- Advanced Descriptor: Unit Conversion
- Advanced Type Hinting: Generics and Protocols
- Advanced Type Hinting: Generics and Union Types
- Asynchronous Task Queue with `asyncio` and `asyncio.Queue`
- Asynchronous Web Request with `asyncio` and `aiohttp`
- Asynchronous Web Request with `asyncio` and `aiohttp`
- Basic Descriptor Example: Validating Attribute Type
- Basic Threading Example: Counting with Threads
- Basic Type Hinting with MyPy
- Basic Type Hints in Python
- Breaking Circular References to Aid Garbage Collection
- Chaining Generators for Data Pipelines
- Class Decorator for Logging Method Calls
- Comparing Threads and Processes for CPU-Bound Tasks
- Custom Context Manager for File Handling
- Custom Context Manager for File Handling
- Custom Iterator for Fibonacci Sequence
- Custom Iterator for a Range with a Step
- Custom Memory Allocation with ctypes
- Custom Range Iterator
- Data Validation with Descriptors
- Data and Non-Data Descriptors in Python
- Decorator with Arguments
- Decorator with Arguments: Implementing Rate Limiting
- Decorators with Arguments: Logging Function Calls
- Efficient Data Processing with Generators
- File Line Iterator
- Generating Fibonacci Sequence with Yield
- Generator Expression for Efficient Data Processing
- Inter-Process Communication with Queues
- Locking for Thread Safety
- Manual Garbage Collection and Object Resurrection
- Parallel Computation of Square Roots using `multiprocessing`
- Preserving Metadata with <code>@wraps</code> in Decorators
- Producer-Consumer Pattern with Asynchronous Queues
- Semaphore for Resource Limiting
- Simple Decorator Example: Logging Function Calls
- Simple Function Decorator
- Thread Pool vs Process Pool Example
- Thread Synchronization: Using Locks to Prevent Race Conditions
- Type Hinting for Improved Code Readability and Error Prevention
- Type Hinting with `typing.List` and `typing.Dict`
- Understanding Object Lifespan and Garbage Collection in Python
- Understanding Reference Counting in Python
- Understanding Weak References in Python
- Understanding the GIL: Demonstrating Limitations with Threading
- Using <code>contextlib.contextmanager</code> Decorator
- Using `contextlib.contextmanager` for Simple Context Managers
- Using `contextlib.suppress` for Ignoring Specific Exceptions
- Using `typing.Tuple` and `typing.Callable`
- Asynchronous Web Request with `asyncio` and `aiohttp`
- Asynchronous Web Server with FastAPI and Uvicorn
- Automating System Tasks with `subprocess`
- Automating Web Interactions with `Selenium`
- Basic Process Creation with Multiprocessing
- Basic Threading Example with the `threading` Module
- Creating Classes with Metaclasses
- Cross-Site Scripting (XSS) Prevention with Escaping
- Custom Context Manager for Resource Handling
- Decorator with Arguments (Decorator Factory)
- Django REST Framework with Class-Based Views
- Efficient Data Structures: Trie for String Searching
- Metaclass for Enforcing Attribute Types
- Pandas DataFrame Optimization for Large Datasets
- Profiling with LineProfiler
- Profiling with cProfile and pstats
- PyQt5 Simple Window
- SQL Injection Prevention with Parameterized Queries
- SQL Injection Prevention with Parameterized Queries
- Simple Decorator Example: Timing Function Execution
- TensorFlow Custom Training Loop with GradientTape
- Thread Synchronization using Locks
- Tkinter Simple Text Editor
- Using Heaps for Efficient Priority Queues
- Using Secrets Module for Generating Secure Random Numbers
- Using a Process Pool for Parallel Computation
- Accessing and Slicing Lists in Python
- Advanced Docstring Example with Type Hints and Exceptions
- Advanced Loop Control: Combining `break`, `continue`, and `else`
- Arithmetic Operators in Python: A Comprehensive Guide
- Assignment Operators in Python
- Basic Float Operations and Representation
- Basic Function Call with Arguments
- Basic List Comprehension: Squaring Numbers
- Basic Printing with `print()`
- Basic Try-Except Block
- Basic Try-Except Block
- Basic User Input with `input()`
- Basic While Loop Example
- Boolean Basics: True and False
- Boolean Operations: and, or, not
- Bytearray Creation and Modification
- Bytearray Slicing and Deletion
- Checking String Membership
- Common String Methods
- Comparison Operators in Python
- Creating and Accessing Dictionaries in Python
