# Python | Numpy np.hermefit() method

• Last Updated : 11 Dec, 2019

With the help of `np.hermefit()` method, we can get the least square fit of hermite series by using `np.hermefit()` method.

Syntax : `np.hermefit(x, y, deg)`
Return : Return the least square fit of given data.

Example #1 :
In this example we can see that by using `np.hermefit()` method, we are able to get the least square fit of hermite series by using this method.

 `# import numpy and hermefit``import` `numpy as np``from` `numpy.polynomial.hermite_e ``import` `hermefit`` ` `x ``=` `np.array([``1``, ``2``, ``3``, ``4``])``y ``=` `np.array([``-``1``, ``-``2``, ``-``3``, ``-``4``])``deg ``=` `3``# using np.hermefit() method``gfg ``=` `hermefit(x, y, deg)`` ` `print``(gfg)`

Output :

[6.52513495e-15 -1.00000000e+00 3.34430164e-15 -4.02985428e-16]

Example #2 :

 `# import numpy and hermefit``import` `numpy as np``from` `numpy.polynomial.hermite_e ``import` `hermefit`` ` `x ``=` `np.array([``11``, ``22``, ``33``, ``44``])``y ``=` `np.array([``1``, ``2``, ``3``, ``4``])``deg ``=` `2``# using np.hermefit() method``gfg ``=` `hermefit(x, y, deg)`` ` `print``(gfg)`

Output :

[-1.00370716e-15 9.09090909e-02 -5.85610278e-19]

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