Complete Python 3 Bootcamp -Python Programming from Scratch

Explore Python basics in the Jupiter environment, write your first hello world, and learn variables, data types, comments, printing, and collecting user input.

Explore Python arithmetic operations, including addition, subtraction, multiplication, exponent, modulus, and floor division, and practice using assignment operators and basic variable handling.

Take user input, cast strings to integers, perform addition and subtraction with intermediate variables, and display the final result while debugging type conversion in Python.

Explore Python arithmetic operators, including addition, subtraction, multiplication, exponent, division, floor division, and modulus, with practical examples and output explanations.

Master assignment operators in Python, including add and assign and subtract and assign, with practical examples using num initialized to 10 and updated to 15.

Explore Python's assign operators for arithmetic, including add and assign, subtract and assign, and multiply and assign. See divide, modulus, exponent, and floor division in action.

Explore how assignment and arithmetic operators interact in Python, show why they aren’t interchangeable, and demonstrate how brackets control precedence for clear, correct calculations.

Practice arithmetic in Python by taking two user inputs, converting them to integers, performing floor division of number one by number two, and updating number one with the result.

Explore Python's built-in math functions and the math library to perform floor, ceil, factorial, and power operations, and see how functions act as input-output modules in a Jupyter notebook.

Learn to use python's math library functions, including truncate, factorial, power, and square root, with examples and proper import usage for arithmetic tasks.

Explore how to combine previously learned Python functions with arithmetic operations in a jupyter notebook, using truncation, square roots, type conversion, and operator precedence for versatile calculations.

Learn to generate random numbers in Python using the random library, including randrange and randint, with import random and seeding to test code and explore square roots of results.

Generate two random numbers, square them, sum the results, and assign the sum to the first random number.

Define functions with def, parameters, and arguments to create reusable code blocks. Explore function calls, returns, scope rules, and how modules like math and random fit in.

Learn to define and call Python functions with def, pass parameters, and return values, using an addition function as a practical example, and explore function scope.

Explains variable scope in Python, distinguishing global and local variables, how global values are accessed and overridden across functions, and the purpose of a main function.

Explore how Python handles function arguments by value and by reference, distinguishing copies versus shared memory when passing numbers and lists, and showing how changes propagate.

Learn how to return values from Python functions by performing operations like addition and subtraction, calling functions within functions, and passing outputs to the main function.

Learn how to create reusable Python modules to separate functions and classes from the main script, enabling modular development, easier changes, and code reuse across projects.

Explore how Python functions implement floor division to get the lower bound and compute the upper bound with user input.

Explore strings as a sequence of characters, learn how to access, manipulate, and format them with escape characters, string methods, and in-place updates.

Learn to access and update string values in Python through indexing and slicing, using positive and negative indices, with single and double quotes, and basic printing.

Learn how strings in Python are immutable and cannot be edited in place. Convert the string to a list to modify a character, then join back to form the string.

Explore escape characters in Python strings, including backslash sequences like backslash b and backslash n, and how they affect printing and new lines. Note Python 3.7 limitations.

Master string formatting in Python by using f-strings, braces, and variables to create readable text. Compare concatenation with the plus operator and learn efficient string construction and escaping quotes.

Explore how Python uses triple quotes to span multiple lines, manage line breaks, and handle escape sequences in strings. Learn to format strings with f-strings and see how variables appear in output.

Develop a Python function to take user input and return it to the main function, showing how to replace the last three characters by mutating a list and joining back.

Explore Python's built-in string methods, such as capitalize, lower, and count, and learn to apply them via string operations, substring searches, and splitting.

Explore Python string methods endswith, find, and index, including how endswith checks the tail, how find locates the first occurrence, and how index differs and raises errors when not found.

Explore Python string validation using isalnum, isalpha, islower, and isupper to ensure inputs are letters, numbers, or both, with no spaces or symbols.

