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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 – calculating the net present value

We will calculate the net present value for a random generated cash flow series:

  1. Generate five random values for the cash flow series. Insert -100 as the start value:
    cashflows = np.random.randint(100, size=5)
    cashflows = np.insert(cashflows, 0, -100)
    print("Cashflows", cashflows)

    The cash flows would be as follows:

    Cashflows [-100   38   48   90   17   36]
    
  2. Call the npv() function to calculate the net present value from the cash flow series we generated in the previous step. Use a rate of 3 percent:
    print("Net present value", np.npv(0.03, cashflows))

    The net present value:

    Net present value 107.435682443
    

What just happened?

We computed the net present value from a random generated cash flow series with the NumPy npv() function (see netpresentvalue.py):

from __future__ import print_function
import numpy as np

cashflows = np.random.randint(100, size=5)
cashflows = np.insert(cashflows, 0, -100)
print("Cashflows&quot...
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