Python Bootcamp : Complete Python Programming Masterclass

Explore Python data types, including integers, floats, complex numbers, strings, lists, tuples, and ranges, plus dictionaries, booleans, binary types, bytes, bytearray, memoryview, and sets and frozensets.

Explore numeric data types in Python by declaring and inspecting integers, floats, and complex numbers in Spyder, printing multiple variables, and using the type function to reveal data types.

Learn how to convert data types in Python using typecasting, converting integers to floats, floats to integers, and strings to numbers, with notes on when complex numbers cannot be cast.

Master common numeric built-in functions such as abs, round, and pow to compute absolute values, round decimals to two places, and square numbers, with practical examples and console output.

Learn basic arithmetic and assignment operators in Python, including addition, subtraction, multiplication, division, exponentiation, and augmented forms like x += 10.

Learn to use the Python math module by importing it and calling common functions such as finite checks, NaN checks, exp, log with different bases, sqrt, and the pi constant.

Practice using Python's math module hands-on by calling isfinite and sqrt on a variable, explore exponent and logarithm functions, and examine the value of pi.

Learn how to work with the string data type in Python. Assign strings with quotes, print them, and create multi-line strings using triple quotes or newline characters.

Learn how Python stores strings as arrays of characters, uses zero-based indices, and slices with end indices (end+1), including negative indexing from the end.

This lecture explains Python string basics: concatenate with the plus sign, check substrings with in and not in, format with %s, %d, %f, and use raw strings with r.

Learn Python string methods such as len, count, find, and index, along with upper and lower case conversion. Practice left and right justification with ljust and rjust and fill characters.

Learn to use Python's split method to break a string into values by a separator. Assign them to multiple variables in one line to process records.

Split the string to extract fields, then check numeric content with is_numeric, and convert to integer or float for reliable mathematical calculations.

Apply the strip method to remove leading and trailing white spaces in strings, or specify characters to strip, including new line symbols, with examples of name, salary, and role data.

Learn how to use Python's replace method to swap substrings in strings, including handling user input for old and new values and optional count to limit replacements.

Learn Python string formatting with the format method, replacing concatenation by using positional braces, numeric indices, and named keys for clear, reliable outputs.

Explain Python conditional statements as control flow, using if and else with equality and not equal checks to decide outcomes like loan approval, and highlight indentation and input type casting.

Learn about shorthand if statements that run a single line when true without an else, see salary greater than 5000, and note why such syntax harms readability and maintenance.

Learn to debug by stepping through Python code with the interactive Python debugger, inspect variables, conditions, and program flow, and handle inputs like salary to verify logic.

Explore how to use boolean variables in if statements, check for empty strings or zero in numbers, and apply the pass statement to handle empty blocks in Python.

Learn to implement loan approval logic with nested if and elif in Python, handling salaries: >=7000 auto approved, 5000-7000 manual approval, <5000 rejected, with emphasis on readability and indentation.

Explore Python conditional statements using logical operators and or to evaluate multiple conditions; see a loan-approval example, user input for salary and age, and a related truth table.

Learn to use the in membership operator with if statements to check if a substring exists in a string, and apply not in for opposite results in basic Python.

Learn how to implement Python loops using while and for constructs to process records from a file, extract items from a sequence, and perform conditional loan processing.

Demonstrate a Python while loop starting with i at 0, printing values as it increments by 2, and ending when i reaches 10, with debugger insights.

This lecture demonstrates using a boolean flag to control a while loop in Python, terminating when i reaches ten, and compares it to a condition-based approach.

Discover how Python for loops iterate over sequences—strings, lists, and ranges—and print each character from a name using formatting.

Explore using range in for loops to generate numeric sequences with configurable start, stop, and step values, and apply range to iterate over strings by index.

Use the enumerate function to iterate over a string, assigning each letter to a variable while auto-incrementing a counter. Compare this with a range-based loop using the string length.

Explore nested loops in Python by building a multiplication table from 1 to 20, with the inner loop running up to 10 for each outer iteration.