Learn how isdigit and isnumeric differ, with isnumeric accepting numbers from any language, and how isupper checks uppercase across all characters.

Explore string manipulation in python, mastering lower and upper case conversions and using join to combine list elements into a single string, with attention to numbers and characters.

Explore Python strings by using the replace method to swap substrings, learn case sensitivity, and apply strip, lstrip, and rstrip to remove spaces from left or right.

Master Python string methods such as strip, lstrip, rstrip, and split, then apply lower, upper, and title to clean, format, and transform text data.

Learn how the split method turns a string into a list using a separator, with examples for spaces, commas, and hashes, and access words by index.

Explore python flow control by using if statements to direct execution, generate ranges with range, and manage loops with break, continue, and else clauses, then study the pass statement.

Master flow control with if statements, evaluating conditions to run or skip code blocks. Use if, elif, and else with age input and type conversion for validation.

Develop hands-on Python string processing by taking user input, splitting, joining, and replacing a search key, while using flow controls and if/elif to avoid loops.

Master Python for loops to iterate over sequences and access list items by index, with if statements to compare strings and drive actions.

Learn to use the Python range function with for loops to generate number sequences, including even numbers, using start, stop, and step, with exclusive upper bound and inclusive lower bound.

Demonstrate how the range function yields values one by one, acts like a list, and can be converted with list(range(...)); use for loops to generate numbers with a fixed step.

Learn how break and continue operate in Python loops, including break exiting the innermost loop in for and while loops, with nested loops and next-iteration skipping.

Study Python flow control: for loops, break and continue, else clauses, and exception handling to manage errors and keep code reliable, including nested loops.

Master Python flow control by using pass statements as placeholders to build skeletons for for loops and if statements without executing code.

Generate a random number, prompt the user to guess, and reveal higher or lower hints for a fixed number of tries using for loops and if statements in Python.

Master Python file processing, covering opening, reading (line by line or by word), writing, and deleting files and directories, plus using with open over open.

Explore how Python's open function handles reading, writing, and appending files, including create, truncate, and exclusive creation modes (r, w, a, x), with binary or text options.

Learn to read from a Python file using read, read(n), and read line, and loop through lines with a for loop, while closing files and handling data as strings.

Discover how to write data to files in Python using write and append modes, avoid buffering pitfalls, and ensure changes persist by closing the file.

Read a line from a file and count its words by counting spaces or using Python split methods. Compare approaches using for loops, if statements, and string operations.

Learn to delete files in Python with the os module, verify file existence before deletion, and understand permission checks, while handling common pitfalls in interactive environments.

This lecture explains reading and writing files in python using with open and file modes, showing how the file handler f is created and automatically closed after operations.

Read a line from a file and count its words in Python, using spaces or the split method, while practicing for loops and if statements.

Learn Python file processing by using glob and regex to pattern-match filenames, filter by date, extension, or digits, and retrieve and read matching records efficiently.

Read a string from a file, reverse it using slice or loops, and append the reversed result back to the file, exploring file processing in Python.

Discover core Python 3 data structures, including lists, dictionaries, and sets, and learn how Python 3 provides handy functions to store, access, and manipulate data efficiently.

Explore Python lists as data structures, learn indexing from zero, and compare append versus extend, then use insert and remove to manage list elements.

Explore list data structures by using pop to remove top elements, count to tally occurrences, and in-place sort to order lists, including ascending and descending options.

Learn Python list operations, including in-place reverse, plus and multiplication to extend and repeat lists, and the list versus array idea; preview slicing and list comprehension.

Practice Python data handling by reading a line of numbers, converting to a list, removing duplicates to create a unique list, and writing the result back to a file.

Explore Python list slicing: select a sublist with a starting index and an exclusive ending index, learn that slicing returns a new list, and pick every third element.

Discover list comprehension in Python, using for loops and conditions to build and filter lists, with examples like selecting even numbers and excluding Toyota cars.

