numpy.ma.corrcoef¶
- numpy.ma.corrcoef(x, y=None, rowvar=True, bias=False, allow_masked=True)¶
The correlation coefficients formed from the array x, where the rows are the observations, and the columns are variables.
corrcoef(x,y) where x and y are 1d arrays is the same as corrcoef(transpose([x,y]))
Parameters: x : ndarray
Input data. If x is a 1D array, returns the variance. If x is a 2D array, returns the covariance matrix.
y : {None, ndarray} optional
Optional set of variables.
rowvar : {False, True} optional
If True, then each row is a variable with observations in columns. If False, each column is a variable and the observations are in the rows.
bias : {False, True} optional
Whether to use a biased (True) or unbiased (False) estimate of the covariance. If True, then the normalization is by N, the number of non-missing observations. Otherwise, the normalization is by (N-1).
allow_masked : {True, False} optional
If True, masked values are propagated pair-wise: if a value is masked in x, the corresponding value is masked in y. If False, raises an exception.
See also
