limix.qc.mean_standardize¶
- limix.qc.mean_standardize(X, axis=- 1, inplace=False)[source]¶
Zero-mean and one-deviation normalisation.
Normalise in such a way that the mean and variance are equal to zero and one. This transformation is taken over the flattened array by default, otherwise over the specified axis. Missing values represented by
NaN
are ignored.- Parameters
- Returns
X – Normalized array.
- Return type
ndarray
Example
>>> import limix >>> from numpy import arange >>> >>> X = arange(15).reshape((5, 3)).astype(float) >>> print(X) [[ 0. 1. 2.] [ 3. 4. 5.] [ 6. 7. 8.] [ 9. 10. 11.] [12. 13. 14.]] >>> X = arange(6).reshape((2, 3)).astype(float) >>> X = limix.qc.mean_standardize(X, axis=0) >>> print(X) [[-1.22474487 0. 1.22474487] [-1.22474487 0. 1.22474487]]