scipy.stats.pearsonr¶
- scipy.stats.pearsonr(x, y)[source]¶
Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. Strictly speaking, Pearson’s correlation requires that each dataset be normally distributed, and not necessarily zero-mean. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so.
Parameters: x : (N,) array_like
Input
y : (N,) array_like
Input
Returns: r : float
Pearson’s correlation coefficient
p-value : float
2-tailed p-value
References
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation