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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python 2. Advanced Data Structures and File Handling FREE CHAPTER 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life Appendix

Introduction


In this chapter, we will learn about the secret sauce behind creating a successful data wrangling pipeline. In the previous chapters, we were introduced to the basic data structures and building blocks of Data Wrangling, such as pandas and NumPy. In this chapter, we will look at the data handling section of data wrangling.

Imagine that you have a database of patients who have heart diseases, and like any survey, the data is either missing, incorrect, or has outliers. Outliers are values that are abnormal and tend to be far away from the central tendency, and thus including it into your fancy machine learning model may introduce a terrible bias that we need to avoid. Often, these problems can cause a huge difference in terms of money, man-hours, and other organizational resources. It is undeniable that someone with the skills to solve these problems will prove to be an asset to an organization.

Additional Software Required for This Section

The code for this exercise depends on...

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