prevent errors, improve clarity, and enhance code reliability.

Explore the Python standard library, a vast toolkit of pre-written modules that save time and boost reliability, including modules like date time, os, csv, json, and more.

Explore Python operators as a universal toolkit for data manipulation, covering arithmetic, comparison, logical, assignment, bitwise, membership, and identity operations across numbers, strings, and collections.

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.

Explore Python operator precedence and the order of operations with examples showing how multiplication, addition, and parentheses determine the result. Understand unary operators, exponentiation, and associativity rules that shape evaluation.

Explore how Python expressions combine values, variables, operators, and function calls to yield results across literal, arithmetic, comparison, logical, and compound expressions, with operator precedence and parentheses.

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.

Learn how to access and extract characters in Python strings using zero-based and negative indexing, and slicing with start, end, and step, including reversing a string and handling index errors.

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.

Explore how the input function captures user responses from the console to create interactive Python programs, then convert inputs to appropriate data types and display results using print.

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 Python's numerical operations from basic arithmetic to floor division, modulus, and exponentiation; apply pemdas to evaluate expressions correctly.

Import and use python's math and random modules to perform advanced numerical operations and generate random values, including square roots, powers, ceiling and floor, and random selections.

Master booleans in Python, using true and false, the bool function, and truthy and falsy values to drive decisions with comparisons and the operators and, or, not.

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.

Master essential Python list methods, from creating lists and appending to inserting, removing, and popping items, then sorting, reversing, indexing, counting, copying, and combining with concatenation and extend.

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 practical uses of tuples in Python masterclass, learning to locate items with index, count occurrences with count, and align data across fixed datasets using names and IDs.

Learn Python dictionaries: mutable key-value structures with unique, immutable keys and any value type, offering fast lookups and insertion order since Python 3.7; practice creating, updating, and deleting items.

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.

Learn how Python performs type conversion through implicit coercion and explicit functions like int, float, str, and list, and ensure type compatibility in operations with try-except.

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.

Explore Python conditionals to make decisions using if, else, and elif, with comparison and logical operators, plus nesting and real-world examples like temperature and age.

Learn how to use multiple elif statements in Python to check conditions in sequence, understand top-to-bottom evaluation, and consider alternatives like dictionaries or functions for readability.

Explore how to write Python in Replit, an online IDE with a free tier and paid options, including logging in via Google or GitHub and creating a new ripple.

Master conditionals in Python through practical examples, including login authentication, email validation, to-do task counts, and dictionary lookups for user food preferences.

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.

Discover how Python's match statement improves readability and enables pattern matching for multiple cases. See examples with days and lists, and learn when to use match over if-elif or dictionaries.

Compare mapping with dictionaries and the match statement to generate motivational messages based on a user’s day, using capitalize, get, and a catch-all underscore case.

Master the range function to generate number sequences in Python with start, stop, and step. Explore defaults, stop not included behavior, and negative steps for loops.

Explore how a while loop uses a condition to repeat, including an infinite loop with while true, then break stops the loop via user input.

Master Python's control flow with break, continue, and pass statements, using break to exit loops, continue to skip iterations, and pass as a placeholder for future code.

Learn to start Python in Replit, using the free tier to create a repl, write hello world in main.py, and run code in the online IDE.

Explore python conditionals through practical examples: verify login with if-else, validate emails, tailor messages for task counts, and look up food preferences in a dictionary.

Practice six practical for loops tasks, including searching for a number, excluding multiples of three, and counting vowels in a string.

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.

Explore practical while loops in Python Masterclass, including infinite loops, break and continue, input handling, counting and summing, and password attempts with validation.

Explore how to model a matrix as a list of lists, iterate through rows and columns with for and while loops, and analyze O(n^2) time complexity.

Explore matrix transposition, matrix multiplication, and element search in matrices using for and while loops, building transposed results and dot products, with practical Python examples.

Learn to emulate a do while loop in Python by using a while loop with extra logic, so the body executes at least once before the condition is checked.