- Creating and Accessing Tuples
- Creating and Manipulating Bytes Objects
- Creating and Manipulating Complex Numbers in Python
- Defining a Simple Function
- Defining and Raising Custom Exceptions
- Dictionary Comprehension
- Dictionary Comprehension: Creating a Price Dictionary
- Dictionary Comprehension: Transforming Keys and Values
- Documenting Python Functions with Docstrings
- Error Handling with <code>else</code> and <code>finally</code>
- Factorial Calculation Using Recursion
- Fibonacci Sequence Using Recursion
- File Handling with 'with' Statement and Line-by-Line Reading
- File Handling with Different Modes
- Float Type Conversion and Special Values
- Formatted Output with f-strings
- Formatted Output with f-strings and .format()
- Function Annotations with Type Hints
- Function Call with Default Argument Values
- Function with Arguments and Return Value
- Generator Expression for Even Numbers
- Generator Expression for Filtering and Transforming Data
- Handling Specific Exceptions: Division by Zero and File Not Found
- Identity Operators: `is` and `is not`
- Integer Declaration and Basic Operations
- Integer Methods and Bitwise Operations
- Introduction to Frozen Sets in Python
- Iterating Through a List with a For Loop
- Iterating with Index using <code>enumerate</code>
- Lambda Function Example: Squaring a Number
- List Comprehension with Conditional Filtering
- List Creation and Basic Operations
- List Methods: Append, Insert, Remove, Pop
- Logical Operators in Python: A Comprehensive Guide
- Loop Control Statements in Python: `break`, `continue`, and `pass`
- Membership Operators: Checking for Element Existence
- Practical Example: Calculating Compound Interest
- Raising Custom Exceptions
- Raising Custom Exceptions in Python
- Returning Multiple Values with Tuples
- Returning a Value from a Function
- Set Comprehension with Conditional Filtering
- Set Comprehension with Multiple Iterables
- Set Creation and Basic Operations
- Simple File Reading and Writing
- Simple Number Comparison
- String Concatenation and Joining
- String Manipulation and Formatting
- String Slicing and Indexing
- Try-Except-Else-Finally
- Tuple Indexing and Slicing
- Tuple Packing and Unpacking
- Understanding *args and **kwargs in Python
- Understanding Bitwise Operators in Python
- Understanding Function Arguments in Python
- Understanding Mutability and Immutability in Python
- Understanding NoneType in Python
- While Loop with Else Clause
- Working with Bytes and Encodings
- Building a Convolutional Neural Network (CNN) for Image Classification
- Building a Simple Neural Network with PyTorch
- Building a Simple Sequential Model with Keras
- Building a Simple Sequential Model with Keras
- Convolutional Neural Network (CNN) for Image Classification
- Creating and Manipulating PyTorch Tensors
- Cross-Validation for Model Evaluation with Scikit-learn
- Custom Autograd Function in PyTorch
- Dynamic Graph Neural Network (GNN) Message Passing
- Dynamic Neural Network with PyTorch
- Grid Search for Hyperparameter Tuning with Scikit-learn
- Image Classification with TensorFlow/Keras
- K-Means Clustering with Scikit-learn
- Linear Regression with Scikit-learn
- Logistic Regression for Classification with Scikit-learn
- Named Entity Recognition with spaCy
- Object Detection with TensorFlow Object Detection API
- Part-of-Speech Tagging with NLTK
- Part-of-Speech Tagging with spaCy
- Pipeline with Feature Union and Grid Search
- Principal Component Analysis (PCA) for Dimensionality Reduction
- PyTorch Autograd Example: Linear Regression
- Regression with a Neural Network using Keras
- Scikit-learn Pipeline for Data Preprocessing and Model Training
- Sentiment Analysis with Keras and IMDB Dataset
- Sentiment Analysis with NLTK's VADER
- Sentiment Analysis with spaCy and a Custom Model
- Simple Neural Network with Keras
- Stemming and Lemmatization with NLTK
- Tensor Operations and Broadcasting
- Text Classification with TensorFlow and Keras
- Tokenization with NLTK: Splitting Text into Words
- Tokenization with spaCy: A More Efficient Approach
- Training a Convolutional Neural Network (CNN) with PyTorch for Image Classification
- AWS Lambda Function for Image Resizing
- Basic Dockerfile for a Python Application
- Building a Docker Image and Running a Container
- Building sdist and wheel distributions
