NumPy interp()
The numpy.interp() function performs one-dimensional linear interpolation for a set of given data points.
It estimates the value of a function at intermediate points based on known discrete values.
Syntax
</>
Copy
numpy.interp(x, xp, fp, left=None, right=None, period=None)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | The x-coordinates where interpolation is performed. |
xp | 1-D sequence of floats | The x-coordinates of known data points. Must be increasing unless period is specified. |
fp | 1-D sequence of float or complex | The corresponding y-values of the known data points. |
left | float or complex, optional | Value to return when x is less than the smallest value in xp. Default is fp[0]. |
right | float or complex, optional | Value to return when x is greater than the largest value in xp. Default is fp[-1]. |
period | None or float, optional | Defines a periodic cycle for xp, normalizing values before interpolation. |
Return Value
Returns an array of interpolated values corresponding to the given x values.
The output has the same shape as x.
Examples
1. Basic Linear Interpolation
Performing interpolation at a given point using known data points.
</>
Copy
import numpy as np
# Define known data points
xp = np.array([1, 2, 3, 4, 5]) # x-coordinates
fp = np.array([2, 4, 6, 8, 10]) # Corresponding y-values
# Interpolate for a new x-value
x = 2.5
result = np.interp(x, xp, fp)
print("Interpolated value at x =", x, ":", result)Output:
Interpolated value at x = 2.5 : 5.0