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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

Generating a heat map based on a correlation matrix

The correlation between two variables is a measure of how much they move together. A correlation of 1 means that the two variables are perfectly positively correlated. As one variable increases in size, so does the other. A value of -1 means that they are perfectly negatively correlated. As one variable increases in size, the other decreases. Correlations of 1 or -1 only rarely happen, but correlations above 0.5 or below -0.5 might still be meaningful. There are several tests that can tell us whether the relationship is statistically significant (such as Pearson, Spearman, and Kendall). Since this is a chapter on visualizations, we will focus on viewing important correlations.

Getting ready

You will need Matplotlib and Seaborn installed to run the code in this recipe. Both can be installed by using pip, with the pip install matplotlib and pip install seaborn commands.

How to do it…

We first show part of a correlation...

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