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Python: Real-World Data Science

You're reading from   Python: Real-World Data Science Real-World Data Science

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Product type Course
Published in Jun 2016
Publisher
ISBN-13 9781786465160
Length 1255 pages
Edition 1st Edition
Languages
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Authors (5):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
Robert Layton Robert Layton
Author Profile Icon Robert Layton
Robert Layton
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Martin Czygan Martin Czygan
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Martin Czygan
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Table of Contents (12) Chapters Close

Table of Contents FREE CHAPTER
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline
1. Course Module 1: Python Fundamentals 2. Course Module 2: Data Analysis 3. Course Module 3: Data Mining 4. Course Module 4: Machine Learning Index

Chapter 12. Python Design Patterns II

In this chapter we will be introduced to several more design patterns. Once again, we'll cover the canonical examples as well as any common alternative implementations in Python. We'll be discussing:

  • The adapter pattern
  • The facade pattern
  • Lazy initialization and the flyweight pattern
  • The command pattern
  • The abstract factory pattern
  • The composition pattern

The adapter pattern

Unlike most of the patterns we reviewed in Chapter 8, Strings and Serialization, the adapter pattern is designed to interact with existing code. We would not design a brand new set of objects that implement the adapter pattern. Adapters are used to allow two pre-existing objects to work together, even if their interfaces are not compatible. Like the display adapters that allow VGA projectors to be plugged into HDMI ports, an adapter object sits between two different interfaces, translating between them on the fly. The adapter object's sole purpose is to perform...

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