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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Modern techniques and Python tools to detect and remove dirty data and extract key insights

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565661
Length 436 pages
Edition 1st Edition
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Authors (2):
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Michael B Walker Michael B Walker
Author Profile Icon Michael B Walker
Michael B Walker
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas 2. Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas FREE CHAPTER 3. Chapter 3: Taking the Measure of Your Data 4. Chapter 4: Identifying Missing Values and Outliers in Subsets of Data 5. Chapter 5: Using Visualizations for the Identification of Unexpected Values 6. Chapter 6: Cleaning and Exploring Data with Series Operations 7. Chapter 7: Fixing Messy Data when Aggregating 8. Chapter 8: Addressing Data Issues When Combining DataFrames 9. Chapter 9: Tidying and Reshaping Data 10. Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning 11. Other Books You May Enjoy

Importing data from SQL databases

In this recipe, we will use pymssql and mysql apis to read data from Microsoft SQL Server and MySQL (now owned by Oracle) databases, respectively. Data from sources such as these tends to be well structured since it is designed to facilitate simultaneous transactions by members of organizations, and those who interact with them. Each transaction is also likely related to some other organizational transaction.

This means that although data tables from enterprise systems are more reliably structured than data from CSV files and Excel files, their logic is less likely to be self-contained. You need to know how the data from one table relates to data from another table to understand its full meaning. These relationships need to be preserved, including the integrity of primary and foreign keys, when pulling data. Moreover, well-structured data tables are not necessarily uncomplicated data tables. There are often sophisticated coding schemes that determine...

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