Learning NumPy Array by Ivan Idris

Author:Ivan Idris [Idris, Ivan]
Language: eng
Format: epub, mobi, azw3, pdf
Publisher: Packt Publishing
Published: 2014-06-12T21:00:00+00:00

The following plots are produced:

As you can see, the bell curve fits the distribution of average daily pressures almost perfectly. The monthly average pressure seems to be constant.

Analyzing atmospheric humidity in De Bilt

Relative atmospheric humidity is the percentage of partial water vapor pressure of the maximum pressure at the same temperature in the atmosphere. During the summer months, high humidity can lead to issues with getting rid of excess heat by sweating. Humidity is also related to rain, dew, and fog. The KNMI De Bilt data file provides data on daily relative average, minimum, and maximum humidity in percentages. We will draw a histogram of the daily relative average humidity and monthly chart:

We will load the dates converted to months, daily relative average humidity, and the minimum and maximum humidity into NumPy arrays. Again, missing values needed to be converted into NaNs:to_float = lambda x: float(x.strip() or np.nan) to_month = lambda x: dt.strptime(x, "%Y%m%d").month months, avg_h, max_h, min_h = np.loadtxt(sys.argv[1], delimiter=',', usecols=(1, 35, 36, 38), unpack=True, converters={1: to_month, 35: to_float, 36: to_float, 38: to_float})

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