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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

Selecting the lowest-budget movies from the top 100

Now that we have covered many of the core pandas algorithms from a theoretical level, we can start looking at more “real world” datasets and touch on common ways to explore them.

Top N analysis is a common technique whereby you filter your data based on how your data performs when measured by a single variable. Most analytics tools have the capability to help you filter your data to answer questions like What are the top 10 customers by sales? or, What are the 10 products with the lowest inventory?. When chained together, you can even form catchy news headlines such as Out of the Top 100 Universities, These 5 Have the Lowest Tuition Fees, or From the Top 50 Cities to Live, These 10 Are the Most Affordable.

Given how common these types of analyses are, pandas offers built-in functionality to help you easily perform them. In this recipe, we will take a look at pd.DataFrame.nlargest and pd.DataFrame.nsmallest and...

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