Troubleshooting Python Application Development [Video]
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Locating Root Causes by Benchmarking and Profiling Your Application
- The Course Overview
- 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 memory_profiler
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Python Idioms for Faster Code
- 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
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Tackling Long Running Loops on Big Lists with NumPy
- The Problem with Using Lists to Perform Vector Calculations
- Using NumPy’s Arrays for More Powerful Vector Representations
- Rewriting Our Problem with NumPy to Speed It up 40x
- Fast MapReduce with NumPy Broadcasting
- Optimize All Calculations in One Go with numexpr
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Speed Up Your I/O Heavy Tasks with Concurrent Programming
- The Problem of Serially Executing Web Scraping Calls
- Simple Asynchronous Programming with coroutines and gevent
- Event-Driven Concurrency with Tornado
- Concurrency and Futures with asyncio
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Make Your Apps Run Faster with Parallel Programming
- 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
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Organize Your Code Better Using Object-Oriented Programming
- 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
- Improve Readability with Abstract Base Classes in Python