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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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Toc

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

Advanced Data Structures


We will start this chapter by discussing advanced data structures. We will do that by revisiting lists. We will construct a stack and a queue, explore multiple element membership checking, and throw a bit of functional programming in for good measure. If all of this sounds intimidating, then do not worry. We will get to things step by step, like in the previous chapter, and you will feel confident once you have finished this chapter.

To start this chapter, you have to open an empty notebook. To do that, you can simply input the following command in a shell. It is advised that you first navigate to an empty directory using cd before you enter the command:

docker run -p 8888:8888 -v 'pwd':/notebooks -it rcshubhadeep/packt-data-wrangling-base:latest

Once the Docker container is running, point your browser to http://localhost:8888 and use dw_4_all as the passcode to access the notebook interface.

Iterator

We will start off this topic with lists. However, before we get into...

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