# numpy.random.power

random.power(a, size=None)

  Draws samples in [0, 1] from a power distribution with positive exponent a - 1.

  Also known as the power function distribution.

  Note

  New code should use the `power` method of a `Generator` instance instead; please see the Quick Start.

  Parameters:
    a float or array_like of floats

      Parameter of the distribution. Must be non-negative.

    size int or tuple of ints, optional

      Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. If size is `None` (default), a single value is returned if `a` is a scalar. Otherwise, `np.array(a).size` samples are drawn.

  Returns:
    out ndarray or scalar

      Drawn samples from the parameterized power distribution.

  Raises:
    ValueError

      If a <= 0.

  See also

  `random.Generator.power`

    which should be used for new code.

  Notes

  The probability density function is

  \[P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.\]

  The power function distribution is just the inverse of the Pareto distribution. It may also be seen as a special case of the Beta distribution.

  It is used, for example, in modeling the over-reporting of insurance claims.

  References

  [1]

  Christian Kleiber, Samuel Kotz, “Statistical size distributions in economics and actuarial sciences”, Wiley, 2003.

  [2]

  Heckert, N. A. and Filliben, James J. “NIST Handbook 148: Dataplot Reference Manual, Volume 2: Let Subcommands and Library Functions”, National Institute of Standards and Technology Handbook Series, June 2003. https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf

  Examples

  Draw samples from the distribution:

      >>> a = 5. # shape
      >>> samples = 1000
      >>> s = np.random.power(a, samples)

  Display the histogram of the samples, along with the probability density function:

      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s, bins=30)
      >>> x = np.linspace(0, 1, 100)
      >>> y = a*x**(a-1.)
      >>> normed_y = samples*np.diff(bins)[0]*y
      >>> plt.plot(x, normed_y)
      >>> plt.show()

  Compare the power function distribution to the inverse of the Pareto.

      >>> from scipy import stats
      >>> rvs = np.random.power(5, 1000000)
      >>> rvsp = np.random.pareto(5, 1000000)
      >>> xx = np.linspace(0,1,100)
      >>> powpdf = stats.powerlaw.pdf(xx,5)

      >>> plt.figure()
      >>> plt.hist(rvs, bins=50, density=True)
      >>> plt.plot(xx,powpdf,'r-')
      >>> plt.title('np.random.power(5)')

      >>> plt.figure()
      >>> plt.hist(1./(1.+rvsp), bins=50, density=True)
      >>> plt.plot(xx,powpdf,'r-')
      >>> plt.title('inverse of 1 + np.random.pareto(5)')

      >>> plt.figure()
      >>> plt.hist(1./(1.+rvsp), bins=50, density=True)
      >>> plt.plot(xx,powpdf,'r-')
      >>> plt.title('inverse of stats.pareto(5)')