def normalize(centered_data, out=None):
return _preprocess(_normalize_fn, centered_data, out)<EOL>
Returns a data set with standard deviation of 1.

The input data must be centered for the operation to
yield valid results: The mean of each column must be 0.
Each column of the returned data set will have standard
deviation 1.

The row vector by which each row of data is divided is
also returned.

Parameters
----------
centered_data : DataFrame or ndarray

out : DataFrame or ndarray, optional
    Alternate output array in which to place the result.
    If provided, it must have the same shape and type
    (DataFrame or ndarray) as the expected output.

Returns
----------
out : tuple of DataFrame or ndarray
    The output data is of the same type as the input.

Notes
----------
To exclude a column (such as a constant feature, which is
usually the first or last column of data) simply don't
include it in the input. For example:

>>> normalized_data, sd_adj = pn.normalize(mydata.iloc[:, 1:])

To perform this operation in place:

>>> _, sd_adj = pn.normalize(mydata.iloc[:, 1:], out=mydata.iloc:, 1:])