correlation
- correlation(x, y, /, *, method='linear')
Pearson's correlation coefficient Return the Pearson's correlation coefficient for two inputs. Pearson's correlation coefficient *r* takes values between -1 and +1. It measures the strength and direction of a linear relationship. >>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9] >>> y = [9, 8, 7, 6, 5, 4, 3, 2, 1] >>> correlation(x, x) 1.0 >>> correlation(x, y) -1.0 If *method* is "ranked", computes Spearman's rank correlation coefficient for two inputs. The data is replaced by ranks. Ties are averaged so that equal values receive the same rank. The resulting coefficient measures the strength of a monotonic relationship. Spearman's rank correlation coefficient is appropriate for ordinal data or for continuous data that doesn't meet the linear proportion requirement for Pearson's correlation coefficient.
© Python Software Foundation. PSF License Version 2 · Official docs ↗
Changed in 3.12
- 3.12: Added support for Spearman’s rank correlation coefficient.
1 Example
>>> # Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune >>> orbital_period = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190] # days >>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km >>> # Show that a perfect monotonic relationship exists >>> correlation(orbital_period, dist_from_sun, method='ranked') 1.0 >>> # Observe that a linear relationship is imperfect >>> round(correlation(orbital_period, dist_from_sun), 4) 0.9882 >>> # Demonstrate Kepler's third law: There is a linear correlation >>> # between the square of the orbital period and the cube of the >>> # distance from the sun. >>> period_squared = [p * p for p in orbital_period] >>> dist_cubed = [d * d * d for d in dist_from_sun] >>> round(correlation(period_squared, dist_cubed), 4) 1.0
from the Python docs