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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 2. Beginning with NumPy Fundamentals FREE CHAPTER 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 – applying the ufunc methods to the add function

Let's call the first four methods on the add() function:

  1. The universal function reduces the input array recursively along a specified axis on consecutive elements. For the add() function, the result of reducing is similar to calculating the sum of an array. Call the reduce() method:
    a = np.arange(9)
    print("Reduce", np.add.reduce(a))

    The reduced array should be as follows:

    Reduce 36
    
  2. The accumulate() method also recursively goes through the input array. But, contrary to the reduce() method, it stores the intermediate results in an array and returns that. The result, in the case of the add() function, is equivalent to calling the cumsum() function. Call the accumulate() method on the add() function:
    print("Accumulate", np.add.accumulate(a))

    The accumulated array is as follows:

    Accumulate [ 0  1  3  6 10 15 21 28 36]
    
  3. The reduceat() method is a bit complicated to explain, so let's call it and go through...
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