ehrapy.preprocessing.minmax_norm#
- ehrapy.preprocessing.minmax_norm(adata, vars=None, copy=False, **kwargs)[source]#
Apply min-max normalization.
Functionality is provided by
minmax_scale()
, see https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html for details.- Parameters:
adata (
AnnData
) –AnnData
object containing X to normalize values in. Must already be encoded usingencode()
.vars (
Union
[str
,Sequence
[str
],None
]) – List of the names of the numeric variables to normalize. If None all numeric variables will be normalized. Defaults to False .copy (
bool
) – Whether to return a copy or act in place. Defaults to False .**kwargs – Additional arguments passed to
minmax_scale()
- Return type:
- Returns:
AnnData
object with normalized X. Also stores a record of applied normalizations as a dictionary in adata.uns[“normalization”].
Examples
>>> import ehrapy as ep >>> adata = ep.dt.mimic_2(encoded=True) >>> adata_norm = ep.pp.minmax_norm(adata, copy=True)