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Pandas Cookbook

You're reading from   Pandas Cookbook Practical recipes for scientific computing, time series, and exploratory data analysis using Python

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836205876
Length 404 pages
Edition 3rd Edition
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Authors (2):
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William Ayd William Ayd
Author Profile Icon William Ayd
William Ayd
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Table of Contents (13) Chapters Close

Preface 1. pandas Foundations FREE CHAPTER 2. Selection and Assignment 3. Data Types 4. The pandas I/O System 5. Algorithms and How to Apply Them 6. Visualization 7. Reshaping DataFrames 8. Group By 9. Temporal Data Types and Algorithms 10. General Usage and Performance Tips 11. The pandas Ecosystem 12. Index

Creating charts from aggregated data

The pandas library makes it easy to visualize data in pd.Series and pd.DataFrame objects, using the pd.Series.plot and pd.DataFrame.plot methods, respectively. In this recipe we are going to start with relatively basic line, bar, area, and pie charts, while also seeing the high-level customization options pandas offers. While, these chart types are simple, using them effectively can be immensely helpful to explore your data, identify trends, and share your research with non-technical associates.

It is important to note that these chart types expect your data to already be aggregated, which our sample data in this recipe will reflect. If you are working with data that is not yet aggregated, you will need to use techniques that you will encounter in Chapter 7, Reshaping DataFrames, and Chapter 8, Group By, or use the techniques shown in the Using Seaborn for advanced plots recipe later in this chapter.

How to do it

Let’s create...

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