- Creating `pyproject.toml` using Poetry
- Creating a `pyproject.toml` file for packaging with Poetry
- Creating a `requirements.txt` file
- Creating a `setup.py` file for packaging
- Creating and Activating Virtual Environments with `venv`
- Creating and Managing Environments with Conda
- Creating and Using a Virtual Environment with `venv`
- Dependency Pinning with `pip freeze`
- Deploying a Simple Flask App to AWS Elastic Beanstalk
- Deploying a Simple Flask App to Heroku
- Deploying a Simple Function to Google Cloud Functions
- Deploying a Simple Web App to PythonAnywhere
- Docker Compose for a Simple Web Application with Redis
- Docker Swarm Compose File (docker-compose.yml)
- Dockerfile Example: A Simple Python Application
- Dockerfile for a Flask Application with Multi-Stage Build
- Dockerfile for a Python Application
- Examining `setup.py` and `pyproject.toml` for Package Metadata
- Generating `requirements.txt` using `pip freeze`
- Installing Dependencies from `requirements.txt`
- Kubernetes Deployment Configuration (YAML)
- Packaging and Publishing a Simple Python Package to PyPI
- Running a Simple Python Application in a Docker Container
- Checking Python Version at Runtime
- Demonstrating PEP 484 (Type Hints) and its Impact on Code Clarity
- Demonstrating PEP 572 (Assignment Expressions - The Walrus Operator) and its Impact on Code Conciseness
- PEP 572 Example: Assignment Expressions (The Walrus Operator)
- PEP 8 Example: Style Guide Compliance
- Structural Pattern Matching in Python 3.10
- Type Hinting Improvements in Python 3.9+
- Adding a Button and Handling Events
- Adding a Button to a PyQt Window
- Advanced PyQt Layout: Using QGridLayout
- Basic Kivy Layout and Widgets
- Basic PyQt Layout Example: Vertical Layout
- Basic Touch Handling with Kivy
- Creating Menus in Tkinter
- Creating a Basic Tkinter Window
- Drawing on the Screen with Touch Input
- Dynamic Widget Creation in Kivy with Kv Language
- Kivy: Adding a Text Input and Label
- PyQt Push Button Example
- PyQt QLineEdit Example
- Simple Kivy App with Kv Language
- Simple Kivy App: Hello World
- Simple PyQt Model-View Example
- Simple PyQt Signal and Slot Example
- Simple PyQt Window
- Tkinter Basic Widgets Example
- Tkinter Event Handling: Button Click
- Tkinter Event Handling: Key Press
- Tkinter Layout: Pack, Grid, and Place
- Tkinter Listbox Example
- Using BoxLayout for Dynamic Widget Arrangement
- Using Dialogs in Tkinter
- Basic JSON Encoding and Decoding in Python
- Basic Mathematical Operations with `math` and `random`
- Basic Module Import
- Basic Multiprocessing Example
- Basic Socket Server
- Basic Threading Example
- Calculate Time Differences and Format Output
- Checking File Existence and Creating Directories
- Combining Iterables with `itertools.chain`
- Complex Number Operations with `cmath`
- Controlling Package Imports with `__all__`
- Creating a Python Package with `__init__.py`
- Creating and Using Python Modules
- Different Forms of Import in Python
- Extracting Data from a Log File Using Regular Expressions
- Handling Custom Objects with JSON Encoding and Decoding
- Importing Modules from a Custom Package
- Infinite Cycling with `itertools.cycle`
- Leveraging `functools.lru_cache` for Memoization
- Reading Command-Line Arguments and Getting the Python Version
- Reloading a Module with `importlib.reload()`
- Selective Module Import
- Simple HTTP Request with `urllib`
- Understanding Python's Module Search Path
- Understanding Relative Imports in Python Packages
- Understanding Subpackages in Python
- Understanding the `__name__` Variable in Python
- Using `__name__` for Conditional Logic in Modules
- Using `functools.partial` for Pre-configuring Functions
- Validating Email Addresses with Regular Expressions
- Working with Dates and Times using `datetime` Module
- Working with JSON Data using `json` Module
- Working with Time Zones using datetime and pytz
- Abstract Base Classes (ABCs) and Inheritance in Python
- BankAccount Example: Methods in Action
- Basic Class with Constructor
- Class with Methods and Attributes
- Constructor with Default Values and Type Hinting
- Creating a Custom Metaclass to Enforce Attribute Naming Conventions
- Creating and Instantiating a Simple Class
- Custom Metaclass to Implement a Singleton Pattern
- Customizing Class Initialization with Metaclasses
