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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data FREE CHAPTER 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

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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