# numpy.interp() function – Python

• Difficulty Level : Hard
• Last Updated : 11 Jun, 2020

`numpy.interp()` function returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.

Syntax : numpy.interp(x, xp, fp, left = None, right = None, period = None)

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Parameters :
x : [array_like] The x-coordinates at which to evaluate the interpolated values.
xp: [1-D sequence of floats] The x-coordinates of the data points, must be increasing if the argument period is not specified. Otherwise, xp is internally sorted after normalizing the periodic boundaries with xp = xp % period.
fp : [1-D sequence of float or complex] The y-coordinates of the data points, same length as xp.
left : [optional float or complex corresponding to fp] Value to return for x < xp[0], default is fp[0].
right : [optional float or complex corresponding to fp] Value to return for x > xp[-1], default is fp[-1].
period : [None or float, optional] A period for the x-coordinates. This parameter allows the proper interpolation of angular x-coordinates. Parameters left and right are ignored if the period is specified.

Return : [float or complex or ndarray] The interpolated values, same shape as x.

Code #1 :

 `# Python program explaining``# numpy.interp() function``      ` `# importing numpy as geek ``import` `numpy as geek ``  ` `x ``=` `3.6``xp ``=` `[``2``, ``4``, ``6``]``fp ``=` `[``1``, ``3``, ``5``]``  ` `gfg ``=` `geek.interp(x, xp, fp)`` ` `print` `(gfg)`

Output :

```2.6
```

Code #2 :

 `# Python program explaining``# numpy.interp() function``      ` `# importing numpy as geek ``import` `numpy as geek ``  ` `x ``=` `[``0``, ``1``, ``2.5``, ``2.72``, ``3.14``]``xp ``=` `[``2``, ``4``, ``6``]``fp ``=` `[``1``, ``3``, ``5``]``  ` `gfg ``=` `geek.interp(x, xp, fp)`` ` `print` `(gfg)`

Output :

```[1.   1.   1.5  1.72 2.14]
```

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