Master truthy and falsy values in Python and their role in if and while statements. Write concise, readable code by leveraging boolean context and avoiding explicit true/false comparisons.

The ternary operator in Python, a compact conditional expression, assigns value if true or else value when the condition is false, avoiding complexity and boosting readability for simple conditional assignments.

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.

Explore the walrus operator through practical examples that compare input validation and list or word-count checks with and without it, showing cleaner, more pythonic, and sometimes more efficient code.

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.

Explore Python functions, including def, parameters and arguments, to write reusable, modular code that follows the dry principle, improves readability, debugging, and testing.

Explore Sublime Text, a lightweight, fast and powerful editor for code and prose that supports multiple languages and extensive customization through package control and plugins.

In this practical session of the Python Masterclass, build a simple multiplication table program with a function, loops, and robust input handling including a graceful exit.

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.

In this practical session, Python demonstrates a voting eligibility checker by defining check voting eligibility(age), looping for user input of age or exit, handling value errors, and printing eligibility results.

Learn how Python docstrings document functions, classes, and modules with triple quotes, accessible via help, and used for readability and automated external documentation.

Master how *args and **kwargs let a function accept a variable number of positional and keyword args. See how these collect extras into a tuple and a dictionary.

Learn how to handle dynamic input in Python functions using *args and **kwargs, gathering positional arguments into a tuple and keyword arguments into a dictionary, and print them.

Unpack iterables and dictionaries in Python using single and double asterisks to pass arguments, demonstrated with a calculate sum function and printing the results in real-world scenarios.

Learn how functions call other functions, including recursion, to build modular, reusable code that follows the dry principle, with examples like prime checks and area calculations.

Create a modular Python calculator that computes final price after discount and tax using separate functions for discount and tax, with a main function to combine results.

Explore how Python functions use multiple return statements to exit early based on conditions, improve readability with clear exit points, and handle nested control flow with practical examples.

Assign default arguments to function parameters and call functions with or without arguments. Enhance flexibility, reduce repeated arguments, and improve readability.

Master positional and named arguments in Python, learn how order and explicit names control values, use defaults, and mix positional and named calls with arcs and quarks.

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.

Develop a shopping cart program using positional, named, and default arguments to add items, set quantities, apply optional discounts, and print totals with flexible extra info.

Learn lambda functions, anonymous Python functions defined with the lambda keyword, unlike def. Use them for simple one-expression operations, multiple arguments, and sorting with a key and ternary results.

In the Python masterclass, learn how to use lambda functions to sort a list of book dictionaries by price with the sorted function, and display titles and prices.

Explore function factories in Python that return other functions, including the power factory creating square and cube functions, enabled by closures and first-class functions.

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.

Discover object oriented programming in Python, where objects with attributes and methods model real world entities such as cars. Build a car class with instances to share behavior and data.

Define a class as a blueprint for creating objects with attributes and a shared species. Create objects via dot notation, note memory allocation, and explore built-in classes such as lists.

Explore how Python's garbage collector manages memory by reclaiming unused objects through reference counting and cyclic garbage collection, with options to manually run or disable it when needed.

Explore attributes and methods in Python classes by building a cat class with instance attributes name, age, color and methods such as meow and hide, using self.

Install Visual Studio Code from the official site on Windows, macOS, or Linux, and explore its terminal, debugging, version control, and extensions for Python development.

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.

Clarifies the difference between class variables and instance variables in object oriented programming. Show how class variables are shared by all instances, while instance variables remain unique to each object.

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.

Learn how self accesses instance data, cls accesses class data, and standalone methods operate without either; see speak, set species, and is animal illustrating instance and class behavior.

Hide complex details behind abstraction to expose only what users need. Explore Python abstraction with functions and classes, illustrated by a bank account example, highlighting readability and reusability.

Encapsulation bundles data and methods in a class, restricting access to protect data integrity. In Python, underscores and name mangling signal privacy, but true private variables do not exist.

Explore inheritance by creating dog and cat subclasses from a base animal class, overriding the makesound method to demonstrate code reuse, the dry principle, and behavior customization.

