The Cambridge University Digital Technology Group have been recording the weather from the roof of their department building since 1995 and make the data available to download in a single CSV file.
The following program determines the correlation coefficient between pressure and temperature at this site.
import numpy as np
import pylab
data = np.genfromtxt('weather-raw.csv', delimiter=',', usecols=(1,4))
# Remove any rows with either missing T or missing p
data = data[~np.any(np.isnan(data), axis=1)]
# Temperatures are reported after multiplication by a factor of 10 so remove
# this factor
data[:,0] /= 10
# Get the correlation coefficient
corr = np.corrcoef(data, rowvar=0)[0,1]
print('p-T correlation coefficient: {:.4f}'.format(corr))
# Plot the data on a scatter plot: T on x-axis, p on y-axis.
pylab.scatter(*data.T, marker='.')
pylab.xlabel('$T$ /$\mathrm{^\circ C}$')
pylab.ylabel('$p$ /mbar')
pylab.show()
The output gives a correlation coefficient of 0.0260: as expected, there is little correlation between air temperature and pressure (since the air density also varies).
