In Python, How Can I Calculate Correlation And Statistical Significance Between Two Arrays Of Data?

I have sets of data with two equally long arrays of data, or I can make an array of two-item entries, and I would like to calculate the correlation and statistical significance rep

Solution 1:

If you want to calculate the Pearson Correlation Coefficient, then scipy.stats.pearsonr is the way to go; although, the significance is only meaningful for larger data sets. This function does not require the data to be manipulated to fall into a specified range. The value for the correlation falls in the interval [-1,1], perhaps that was the confusion?

If the significance is not terribly important, you can use numpy.corrcoef().

The Mahalanobis distance does take into account the correlation between two arrays, but it provides a distance measure, not a correlation. (Mathematically, the Mahalanobis distance is not a true distance function; nevertheless, it can be used as such in certain contexts to great advantage.)

Solution 2:

You can use the Mahalanobis distance between these two arrays, which takes into account the correlation between them.

The function is in the scipy package: scipy.spatial.distance.mahalanobis

There's a nice example here

Solution 3:

scipy.spatial.distance.euclidean()

This gives euclidean distance between 2 points, 2 np arrays, 2 lists, etc

import scipy.spatial.distanceas spsd
spsd.euclidean(nparray1, nparray2)

You can find more info here http://docs.scipy.org/doc/scipy/reference/spatial.distance.html