•Getting Started with Modern Python
•The Course Overview
•Setting Up Your Development Environment
•Writing Your First Python Program
•Writing Your Second Python Program
•Debugging Your Second Python Program
•Printing and Formatting Strings
•Parsing Simple Strings
•Working with the Re Module
•Working with Dates
•Ensuring Data Integrity and Security
•Working with Files and Filelike Interfaces
•Introduction to Lists and for Loops
•Working with List Comprehensions
•Filtering Data in List Comprehensions
•Some Problems with List Comprehensions
•Working with Generators
•Going to Infinity and Beyond with Itertools
•Introduction to Functions
•Various Concepts of Functions
•Exploring Function Decorators
•Document Code and Verify Code Correctness
•Classes Modules When and Why to Use Them
•Test Your Knowledge
•Building REST APIs with Python
•The Bigger Picture
•Your Development Environment
•Installing PostgreSQL
•Django Projects and Apps
•Using the Django Development Environment
•MVC and MVT Framework
•Creating and Working with Models
•Migrations and Database Queries
•Writing Our First View
•Routing and HTTP Methods
•Using Templates
•Testing
•Exploring RESTful APIs
•Writing a Simple Hello World API
•Exploring the DRF
•Serializing Models
•Refactoring Our API with the DRF
•Generic Django REST Framework Views
•Extra Viewset Actions and Routers
•Testing the API
•Tips Tricks and Techniques for Python Application Development
•Identifying Bottlenecks
•Speeding Up IObound Tasks
•Speeding Up CPUbound Tasks
•Move Timecritical Tasks to CC Libraries
•Alternative Python Runtimes
•Setuptools
•Source Distribution and Wheels
•Standalone Executables
•Docker
•Unit Testing and Coverage
•Complex Tests and Coverage
•Python Test Driven Development
•Integration Testing
•Python Behavior Driven Development
•Managing Python Environments Efficiently
•Python Package Management
•Debugging and Navigating Python Bytecode
•Prototyping Using the Python Shell and Jupyter
•Linting
•Static Code Analysis
•Type Checking
•Troubleshooting Python Application Development
•Measuring Time Between Two Lines of Code with timeit
•Figuring out Where Time Is Spent with the Profile Module
•More Precise Time Tracking with cProfile
•Looking at Memory Consumption with memoryprofiler
•Reduce Execution Time and Memory Consumption with slots
•Use Tuples Instead of Lists When Your Data Does Not Change
•Save on Memory Consumption with Generators Instead of Lists
•When to Use Lists Instead of Generators
•Leveraging Itertools to Create Generator Pipelines
•The Problem with Using Lists to Perform Vector Calculations
•Using NumPys Arrays for More Powerful Vector Representations
•Rewriting Our Problem with NumPy to Speed It up x
•Fast MapReduce with NumPy Broadcasting
•Optimize All Calculations in One Go with numexpr
•The Problem of Serially Executing Web Scraping Calls
•Simple Asynchronous Programming with coroutines and gevent
•EventDriven Concurrency with Tornado
•Concurrency and Futures with asyncio
•Getting Started with Parallel Programming
•Doubling the Speed of Your List Processing with Tuples
•Easily Speed up a Group of Processes with Pool
•Stop Processes from Interfering with Each Other with Locks
•Logging What Happens When You Have Many Processes
•Stop Modifying the Wrong Object Instance with Correct Object Cloning
•Speed Up Your OOP with namedtuples
•Reduce Getters and Setters with Static Methods and Properties
•Comparing Two Different Objects
•Working with Files, and File-like Interfaces
•Classes, Modules – When and Why to Use Them?
•Tips, Tricks, and Techniques for Python Application Development
•Speeding Up I/O-bound Tasks
•Speeding Up CPU-bound Tasks
•Move Time-critical Tasks to C/C++ Libraries
•Looking at Memory Consumption with memory_profiler
•Reduce Execution Time and Memory Consumption with __slots__
•Using NumPy’s Arrays for More Powerful Vector Representations
•Rewriting Our Problem with NumPy to Speed It up 40x
•Event-Driven Concurrency with Tornado
•Improve Readability with Abstract Base Classes in Python
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