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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

Using an RDBMS (MySQL/PostgreSQL/SQLite)


In this topic, we will focus on how to write some basic SQL commands, as well as how to connect to a database from Python and use it effectively within Python. The database we will choose here is SQLite. There are other databases, such as Oracle, MySQL, Postgresql, and DB2. The main tricks that you are going to learn here will not change based on what database you are using. But for different databases, you will need to install different third-party Python libraries (such as Psycopg2 for Postgresql, and so on). The reason they all behave the same way (apart for some small details) is the fact that they all adhere to PEP249 (commonly known as Python DB API 2).

This is a good standardization and saves us a lot of headaches while porting from one RDBMS to another.

Note

Most of the industry standard projects which are written in Python and use some kind of RDBMS as the data store, most often relay on an ORM or Object Relational Mapper. An ORM is a high...

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