Advanced Python Programming: Build High Performance, Concurrent, and Multi-Threaded Apps with Python Using Proven Design Patterns

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With this Learning Path, you will gain complete knowledge to solve problems by building high performing applications loaded with asynchronous, multithreaded code and proven design patterns.

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Title Statements

Full title :Advanced Python Programming : Build High Performance, Concurrent, and Multi-Threaded Apps with Python Using Proven Design Patterns.

Summary

With this Learning Path, you will gain complete knowledge to solve problems by building high performing applications loaded with asynchronous, multithreaded code and proven design patterns.

Edition Statements

Publication Date:2019.

Publication Statements

Publisher(s):Packt Publishing Ltd
Place of Publication:Birmingham
Language:eng
Publication Date:2019.

Notes

The future

The Physical Object

Description:1 online resource (652 pages)

Edition Identifiers

ISBN:9781838553692, 183855369X
oclc:on1089525376
LC Classification:QA76.73.P98
Dewey Decimal:005.72

Cover; Title Page; Copyright; About Packt; Contributors; 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

TriesCaching 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

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