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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 FREE CHAPTER 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data 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

Using PandasAI for imputation

Many of the missing value imputation tasks we have explored in this chapter can also be completed using PandasAI. As we have discussed in previous chapters, AI tools can help us check the work we have done with traditional tools and can suggest alternative approaches that did not occur to us. It always makes sense, though, to look under the hood and be sure we understand what PandasAI, or other AI tools, are doing.

We will use PandasAI in this recipe to identify missing values, impute missing values based on summary statistics, and assign missing values based on machine learning algorithms.

Getting ready

We will work with PandasAI in this recipe. It can be installed with pip install pandasai. You also need to get a token from openai.com to send a request to the OpenAI API.

How to do it...

In this recipe, we will carry out many of the tasks we have done earlier in this chapter using AI tools instead.

  1. We start by importing...
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