Master nested loops in Python by building and printing a multiplication table from 1 to 19 with nested for loops, using range and print optimizations to format a horizontal table.

Learn to use continue and break in Python loops to filter even numbers with the modulo operator, control loop flow, and exit when conditions like 10 are met.

Explore Python lists and other core collections: lists, tuples, dictionaries, and sets, learning mutability, indexing, duplicates, and list syntax with square brackets used in data processing libraries like pandas.

Learn how to declare and initialize a list using square brackets, an empty list, or the list() function, then distinguish assignment from copying and use append to add elements.

Explore accessing list elements in python using positive and negative indices and slices, looping with for and printing, then search with in to report whether a name is found.

Learn to add, update, and delete elements in Python lists using append, insert, del, remove, pop, and clear. Update by index and manage list contents with these methods.

Build a dynamic python list from user input and explore core list methods such as sum, average, concatenation with plus, extend, sort, and index to locate elements.

Explore multidimensional lists in Python by creating nested lists, accessing elements with multiple indices, and manipulating sublists to support data processing and data science tasks.

Explore Python tuples: immutable sequences accessed by index, defined with parentheses, faster than lists for certain data, with length retrieval and concatenation via plus.

Explore Python dictionaries as key-value data structures that map keys to values, access data by key, and convert between dictionaries and JSON formats.

Learn how to work with Python dictionaries, including initializing, accessing case-sensitive keys, and adding, updating, and deleting key-value pairs.

Define and manipulate dictionaries by accessing, adding, updating, and deleting key-value pairs, use len to count entries, extract keys or values with keys(), values(), and convert to lists using list().

Build a menu-driven phonebook using lists and multidimensional lists in a command-line interface, enabling add of name, number, and email; search and display, with a continuous loop until exit.

Develop a menu-driven Python phone book that uses a multidimensional list to capture and append contacts (name, number, email) and test options including exit and wrong selections.

Search for a contact in a phonebook built with nested lists by prompting for a name, looping through sublists, and using a found flag to report whether the contact exists or not.

Modify date components with replace, noting that dates are immutable. Convert dates to strings with str or ISO format, parse ISO strings with fromisoformat, and find weekdays with isoweekday.

Learn to format and parse dates in Python using strftime and strptime, mastering common codes for day, month, year, and time to produce flexible date strings.

Open a file in Python, read or write, or use the default read mode, then close it. Explore modes r, w, a, x and how directories shape access.

Read file records in Python by using read line and read lines, track the file pointer with seek, and learn to open, close, and process order ID and transaction value.

Read records from a file, split into id and transaction value, sum the floats, and round to two decimals; compare reading all lines with iterating the file for memory efficiency.

Learn how to write to a file in Python by reading and categorizing transaction records into high, medium, and low values, then saving to a new file.

Compare traditional open statements with Python's with open for safe file reading and writing. Learn to close files, monitor status, and avoid common pitfalls.

Learn how Python functions create reusable blocks of code, illustrated by tax calculations. Distinguish builtin from user defined functions defined with def, parameters, and return, explore recursion and lambda functions.

Define and call your first function to print a message, then implement a multiply function that takes two numbers, casts input to int, returns the product, and prints the result.

Learn to implement a Python function to compute tax using income brackets, apply a female rebate, prevent negative tax, and refactor repetitive logic into a reusable function.

Calculate tax using a Python function exercise, based on income and gender, with 30% for over 100k, 20% for 50k–100k, a female rebate, and nonnegative safeguards.

Explore local and global variables in Python functions, showing how locals stay inside a function, globals span the program, and how name shadowing affects references.

Explore function arguments in Python, defining parameters versus arguments, and master four types—required (positional), default, keyword, and variable arguments—through practical examples.

Explore required arguments in Python functions by ensuring the number of arguments matches parameters and their positions remain fixed, as shown by a two-argument add or concatenate example.

Learn how default arguments let a function accept zero to a chosen number of parameters, using default values, and mix default with required parameters while noting positional parameters precede defaults.

Explore keyword arguments in Python by using parameter names as keys to pass values, showing order independence, variables as keys, and combining with positional and default arguments.

