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Learning pandas

You're reading from Learning pandas High performance data manipulation and analysis using Python

Product type Paperback

Published in Jun 2017

Publisher

ISBN-13 9781787123137

Length 446 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Michael Heydt

View More author details

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. pandas and Data Analysis FREE CHAPTER

2. Up and Running with pandas

3. Representing Univariate Data with the Series

4. Representing Tabular and Multivariate Data with the DataFrame

5. Manipulating DataFrame Structure

6. Indexing Data

7. Categorical Data

8. Numerical and Statistical Methods

9. Accessing Data

10. Tidying Up Your Data

11. Combining, Relating, and Reshaping Data

12. Data Aggregation

13. Time-Series Modelling

14. Visualization

15. Historical Stock Price Analysis

Merging and joining data

Pandas allows the merging of pandas objects with database-like join operations, using the pd.merge() function and the .merge() method of a DataFrame object. A merge combines the data of two pandas objects by finding matching values in one or more columns or row indexes. It then returns a new object that represents a combination of the data from both, based on relational-database-like join semantics applied to those values.

Merges are useful as they allow us to model a single DataFrame for each type of data (one of the rules of having tidy data), but to be able to relate data in different DataFrame objects using values existing in both sets of data.

Merging data from multiple pandas objects

A practical example of merges would be that of looking up customer names from orders. To demonstrate this in pandas, we will use the following two DataFrame objects. One represents a list of customer details and the other represents the orders made by the customers and what day the...

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Authors (1)

Michael Heydt

Michael Heydt

Michael Heydt is an independent consultant, programmer, educator, and trainer. He has a passion for learning and sharing his knowledge of new technologies. Michael has worked in multiple industry verticals, including media, finance, energy, and healthcare. Over the last decade, he worked extensively with web, cloud, and mobile technologies and managed user experiences, interface design, and data visualization for major consulting firms and their clients. Michael's current company, Seamless Thingies , focuses on IoT development and connecting everything with everything. Michael is the author of numerous articles, papers, and books, such as D3.js By Example, Instant Lucene. NET, Learning Pandas, and Mastering Pandas for Finance, all by Packt. Michael is also a frequent speaker at .NET user groups and various mobile, cloud, and IoT conferences and delivers webinars on advanced technologies.

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