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Learning NumPy Array

You're reading from   Learning NumPy Array Supercharge your scientific Python computations by understanding how to use the NumPy library effectively

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783983902
Length 164 pages
Edition 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 (14) Chapters Close

Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with NumPy FREE CHAPTER 2. NumPy Basics 3. Basic Data Analysis with NumPy 4. Simple Predictive Analytics with NumPy 5. Signal Processing Techniques 6. Profiling, Debugging, and Testing 7. The Scientific Python Ecosystem Index

Analyzing intra-year daily average temperatures


We are going to have a look at the temperature variation within a year by converting dates to the corresponding day of the year in numbers. This number is between 1 and 366, where 1 corresponds to January 1st and 365 (or 366) corresponds to December 31st. Perform the following steps to analyze the intra-year daily average temperature:

  1. Initialize arrays for the range 1-366 with averages initialized to zeros:

    rng = np.arange(1, 366)
    avgs = np.zeros(365)
    avgs2 = np.zeros(365)
  2. Calculate averages by the day of the year before and after a cutoff point:

    for i in rng: 
       indices = np.where(days[:cutoff] == i)
       avgs[i-1] = temp[indices].mean()
       indices = np.where(days[cutoff+1:] == i)
       avgs2[i-1] = temp[indices].mean()
  3. Fit the averages before the cutoff point to a quadratic polynomial (just a first-order approximation):

    poly = np.polyfit(rng, avgs, 2)
    print poly

    The following polynomial coefficients in descending power are printed:

    [ -4.91329859e-04...
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