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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

Using OpenAI for Series operations

Many of the Series operations demonstrated in the previous recipes in this chapter can be assisted by AI tools, including by PandasAI, with the large language model from OpenAI. In this recipe, we examine how to use PandasAI to query Series values, create new Series, set Series values conditionally, and do some rudimentary reshaping of DataFrames.

Getting ready

We will work with the NLS and COVID-19 case daily data again in this recipe. We will also work with PandasAI, which 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...

The following steps create a PandasAI SmartDataframe object, and then use the chat method of that object to submit natural language instructions for a range of Series operations:

  1. We first need to import the OpenAI and SmartDataframe modules from PandasAI. We also have to instantiate an llm object:
    import...
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