1 Understanding Python’s Runtime Environment
1.1 The Python Interpreter: CPython, PyPy, and Beyond
1.2 Python Execution Model and Bytecode
1.3 Global Interpreter Lock (GIL)
1.4 Memory Management and Garbage Collection
1.5 Error Handling and Exception Management
1.6 Dynamic Typing and Runtime Evaluation
2 Efficient Data Handling and Storage
2.1 Data Structures and Their Impact on Performance
2.2 Efficient Use of Lists and Tuples
2.3 Dictionaries and Sets for Fast Lookups
2.4 Working with Large Datasets using Pandas
2.5 Optimizing File I/O Operations
2.6 Data Persistence with SQLite and Other Databases
2.7 Serialization and Object Storage
3 Advanced Algorithms and Data Structures
3.1 Algorithmic Complexity: Time and Space Trade-offs
3.2 Sorting and Searching Algorithms
3.3 Graph Algorithms and Network Analysis
3.4 Tree Structures for Optimized Searching
3.5 Dynamic Programming and Memoization
3.6 Advanced Hashing Techniques
3.7 Leveraging Recursive and Iterative Approaches
4 Networking and Concurrency for Performance
4.1 Concurrency and Parallelism Basics
4.2 Socket Programming for Real-time Networking
4.3 Asynchronous I/O Operations
4.4 Threading vs. Multiprocessing
4.5 Event-driven Programming with Asyncio
4.6 Using Message Queues for Distributed Systems
4.7 Load Balancing and Scaling Strategies
5 Optimizing Memory Usage
5.1 Understanding Python’s Memory Model
5.2 Identifying and Reducing Memory Leaks
5.3 Efficient Data Representation
5.4 Using Generators for Memory Efficiency
5.5 Memory Profiling Tools and Techniques
5.6 Optimizing with __slots__ and Data Classes
5.7 Garbage Collection Tuning
6 Leveraging Built-in Libraries for Optimization
6.1 Exploring the Python Standard Library
6.2 Optimizing Code with the Collections Module
6.3 File and Directory Management with the OS and Shutil Modules
6.4 Concurrent Execution with the Concurrent.Futures Module
6.5 Data Handling with the JSON and CSV Modules
6.6 Profiling and Debugging with the CProfile and PDB Modules
6.7 Mathematical Computation with the Math and Statistics Modules
7 Profiling and Benchmarking Code
7.1 Understanding the Need for Profiling
7.2 Using CProfile for Performance Insights
7.3 Line-by-Line Analysis with LineProfiler
7.4 Visualizing Profiling Data with SnakeViz
7.5 Custom Benchmarking with Timeit
7.6 Continuous Performance Monitoring
7.7 Interpreting and Acting on Profiling Results
8 Exploring Compiled Python with Cython
8.1 Understanding Cython and Its Benefits
8.2 Setting Up the Cython Environment
8.3 Converting Python Code to Cython
8.4 Optimizing Python Code with Static Typing
8.5 Building and Compiling Cython Modules
8.6 Interfacing with C/C++ Libraries
8.7 Debugging and Profiling Cython Code
9 Harnessing Asynchronous Programming
9.1 Principles of Asynchronous Programming
9.2 Asyncio: The Asynchronous Framework
9.3 Defining and Running Async Coroutines
9.4 Handling Asynchronous I/O Bound Operations
9.5 Task Scheduling and Concurrency with Await
9.6 Combining Sync and Async Code
9.7 Error Handling in Asynchronous Programming
10 Mastering Multithreading and Multiprocessing
10.1 Distinguishing Between Multithreading and Multiprocessing
10.2 Thread Management with the Threading Module
10.3 Synchronization and Locks in Multithreading
10.4 Harnessing the Power of the Multiprocessing Module
10.5 Shared Memory and Data Exchange
10.6 Implementing Process Pools
10.7 Debugging and Troubleshooting Concurrency Issues

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