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# scipy.interpolate.lagrange

scipy.interpolate.lagrange(x, w)

  Return a Lagrange interpolating polynomial.

  Given two 1-D arrays x and w, returns the Lagrange interpolating polynomial through the points `(x, w)`.

  Warning: This implementation is numerically unstable. Do not expect to be able to use more than about 20 points even if they are chosen optimally.

  Parameters:
    x array_like

      x represents the x-coordinates of a set of datapoints.

    w array_like

      w represents the y-coordinates of a set of datapoints, i.e., f(x).

  Returns:
    lagrange `numpy.poly1d` instance

      The Lagrange interpolating polynomial.

  Examples

  Interpolate \(f(x) = x^3\) by 3 points.

      >>> import numpy as np
      >>> from scipy.interpolate import lagrange
      >>> x = np.array([0, 1, 2])
      >>> y = x**3
      >>> poly = lagrange(x, y)

  Since there are only 3 points, Lagrange polynomial has degree 2. Explicitly, it is given by

  \[\begin{split}\begin{aligned} L(x) &= 1\times \frac{x (x - 2)}{-1} + 8\times \frac{x (x-1)}{2} \\ &= x (-2 + 3x) \end{aligned}\end{split}\]

      >>> from numpy.polynomial.polynomial import Polynomial
      >>> Polynomial(poly.coef[::-1]).coef
      array([ 0., -2.,  3.])

      >>> import matplotlib.pyplot as plt
      >>> x_new = np.arange(0, 2.1, 0.1)
      >>> plt.scatter(x, y, label='data')
      >>> plt.plot(x_new, Polynomial(poly.coef[::-1])(x_new), label='Polynomial')
      >>> plt.plot(x_new, 3*x_new**2 - 2*x_new + 0*x_new,
      ...          label=r"$3 x^2 - 2 x$", linestyle='-.')
      >>> plt.legend()
      >>> plt.show()