# Python | Numpy np.hermfit() method

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

Syntax : `np.hermfit(x, y, deg)`
Return : Return the least square fits of hermite series.

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

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

Output :

[4.00000000e+00 5.00000000e-01 1.56777498e-16]

Example #2 :

 `# import numpy and hermfit ` `import` `numpy as np ` `from` `numpy.polynomial.hermite ``import` `hermfit ` ` `  `x ``=` `np.array([``-``2``, ``-``1``, ``0``, ``1``, ``2``]) ` `y ``=` `np.array([``0.1``, ``0.2``, ``0.3``, ``0.4``, ``0.5``]) ` `deg ``=` `3` ` `  `# using np.hermfit() method ` `gfg ``=` `hermfit(x, y, deg) ` ` `  `print``(gfg) `

Output :

[ 3.00000000e-01 5.00000000e-02 2.76178300e-18 -1.46465661e-18]

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