Learn how to filter a list in Python using the filter function and lambda expressions, including filtering a list of lists to keep students with grades above a threshold.

Explain how to implement stacks and queues in Python using lists, demonstrate push and pop operations, illustrate LIFO and FIFO behavior, and show practical examples like bracket validation.

Implement a stack to verify bracket sequences by pushing opening brackets, popping on closing ones, ignoring spaces, and comparing types to ensure correct order.

Explore shallow copy and deep copy in Python lists, demonstrating shared memory and independent copies. Learn how Python's copy method prevents unintended modifications by creating true copies.

Learn how to insert a new value into a sorted Python list using the bisect module, perform in place sorting, and determine the insertion index.

Study how to count word frequencies in a text file by extracting unique words, building counters, and tallying occurrences using a function and list-based approaches.

Practice solving Python tasks by reading numbers from a file, converting them to integers, summing them, and counting strings with length two or more whose first and last characters match.

Solve practical Python problems by writing a function that checks for any common elements between two lists and explores list difference by removing shared items, plus a concatenation exercise.

Explore nested for loops to generate paired outputs from lists, converting numbers to strings and building a concatenated result for each iteration.

This lecture explores tuples in Python, contrasts them with lists, and covers immutability, constructing tuples, single-element syntax, concatenation, augmented assignments, and sequence repetition.

Learn how to use Python list methods index and count to find element positions, count occurrences, and manage nested lists, including handling missing values and first occurrences.

Learn how to build dictionaries from pairs, access values with get, and handle missing keys with default values, while exploring key-value data structures in Python.

Learn how to assign multiple values to variables in Python using parallel assignment and range-based initialization. Discover packing and unpacking concepts, and how swapping values works.

Explore double unpacking and returning multiple values in Python by computing the top three maximum numbers from a list and unpacking them into separate variables.

Explore data structures by swapping variables with tuples, compare mutable lists with immutable tuples and strings, and practice nested lists, unpacking, and function-based swapping, plus slicing and length concepts.

Learn how Python 3 lists and tuples use the len function to get length, and master slicing with start, end (exclusive), and optional step, plus indexing practice.

Explore how lists interact with immutability, copying, and mutation in Python, comparing shallow and deep copies, and practicing append and pop operations to modify mutable elements.

Explore Python data structures and operators, including strings, lists, type conversion, in checks, and max/min computations, preparing you for dictionaries in the next lesson.

Explore dictionaries as Python key-value stores with unique, case-sensitive keys, supporting heterogeneous values like lists, and learn to retrieve keys, values, and test membership.

Master Python dictionary operations by iterating over items, accessing keys and values, and unpacking dictionary pairs; learn to search for keys and handle missing keys.

Explore the get and pop methods for Python dictionaries, noting that has_key is deprecated in Python 3 and how missing keys return None or a default, while pop removes items.

Read a text file, split into words, and tally frequencies with a dictionary, showing how this approach is more efficient than a list-based method.

Explore python dictionaries by comparing shallow and deep copies, then practice copying, clearing, and fromkeys to manage keys and values.

Learn how to merge two or more dictionaries in Python using the update method, handling key conflicts and appending new entries.

Explore iterating dictionaries, retrieving keys and values, and using set operations to find differences and common keys, while removing duplicate keys across dictionaries.

Explore how Python sets enforce uniqueness, do not preserve order, and support union, intersection, difference, subset, and symmetric difference with practical examples.

Explore Python dictionaries, learn to merge data with update, manipulate key-value pairs in place, and compute sums of values while practicing practical data-structure problems.

Explore Python dictionaries by merging d1 and d2 with the update method, demonstrate in-place updates versus assignment, and compute the sum of all dictionary values.

Explore solving data structures practice tasks by implementing custom methods to merge key-value pairs, manage values and keys, and handle conditions with loops in Python.

Explore advanced concepts of Python through functional programming, focusing on map, filter, and reduce, and how they map elements, filter data, and reduce results.

