# 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.

      >>> 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
      array([ 3., -2.,  0.])

scipy.interpolate.bisplev scipy.interpolate.approximate_taylor_polynomial