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# numpy.cov

`numpy.``cov`(m, y=None, rowvar=1, bias=0)

  Estimate a covariance matrix, given data.

  Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, , then the covariance matrix element is the covariance of and . The element is the variance of .

  Parameters: | m : 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 form as that of 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 given (unbiased estimate). If bias is 1, then normalization is by `N`.
  Returns: | out : ndarray The covariance matrix of the variables.

  See also

  `corrcoef`
    Normalized covariance matrix

  Examples

  Consider two variables, and , which correlate perfectly, but in opposite directions:

      >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
      >>> x
      array([[0, 1, 2],
             [2, 1, 0]])

  Note how increases while decreases. The covariance matrix shows this clearly:

      >>> np.cov(x)
      array([[ 1., -1.],
             [-1.,  1.]])

  Note that element , which shows the correlation between and , is negative.

  Further, note how x and y are combined:

      >>> x = [-2.1, -1,  4.3]
      >>> y = [3,  1.1,  0.12]
      >>> X = np.vstack((x,y))
      >>> print np.cov(X)
      [[ 11.71        -4.286     ]
       [ -4.286        2.14413333]]
      >>> print np.cov(x, y)
      [[ 11.71        -4.286     ]
       [ -4.286        2.14413333]]
      >>> print np.cov(x)
      11.71

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