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High Performance Python Research Based PDF Notes

Download High Performance Python Free in PDF. Python is a popular language and easy to understand. This Notes shows that how you can maintain the performance and speed up your code to run fast and correctly. This notes help you to clear your concept and you’ll learn how to implement the code that easy to modify.

In this notes you’ll learn to accomplish the performance from python application. This notes for who interested in python and wants to learn more. This notes for advance python programmers who needed to better their performance and achieve their goals.  You can download complete PDF Notes Free 

You Learn These Topics From This Notes:

1. Understanding Python Performance

  • The Fundamental Computer System
  • Computing Units
  • Memory Units
  • Communication Layers
  • Putting the Fundamental Elements Together

2. Profiling to Find Bottlenecks

  • Profiling Efficiently
  • Introducing the Julia Set
  • Calculating the Full Julia Set
  • Using the cProfile Module
  • Strategies to Profile Your Code Successfully

3. List of Tuples

  • A More Efficient Search
  • Lists Versus Tuples
  • Lists As Dynamic Arrays
  • Tuple As Static Arrays

4. Dictionaries and Sets

  • How Do Dictionaries and Sets Works?
  • Inserting and Retrieving
  • Deletion
  • Resizing
  • Dictionaries and Namespace

5. Iterators and Generators

  • Iterators for Infinite Series
  • Lazy Generator Evaluation
  • Wrap-Up

6. Matrix and Vector Computation

  • Introduction to the Problems
  • Memory Fragmentation
  • Understanding Perf
  • Enter NumPy
  • Apply NumPy to the Diffusion Problem

7. Compiling to C

  • What Sort of Speed Gains Are Possible?
  • JIT Versus AOT Compiler
  • Cython
  • Shed Skin
  • Cython and NumPy
  • When to Use Each Technology
  • Foreign Function Interface

8. Concurrency

  • Introduction to Asynchronous Programing
  • Serial Crawler 
  • Gevent
  • Tornado
  • Database Example

9. The Multiprocessing Module

  • The Overview of the Multiprocessing Module
  • Estimating Pi Using Processes and Threads
  • Finding Prime Numbers
  • Verify Primes Using Interprocess Communication
  • Serial Solution
  • Naive Pool Solution

10. Cluster and Job Queues

  • Benefits of Clustering
  • Drawbacks of Clustering
  • Commons  Cluster Design
  • How to Start a Cluster Solution
  • Three Cluster Solution
  • NSQ for Robust Production Clustering 

11. Using Less RAM

  • Objects for Primitives are Expensive
  • Understanding the RAM Used in a Collection
  • Bytes Versus Unicode
  • Tips for Less RAM
  • Probabilistic Data Structure
  • Bloom Filters

12. Lesson from the Field

  • Adaptive Lab’s Social Media Analytics
  • Our Development Technology
  • The Sweet Spot
  • Lesson in Optimizing
  • Cluster Design
  • Large Scale Social Media Analysis in Smesh
  • PyPy for Successful Web and Data Processing System
  • Task Queue at



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