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Numpy Beginner's Guide (Update)

You're reading from   Numpy Beginner's Guide (Update) Build efficient, high-speed programs using the high-performance NumPy mathematical library

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781785281969
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. NumPy Quick Start FREE CHAPTER 2. Beginning with NumPy Fundamentals 3. Getting Familiar with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Moving Further with NumPy Modules 7. Peeking into Special Routines 8. Assuring Quality with Testing 9. Plotting with matplotlib 10. When NumPy Is Not Enough – SciPy and Beyond 11. Playing with Pygame A. Pop Quiz Answers B. Additional Online Resources C. NumPy Functions' References
Index

Time for action – using the datetime64 data type


The datetime64 data type was introduced in NumPy 1.7.0 (see http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html).

  1. To learn about the datetime64 data type, start a Python shell and import NumPy as follows:

    $ python
    >>> import numpy as np
    

    Create a datetime64 from a string (you can use another date if you like):

    >>> np.datetime64('2015-04-22')
    numpy.datetime64('2015-04-22')
    

    In the preceding code, we created a datetime64 for April 22, 2015, which happens to be Earth Day. We used the YYYY-MM-DD format, where Y corresponds to the year, M corresponds to the month, and D corresponds to the day of the month. NumPy uses the ISO 8601 standard (see http://en.wikipedia.org/wiki/ISO_8601). This is an international standard to represent dates and times. ISO 8601 allows the YYYY-MM-DD, YYYY-MM, and YYYYMMDD formats. Check for yourself, as follows:

    >>> np.datetime64('2015-04-22')
    numpy.datetime64('2015-04-22')
    >&gt...
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