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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

Using scatter plots to view bivariate relationships

My sense is that there are few plots that data analysts rely more on than scatter plots, with the possible exception of histograms. We are all very used to looking at relationships that can be illustrated in two dimensions. Scatter plots capture important real-world phenomena (the relationship between variables) and are quite intuitive for most people. This makes them a valuable addition to our visualization toolkit.

Getting ready

You will need Matplotlib and Seaborn for this recipe. We will be working with the landtemps dataset, which provides the average temperature in 2019 for 12,095 weather stations across the world.

How to do it...

We level up our scatter plot skills from the previous chapter and visualize more complicated relationships. We display the relationship between average temperature, latitude, and elevation by showing multiple scatter plots on one chart, creating 3D scatter plots, and showing multiple regression...

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