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Healthcare Analytics Made Simple

You're reading from   Healthcare Analytics Made Simple Techniques in healthcare computing using machine learning and Python

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781787286702
Length 268 pages
Edition 1st Edition
Languages
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Authors (2):
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Vikas (Vik) Kumar Vikas (Vik) Kumar
Author Profile Icon Vikas (Vik) Kumar
Vikas (Vik) Kumar
Shameer Khader Shameer Khader
Author Profile Icon Shameer Khader
Shameer Khader
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Healthcare Analytics 2. Healthcare Foundations FREE CHAPTER 3. Machine Learning Foundations 4. Computing Foundations – Databases 5. Computing Foundations – Introduction to Python 6. Measuring Healthcare Quality 7. Making Predictive Models in Healthcare 8. Healthcare Predictive Models – A Review 9. The Future – Healthcare and Emerging Technologies 10. Other Books You May Enjoy

Introduction to pandas

Almost all of the features we've discussed so far are features of base Python; that is, no external packages or libraries were required. The truth of the matter is that the majority of the code we write in this book will pertain to one of several external Python packages commonly used for analytics. The pandas library (http://pandas.pydata.org) is an integral part of the later programming chapters. The functions of pandas for machine learning are threefold:

  • Import data from flat files into your Python session
  • Wrangle, manipulate, format, and cleanse data using the pandas DataFrame and its library of functions
  • Export data from your Python session to flat files

Let's review each of these functions in turn.

Flat files are popular methods of storing healthcare-related data (along with HL7 formats, which are not covered in this book). A flat file...

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