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# scipy.stats.boxcox

`scipy.stats.``boxcox`(x, lmbda=None, alpha=None)

  Return a positive dataset transformed by a Box-Cox power transformation.

  Parameters: | x : ndarray Input array. Should be 1-dimensional. lmbda : {None, scalar}, optional If lmbda is not None, do the transformation for that value. If lmbda is None, find the lambda that maximizes the log-likelihood function and return it as the second output argument. alpha : {None, float}, optional If `alpha` is not None, return the `100 * (1-alpha)%` confidence interval for lmbda as the third output argument. Must be between 0.0 and 1.0.
  Returns: | boxcox : ndarray Box-Cox power transformed array. maxlog : float, optional If the lmbda parameter is None, the second returned argument is the lambda that maximizes the log-likelihood function. (min_ci, max_ci) : tuple of float, optional If lmbda parameter is None and `alpha` is not None, this returned tuple of floats represents the minimum and maximum confidence limits given `alpha`.

  See also

  `probplot`, `boxcox_normplot`, `boxcox_normmax`, `boxcox_llf`

  Notes

  The Box-Cox transform is given by:

      y = (x**lmbda - 1) / lmbda,  for lmbda > 0
          log(x),                  for lmbda = 0

  `boxcox` requires the input data to be positive. Sometimes a Box-Cox transformation provides a shift parameter to achieve this; `boxcox` does not. Such a shift parameter is equivalent to adding a positive constant to x before calling `boxcox`.

  The confidence limits returned when `alpha` is provided give the interval where:

  \[llf(\hat{\lambda}) - llf(\lambda) < \frac{1}{2}\chi^2(1 - \alpha, 1),\]

  with `llf` the log-likelihood function and \(\chi^2\) the chi-squared function.

  References

  G.E.P. Box and D.R. Cox, “An Analysis of Transformations”, Journal of the Royal Statistical Society B, 26, 211-252 (1964).

  Examples

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

  We generate some random variates from a non-normal distribution and make a probability plot for it, to show it is non-normal in the tails:

      >>> fig = plt.figure()
      >>> ax1 = fig.add_subplot(211)
      >>> x = stats.loggamma.rvs(5, size=500) + 5
      >>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
      >>> ax1.set_xlabel('')
      >>> ax1.set_title('Probplot against normal distribution')

  We now use `boxcox` to transform the data so it’s closest to normal:

      >>> ax2 = fig.add_subplot(212)
      >>> xt, _ = stats.boxcox(x)
      >>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
      >>> ax2.set_title('Probplot after Box-Cox transformation')

      >>> plt.show()

  (Source code)