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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 FREE CHAPTER 2. Advanced Data Structures and File Handling 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


So far in this book, we have focused on learning pandas DataFrame objects as the main data structure for the application of wrangling techniques. Now, we will learn about various techniques by which we can read data into a DataFrame from external sources. Some of those sources could be text-based (CSV, HTML, JSON, and so on), whereas some others could be binary (Excel, PDF, and so on), that is, not in ASCII format. In this chapter, we will learn how to deal with data that is present in web pages or HTML documents. This holds very high importance in the work of a data practitioner.

Note

Since we have gone through a detailed example of basic operations with NumPy and pandas, in this chapter, we will often skip trivial code snippets such as viewing a table, selecting a column, and plotting. Instead, we will focus on showing code examples for the new topics we aim to learn about here.

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