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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
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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 atmospheric pressure in De Bilt


Atmospheric pressure is the pressure exerted by air in the atmosphere. It is defined as force divided by area. The KNMI De Bilt data file has measurements in 0.1 hPa for average, minimum, and maximum daily pressures. We will plot a histogram of the average pressure and monthly minimums, maximums, and averages:

  1. We will load the dates converted to months, average, minimum, and maximum pressure into NumPy arrays. Again, missing values needed to be converted to NaNs. The code is as follows:

    to_float = lambda x: 0.1 * float(x.strip() or np.nan)
    to_month = lambda x: dt.strptime(x, "%Y%m%d").month
    months, avg_p, max_p, min_p = np.loadtxt(sys.argv[1], delimiter=',', usecols=(1, 25, 26, 28), unpack=True, converters={1: to_month, 25: to_float, 26: to_float, 28: to_float})
  2. Values are missing from the pressure value columns, so we have to create masked arrays out of NumPy arrays. The following code snippet prints some simple statistics:

    max_p = ma.masked_invalid...
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