The NumPy interp() function allows you to get the one-dimensional linear interpolation to a function with the provided discrete data points (xp, fp), evaluated at x.

## Function Syntax

The function syntax is as shown below:

1 | numpy.interp(x, xp, fp, left=None, right=None, period=None) |

## Parameters

The function parameters are discussed below:

**x**– defines the x-coordinates at which the interpolated values are evaluated.**xp**– represents the x-coordinates of the data points.**fp**– represents the y-coordinates of the data points. They must be of the same length as xp.**left**– defines the value to return for x < xp[0].**right**– defines the value to return for x > xp[-1].**period**– specifies the period for the x-coordinates.

**Resource**: https://en.wikipedia.org/wiki/Linear_interpolation

## Return Value

The function returns the interpolated values with the same shape as the input (x).

## Example

The following example illustrates how to use the interp() function in NumPy.

1 2 3 4 5 6 | # import numpy import numpy as np x = 1.4 xp = [6,4,2] fp = [1,2,3] print(np.interp(x, xp, fp)) |

The code above should return:

1 | 1.0 |

## Example #2

Consider the code below with periodic coordinates.

1 2 3 4 | x = [0, 1.8, 2.4, 1., 2] xp = [100, 90, 45, 33] fp = [4,3,2,1] print(np.interp(x, xp, fp, period=240)) |

The above code should return:

1 | [1.57225434 1.54104046 1.53063584 1.55491329 1.53757225] |

## Conclusion

This article covers the basics of the interp function in NumPy. Feel free to explore the docs for more.

Happy coding!!