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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

Combining DataFrames vertically

There are times when we need to append rows from one data table to another. This will almost always be rows from data tables with similar structures, along with the same columns and data types. For example, we might get a new CSV file containing hospital patient outcomes each month and need to add that to our existing data. Alternatively, we might end up working at a school district central office and receive data from many different schools. We might want to combine this data before conducting analyses.

Even when the data structure across months and across schools (in these examples) is theoretically the same, it may not be in practice. Business practices can change from one period to another. This can be intentional or happen inadvertently due to staff turnover or some external factor. One institution or department might implement practices somewhat differently than another, and some data values might be different for some institutions or missing...

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