Explore mixing required, default, and keyword parameters in a function and master the rules: defaults come after non-defaults, and keywords come after positionals.

Learn how to handle variable length or arbitrary arguments in python with *args, accepting any number of inputs as a tuple and iterating over them with a for loop.

Explore how variable-length keyword arguments are parsed as dictionaries, contrast with *args, and learn to combine a required argument with both arbitrary and keyword arguments.

Learn how Python uses pass-by-object-reference to handle arguments, the difference between pass-by-value and pass-by-reference, and how immutability of ints and strings contrasts with mutable lists.

The lecture demonstrates how Python passes arguments by object reference, contrasting immutable and mutable objects, showing that reassignment creates a local reference, while mutating lists via append changes the object.

Learn how the map function applies a single function to every item in a list to compute square areas and compare it to a for loop.

Explore how to use Python's filter function to remove non values and keep numbers greater than five, handle None returns, and filter out empty strings and zeros.

Explore Python lambda functions, aka anonymous functions, and their syntax, including returning lambdas, and apply them with map, filter, and sort for operations like square and cube.

Master lambda usage with map, filter, and sort to write concise, readable Python code. Learn one-line functions, and apply lambda to sort and map over a two dimensional list.

Explore object oriented programming in Python, defining objects with properties and methods and using classes as blueprints to create instances, illustrated with real-world examples like customers and credit cards.

Explore the four pillars of object oriented programming: abstraction, encapsulation, inheritance, and polymorphism, through practical analogies to actors, capsules, chairs, and ui elements, and see how Python implements these concepts.

Define a class and create an object in Python, use self to reference the instance, and explore abstraction, encapsulation, inheritance, and polymorphism alongside class naming and indentation.

Define a Python class with __init__ to initialize object attributes using self, first name, last name, gender, and enrollment number, then access them via dot notation in methods and outside.

Learn how class attributes, or static variables, share data across all objects by storing country information, accessible via class or instance references, and observe how changes propagate.

Explore object-oriented programming by building a student class with methods to input details, compute averages, and handle variable-length marks via a looping user interface.

Create a Python student class that collects first name, last name, gender, and enrollment number from user input, constructs an object, and adds a method to print all attributes.

Define an instance method to print a student’s attributes, create a student object, and call the method to display first name, last name, gender, and enrollment before computing average marks.

refactor input handling to use variables and a loop with a list of three subject marks, then compute and print the average using a class method.

Solve exercise four by collecting up to seven marks, allowing exit with E, and adjusting the loop to range 1 to 8; compute and print the average.

Explore how instance, class, and static methods manipulate attributes, using getters (accessors) and setters (mutators) to safely access and update enrollment numbers in Python classes.

Explore class and static methods in Python, access and modify class attributes without creating objects, and apply the classmethod and staticmethod decorators to enforce proper design.

Explore inheritance in Python by modeling a university and its student as parent and child classes, sharing attributes and methods, and accessing inherited behavior, with multilevel inheritance hinted via campus.

Demonstrate multilevel inheritance in Python with university, campus, and student, then introduce multiple inheritance using grants to show method and attribute access across parent classes.

Explore how constructor inheritance works in Python, showing that methods are inherited by default while attributes are not, and how to use super to initialize parent attributes.

Explore how Python uses super and method resolution order (MRO) to resolve attributes when a class has two super classes, showing how parent order affects method execution.

Explore polymorphism in Python by demonstrating function polymorphism with len on lists and strings. Learn class method polymorphism and inheritance-based polymorphism with method overriding, then apply hands-on implementations.

Demonstrate polymorphism of functions and class methods by square and circle classes sharing an area method; use a generic area function to operate on different objects and print their results.

Explore method overriding and polymorphism in Python through a geometry example, showing how a child class overrides a parent’s describe method and uses super to access the base implementation.

Master operator overloading in Python by implementing dunder methods like __add__ to customize how objects of user-defined classes interact, revealing polymorphism.

Learn how dunder methods enable operator overloading and customize object string representations by overriding __str__, so printing and file writing produce meaningful, comma-separated output.