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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

Reading and writing data in Excel format

Pandas supports reading data in Excel 2003 and newer formats, using the pd.read_excel() function or via the ExcelFile class. Internally, both techniques use either the XLRD or OpenPyXL packages, so you will need to ensure that one of them is installed in your Python environment.

For demonstration, a data/stocks.xlsx file is provided with the sample data. If you open it in Excel, you will see something similar to what is shown in the following screenshot:

The workbook contains two sheets, msft and aapl, which hold the stock data for each respective stock.

The following then reads the data/stocks.xlsx file into a DataFrame:

This has read only content from the first worksheet in the Excel file (the msft worksheet), and has used the contents of the first row as column names. To read the other worksheet, you can pass the name of the worksheet using the sheetname parameter:

Like with pd.read_csv(), many assumptions are made about column names, data types, and...

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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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