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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


This chapter of our data journey is focused on RDBMS (Relational Database Management Systems) and SQL (Structured Query Language). In the previous chapter, we stored and read data from a file. In this chapter, we will read structured data, design access to the data, and create query interfaces for databases.

Data has been stored in RDBMS format for years. The reasons behind it are as follows:

  • RDBMS is one of the safest ways to store, manage, and retrieve data.

  • They are backed by a solid mathematical foundation (relational algebra and calculus) and they expose an efficient and intuitive declarative language – SQL – for easy interaction.

  • Almost every language has a rich set of libraries to interact with different RDBMS and the tricks and methods of using them are well tested and well understood.

  • Scaling an RDBMS is a pretty well-understood task and there are a bunch of well trained, experienced professionals to do this job (DBA or database administrator).

As we can see in the following...

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