- Defining a Simple Class in Python
- Defining and Using Instance Variables
- Duck Typing: Embrace Flexibility in Python
- Encapsulation and Access Modifiers in Python
- Encapsulation and Data Hiding with Private Attributes
- Encapsulation with Properties: Managing Temperature
- Extending Functionality with New Methods in Subclasses
- Instantiation with Default Attribute Values
- Metaclass Basics: Creating Classes with Control
- Metaclass for Automatic Attribute Validation
- Metaclass for Singleton Implementation
- Modifying Instance Variables After Object Creation
- Modifying Object Attributes Using 'self'
- Multiple Inheritance in Python: A Practical Example
- Object Identity vs. Equality in Python
- Overriding Methods in Python Inheritance
- Polymorphism and Method Overloading with Decorators
- Polymorphism and Operator Overloading with Vectors
- Single Inheritance Example: Animal and Dog
- Single Inheritance: Vehicle and Car
- Understanding Destructors in Python
- Understanding Method Resolution Order (MRO) in Python
- Understanding Methods in Python Classes
- Understanding the 'self' Parameter in Python Classes
- Using `super()` for Method Resolution Order (MRO)
- Accessing the Official Python Documentation Using `pydoc`
- Connecting to Meetup API to Fetch Events
- Exploring Python's Standard Library with `inspect` and Documentation Strings
- Fetching Conference Data from a Website using Web Scraping
- Fetching Stack Overflow Question Data
- Finding Python User Groups (PUGs)
- Finding and Installing Packages with pip
- Finding and Subscribing to Python Mailing Lists
- Listing Package Licenses with `pip show`
- Programmatically Reading License Files from a Package
- Searching Stack Overflow for Questions
- Adhering to PEP 8 for Enhanced Readability and Maintainability
- Advanced Unit Testing: Mocking with `unittest.mock`
- Automating Code Formatting with Black
- Automating Git Commit Messages with Pre-commit Hooks
- Basic Git Operations in Python using `subprocess`
- Basic Unit Test Example with `unittest`
- Basic pytest Function Test
- Checking Git Repository Status with `GitPython`
- Comprehensive Docstring Example: Function with Arguments and Return
- Doctests for Function Verification
- Git Branching Strategy Simulation
- Illustrating Basic Git Workflow within Python
- Measuring Test Coverage with `pytest-cov`
- Mocking external dependencies with unittest.mock
- Module-Level Docstring and Class Docstring
- Setting up Read the Docs for Your Python Project
- Sphinx Configuration Example
- Using Docstrings for Documentation
- Using Meaningful Variable Names
- Using Pylint to Enforce Code Style
- Using Pytest Fixtures
- Using autopep8 for PEP 8 Compliance
- Using pytest fixtures for test setup and teardown
- Analyzing Test Coverage with `coverage.py`
- Asserting Exceptions with `assertRaises`
- Basic Assertions with unittest
- Basic Pytest Fixture Example
- Basic Pytest Parametrization
- Basic Unit Test with `unittest`
- Basic Unit Testing with unittest
- Doctests: Testing within Docstrings
- Measuring Test Coverage with `coverage.py`
- Mocking a Class with `unittest.mock.patch.object`
- Mocking a Function with `unittest.mock.patch`
- Parameterized Fixtures
- Patching a Class Attribute for Testing
- Patching a Function's Return Value
- Running Doctests in Python
- Simple Test Function with pytest
- Simple pytest Test Function
- Testing Exceptions with `unittest`
- Testing with Assertions for Different Data Types
- Unit Testing with Fixtures in Python
- Using Fixtures in pytest
- Using setUp and tearDown for Test Fixtures
- API Authentication with OAuth 2.0 (Authorization Code Grant)
- API Authentication with Requests and API Keys
- Accessing Request Data in Flask
- Advanced Routing with Path Conversion
- AsyncIO and Aiohttp for Asynchronous Data Fetching and Validation
- Basic Django Project Setup
- Basic Django URL Configuration
- Basic Django URL Routing
- Basic Flask Routing Example
- Basic Routing in FastAPI
- Building a Simple RESTful API with Flask
- Crafting Custom Responses in Flask
- Customizing the Django Admin Interface
- Defining Model Relationships (ForeignKey)
- Defining a Simple Django Model
- Defining a Simple Model in Django
- Django 'Hello, World!' Example
- Django Class-Based View (TemplateView)
- Django Cookie Example: Setting and Retrieving Cookies
