scipy.stats.boxcox(x, lmbda=None, alpha=None)[source]

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


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.


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.


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:

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

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


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


>>> 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')

(Source code)


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