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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 6. When to Use Object-oriented Programming

In previous chapters, we've covered many of the defining features of object-oriented programming. We now know the principles and paradigms of object-oriented design, and we've covered the syntax of object-oriented programming in Python.

Yet, we don't know exactly how and when to utilize these principles and syntax in practice. In this chapter, we'll discuss some useful applications of the knowledge we've gained, picking up some new topics along the way:

  • How to recognize objects
  • Data and behaviors, once again
  • Wrapping data in behavior using properties
  • Restricting data using behavior
  • The Don't Repeat Yourself principle
  • Recognizing repeated code

Treat objects as objects

This may seem obvious; you should generally give separate objects in your problem domain a special class in your code. We've seen examples of this in the case studies in previous chapters; first, we identify objects in the problem and then...

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