Finding derivative of a spline in Python using SciPy

In this tutorial, we will learn how to find derivative of a spline in Python using SciPy.

Here we have used:


First of all, we have to be familiar with the word spline. The spline is a piecewise polynomial function and this function is used in interpolating problems, specifically spline interpolation is mostly preferred as a method of estimating values between known data points.

The derivative of  a spline РSciPy

here, we are focusing on the cubic spline. we can easily get cubic spline of any data by using the following library

from scipy.interpolate import CubicSpline


here, for the x-axis, we are considering an array of nine elements

and for the y-axis, we are considering the array of sine values of nine elements.

from scipy.interpolate import CubicSpline
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
y = np.sin(x)
cs = CubicSpline(x, y)
s = np.arange(-1, 10, 0.1)
fig, p = plt.subplots(figsize=(8, 4))
p.plot(x, y, 'o', label='value')
p.plot(s, np.sin(s), label='original')
p.plot(s, cs(s), label="C")
p.plot(s, cs(s, 1), label="C1")
p.plot(s, cs(s, 2), label="linear")
p.set_xlim(-0.5, 14)
p.legend(loc='upper right', ncol=3)

Output :

how to find derivative of a spline in Python using SciPy

Changes in values can be observed in the graph.

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