Explore Python's functional programming using the map function to apply an uppercasing operation to each character of a string and observe how map maps inputs to outputs.

Explore how Python's map function acts as a generator, producing output only upon iteration. See how listing or looping triggers function calls and yields the mapped results.

Explore how Python's map creates a generator yielding values on demand, not a full list. See how this on-demand approach saves memory when processing large data.

discover the method list by replacing brackets with square brackets, applying map to strings, and printing results. explore how generators and converting maps to lists are used in this approach.

Explore how the map function returns a generator and how mapped elements can be converted to lists or sets, showing how duplicates are removed.

Explore how lambda functions work with map in Python, see how to pass a function and an iterable, and learn when to use simple lambdas versus full functions for readability.

Explore the map function in Python to combine two iterables with a lambda, adding corresponding elements. Learn behavior with unequal lengths and multi-argument mapping.

Explore functional programming by comparing filter and map in Python, using strings and lists to filter vowels, even numbers, and names by length; understand generator vs list behavior.

Explore converting filter to map, generating sequences with Python, and using generators for efficient iteration; learn to eliminate even numbers and convert between lists and generators while understanding generator exhaustion.

Learn how to use lambda with filter in Python to select even numbers, understand lambda’s simple boolean checks, and note its limitations with complex data like dictionaries.

Discover how to pass arguments to map and filter with lambda functions, learn why filter accepts one argument, and see why multiple values can’t be passed in a single lambda.

Explore how the map and reduce functions apply operations to data, using lambda expressions to accumulate sums across a list with a starting value.

Explore the object oriented paradigm by using classes and objects to combine data and functionality, enabling code reuse, easier maintenance, and clearer program structure.

Define classes, attributes, and methods in Python to model real entities like students, teachers, and courses. Experience self-based access, global versus instance scope, and basic class interactions such as enrollments.

Dive into the object oriented paradigm, examining classes, objects, and blueprints. Learn to create reusable code by defining attributes and methods and instantiating class instances.

Explore object oriented programming by defining classes as blueprints and creating objects as instances, and use constructors to initialize data at instantiation, passing values via self.

Discover how python uses classes as blueprints to create multiple objects, each with its own state and behavior, via constructors, attributes, and methods.

Discover how class variables are shared by all instances while instance variables stay unique to each object, and how to define them at the class or the instance level.

Discover how instance variables are owned by each object instance and hold unique values defined in constructors. Learn how class variables are shared across instances and how to set them.

Explore working with class variables and instance variables together in Python. Learn how changes to class variables compare with instance-specific values across objects.

Design a bank account class with instance variables for account number and balance, implement an initialize method, and create an instance to display its account details.

Learn how inheritance allows a child class to inherit methods and attributes from a parent class, such as person, student, and teacher, reducing repetition.

Learn inheritance in Python, override parent methods with super, and apply multiple inheritance to build reusable class hierarchies with bank accounts and animals.

Explore how child classes inherit methods from a parent class, and how to override or use default behavior with subclass examples like fish, goldfish, and trout, including constructors and parameters.

Explore object-oriented programming with inheritance by building a fish hierarchy, creating a clown fish subclass, and overriding and accessing parent class methods and attributes.

Define bank account, current account, and saving account classes via inheritance, instantiate objects, and initialize account numbers and balances through constructors.

Explore inheritance and overriding in Python classes, focusing on parent and child relationships. See how constructors and the super function shape behavior in overridden methods.

Learn how to use the super function to access and extend overridden parent methods in Python inheritance, including calling the parent init to merge with child-specific behavior.

Implement a bank account check balance method and override it in current and saving accounts to display their balances, then call the super method to display the balance.

Explore how Python supports multiple inheritance, inheriting from multiple base classes, and how method resolution order handles potential ambiguity and the diamond problem.

Explore the diamond problem in object-oriented inheritance by drawing class A, B, C, and D, and analyzing how the shared base method e appears in both branches.