Create a common interface for related classes with abstract classes, backed by ABC and the abstractmethod decorator, ensuring subclasses implement methods like speak.

Explore abstraction, encapsulation, and inheritance in a Python library management scenario by defining an abstract borrow book method, protected and private attributes, and derived student and teacher classes.

Learn how the super function enables a child class to access and extend parent class methods and attributes, including overridden methods like greet in the person and employee example.

Explore how to use the super function in Python to call parent class methods within a staff member and teacher hierarchy, illustrating maintainability and reduced redundancy.

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.

Learn how Python uses method resolution order (mro), via c3 linearization, to resolve methods in multiple inheritance and how super follows this ordered lookup.

Explore how multiple inheritance combines features from device, phone, and camera in a smart device hierarchy. Visualize how Python's method resolution order resolves method calls using the MRO attribute.

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 polymorphism through a shape hierarchy, implementing shape, rectangle, and circle classes with area and perimeter methods, and demonstrate polymorphic calls via a display shape info function.

Explore static methods and class methods in Python, learning how they belong to the class and can be called with or without an instance, often via decorators.

Explore static and class methods in Python by implementing a logger class, using a static format_message utility and class methods to create debug and error loggers.

Master dunder methods in python, using double underscores to customize object representations, comparisons, operations, and deletion through __str__, __repr__, __len__, __add__, __eq__, and __del__.

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.

Learn how Python handles object references, and compare shallow and deep copies to prevent unintended changes; use shallow copy for non-nested objects, deep copy for fully independent objects.

Explore how method overriding lets subclasses customize inherited behavior, and how Python mimics overloading using default arguments and *args/**kwargs, unlike Java or C++ that rely on compile-time types.

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.

Learn exception handling with try and except, else and finally, including nested blocks, custom raises, input validation, and management of value errors and zero division errors.

Refactor a simple Python program to handle invalid input and division by zero using try and except, making it robust, user friendly, and prompting again via recursion.

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.

Refactor the program by centralizing exception handling with a handle_exceptions helper. Provide consistent feedback for value errors, division by zero, and index errors when dividing numbers or fetching items.

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.

Create custom exceptions in Python by inheriting from the base exception, store data like balance and amount, override the string method, and improve error reporting in try and except blocks.

Refactor a simple order system to use custom exceptions, raising insufficient stock and invalid quantity errors, and implement try/except blocks for robust, contextual error handling.

Adopt specific exception handling by avoiding broad except clauses, targeting errors like zero division, valueerror, and type error to improve readability and debugging, while logging errors to a file.

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.

Master debugging techniques by isolating code sections, validating assumptions with asserts, and reading stack traces, then use pdb and breakpoints for interactive, step-by-step problem solving.

Use pdb to debug a simple Python program that computes student average grades, pausing at set_trace, inspecting variables, and correcting a type mismatch between ints and strings.

Explore how popular integrated development environments debug Python code, using PyCharm and Visual Studio Code to set breakpoints, step through code, inspect variables, and use the built-in console.

Debug an ATM system withdrawal logic by inspecting variables, setting breakpoints, and using the Python debugger in VS Code to fix the condition to allow withdrawals up to the balance.

Learn how the Python assert statement serves as a debugging aid by enforcing preconditions, postconditions, and invariants, raising assertion errors with optional messages.

Use Python's warnings module to issue, filter, and manage warnings, informing about deprecations and configuration issues, while understanding how to ignore, suppress, or transform them without halting execution.

Master Python debugging by reading stack traces from the bottom up, identifying root causes such as zero division, isolating the bug, and applying targeted solutions with logging and PDB debugger.

Explore functional programming, a paradigm that treats computation as evaluating mathematical functions to transform data with pure functions, immutability, and avoids side effects.

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.

Demonstrate pure functions and immutability by implementing add, remove, update, and total items in a shopping list, returning new lists without altering the original.

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.

Learn how function composition combines simple functions in Python to create a new, reusable operation by passing outputs as inputs, with examples using map and filter.

