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# scipy.stats.boxcox_normplot

`scipy.stats.``boxcox_normplot`(x, la, lb, plot=None, N=80)

  Compute parameters for a Box-Cox normality plot, optionally show it.

  A Box-Cox normality plot shows graphically what the best transformation parameter is to use in `boxcox` to obtain a distribution that is close to normal.

  Parameters: | x : array_like Input array. la, lb : scalar The lower and upper bounds for the `lmbda` values to pass to `boxcox` for Box-Cox transformations. These are also the limits of the horizontal axis of the plot if that is generated. plot : object, optional If given, plots the quantiles and least squares fit. plot is an object that has to have methods “plot” and “text”. The `matplotlib.pyplot` module or a Matplotlib Axes object can be used, or a custom object with the same methods. Default is None, which means that no plot is created. N : int, optional Number of points on the horizontal axis (equally distributed from la to lb).
  Returns: | lmbdas : ndarray The `lmbda` values for which a Box-Cox transform was done. ppcc : ndarray Probability Plot Correlelation Coefficient, as obtained from `probplot` when fitting the Box-Cox transformed input x against a normal distribution.

  See also

  `probplot`, `boxcox`, `boxcox_normmax`, `boxcox_llf`, `ppcc_max`

  Notes

  Even if plot is given, the figure is not shown or saved by `boxcox_normplot`; `plt.show()` or `plt.savefig('figname.png')` should be used after calling `probplot`.

  Examples

      >>> from scipy import stats
      >>> import matplotlib.pyplot as plt

  Generate some non-normally distributed data, and create a Box-Cox plot:

      >>> x = stats.loggamma.rvs(5, size=500) + 5
      >>> fig = plt.figure()
      >>> ax = fig.add_subplot(111)
      >>> prob = stats.boxcox_normplot(x, -20, 20, plot=ax)

  Determine and plot the optimal `lmbda` to transform `x` and plot it in the same plot:

      >>> _, maxlog = stats.boxcox(x)
      >>> ax.axvline(maxlog, color='r')

      >>> plt.show()

  (Source code)