Python High Performance - Second Edition

Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Benchmarking and Profiling -- Designing your application -- Writing tests and benchmarks -- Timing your benchmark -- Better tests and benchmarks with pytest-benchmark -- Finding bottlenecks with cProfile -- Profile line by line with line\_profiler -- Optimizing Read more... Abstract: Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Benchmarking and Profiling -- Designing your application -- Writing tests and benchmarks -- Timing your benchmark -- Better tests and benchmarks with pytest-benchmark -- Finding bottlenecks with cProfile -- Profile line by line with line\_profiler -- Optimizing our code -- The dis module -- Profiling memory usage with memory\_profiler -- Summary -- Chapter 2: Pure Python Optimizations -- Useful algorithms and data structures -- Lists and deques -- Dictionaries -- Building an in-memory search index using a hash map -- Sets -- Heaps -- Tries -- Caching and memoization -- Joblib -- Comprehensions and generators -- Summary -- Chapter 3: Fast Array Operations with NumPy and Pandas -- Getting started with NumPy -- Creating arrays -- Accessing arrays -- Broadcasting -- Mathematical operations -- Calculating the norm -- Rewriting the particle simulator in NumPy -- Reaching optimal performance with numexpr -- Pandas -- Pandas fundamentals -- Indexing Series and DataFrame objects -- Database-style operations with Pandas -- Mapping -- Grouping, aggregations, and transforms -- Joining -- Summary -- Chapter 4: C Performance with Cython -- Compiling Cython extensions -- Adding static types -- Variables -- Functions -- Classes -- Sharing declarations -- Working with arrays -- C arrays and pointers -- NumPy arrays -- Typed memoryviews -- Particle simulator in Cython -- Profiling Cython -- Using Cython with Jupyter -- Summary -- Chapter 5: Exploring Compilers -- Numba -- First steps with Numba -- Type specializations -- Object mode versus native mode -- Numba and NumPy -- Universal functions with Numba -- Generalized universal functions -- JIT classes -- Limitations in Numba -- The PyPy project