Master function chaining in Python by applying multiple methods on strings and lists in one line, improving readability and reducing intermediate variables through data transformations.

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 closures that remember their creation environment and enable function factories to generate specialized functions with preset variables, including greetings and comparisons to functools.partial.

Develop a discount factory that creates apply discount functions using closures to apply either percentage or fixed discounts to product prices, demonstrated with 10% and $50 discounts.

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.

Learn how recursive functions solve problems by breaking them into base and recursive cases, with factorial and list-sum examples, and understand readability, memory use, and Python's recursion limit.

Learn to use recursion for string manipulation and pattern matching in Python. Implement a find pattern function that returns all substring indices.

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.

Explore Python file modes, including write and append, to control overwriting or adding data; learn how write and writelines format text with newlines, and why with statement closes files.

Build a basic Python file processor that reads from input.txt, swaps the content's case, and writes to output.txt while handling missing files and other 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 reading and writing csv files in Python with csv module, including csv.reader, csv.writer, and csv.DictReader. Use list comprehensions and data edits before writing back with writerows, ensuring consistent fields.

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

Learn how to read and write JSON in Python using the json module, perform serialization and deserialization with dumps and loads, and store user settings and API data.

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.

Explore how to work with binary files in Python by opening files in binary mode, reading and writing bytes, and handling non-text data like images, audio, and PDFs.

Discover efficient handling of large files by reading in chunks, leveraging memory mapping and the walrus operator, using line by line iterables and optimized IO with libraries like Pandas.

Create a Python data pipeline that reads a CSV, filters rows by age greater than 30, and writes the results to a JSON file using the CSV and JSON modules.

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.

Serialize and deserialize Python objects with the pickle module using dump and load to store and restore data as binary. Avoid untrusted sources; consider safer alternatives like JSON when needed.

Learn to build a python script that organizes files by extension into categorized subdirectories, move files, and record distribution metadata in a json file for scalable data management.

Explore data serialization and deserialization in python by comparing pickle and json, saving and loading a dict with name alice, age 30, and skills, and weighing python-specific performance versus readability.

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.

Explore how the import statement brings modules into a Python script, enabling reuse of functions and classes, and compare importing an entire module with importing specific names.

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.

Master built-in Python modules such as math, random, date/time, OS, and SSIS to simplify common coding tasks, with key functions like sqrt, sin, randint, choice, date, makedir, and sys.exit.

Organize related modules into a folder and convert it to a package with a dunder init.py, then import commonly used functions in __init__.py for direct access.

Organize large Python projects by modularizing code into modules and packages, establishing a clear folder structure, and isolating tests to improve readability, reuse, and maintainability.

Understand how Python's dunder name variable distinguishes main execution from module import. Learn the if __name__ == '__main__' pattern for reusable, testable code with examples.

Create and structure custom Python modules and packages, implement a finance package with a cli, and perform currency conversion using a json exchange rate file, tax calculation, and loan interest.

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.

Explore how pip, the Python package installer, installs, upgrades, and uninstalls packages from PyPI using commands like pip install, pip uninstall, and pip list.

Learn how namespaces map identifiers to objects and how Python uses the LEGB scope across modules. Use import module syntax and dunder all to prevent conflicts and control access.

Explore python namespace and scope, including global and local scopes, validate emails, phones, and passwords with regular expressions, and design a standalone, importable user management module with token generation.

Generate a temporary token and update a global auth token to illustrate namespace and scope, including regular expressions, in a Python CLI workflow for input validation.

Build a modular Task Manager in Python with tasks, storage, and validation modules, saving data to json and enabling an argparse CLI for both standalone and importable module.

Explore regular expressions in Python, using the re module to search, match, and replace text with concise patterns. Learn how to validate emails, find phone numbers, and extract text efficiently.

Build a modular python package for a personal expense tracker, featuring input handler with regex validation, storage in json or csv, and pandas-based analysis to generate reports.

Develops a personal expense tracker that validates amount, category, and date, saves expenses to data/expenses.json, analyzes totals by category with pandas, and supports add, remove, list, and analyze via argparse.