numpy.corrcoef¶
-
numpy.
corrcoef
(x, y=None, rowvar=1, bias=0, ddof=None)[source]¶ Return correlation coefficients.
Please refer to the documentation for
cov
for more detail. The relationship between the correlation coefficient matrix, P, and the covariance matrix, C, isP_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }
The values of P are between -1 and 1, inclusive.
Parameters: x : array_like
A 1-D or 2-D array containing multiple variables and observations. Each row of m represents a variable, and each column a single observation of all those variables. Also see rowvar below.
y : array_like, optional
An additional set of variables and observations. y has the same shape as m.
rowvar : int, optional
If rowvar is non-zero (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.
bias : int, optional
Default normalization is by
(N - 1)
, whereN
is the number of observations (unbiased estimate). If bias is 1, then normalization is byN
. These values can be overridden by using the keywordddof
in numpy versions >= 1.5.ddof : {None, int}, optional
New in version 1.5.
If not
None
normalization is by(N - ddof)
, whereN
is the number of observations; this overrides the value implied bybias
. The default value isNone
.Returns: out : ndarray
The correlation coefficient matrix of the variables.
See also
cov
- Covariance matrix