Book Image

Learning NumPy Array

By : Ivan Idris
Book Image

Learning NumPy Array

By: Ivan Idris

Overview of this book

Table of Contents (14 chapters)
Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
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...