numpy.ma.corrcoef#

ma.corrcoef(x, y=None, rowvar=True, allow_masked=True)[source]#

Return Pearson product-moment correlation coefficients.

Except for the handling of missing data this function does the same as numpy.corrcoef. For more details and examples, see numpy.corrcoef.

Parameters:

xarray_like: A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see rowvar below.
yarray_like, optional: An additional set of variables and observations. y has the same shape as x.
rowvarbool, optional: If rowvar is True (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.
allow_maskedbool, 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. Because bias is deprecated, this argument needs to be treated as keyword only to avoid a warning.

See also

numpy.corrcoef: Equivalent function in top-level NumPy module.
cov: Estimate the covariance matrix.

Examples

>>> import numpy as np
>>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
>>> np.ma.corrcoef(x)
masked_array(
  data=[[--, --],
        [--, --]],
  mask=[[ True,  True],
        [ True,  True]],
  fill_value=1e+20,
  dtype=float64)