Wind speed is a very important value. The KNMI De Bilt data file has daily average wind speed data expressed in meters per second as well.
We will load the wind direction, wind speed, and average temperature into NumPy arrays. Wind direction and speed have missing values, so some conversion is in order. We will create a masked array from the wind direction and speed values:
to_float = lambda x: float(x.strip() or np.nan)
wind_direction, wind_speed, avg_temp = np.loadtxt(sys.argv[1], delimiter=',', usecols=(2, 4, 11), unpack=True, converters={2: to_float, 4: to_float})
wind_direction = ma.masked_invalid(wind_direction)
wind_speed = ma.masked_invalid(wind_speed)
print "# Wind Speed values", len(wind_speed.compressed())
print "Min speed", wind_speed.min(), "Max speed", wind_speed.max()
print "Average", wind_speed.mean(), "Std. Dev", wind_speed.std()
print "Correlation of wind speed and temperature", np.corrcoef(avg_temp[~wind_speed.mask], wind_speed.compressed())[0][1]