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Data Wrangling with Python

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

Product type Paperback

Published in Feb 2019

Publisher Packt

ISBN-13 9781789800111

Length 452 pages

Edition 1st Edition

Languages

Python

Tools

NumPy

Concepts

Data Analysis

Authors (2):

Shubhadeep Roychowdhury

Dr. Tirthajyoti Sarkar

View More author details

Table of Contents (12) Chapters

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

Summary

In this chapter, we learned what the term data wrangling means. We also got examples from various real-life data science situations where data wrangling is very useful and is used in industry. We moved on to learn about the different built-in data structures that Python has to offer. We got our hands dirty by exploring lists, sets, dictionaries, tuples, and strings. They are the fundamental building blocks in Python data structures, and we need them all the time while working and manipulating data in Python. We did several small hands-on exercises to learn more about them. We finished this chapter with a carefully designed activity, which let us combine a lot of different tricks from all the different data structures into a real-life situation and let us observe the interplay between all of them.

In the next chapter, we will learn about the data structures in Python and utilize them to solve real-world problems.

You have been reading a chapter from

Data Wrangling with Python

Published in: Feb 2019

Publisher: Packt

ISBN-13: 9781789800111

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Authors (2)

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

Shubhadeep Roychowdhury

Shubhadeep 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.

See other products by Shubhadeep Roychowdhury

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