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Pandas Cookbook

You're reading from   Pandas Cookbook Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781784393878
Length 532 pages
Edition 1st Edition
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Author (1):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
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Table of Contents (12) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Beginning Data Analysis 4. Selecting Subsets of Data 5. Boolean Indexing 6. Index Alignment 7. Grouping for Aggregation, Filtration, and Transformation 8. Restructuring Data into a Tidy Form 9. Combining Pandas Objects 10. Time Series Analysis 11. Visualization with Matplotlib, Pandas, and Seaborn

Plotting basics with pandas

Pandas makes plotting quite easy by automating much of the procedure for you. All pandas plotting is handled internally by matplotlib and is publicly accessed through the DataFrame or Series plot method. We say that the pandas plot method is a wrapper around matplotlib. When you create a plot in pandas, you will be returned a matplotlib Axes or Figure. You can use the full power of matplotlib to modify this object until you get the desired result.

Pandas is only able to produce a small subset of the plots available with matplotlib, such as line, bar, box, and scatter plots, along with kernel density estimates (KDEs) and histograms. Pandas excels at the plots it does create by making the process very easy and efficient, usually taking just a single line of code, saving lots of time when exploring data.

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