numpy.interp() function Python (original) (raw)

Last Updated : 24 Sep, 2024

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)

**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 `

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 `

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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