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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

Changing series values conditionally

So, changing series values is often more complicated than the previous recipe suggests. We often need to set series values based on the values of one or more other series for that row of data. This is complicated further when we need to set series values based on values from other rows; say, a previous value for an individual, or the mean for a subset. We will deal with these complications in this and the next recipe.

Getting ready

We will work with land temperature data and the National Longitudinal Survey data in this recipe.

Data note

The land temperature dataset contains the average temperature readings (in Celsius) in 2019 from over 12,000 stations across the world, though the majority of the stations are in the United States. The raw data was retrieved from the Global Historical Climatology Network integrated database. It has been made available for public use by the United States National Oceanic and Atmospheric Administration...

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