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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

Summary

This chapter covered key steps we need to take the day after we convert our raw data into a pandas DataFrame. We explored techniques for examining the structure of our data, including the number of rows and columns, and data types. We also learned how to generate frequencies for categorical variables, and began to look at how values for one variable change with the values of another variable. Finally, we saw how to examine the distribution of continuous variables, including with sample statistics such as the mean, minimum, and max, and by plotting. This sets us up for the topics in the next chapter, where we will use techniques to identify outliers in our data.

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