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Description of  Python High Performance: Build high-performing, concurrent, and distributed applications

1787282899 pdf B071SGSHZ5 pdf Learn how to use Python to create efficient applicationsKey FeaturesIdentify the bottlenecks in your applications and solve them using the best profiling techniquesIdentify the bottlenecks in your applications and solve them using the best profiling techniquesWrite efficient numerical code in NumPy, Cython, and PandasWrite efficient numerical code in NumPy, Cython, and PandasAdapt your programs to run on multiple processors and machines with parallel programmingAdapt your programs to run on multiple processors and machines with parallel programmingBook DescriptionPython is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language.Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.By the end of the book, readers will have learned to achieve performance and scale from their Python applications.What you will learnWrite efficient numerical code with the NumPy and Pandas librariesWrite efficient numerical code with the NumPy and Pandas librariesUse Cython and Numba to achieve native performanceUse Cython and Numba to achieve native performanceFind bottlenecks in your Python code using profilersFind bottlenecks in your Python code using profilersWrite asynchronous code using Asyncio and RxPyWrite asynchronous code using Asyncio and RxPyUse Tensorflow and Theano for automatic parallelism in PythonUse Tensorflow and Theano for automatic parallelism in PythonSet up and run distributed algorithms on a cluster using Dask and PySparkSet up and run distributed algorithms on a cluster using Dask and PySparkTable of ContentsBenchmarking and profilingBenchmarking and profilingPure Python Optimization techniquesPure Python Optimization techniquesFast array operationsFast array operationsC performance with CythonC performance with CythonExploring CompilersExploring CompilersImplementing concurrencyImplementing concurrencyParallel processingParallel processingDistributed ProcessingDistributed ProcessingDesigning for High PerformanceDesigning for High Performance