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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

Advanced List Comprehension and the zip Function


In this topic, we will deep dive into the heart of list comprehension. We have already seen a basic form of it, including something as simple as a = [i for i in range(0, 30)] to something a bit more complex that involves one conditional statement. However, as we already mentioned, list comprehension is a very powerful tool and, in this topic, we will explore the power of this amazing tool further. We will investigate another close relative of list comprehension called generators, and also work with zip and its related functions and methods. By the end of this topic, you will be confident in handling complicated logical problems.

Introduction to Generator Expressions

Previously, while discussing advanced data structures, we witnessed functions such as repeat. We said that they represent a special type of function known as iterators. We also showed you how the lazy evaluation of an iterator can lead to an enormous amount of space saving and time...

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