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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 FREE CHAPTER 2. Essential DataFrame Operations 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

Understanding the differences between concat, join, and merge

The merge and join DataFrame (and not Series) methods and the concat function all provide very similar functionality to combine multiple pandas objects together. As they are so similar and they can replicate each other in certain situations, it can get very confusing when and how to use them correctly. To help clarify their differences, take a look at the following outline:

  • concat:
    • Pandas function
    • Combines two or more pandas objects vertically or horizontally
    • Aligns only on the index
    • Errors whenever a duplicate appears in the index
    • Defaults to outer join with option for inner
  • join:
    • DataFrame method
    • Combines two or more pandas objects horizontally
    • Aligns the calling DataFrame's column(s) or index with the other objects' index (and not the columns)
    • Handles duplicate values on the joining columns/index by...
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