Best fit polynomial

Date: March 2nd 2016
Last updated: March 2nd 2016

Fitting a polynomial line using numpy. This example is adapted from here:

Import modules

import numpy.polynomial.polynomial as poly
import numpy as np
import matplotlib.pyplot as plt

Create data

x = np.arange(1, 50, 1)
y = np.random.randint(50, size=x.shape)

Create linspace
"Return evenly spaced numbers over a specified interval". See

x_new = np.linspace(x[0], x[-1], num=len(x)*10)

Create polyfit

# modify the third number to adjust fit
coefs = poly.polyfit(x, y, 3)
ffit = poly.polyval(x_new, coefs)

Plot data

fig, ax = plt.subplots()
ax.scatter(x, y, facecolors='None')
ax.plot(x_new, ffit)

best fit 3rd order polynomial screenshot

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