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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Joining DataFrames


To demonstrate joining, we will use two CSV files: dest.csv and tips.csv. The use case behind it is that we are running a taxi company. Every time a passenger is dropped off at his or her destination, we add a row to the dest.csv file with the employee number of the driver and the destination:

EmpNr,Dest
5,The Hague
3,Amsterdam
9,Rotterdam

Sometimes drivers get a tip, so we want that registered in the tips.csv file (if this doesn't seem realistic, please feel free to come up with your own story):

EmpNr,Amount
5,10
9,5
7,2.5

Database-like joins in pandas can be done with either the merge() function or the join() DataFrame method. The join() method joins on indices by default, which might not be what you want. In SQL—a relational database query language—we have the inner join, left outer join, right outer join, and full outer join.

Note

An inner join selects rows from two tables, if and only if values match, for columns specified in the join condition. Outer joins do not require...

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