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numpy.corrcoef
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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, is
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), where N is the number of
observations (unbiased estimate). If bias is 1, then
normalization is by N. These values can be overridden by using
the keyword ddof in numpy versions >= 1.5.
ddof : {None, int}, optional
New in version 1.5.
If not None normalization is by (N - ddof), where N is
the number of observations; this overrides the value implied by
bias. The default value is None.
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Returns : | out : ndarray
The correlation coefficient matrix of the variables.
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See also
- cov
- Covariance matrix