- Django Form Example: Simple Contact Form
- Django Function-Based View
- Django Middleware for Handling Maintenance Mode
- Django Middleware for Request Logging
- Django Permission-Based Authorization
- Django Project Structure Example
- Django Session Example: Setting and Retrieving User Data
- Django User Authentication with Built-in Forms
- Dynamic Blueprints with Subdomains
- FastAPI Dependency Injection with Asynchronous Dependencies
- FastAPI Request Body and Response Model Example
- FastAPI Response Model with Status Code
- FastAPI with Type Hints for Data Validation
- Fetching Data from a REST API with Requests
- Fetching and Parsing JSON Data from an API
- Fetching and Parsing XML Data from an API
- Flask 'Hello, World!' Example
- Flask App with SQLAlchemy Integration
- Flask Blueprints: Structuring Your Application
- Flask Form Example: Field-Specific Validation
- Flask Form Example: Simple Input Form
- Flask Route with Variable Parameters
- Flask Session Management with Cookies
- Including URLconf Files
- ModelSerializer Example with Django REST Framework
- Rendering Dynamic Content in Django Templates
- Rendering HTML with Jinja2 Templates in Flask
- Simple API Endpoint with Django REST Framework
- Simple Asynchronous API with FastAPI
- Simple Flask Application
- Template Inheritance in Flask with Jinja2
- Testing Django Views with `TestCase`
- Testing Flask Application Context
- Testing a Flask Application with Pytest
- Using Template Filters in Django
- Using `mock` to Isolate Dependencies in Django Tests
- Using the `requests` library to interact with an API
- Advanced NumPy Indexing: Integer Array Indexing
- Applying Trigonometric Functions with NumPy
- Array Indexing and Slicing
- Array Slicing and Indexing in NumPy
- Basic NumPy Indexing and Slicing
- Calculating Eigenvalues and Eigenvectors with NumPy
- Calculating the Mean and Standard Deviation using NumPy
- Connecting to MongoDB and Inserting a Document
- Connecting to MySQL using mysql.connector
- Connecting to PostgreSQL using psycopg2
- Creating and Inspecting NumPy Arrays
- Creating and Manipulating Pandas DataFrames
- Creating and Manipulating Pandas Series
- Creating and Querying a SQLite Database
- Data Type Conversion in Pandas
- Element-wise Array Operations with NumPy
- Handling Missing Data with Interpolation
- Handling Missing Data with Pandas
- Identifying and Handling Missing Data with Pandas
- Joining DataFrames on Index
- Merging DataFrames with Pandas
- Pandas Boolean Indexing for Data Selection
- Pandas DataFrame Selection and Indexing with .loc and .iloc
- Pandas GroupBy and Aggregation: Sales Analysis
- Pandas GroupBy and Transformation: Applying Custom Functions
- Parsing XML with lxml
- Parsing XML with xml.etree.ElementTree
- Parsing YAML Data with PyYAML
- Pickling a Custom Class
- Pickling a Python Dictionary
- Querying MongoDB with `pymongo`
- Reading and Writing CSV Files with Pandas
- Reading and Writing CSV Files with the `csv` Module
- Reading and Writing Excel Files with Pandas
- Reading and Writing JSON Data in Python
- SQLAlchemy: Defining a Simple Model and Creating a Table
- Solving a System of Linear Equations with NumPy
- Using Parameterized Queries for Security
- Working with Gzip Compressed Files
- Working with Zip Archives
- Accessing a SOAP API with Suds
- Accessing a Weather API with API Key Authentication
- Accessing and Understanding API Documentation with `requests` and `help()`
- Advanced Web Scraping with Scrapy
- Basic HTTP GET Request with `requests`
- Fetching Data from a RESTful API using `requests`
- Fetching Data from a URL using urllib.request
- Parsing XML with ElementTree
- Rate Limiting API Requests with `requests` and `ratelimit`
- Sending Data to a RESTful API using `requests` (POST Request)
- Sending POST Requests with JSON Data
- Simple Socket Server and Client in Python
- Simple Web Scraping with Beautiful Soup
- Submitting a Form with Requests
- Token Bucket Rate Limiting with Redis
- Using an API Key with Headers for Authentication
- Web Scraping with Beautiful Soup
- Web Scraping with Beautiful Soup: Selecting Elements using CSS Selectors
- Web Scraping with Scrapy: Selecting Elements using XPath
- Web Scraping: Following Links with Beautiful Soup and Requests
- Working with URL Parameters using urllib.parse