The Complete Python Intermediate Features

Learn how decorators extend a greet function's behavior without altering its code, using a method-based decorator to require a full name (first and last name) before greeting.

Discover how a decorator adds functionality to a function or class without modification and understand method based, class based, and parametric decorators.

Learn how a method-based decorator extends a greet function without modifying it and ensures greeting occurs only when both first and last names exist.

Implement a Python grid function that takes first name and last name and returns its representation. Create a decorator that wraps a decorator and prints wrapper function executed before function.

Implement a python decorator that wraps a function, prints a pre-call message 'wrapper function executed' before the function, and returns a formatted result using first name and last name.

Explore class based decorator in Python. Learn how a class accepts a function, implements a core method, and calls the stored function inside the core implementation, via an example.

Transform a function decorator into a class based decorator in Python by defining __init__, a core method, and a print method, while handling *args and **kwargs to preserve behavior.

Demonstrates implementing a class-based decorator in Python that raises a function result to a configurable power, illustrated with double and triple raise functions and variable arguments.

Learn lambda functions as anonymous, small one-expression functions used for single tasks, and compare them with standard functions. See usage with built-ins like map and filter through coding examples.

Learn how lambda functions add numbers with arguments and return results, compare with standard functions, and see concise one-line code versus longer multi-line implementations.

Explore how to use lambda inside a standard function and run both together to solve the same problem, illustrated by example-01B in PyCharm.

Combine a standard function with a lambda to reproduce a result, showing lambda's power in a single line and printing the outcome with f-strings.

Explore how a lambda function can accept multiple arguments but return a single expression, and compare it with a standard function using the same three arguments in example-02.py.

Explore how to create a lambda function that takes three arguments and multiplies them, compare it with a standard function, and print the result using f-strings.

Explore how to combine standard and lambda functions to handle multiple arguments and reproduce the same result shown in example-02, through hands-on coding.

Demonstrate a standard modulo function, apply a filter over the range 1 to 25, and contrast a named function with a lambda while printing the result.

Explore the map function in Python, which applies a transformation to each item in a sequence and returns a new sequence of the results.

Explore how the map function handles a customer demand to add a parameterized number to a list of items and how to solve this using a map-based approach.

Explore the map function by transforming a sequence of numbers, adding one or a configurable value to each item, and comparing a loop approach with Python's built-in map and lambda.

Practice using the map function and a lambda to capitalize a list of fruits like bananas, pineapples, and mangoes in a do-it-yourself exercise.

Learn to use the map function to capitalize each fruit in a list with a lambda, producing capitalized fruits.

Discover how the filter function processes an iterable, returning a generator of items that pass a criterion, while map and filter illustrate a functional approach with a student class.

Explore using filter to select students by age and compare it with for loops and map, then apply case-insensitive filtering on fruits.

Learn how the reduce function from the functools module aggregates a sequence by summing the first two elements, then cumulatively adding each next item until the final result is returned.

Learn to use the reduce function from functools to multiply a sequence of numbers with a lambda, producing a final product like 720 from 3, 2, 4, 5, and 6.

Demonstrate using a for loop to aggregate numbers by computing a product, matching the reduce result and illustrating how reduce works behind the scenes.

Use reduce with a lambda to print a word list after importing functools. Find the minimum and maximum in a numbers list with reduce using min and max.

Discover how map, filter, and reduce solve problems with a bike list in a functional programming style. Build a bike class and find the cheapest bike using lambda.

Add five to the price of Ebony bikes in a list and print the results. Learn to combine map, filter, and lambda in Python to solve this task.

Explore how Python's built-in logging module writes status messages to files, sockets, or streams, defaults to warning, can start at debug, and enables severity-based logging to diagnose problems.

Explore Python logging by importing logging and using debug, info, warning, error, and critical levels; learn default warning start and the path to log to files in the next video.

Log developer details to developer.log using Python's logging, create a developer class storing full name, email, and team, and configure formatting and basic logging to a file.

Explore how generators yield values to create iterators on the fly, and how __iter__ and __next__ work with iterables like lists and dictionaries for memory efficiency.

Explore how to use Python's built-in iter and next to create an iterator from a list of car makes, print each item, and handle stop iteration when exhausted.

Learn to implement a custom iterator in Python by building a class with an iter method and a next method, iterating a car list and handling stop iteration.

Compare a list-based square calculator with a generator-based version, showing how generators yield results on the fly without storing in memory, using yield, next, and for loops.

Explore generator-based Fibonacci sequences, printing the first 15 numbers by yielding values and unpacking tuples in Python, with step-by-step explanation of a and b updates.

Develop a custom IRange iterator that mirrors the built-in Python range, handling start, stop, and step, with iter and next methods demonstrated in a for loop.

Explore how iterables such as lists, strings, and files power for loops, and how list comprehensions create lists, highlighting memory use and the on demand nature of generators and iterators.

Discover how the context manager closes files automatically, ensuring resources are released after errors. Use with, enter, and exit to convert direct file opening to context-managed workflows.

Learn how to open and write to a file using direct open, compare with context manager, and see how with handles automatic closing and resource management.

Learn to implement a custom context manager for file handling by building an open file class with __enter__ and __exit__, and using with to write safely.

Understand that __enter__ returns the open file objects and __exit__ closes the open file objects regardless of exceptions, demonstrating the power of the context manager.