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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

Representing durations of time using Period

Many useful analysis operations on time-series data require that events within a specific time interval be analyzed. A simple example would be to determine how many financial transactions occurred in a specific period.

These types of analyses can be performed using Timestamp and DateOffset, where the bounds are calculated and then items filtered based on these bounds. However, this becomes cumbersome when you need to deal with events that must be grouped into multiple periods of time, as you start to need to manage sets of the Timestamp and DateOffset objects.

To facilitate these types of data organization and calculations, pandas makes intervals of time a formal construct using the Period class. pandas also formalizes series of Period objects using PeriodIndex, which provides capabilities of aligning data items based on the indexes...

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