Chapter 4. Profiling and Optimization
In this chapter, we will cover the following topics:
- Evaluating the time taken by a statement in IPython
- Profiling your code easily with cProfile and IPython
- Profiling your code line-by-line with line_profiler
- Profiling the memory usage of your code with memory_profiler
- Understanding the internals of NumPy to avoid unnecessary array copying
- Using stride tricks with NumPy
- Implementing an efficient rolling average algorithm with stride tricks
- Making efficient array selections in NumPy
- Processing huge NumPy arrays with memory mapping
- Manipulating large arrays with HDF5 and PyTables
- Manipulating large heterogeneous tables with HDF5 and PyTables
