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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

Calculating new dates using offsets

Frequencies in pandas are represented using date offsets. We have touched on this concept at the beginning of the chapter when discussing Timedelta objects. pandas extends the capabilities of these using the concept of DateOffset objects. They are objects which represent knowledge of how to integrate time offsets and frequencies relative to DatetimeIndex objects.

Representing data intervals with date offsets

DatetimeIndex objects are created at various frequencies by using passing frequency strings such as 'M', 'W', and 'BM' using the freq parameter of pd.date_range(). Under the hood, these frequency strings are translated into an instance of the pandas DateOffset...

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