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The Data Wrangling Workshop

You're reading from The Data Wrangling Workshop Create your own actionable insights using data from multiple raw sources

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781839215001

Length 576 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Data Processing

Authors (3):

Dr. Tirthajyoti Sarkar

Shubhadeep Roychowdhury

Brian Lipp

View More author details

Table of Contents (11) Chapters

Preface

1. Introduction to Data Wrangling with Python

2. Advanced Operations on Built-In Data Structures 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. Applications in Business Use Cases and Conclusion of the Course

Appendix

Summary

In this chapter, we went through several important concepts and learning modules related to advanced data gathering and web scraping. We started by reading data from web pages using two of the most popular Python libraries – requests and BeautifulSoup. In this task, we utilized the knowledge we gained in the previous chapter about the general structure of HTML pages and their interaction with Python code. We extracted meaningful data from the Wikipedia home page during this process.

Then, we learned how to read data from XML and JSON files – two of the most widely used data streaming/exchange formats on the web. For XML, we showed you how to traverse the tree-structure data string efficiently to extract key information. For JSON, we mixed it with reading data from the web using an API. The API we consumed was RESTful, which is one of the major standards in web APIs.

At the end of this chapter, we went through a detailed exercise using regex techniques in...

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Authors (3)

Brian Lipp

Brian Lipp

Brian Lipp is a Technology Polyglot, Engineer, and Solution Architect with a wide skillset in many technology domains. His programming background has ranged from R, Python, and Scala, to Go and Rust development. He has worked on Big Data systems, Data Lakes, data warehouses, and backend software engineering. Brian earned a Master of Science, CSIS from Pace University in 2009. He is currently a Sr. Data Engineer working with large Tech firms to build Data Ecosystems.

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Roychowdhury

Roychowdhury

Shubhadeep Roychowdhury holds a master's degree in computer science from West Bengal University of Technology and certifications in machine learning from Stanford. He works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics.

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Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.

See other products by Dr. Tirthajyoti Sarkar

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