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Mastering Pandas for Finance

You're reading from   Mastering Pandas for Finance Master pandas, an open source Python Data Analysis Library, for financial data analysis

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Product type Paperback
Published in May 2015
Publisher Packt
ISBN-13 9781783985104
Length 298 pages
Edition 1st 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 (11) Chapters Close

Preface 1. Getting Started with pandas Using Wakari.io FREE CHAPTER 2. Introducing the Series and DataFrame 3. Reshaping, Reorganizing, and Aggregating 4. Time-series 5. Time-series Stock Data 6. Trading Using Google Trends 7. Algorithmic Trading 8. Working with Options 9. Portfolios and Risk Index

Creating time-series with specific frequencies

Time-series data in pandas can also be created to represent intervals of time other than daily frequency. Different frequencies can be generated with pd.date_range() by utilizing the freq parameter. This parameter defaults to a value of D, which represents daily frequency.

To introduce the creation of nondaily frequencies, the following command creates a DatetimeIndex with one-minute intervals using freq='T':

In [20]:
   bymin = pd.Series(np.arange(0, 90*60*24),
                     pd.date_range('2014-08-01', 
                                   '2014-10-29 23:59:00',
                                   freq='T'))
   bymin

Out[20]:
   2014-08-01 00:00:00         0
   2014-08-01 00:01:00         1
   2014-08-01 00:02:00         2
                           ...  
   2014-10-29 23:57:00    129597
   2014-10-29 23:58:00    129598
   2014-10-29 23:59:00    129599
   Freq: T, dtype: int64

This time-series allows...

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