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# numpy.polynomial.hermite.hermder

`numpy.polynomial.hermite.``hermder`(c, m=1, scl=1, axis=0)

  Differentiate a Hermite series.

  Returns the Hermite series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl (the scaling factor is for use in a linear change of variable). The argument c is an array of coefficients from low to high degree along each axis, e.g., [1,2,3] represents the series `1*H_0 + 2*H_1 + 3*H_2` while [[1,2],[1,2]] represents `1*H_0(x)*H_0(y) + 1*H_1(x)*H_0(y) + 2*H_0(x)*H_1(y) + 2*H_1(x)*H_1(y)` if axis=0 is `x` and axis=1 is `y`.

  Parameters : | c : array_like Array of Hermite series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. m : int, optional Number of derivatives taken, must be non-negative. (Default: 1) scl : scalar, optional Each differentiation is multiplied by scl. The end result is multiplication by `scl**m`. This is for use in a linear change of variable. (Default: 1) axis : int, optional Axis over which the derivative is taken. (Default: 0). New in version 1.7.0.
  Returns : | der : ndarray Hermite series of the derivative.

  See also

  `hermint`

  Notes

  In general, the result of differentiating a Hermite series does not resemble the same operation on a power series. Thus the result of this function may be “unintuitive,” albeit correct; see Examples section below.

  Examples

      >>> from numpy.polynomial.hermite import hermder
      >>> hermder([ 1. ,  0.5,  0.5,  0.5])
      array([ 1.,  2.,  3.])
      >>> hermder([-0.5,  1./2.,  1./8.,  1./12.,  1./16.], m=2)
      array([ 1.,  2.,  3.])