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Learning pandas

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

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis 2. Up and Running with pandas FREE CHAPTER 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

Handling holidays using calendars

Earlier, when we calculated the next business day from August 29, 2014, we were told by pandas that this date is September 1, 2014. This is actually not correct in the United States: September 1, 2014 is a US federal holiday and banks and exchanges are closed on this day. The reason for this is that pandas uses a specific default calendar when calculating the next business day, and this default pandas calendar does not include September 1, 2014 as a holiday.

The solution to this issue is to either create a custom calendar (which we will not get into the details of), or use the one custom calendar provided by pandas for just this situation, USFederalHolidayCalendar. This custom calendar can then be passed to a CustomBusinessDay object that will be used instead of a BusinessDay object. The calculation using this CustomBusinessDay object will then...

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